#OpenCV Projects

[techieyan]

6 Fun and Educational OpenCV Projects for Coding Enthusiasts

OpenCV (Open Source Computer Vision Library) is an open source computer vision and machine learning software library used to detect and recognize objects in images and videos. It is one of the most popular coding libraries for the development of computer vision applications. OpenCV supports many programming languages including C++, Python, Java, and more.

Coding enthusiasts who are looking for fun and educational OpenCV projects can find plenty of interesting ones across the web. From creating facial recognition applications to motion detection and tracking, there are numerous projects that can help hone coding skills and gain a better understanding of OpenCV. Here are 6 fun and educational OpenCV projects for coding enthusiasts:

1. Facial Recognition Application: This project involves creating an application that can detect faces in images and videos and recognize them. It can be used to create face authentication systems, such as unlocking a smartphone or computer with a face scan.

2. Motion Detection and Tracking: This project involves creating a program that can detect and track moving objects in videos. It can be used for applications such as surveillance cameras and self-driving cars.

3. 3D Augmented Reality: This project involves creating an augmented reality application that can track 3D objects in real time. It can be used for applications such as gaming and virtual reality.

4. Image Processing: This project involves creating a program that can manipulate and process images. It can be used for applications such as image recognition and filtering.

5. Object Detection: This project involves creating a program that can detect objects in images and videos. It can be used for applications such as autonomous vehicles, robotics, and medical imaging.

6. Text Detection: This project involves creating a program that can detect text in images and videos. It can be used for applications such as optical character recognition and document scanning.

These are just some of the many fun and educational OpenCV projects that coding enthusiasts can explore. With a little bit of research and practice, anyone can create amazing applications with OpenCV.

[takeoffproject]

OpenCV projects using Raspberry Pi | Takeoff

The Blind Reader is a small, affordable device designed to help blind people read printed text. Traditional Braille machines can be very expensive, so not everyone can buy them. The Blind Reader is different because it's cheaper and easier for more people to use.

This device works using a small computer called a Raspberry Pi and a webcam. Here’s how it works step-by-step:

1. Capture the Text: You place a page with printed text under the webcam. The webcam takes a picture of the page.

2. Convert Image to Text: The Raspberry Pi uses a technology called Optical Character Recognition (OCR) to convert the picture into digital text. This means it reads the image and understands what letters and words are on the page.

3. Process the Text: The device makes sure the text is clear and readable. It fixes problems like the text being at an angle or having multiple columns. This process is called skew correction and segmentation.

4. Read Aloud: Once the text is clear, the Raspberry Pi uses a Text-to-Speech (TTS) program to change the text into spoken words.

5. Output the Audio: The spoken words are sent to speakers through an audio amplifier, so the sound is loud and clear.

At Takeoffprojects, The Blind Reader is a complete system that helps visually impaired people by reading printed text out loud. It's easy to use and portable, which means you can carry it around and use it anywhere. This device makes reading more accessible for blind people, helping them become more independent and gain better access to information. Projects on OpenCV like this demonstrate the power of technology in improving lives.

[meloncolle]

I had to clean up 1000+ images for this translation, so I made a tool that does a convex hull flood-fill in 1 click. It's like a magic wand selection that also fills any interior holes in the selection

It's not hard to do this manually in GIMP or w/e, but it takes several clicks/mouse movements each time, and i wanted to spare my wrist over thousands of images lol. The use case is pretty limited, but here's a link:

[linkyu]

tell me about your defense contract pleage

Oh boy!

To be fair, it's nothing grandiose, like, it wasn't about "a new missile blueprint" or whatever, but, just thinking about what it could have become? yeesh.

So, let's go.

For context, this is taking place in the early 2010s, where I was working as a dev and manager for a company that mostly did space stuff, but they had some defence and security contracts too.

One day we got a new contract though, which was... a weird one. It was state-auctioned, meaning that this was basically a homeland contract, but the main sponsor was Philip Morris. Yeah. The American cigarette company.

Why? Because the contract was essentially a crackdown on "illegal cigarette sales", but it was sold as a more general "war on drugs" contract.

For those unaware (because chances are, like me, you are a non-smoker), cigarette contraband is very much a thing. At the time, ~15% of cigarettes were sold illegally here (read: they were smuggled in and sold on the street).

And Phillip Morris wanted to stop that. After all, they're only a small company worth uhhh... oh JFC. Just a paltry 150 billion dollars. They need those extra dollars, you understand?

Anyway. So they sponsored a contract to the state, promising that "the technology used for this can be used to stop drug deals too". Also that "the state would benefit from the cigarettes part as well because smaller black market means more official sales means a higher tax revenue" (that has actually been proven true during the 2020 quarantine).

Anyway, here was the plan:

Phase 1 was to train a neural network and plug it in directly to the city's video-surveillance system, in order to detect illegal transactions as soon as they occur. Big brother who?

Phase 2 was to then track the people involved in said transaction throughout the city, based on their appearance and gait. You ever seen the Plainsight sheep counting video? Imagine something like this but with people. That data would then be relayed to police officers in the area.

So yeah, an automated CCTV-based tracking system. Because that's not setting a scary precedent.

So what do you do when you're in that position? Let me tell you. If you're thrust unknowingly, or against your will, into a project like this,

Note. The following is not a legal advice. In fact it's not even good advice. Do not attempt any of this unless you know you can't get caught, or that even if you are caught, the consequences are acceptable. Above all else, always have a backup plan if and when it backfires. Also don't do anything that can get you sued. Be reasonable.

Let me introduce you to the world of Corporate Sabotage! It's a funny form of striking, very effective in office environments.

Here's what I did:

First of all was the training data. We had extensive footage, but it needed to be marked manually for the training. Basically, just cropping the clips around the "transaction" and drawing some boxes on top of the "criminals". I was in charge of several batches of those. It helped that I was fast at it since I had video editing experience already. Well, let's just say that a good deal of those markings were... not very accurate.

Also, did you know that some video encodings are very slow to process by OpenCV, to the point of sometimes crashing? I'm sure the software is better at it nowadays though. So I did that to another portion of the data.

Unfortunately the training model itself was handled by a different company, so I couldn't do more about this.

Or could I?

I was the main person communicating with them, after all.

Enter: Miscommunication Master

In short (because this is already way too long), I became the most rigid person in the project. Like insisting on sharing the training data only on our own secure shared drive, which they didn't have access to yet. Or tracking down every single bug in the program and making weekly reports on those, which bogged down progress. Or asking for things to be done but without pointing at anyone in particular, so that no one actually did the thing. You know, classic manager incompetence. Except I couldn't be faulted, because after all, I was just "really serious about the security aspect of this project. And you don't want the state to learn that we've mishandled the data security of the project, do you, Jeff?"

A thousand little jabs like this, to slow down and delay the project.

At the end of it, after a full year on this project, we had.... a neural network full of false positives and a semi-working visualizer.

They said the project needed to be wrapped up in the next three months.

I said "damn, good luck with that! By the way my contract is up next month and I'm not renewing."

Last I heard, that city still doesn't have anything installed on their CCTV.

tl;dr: I used corporate sabotage to prevent automated surveillance to be implemented in a city--

hey hold on

wait

what

HEY ACTUALLY I DID SOME EXTRA RESEARCH TO SEE IF PHILLIP MORRIS TRIED THIS SHIT WITH ANOTHER COMPANY SINCE THEN AND WHAT THE FUCK

HUH??????

well what the fuck was all that even about then if they already own most of the black market???

[breederbreederpumpkineater]

Whats the coolest (computer) project you want to work on?

Why aren't you working on it, if you aren't already?

oh gosh coolest project i want to work on is an input-output function search for wikifunctions. but!!! im working on a project right now to add a BUNCH of nigerian politicians to wikidata. got some scans and extracted the text using some opencv and ocr and now i got a huge as spreadsheet. just gotta match up each row with the corresponding person in the wikidata knowledge graph but oh my god is it tedious. one day ill post my code online after im done lol

[beeapothecary]

AI Pollen Project Update 1

Hi everyone! I have a bunch of ongoing projects in honey and other things so I figured I should start documenting them here to help myself and anyone who might be interested. Most of these aren’t for a grade, but just because I’m interested or want to improve something.

One of the projects I’m working on is a machine learning model to help with pollen identification under visual methods. There’s very few people who are specialized to identify the origins of pollens in honey, which is pretty important for research! And the people who do it are super busy because it’s very time consuming. This is meant to be a tool and an aid so they can devote more time to the more important parts of the research, such as hunting down geographical origins, rather than the mundane parts like counting individual pollen and trying to group all the species in a sample.

The model will have 3 goals to aid these researchers:

Count overall pollen and individual species of pollen in a sample of honey

Provide the species of each pollen in a sample

Group pollen species together with a confidence listed per sample

Super luckily there’s pretty large pollen databases out there with different types of imaging techniques being used (SEM, electron microscopy, 40X magnification, etc). I’m kind of stumped on which python AI library to use, right now I’ve settled on using OpenCV to make and train the model, but I don’t know if there’s a better option for what I’m trying to do. If anyone has suggestions please let me know

This project will be open source and completely free once I’m done, and I also intend on making it so more confirmed pollen species samples with confirmed geographical origins can be added by researchers easily. I am a firm believer that ML is a tool that’s supposed to make the mundane parts easier so we have time to do what brings us joy, which is why Im working on this project!

I’m pretty busy with school, so I’ll make the next update once I have more progress! :)

Also a little note: genetic tests are more often used for honey samples since it is more accessible despite being more expensive, but this is still an important part of the research. Genetic testing also leaves a lot to be desired, like not being able to tell the exact species of the pollen which can help pinpoint geographical location or adulteration.

[tsreviews]

AvatoAI Review: Unleashing the Power of AI in One Dashboard

Here's what Avato Ai can do for you

Data Analysis:

Analyze CV, Excel, or JSON files using Python and libraries like pandas or matplotlib.

Clean data, calculate statistical information and visualize data through charts or plots.

Document Processing:

Extract and manipulate text from text files or PDFs.

​Perform tasks such as searching for specific strings, replacing content, and converting text to different formats.

Image Processing:

Upload image files for manipulation using libraries like OpenCV.

​Perform operations like converting images to grayscale, resizing, and detecting shapes or

Machine Learning:

Utilize Python's machine learning libraries for predictions, clustering, natural language processing, and image recognition by uploading

Versatile & Broad Use Cases:

An incredibly diverse range of applications. From creating inspirational art to modeling scientific scenarios, to designing novel game elements, and more.

User-Friendly API Interface:

Access and control the power of this advanced Al technology through a user-friendly API.

​Even if you're not a machine learning expert, using the API is easy and quick.

Customizable Outputs:

Lets you create custom visual content by inputting a simple text prompt.

​The Al will generate an image based on your provided description, enhancing the creativity and efficiency of your work.

Stable Diffusion API:

Enrich Your Image Generation to Unprecedented Heights.

Stable diffusion API provides a fine balance of quality and speed for the diffusion process, ensuring faster and more reliable results.

Multi-Lingual Support:

Generate captivating visuals based on prompts in multiple languages.

Set the panorama parameter to 'yes' and watch as our API stitches together images to create breathtaking wide-angle views.

Variation for Creative Freedom:

Embrace creative diversity with the Variation parameter. Introduce controlled randomness to your generated images, allowing for a spectrum of unique outputs.

Efficient Image Analysis:

Save time and resources with automated image analysis. The feature allows the Al to sift through bulk volumes of images and sort out vital details or tags that are valuable to your context.

Advance Recognition:

The Vision API integration recognizes prominent elements in images - objects, faces, text, and even emotions or actions.

Interactive "Image within Chat' Feature:

Say goodbye to going back and forth between screens and focus only on productive tasks.

​Here's what you can do with it:

Visualize Data:

Create colorful, informative, and accessible graphs and charts from your data right within the chat.

​Interpret complex data with visual aids, making data analysis a breeze!

Manipulate Images:

Want to demonstrate the raw power of image manipulation? Upload an image, and watch as our Al performs transformations, like resizing, filtering, rotating, and much more, live in the chat.

Generate Visual Content:

Creating and viewing visual content has never been easier. Generate images, simple or complex, right within your conversation

Preview Data Transformation:

If you're working with image data, you can demonstrate live how certain transformations or operations will change your images.

This can be particularly useful for fields like data augmentation in machine learning or image editing in digital graphics.

Effortless Communication:

Say goodbye to static text as our innovative technology crafts natural-sounding voices. Choose from a variety of male and female voice types to tailor the auditory experience, adding a dynamic layer to your content and making communication more effortless and enjoyable.

Enhanced Accessibility:

Break barriers and reach a wider audience. Our Text-to-Speech feature enhances accessibility by converting written content into audio, ensuring inclusivity and understanding for all users.

Customization Options:

Tailor the audio output to suit your brand or project needs.

​From tone and pitch to language preferences, our Text-to-Speech feature offers customizable options for the truest personalized experience.

>>>Get More Info<<<

[rabbivole]

they loaned me a webcam for the vr project. if i plug it into my pc, opencv by default crops the image and only gives me 640x480 or something. i can manually set it higher, but then the feed has an insane delay, without even doing any processing

i assumed that was just an opencv issue but i plugged it into my macbook- which is much shittier than my pc- and it natively does a high-res feed with way, way less delay

i am going to chew my own arms off. i cannot figure out why this is happening

[topartodyssey]

What is best programming language for Artificial Intelligence projects?

There isn’t a single “best” programming language for artificial intelligence (AI) projects, as the choice of language depends on various factors such as the specific AI task, the libraries and frameworks available, your familiarity with the language, and the requirements of the project.

However, here are some popular programming languages often used in AI development:

Python: Python is one of the most widely used languages in the AI community due to its simplicity, readability, and availability of numerous AI libraries and frameworks. Libraries like TensorFlow, PyTorch, and sci-kit-learn provide powerful tools for machine learning and deep learning tasks. Python’s versatility also allows for rapid prototyping and experimentation.

R: R is a programming language specifically designed for statistical computing and data analysis. It has a rich collection of packages and libraries focused on machine learning, statistical modeling, and data visualization. R is often preferred by statisticians and researchers working in AI and data science domains

. Java: Java is a popular general-purpose programming language that is widely used in enterprise applications. It has strong support for large-scale systems and offers a range of libraries and frameworks for AI development, such as Deeplearning4j and Weka. Java’s performance and scalability make it a good choice for AI projects that require efficient execution.

C++: C++ is a powerful, low-level programming language known for its performance and efficiency. It is commonly used in AI projects that require high computational speed or have strict resource constraints. Frameworks like TensorFlow and OpenCV provide C++ APIs for AI tasks, and libraries like Eigen can be useful for linear algebra and numerical computations.

Julia: Julia is a relatively new language specifically designed for high-performance numerical computing. It combines the ease of use of dynamic languages like Python with the performance of languages like C++. Julia’s strengths lie in scientific computing and machine learning applications, and it aims to provide a productive and efficient environment for AI development.

MATLAB: MATLAB is a proprietary programming language and environment that is widely used in various scientific and engineering disciplines. It offers powerful tools for numerical computing, data analysis, and visualization. MATLAB’s extensive set of toolboxes, including those for machine learning and deep learning, make it a popular choice for AI researchers and practitioners.

Lisp: Lisp is a family of programming languages known for their flexibility and expressive power. Common Lisp and Scheme are popular variants used in AI development. Lisp’s features, such as support for symbolic processing and its ability to manipulate code as data, make it well-suited for tasks like natural language processing, expert systems, and AI research.

Prolog: Prolog is a declarative programming language based on logic programming. It is particularly useful for tasks involving rule-based reasoning and symbolic computation. Prolog is often employed in areas such as expert systems, natural language processing, and knowledge representation.

Scala: Scala is a statically typed programming language that runs on the Java Virtual Machine (JVM). It combines object-oriented and functional programming paradigms and offers a concise syntax. Scala’s interoperability with Java and its strong support for concurrent programming make it a suitable choice for AI projects that require scalability and parallel processing.

Julia: I mentioned Julia earlier, but it’s worth highlighting again. Julia is gaining popularity in the AI community due to its speed, ease of use, and extensive mathematical libraries. Its just-in-time (JIT) compilation capabilities allow for fast execution, and its focus on numerical computing makes it a good fit for scientific computing and machine learning tasks.

It’s worth noting that the choice of programming language is often influenced by the existing ecosystem and community support. Python, with its extensive libraries and frameworks, is generally considered a good starting point for most AI projects due to its flexibility, ease of use, and rich ecosystem. However, depending on the specific requirements and constraints of your project, other languages may also be suitable.

[techieyan]

Making Science Fiction a Reality through Cutting-Edge OpenCV Projects

Science fiction has long captivated the minds and imaginations of people, offering glimpses of a fantastical future filled with advanced technology, interstellar travel, and otherworldly beings. While many may view these stories as pure fantasy, the truth is that science fiction has often served as a source of inspiration for real-life innovation and progress. In fact, with the rapid advancement of technology and the rise of cutting-edge OpenCV projects, we are closer than ever to turning science fiction into reality.

One of the most exciting areas where science fiction is becoming a reality is in space exploration. For decades, science fiction has envisioned a future where humans travel to other planets and establish colonies on distant worlds. This idea may have seemed far-fetched, but with the development of space technologies such as reusable rockets and advanced propulsion systems, space agencies and private companies are now actively working towards making this vision a reality.

For instance, SpaceX, founded by entrepreneur Elon Musk, has made significant strides in developing reusable rockets that can drastically reduce the cost of space travel. The company has successfully launched and landed multiple rockets, and plans to use them in future missions to Mars and beyond. Similarly, NASA has announced its ambitious plan to send humans back to the moon and establish a sustainable presence there, with the goal of eventually sending astronauts to Mars.

In addition to space exploration, science fiction has also inspired advancements in the field of artificial intelligence (AI). In countless science fiction stories, AI is portrayed as a powerful and intelligent being capable of surpassing human intelligence. While we may not have reached that level yet, AI technology has made significant progress in recent years and is being used in various industries, from healthcare to finance.

One of the most notable examples of AI in action is self-driving cars. This technology was once only seen in science fiction movies, but today, companies like Tesla and Google's Waymo are testing and implementing self-driving cars on the roads. These vehicles use advanced AI algorithms and sensors to navigate through traffic, making driving safer and more efficient.

Another field where science fiction is becoming a reality is in the development of advanced prosthetics. In science fiction, we often see characters with robotic limbs that are not only functional but also enhance their abilities. With advancements in robotics and bioengineering, we are now seeing the first steps towards creating such advanced prosthetics.

For instance, the Defense Advanced Research Projects Agency (DARPA) has been working on a project called the 'LUKE Arm,' inspired by the robotic arm used by Luke Skywalker in the Star Wars franchise. This prosthetic arm is designed to provide amputees with a greater range of motion and control, allowing them to perform tasks that were once impossible.

Apart from these examples, there are countless other cutting-edge projects that are bringing science fiction to life. From 3D printing organs to developing mind-controlled prosthetics, the possibilities are endless. These projects not only showcase the incredible advancements in technology but also highlight the power of imagination and how science fiction can drive innovation.

However, with every technological advancement comes ethical and moral concerns. Science fiction has often warned us of the potential dangers of technology, and it is crucial to address these concerns as we move towards a sci-fi-inspired future. This requires responsible and ethical development, as well as proper regulations and guidelines in place to ensure the safety and well-being of society.

In conclusion, science fiction has long been a source of inspiration for groundbreaking ideas and developments. With the rise of cutting-edge projects, we are witnessing the transformation of science fiction into reality. From space exploration to artificial intelligence, these advancements are not only pushing the boundaries of what we once thought was possible but also shaping the future of humanity. As we continue to push the limits of technology, we must also remember to do so with caution and responsibility, ensuring a brighter and more equitable future for all.

[hiringjournal]

In-House vs. Remote: What’s the Best Way to Hire a Computer Vision Engineer?

Computer vision has been reshaping industries from facial recognition, autonomous vehicles, to medical imaging and retail analysis. As demand proliferates, so does the challenge to find the right experts. Whether a startup or a growing tech company investing in AI, the key question in the picture is: should you opt to hire remote developers or in-house professionals for computer vision roles. 

Both options offer their own pros and cons, but the best choice depends on your project scope, budget, and team structure. To understand this in more detail let’s read furthermore.

In-House vs. Remote: What’s the Best Way to Hire a Computer Vision Engineer?

Let’s first explore the traditional and most-preferred hiring approach of in-house developers. Hiring computer vision engineers in-house brings obvious advantages, especially when your project involves ongoing development, close collaboration, or sensitive data. 

Having a dedicated engineer on-site will make it easier to coordinate with product managers, data scientists, and developers. You must opt for in-house when:

You must work closely with cross-functional teams.

The project is crucial to your roadmap for products.

Working with sensitive or private datasets.

You intend to make long-term innovation investments.

An internal computer vision specialist can fully own the design of the vision system, contribute continuously to the tech stack, and become a part of the corporate culture.

Why Remote Hiring Is Gaining Ground

Unlike advancements in technology, hiring approaches have also evolved, especially post-pandemic. Hiring remotely from a global talent pool has become a preferred approach among several tech companies and startups. 

Many tech companies now prefer to hire AI engineers remotely - especially when looking for rare or specialized skills. Remote hiring offers you access to a global talent network, growing your chances of finding the exact expertise you need. Go remote when:

For a particular use case, such as object tracking or picture segmentation, you require a specialist.

Hiring time is crucial or limited.

You're operating on a tight budget.

You desire flexibility without committing to anything long-term.

Hiring remote developers lowers overhead expenses, particularly in the fields of AI and machine learning. In many areas, pay can be lower without compromising quality, and there's no need to move staff or offer office space.

Consider Hybrid or Project-Based Models

Sometimes combining the two is the best course of action. As you gradually assemble an internal team, you may hire a remote engineer for temporary project or consulting work. This enables you to make rapid progress while developing long-term skills.

Depending on the size of the project, many teams also employ ML or AI engineers to collaborate with computer vision experts. A flexible model keeps your core staff small and concentrated while filling in gaps.

Tips for Hiring the Right Talent

Hiring the best candidate, whether in-house or remote, necessitates having a thorough grasp of your requirements. Identify the precise issues you wish to resolve, such as video analytics, facial recognition, or image classification, and compare them to the engineer's background.

Look for the following when hiring computer vision engineers:

A solid foundation in PyTorch, TensorFlow, OpenCV, or Python.

Knowledge of practical applications (not simply scholarly research).

Case studies or a portfolio demonstrating quantifiable impact.

Excellent communication abilities, particularly for jobs requiring remote work.

Screening for collaborative style is also beneficial. Working across time zones with platforms like Slack, GitHub, and project boards requires self-motivation and comfort on the part of remote engineers.

[literaturereviewhelp]

Abstract This paper is an exploration of the internet and published article sources that give a glimpse of Microsoft Kinect and its utility in the consumer market. It shows the interaction of the human/virtual environment without necessarily using the actual controllers or buttons but by using natural speech or gestures. The paper will also scrutinize the field’s advancement, since Kinect was released finding its application in diverse fields having in mind that it was anticipated to be used in games. Following the introduction of Kinect, the prospects of Natural User Interface (NUI) appear to be extended. It has permitted an almost prompt human-machine communication. Introduction: Microsoft Kinect Application Intuitively, technology should understand us and work for us but not the other way round. Kinect for windows has helped change the way how people and computer relate by providing developers and businesses with the necessary tools to produce new solutions (Borenstein, 2012). This has enabled people to communicate naturally through speaking or gesturing. Companies worldwide are using Kinect sensor and the Software Development Kit (SDK) to improve and set up pioneering solutions for healthcare, education and retail markets. Method: Microsoft Kinect Hardware Depth sensing cameras have for a long time been used in research owing to the high costs associated with such specialized gadgets. Following the introduction of the Kinect, imaging real time depth has been made possible for the everyday developer at reduced rates. Formally referred to as “Project Natal”, Kinect is a gadget that was intended for the Xbox 360 video games to control the video game without using a controller. It has four vital components namely a transmitter, an accelerometer, a specialized chip and an infrared camera that collectively analyzes received information. The depth sensor is what makes all the difference by detecting the precise player’s position in a room. This has been made latent, since the reflected rays gathered from the sensor are converted into data that defines the distance between the device and the object. The obtained infrared image is then meshed with an RGB image, and it is processed in real time. The software in this case determines the various joint positions of the player and then pinpoints their position constructing the skeleton outline. This analysis software also determines the system’s latency, and if it processes too slowly, the image reproduction on the screen is delayed (Zhang, 2012). To provide voice recognition capabilities and to improve the player position detection, Kinect uses multi array microphones to detect sound. The microphones are capable of capturing sound from a particular way identifying its source and the audio wave course. A 2G range configured accelerometer is also mounted on the Kinect, which helps determine the current sensor position allowing it to measure the object as close as 40 cm with precision and accuracy. This enhances a smoothing degradation of up to 3 m (Seguin & Buisson, n.d). The Kinect SDK can be utilized on a computer that has a maximum of four sensors and on different virtual machines supporting Windows. This kind of flexibility enables developers and businesses to implement what is right with regard to their requirements at their own discretion. SDK newest version includes a sensor, which connects to the web browsers, that has been possible through HTML samples. The developers have the capacity to use such programs as OpenCV to create cutting-edge Kinect applications utilizing available developer’s typical libraries. Results Human Health and Kinect Kinect for Windows has expanded awareness of the human features. This includes face tracking and body movemennt, and acknowledgement of human body actions. Another add-on is voice recognition that enhances the comprehension of human. Together with Kinect fusion they help capture the scene’s depth and color that help in reconstruction of a three-dimensional model that is printable. Healthcare providers have been fast in recognizing Kinect’s cost effectiveness in improving care for patients at the same time enhancing smooth clinical workflow (Cook, Couch, Couch, Kim, and Boonn, 2013). A practical application of the technology is in Reflexion Rehabilitation Measurement Tool (RMT) developed by San Diego’s Naval Medical Center. This physical therapy gadget allows doctors to modify patients’ schedules and to remotely observe patients. The program uses a personal computer operating Windows 7 and Microsoft Kinect motion camera. Such capabilities of the gadget have helped the physical therapist improve patients’ adherence to any given prescription. RMT is sold with installed educational directions from a specific therapist. The on-screen guide or avatar directs them on how to conduct the exercises correcting them when they do something wrong. The patient’s therapist has the capacity to review the session’s records before the patient visits them hence assessing their compliance. With the ability to track three-dimensional motion, the Kinect serves as a vital analysis tool for numerous medical conditions. The patients’ experiences, on the other hand, are immersed in the virtual healthcare that is convenient and simplified (Borenstein, 2012). Patients can now attend any clinic and be connected instantly with a doctor from any part of the globe (Boulos, Blanchard, Walker, Montero, Tripathy, and Gutierrez-Osuna, 2011). The doctors have simultaneously experienced new precision and productivity levels allowing them to meet with more patients every day with specialists attending to specific patients despite the distances. Therefore, doctors can use Microsoft Kinect to operate varying equipment remotely that aids in running analysis, collecting data and relaying instructions (Boulos et al., 2011). Kinect and the Gaming World With Kinect sensor for gaming hitting sales of 10 million units in 2011, Microsoft earned a Guinness World Record Award for this peripheral. The device became the best-selling electronic device for the consumer shifting 133 333 units every day since its launch (4th November 2010 and 3rd January 2013). This figure outstripped that of the Nintendo Wii that took two years to hit such a sale. Microsoft Kinect has changed the way people play games and watch movies. With Kinect, remotes and controllers have become a thing of the past. The experience has allowed complete body gaming responding to how one moves (Ungerleider, 2013). Once a person waves a hand, the sensor is activated hence recognizing a person’s image allowing their avatar to be opened. Kinect also has an advanced voice recognition technology that responds to peoples’ voices that helps them in revealing preloaded voice commands (Benedetti, 2010). Peoples’ voices can be used to control movies with no remote required. The technology has been versatile with fun and secure involvements, since it has installed parental control parameters for decent family movies. Microsoft Kinect and the Future   Numerous technologies have emerged following Kinect launch where a prototype called Holodesk, which uses Kinect camera technology, has been coined. This innovation, once it has been tested, will offer a possibility to manipulate three-dimensional objects after projecting them from the device by mirrors with semi-reflective surfaces. To track and pinpointing the locations of the hands, the device will work in collaboration with a Kineck camera. Holodesk . Other applications could utilize Kinects’s ability to respond to human gestures and mapping objects in three-dimensional that can be incorporated with existing gadgets such as aerial drones in responding to disasters such as the KinectBot. KinectBot image . To remain at the top of the gaming world, Microsoft has to incorporate and improve its existing gaming consoles so that they can have a competitive edge. For example, there are possibilities of Microsoft Kinect 2.0 being released soon that will have the capabilities of tracking game players with an average height of one meter. The device might also have a feature that will enable players to play while standing or sitting detecting their hands status. The device will also detect rotated or extra joints enabling more than six people to play at the same time. Furthermore, to enhace continuous communication, this device will have improved displays requiring larger playing spaces. Its RGB streams will have enhanced resolution and quality with the depth stream being able to detect and resolve tiny objects in the game. An active infrared camera will come handy in permiting independent procesings of the lighting and recognition of human features. The device is expected to have a 33 ms latency improvement making the device a must-have in the entertainment field. The most outstanding component will be the 3.0 USB cable that will enhace faster transmission of data. Conclusion Kinect has opened many augmented and virtual doors to everyone, but this does not make it a perfect device. It still needs better sensors, microphones and cameras and associated components such as robotics and screens to improve the Kinect’s capacity. Through its SDK, the Kinect has enabled lone developers to produce numerous functions for this application (Seguin& Buisson, n.d). Eventually, this has opened the virtual reality doors that had been reserved for research and big companies. The interactive and instinctive communication that human would want can only be achieved through use of Kinect. References   Benedetti, W. (2010). After passing on Kinect, Sony makes a move on hardcore gamers. Web. Borenstein, G. ( 2012). Making things see: 3D vision with Kinect, Processing, Arduino, and MakerBot. Sebastopol, CA.: O’Reilly Media, Inc. Boulos, M. N., Blanchard, B. J., Walker, C., Montero, J., Tripathy, A., and Gutierrez-Osuna, R. (2011). Web GIS in practice X: A Microsoft Kinect natural user interface for Google Earth Navigation. International Journal of Health Geographics, 10 (1). Cook, T. S., Couch, G., Couch, T. J., Kim, W., and Boonn, W. W. (2013). Using the Microsoft Kinect for patient size estimation and radiation dose normalization: Proof of concept and initial validation. Journal of Digital Imaging, 26(4), 657-662. Read the full article

[nitte-university-blog]

Top Skills You’ll Learn in a Robotics and Artificial Intelligence Course

In a world that’s rapidly embracing automation, machine intelligence, and smart systems, careers in robotics and artificial intelligence (AI) are more promising than ever. From healthcare robots to self-driving cars and intelligent customer support systems, AI and robotics are becoming integral to modern life.

If you're considering robotics and artificial intelligence courses, you're not just choosing a degree — you're preparing to be part of a technological revolution. But what exactly will you learn in such a program? Let’s explore the most important skills these courses help you develop, and how they prepare you for the future of innovation.

Programming Fundamentals for AI and Robotics

Whether a robot arm on a manufacturing floor or a chatbot handling customer queries, everything begins with programming. Students learn core languages such as:

Python: Widely used in AI and machine learning applications.

C/C++: Essential for embedded systems and robotic control.

Java: Useful in software development and some machine learning frameworks.

Understanding data structures, control flow, and algorithms is foundational for writing efficient code for intelligent systems.

Machine Learning and Deep Learning Techniques

At the heart of AI lies machine learning — the ability for machines to learn from data. Students gain practical knowledge of:

Supervised and unsupervised learning

Neural networks and deep learning frameworks like TensorFlow and PyTorch

Natural Language Processing (NLP) for text and voice-based AI systems

These skills are critical for creating models that can analyze data, make predictions, and improve over time.

Robotics System Design and Control

In robotics, it’s all about building machines that sense, think, and act. You'll learn how to:

Design mechanical structures and integrate them with electronics

Work with sensors (like LIDAR, cameras, gyros) and actuators

Apply control systems theory to ensure precise movements and decisions

These concepts are essential in developing autonomous systems, from robotic arms to drones.

Embedded Systems and IoT Integration

Modern robots and smart devices often rely on embedded systems — mini-computers that perform dedicated functions. You'll learn to:

Program microcontrollers (like Arduino or Raspberry Pi)

Work with real-time operating systems

Connect devices using IoT protocols (like MQTT)

This hands-on knowledge is critical for building responsive and connected devices.

Computer Vision and Image Processing

Robots and AI systems need eyes — and that’s where computer vision comes in. This skill allows machines to:

Interpret visual data from cameras or sensors

Recognize objects, track movements, and detect patterns

Use tools like OpenCV to process and analyze images

Applications range from facial recognition to robotic navigation.

AI Ethics and Responsible Innovation

With great power comes great responsibility. As AI systems become more influential, engineers must understand:

Ethical implications of automation and decision-making

Bias in AI models

Data privacy and security concerns

Courses now include modules that prepare students to design responsible and inclusive technologies.

Soft Skills for Cross-Disciplinary Collaboration

It’s not all about tech. Robotics and AI projects often involve teamwork across domains. You’ll develop:

Communication and presentation skills

Project management techniques

Creative thinking and problem-solving abilities

These soft skills ensure that your innovative ideas are clearly conveyed and efficiently executed in real-world scenarios.

Real-World Projects and Internships

A good robotics and AI course doesn't end with classroom theory. Students gain experience through:

Capstone projects where they design, build, and deploy AI or robotic systems

Industry internships that provide exposure to real-world applications

Hackathons and competitions that encourage innovation under pressure

This kind of hands-on experience is crucial in standing out during placements and job interviews.

Choosing the Right Institution Matters

The quality of your learning experience depends heavily on where you study. The best robotics and artificial intelligence courses provide a mix of strong academic foundation, practical labs, and industry exposure.

At NITTE University, particularly through its NMAM Institute of Technology (NMAMIT), students receive a future-focused education that combines cutting-edge theory with real-world skills. With dedicated labs, advanced AI and robotics curriculum, and partnerships with industry leaders, NMAMIT prepares students not just for today’s tech world—but for the challenges of tomorrow.

[jaspinder123]

Can You Build a Global AI Career with a BE in Computer Science Artificial Intelligence from Chitkara University?

Artificial Intelligence (AI) is no longer science fiction���it's today's reality and tomorrow's future. From autonomous vehicles and voice assistants to predictive healthcare and smart cities, AI is transforming every industry. But to thrive in this new tech landscape, students need more than just theoretical knowledge—they need future-focused education, global exposure, and real-world experience.

If you’re considering pursuing a BE Computer Science Artificial Intelligence degree, one question naturally arises: Can this program from Chitkara University truly pave the way for a global AI career?

Why AI? Why Now?

Before diving into what Chitkara offers, it’s important to understand why AI careers are in such high demand globally:

AI Job Market is Booming: According to LinkedIn and Forbes, AI specialist roles have seen over 70% year-over-year growth worldwide.

High-Paying Roles: AI engineers, machine learning experts, and data scientists earn some of the highest salaries in tech, especially in countries like the US, Canada, UK, and Germany.

Cross-Industry Applications: AI isn't just limited to tech companies. It powers finance, healthcare, retail, automotive, cybersecurity, agriculture, and even entertainment.

Global Talent Shortage: Countries are actively recruiting international AI talent, making this a career with real global mobility.

And that’s where a robust, industry-integrated academic foundation like Chitkara’s comes in.

About Chitkara University’s BE Computer Science Artificial Intelligence Program

Chitkara University, a recognized leader in innovation-driven education, has built a strong reputation for its tech programs. Its BE in Computer Science Artificial Intelligence program is tailored to meet the evolving demands of global employers.

Program Highlights:

Curriculum Designed with Industry Experts from top companies like IBM, Intel, and Microsoft.

Covers AI, Machine Learning, Deep Learning, Natural Language Processing, Computer Vision, Robotics, Data Science, and more.

Strong emphasis on hands-on labs, live projects, and global certification programs.

Mentorship and guest sessions from AI professionals working in Silicon Valley, Europe, and Southeast Asia.

Global-Ready Learning Environment

One of the standout features of Chitkara’s BE CSE (AI) program is its international orientation.

International Exposure

Student Exchange Programs with global universities in Canada, France, Australia, and the UK.

Opportunities to participate in international AI competitions, research collaborations, and conferences.

Real-time projects with multinational corporations (MNCs) and joint certifications from global tech partners.

AI-Focused Research Opportunities

Dedicated Centre of Excellence in AI and Robotics on campus.

Funded research projects in collaboration with DST, Microsoft AI for Good, and global research labs.

Opportunity to co-author and publish papers in international journals and tech summits.

Cutting-Edge Tools & Technologies You’ll Learn

Chitkara’s BE Computer Science Artificial Intelligence program trains students in real-world technologies such as:

Python, R, and TensorFlow

OpenCV, PyTorch, and Keras

Scikit-learn and AWS AI/ML services

Google Cloud AI & Azure ML Studio

ChatGPT, GPT-4, and emerging LLM tools

AI Ethics and Explainable AI (XAI)

Mastering these technologies puts students on par with global AI talent.

Strong Global Placement Support

Chitkara University's placement cell has built a solid reputation for helping students land international roles and placements with multinational tech firms.

Top Global Recruiters

Some of the major companies hiring BE CSE (AI) graduates from Chitkara include:

Google (India, Singapore)

Amazon AWS AI (Germany)

Microsoft AI Labs

Zscaler, IBM, Accenture AI

TCS, Infosys, Cognizant AI Labs

Nvidia, Samsung R&D, HCL Tech

Startups and AI Think Tanks in Europe and Canada

Roles You Can Aim For:

AI Engineer

Machine Learning Specialist

Computer Vision Developer

Data Scientist

NLP Engineer

Robotics Software Engineer

AI Research Associate

Salaries for international roles can range from ₹20 LPA to ₹60 LPA, while domestic placements also offer competitive packages up to ₹30 LPA.

Alumni Success Stories: Proof of Global Reach

After completing my BE in Computer Science Artificial Intelligence at Chitkara, I got placed in a Germany-based robotics startup. The international exposure and research training truly gave me an edge. — Kiran Deep , Batch of 2022

Chitkara’s AI labs, faculty mentorship, and international internships helped me secure a role as a Machine Learning Engineer with AWS AI in Singapore. — Ankush Thakur, Batch of 2021

These stories reflect the global success potential of Chitkara’s AI program.

Entrepreneurial Edge in the AI Startup Ecosystem

If you dream of building your own AI startup, Chitkara University is equally supportive.

Chitkara Innovation Incubator offers funding, mentorship, and co-working space.

Regular AI Startup Bootcamps and Hackathons with investor access.

Partnerships with NASSCOM 10,000 Startups, Google Launchpad, and other accelerators.

Whether you want to join a global AI company or start one, you’ll find a strong foundation here.

Key Benefits of Choosing Chitkara for BE in CSE AI

Industry-Aligned Curriculum

Global University & Research Collaborations

Placement Support for International Careers

Practical Exposure with Real Projects

Cutting-Edge Tech Labs and Tools

Mentorship from AI Leaders

Innovation and Startup Ecosystem

Strong Alumni Network across the Globe

Final Verdict: Can You Build a Global AI Career?

Absolutely. Chitkara University’s BE Computer Science Artificial Intelligence program is designed to meet international standards of education, innovation, and employability. With hands-on learning, AI research opportunities, and strong global placement support, you’re not just earning a degree—you’re stepping into a global career in artificial intelligence.

If you're passionate about creating smart solutions, exploring intelligent systems, or building tech for tomorrow, this program is your launchpad to success on a global stage.

FAQs

Q1. What is the global career scope after completing BE Computer Science Artificial Intelligence from Chitkara University?

A: Graduates can explore high-demand roles such as AI Engineer, Data Scientist, and ML Specialist in countries like the US, Germany, Canada, and Singapore. Chitkara’s international tie-ups and placement support give students a competitive global edge.

Q2. Does Chitkara University provide international placement assistance for BE Computer Science Artificial Intelligence students?

A: Yes, Chitkara offers strong international placement support, connecting students with global companies and startups through dedicated career cells, internships, and alumni networks across Europe, North America, and Asia-Pacific.

Q3. What kind of projects and tools do students work on during the BE Computer Science Artificial Intelligence program?

A: Students work on real-world AI applications using tools like Python, TensorFlow, PyTorch, OpenCV, and GPT-based platforms. Projects include computer vision, natural language processing, and AI-based automation systems.

Q4. Are there research opportunities available in the BE Computer Science Artificial Intelligence program at Chitkara?

A: Absolutely. Chitkara offers AI-focused research labs and collaborates with international institutions and companies, allowing students to publish papers, join innovation challenges, and contribute to cutting-edge AI developments.

Q5. How early can students begin specializing in AI within the BE Computer Science Artificial Intelligence program?

A: Students start exploring AI and ML fundamentals as early as the second year, with progressive specialization through electives, labs, internships, and capstone projects by the third and fourth year.

[christianbale121]

The Ultimate Guide to AI Development: How to Build Intelligent Systems from Scratch

Artificial Intelligence (AI) is no longer a futuristic concept—it's here, it's evolving rapidly, and it's transforming the world around us. From chatbots and self-driving cars to recommendation engines and intelligent assistants, AI systems are being integrated into virtually every industry. But how do you actually build an intelligent system from scratch?

This ultimate guide walks you through everything you need to know to begin your journey in AI development. Whether you’re a beginner or someone with coding experience looking to break into AI, this blog will lay down the foundations and give you a roadmap for success.

What Is AI Development?

AI development involves designing and implementing systems that can mimic human intelligence. This includes tasks like learning from data, recognizing patterns, understanding language, making decisions, and solving problems. The goal is to create machines that can think, reason, and act autonomously.

Key Branches of AI:

Machine Learning (ML): Algorithms that allow systems to learn from data and improve over time.

Deep Learning: A subset of ML that uses neural networks to simulate human brain processes.

Natural Language Processing (NLP): Teaching machines to understand and generate human language.

Computer Vision: Enabling systems to interpret and analyze visual data.

Robotics: Combining AI with mechanical systems for real-world applications.

Step-by-Step: How to Build AI Systems from Scratch

1. Understand the Problem You Want to Solve

AI is a tool—start with a clearly defined problem. Do you want to build a recommendation engine? A fraud detection system? A chatbot? Defining the scope early will determine the approach, dataset, and tools you’ll need.

2. Learn the Prerequisites

Before diving into building AI systems, you’ll need some foundational knowledge:

Programming: Python is the go-to language for AI development.

Math: Focus on linear algebra, statistics, and probability.

Algorithms and Data Structures: Essential for building efficient AI models.

Data Handling: Understand how to clean, manipulate, and analyze data using tools like Pandas and NumPy.

3. Choose the Right Tools and Frameworks

Here are some of the most popular tools used in AI development:

TensorFlow & PyTorch: Deep learning frameworks.

Scikit-learn: For classical machine learning.

Keras: High-level neural networks API.

OpenCV: For computer vision applications.

NLTK & SpaCy: For NLP tasks.

4. Gather and Prepare Your Data

AI systems rely on data. The more relevant and clean your data, the better your model performs. Tasks here include:

Data collection (from public datasets or APIs)

Data cleaning (handling missing values, noise, duplicates)

Feature engineering (extracting meaningful features)

5. Train a Machine Learning Model

Once your data is ready:

Choose the appropriate model (e.g., regression, decision tree, neural network).

Split your data into training and testing sets.

Train the model on your data.

Evaluate performance using metrics like accuracy, precision, recall, or F1-score.

6. Tune and Optimize

Hyperparameter tuning and model optimization are crucial for improving performance. Use techniques like:

Grid Search

Random Search

Cross-Validation

Regularization

7. Deploy the Model

A working model is great—but you’ll want to put it to use!

Use platforms like Flask or FastAPI to serve your model via an API.

Deploy on cloud platforms (AWS, GCP, Azure, or Heroku).

Monitor performance and gather user feedback for further improvements.

Best Practices for AI Development

Start small, scale smart: Don’t try to build a self-aware robot from day one. Begin with basic projects and iterate.

Ethics matter: Consider fairness, accountability, and transparency in your AI systems.

Keep learning: AI is evolving—stay updated with research papers, online courses, and developer communities.

Document everything: From data preprocessing steps to model decisions, good documentation helps others (and your future self).

Recommended Learning Resources

Courses: Coursera (Andrew Ng’s ML course), Fast.ai, edX, Udacity

Books: "Hands-On Machine Learning with Scikit-Learn, Keras & TensorFlow" by Aurélien Géron, "Deep Learning" by Ian Goodfellow

Communities: Kaggle, Stack Overflow, Reddit’s r/MachineLearning, AI Alignment Forum

Final Thoughts

Building intelligent systems from scratch is both a challenge and a rewarding experience. It’s a blend of logic, creativity, and continuous learning. With the right mindset and resources, you can go from a curious beginner to a capable AI developer.

[rabbivole]

holy fucking shit, it works

i mean i'm just following tutorials but it does work. although i have had to do a bunch of extra research and add stuff to the tutorial because opencv dynamited their aruco library 6 months ago and it’s in fucking ashes and now every single tutorial on the planet is dead

(lmao this is like 15 very difficult steps away from my actual final project, i’m so fucking owned)