#Computer Vision Engineer

[kamalkafir-blog]

Senior Planning Engineer - Construction

Job title: Senior Planning Engineer – Construction Company: Turner & Townsend Job description: our website: Job Description We are currently recruiting for a Senior Planning Engineer to undertake duties… on a number of high profile construction projects. MAIN PURPOSE OF ROLE: The Senior Planning Engineer will work as part of our project… Expected salary: Location: Bristol Area Job date: Sat, 28…

[upatov]

Building the World's First True Reverse Video Search Engine

While it may not be widely known, no true video search engine currently exists. Services that claim to offer "reverse video search" are, in fact, merely searching via static images or individual frames, and they are transparent about these limitations.

The reason for this is simple: searching entire videos is a highly complex and resource-intensive task. The technical challenges and costs associated with processing full-length videos have made it prohibitive—until now. 

At Comexp Research Lab, we have developed a new approach, based on the Theory of Active Perception (TAPe), that allows us to tackle these challenges with a fraction of the usual computational resources.

Leveraging TAPe, we’ve created an innovative solution capable of handling complex information processing problems in a more efficient manner. This breakthrough has enabled us to develop a new video search engine—essentially Google for videos—which we’ve named TAPe Reverse Video Search (RVS). In this article, we detail the journey toward building this pioneering technology and outline the progress we’ve made to date.

A Tangible Milestone: Creating the First True Reverse Video Search Engine

At Comexp Research Lab, our work has focused on developing services based on our proprietary video-by-video search technology, which mimics the efficiency of human perception. The TAPe model represents a significant departure from traditional search methods by utilizing a perceptual approach rooted in group theory.

In our discussions with peers, investors, and the general public, we typically delve into the Theory of Active Perception (TAPe) and present demonstrations that are conceptually straightforward. Yet, the feedback is often the same: “This is fascinating, but can you show us something more concrete?”

This year, we reached that milestone. We’ve launched a prototype of our video-by-video search engine. Although still in its early stages, the engine indexes videos much like how Google began by indexing text-based websites. In Google’s case, as the volume of indexed sites grew, so did its ability to deliver rapid, relevant search results. The same principle applies to video search, albeit with far greater challenges.

Indexing video content requires substantially more computational resources than indexing text. Even with modern technologies, the process remains slow, costly, and inefficient. For this reason, no major company—Google included—currently offers a fully realized video search engine that searches entire videos. This is where TAPe provides a significant advantage.

Revolutionizing Video Search with TAPe

Our search engine, powered by TAPe, enables users to search a vast archive of indexed video content to locate specific videos. The process begins by comparing the user’s video query against the indexed database and delivering the most relevant matches.

The Theory of Active Perception (TAPe) is a set of novel methods we’ve developed that fundamentally changes how information is processed. This approach allows us to achieve results that are orders of magnitude more efficient than conventional methods—using thousands of times less computational power, less time, and fewer resources overall.

Given the rapidly increasing volume of video content, we began by indexing feature films, documentaries, and TV series. As of now, our system has indexed 80,000 movies. This forms the foundation of our search engine, similar to how textual search engines require comprehensive indexing to be effective.

Additionally, we’ve expanded our capabilities to include television search. Our system tracks broadcasts from major global TV channels, allowing users to discover when and where specific video content, such as TV episodes, has aired. Our next major goal is to index YouTube content, which will significantly enhance the power of TAPe RVS.

Introducing ComexpBot: A Practical Application of TAPe Video Search

To facilitate the use of TAPe RVS and explore potential applications, we’ve developed a Telegram-based bot called ComexpBot. This tool allows users to search for films, TV series, and broadcasts by submitting video fragments instead of traditional text or image queries.

For example, a user might upload a brief clip or GIF, and the bot will quickly identify the corresponding film or series if it exists in our database. The bot returns detailed information, such as the title of the content, links to related websites (like IMDB), and even available trailers.

One of the most striking features of the bot is its ability to recognize videos from small, low-resolution snippets—sometimes as small as 260 pixels. This showcases the efficiency of TAPe’s video sequence processing, which significantly reduces the computational overhead compared to traditional frame-by-frame analysis.

The Underlying Technology: How TAPe Works

Unlike traditional computer vision techniques that rely heavily on convolutional neural networks (CNNs) and deep learning, TAPe employs a unique methodology. Rather than focusing on individual frames, TAPe processes sequences of frames—typically around 5 seconds of video—at once. This approach is counterintuitive but far more efficient than analyzing frames individually, especially considering that a 5-second video segment can consist of 120 to 300 frames.

Importantly, TAPe does not require pre-trained models, as most computer vision systems do. Instead, it learns in real time during the recognition process, much like human perception. This real-time learning enables TAPe to bypass many of the computationally expensive steps involved in traditional video processing. As a result, TAPe can extract the minimal number of features necessary to identify video content, leading to a significantly more efficient search process.

By creating a lightweight “cast” of each video—known as a tape-index—TAPe captures the essential characteristics of the content, which allows for fast and accurate searches. This method drastically reduces storage requirements and computational complexity.

Looking Forward: The Future of TAPe and Video Search

TAPe’s potential extends far beyond its current application in video search. While we are focusing on video recognition and analytics, the underlying technology has broader implications for fields such as artificial intelligence, machine learning algorithms, CPU and GPU development, autopilot systems, and real-time video analytics.

We are also planning to offer the TAPe Video Search API, which will enable researchers and enterprises to analyze vast amounts of video content more efficiently. Additionally, we are developing an extension of the TAPe API for website developers, making it accessible to a wider audience.

One of our most ambitious goals is to index YouTube content, beginning with the platform’s most popular videos. Although this represents only a small fraction of the total content on YouTube, it still amounts to a staggering 2,500 years' worth of video footage. We are confident that TAPe’s efficiency will allow us to tackle this challenge within a reasonable timeframe.

As the use of video content continues to grow exponentially, the demand for efficient, large-scale video search solutions will only increase. TAPe’s revolutionary approach positions it to play a key role in meeting this demand, providing a sustainable and scalable solution for video search in the digital age.

[juvederm]

bbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbb

[sahargjr]

[2/100 days of productivity]

Spent most of the day on my computer vision assignment, writing the code and later on writing the report. It was submitted on time.

The good thing is I've spent 7 hours of concentrated work, i haven't been able to do that since a very long time.

Used the 50min/10min pomodoro technique. I am trying to advance in my reading during the breaks. I've been suffering from constant reading slumps.

[hiringjournal]

Top Skills to Look for When You Hire Computer Vision Developers

One of the most fascinating and quickly developing areas of technology is computer vision. Tech organizations must make sure they hire the right people to advance these projects as businesses depend more and more on image processing and real-time data interpretation. In order to create systems that can identify, process, and evaluate photos and videos and derive insightful information, computer vision developers are essential. 

Knowing the core competencies needed for a computer vision developer is crucial if you want to create innovative computer vision applications. In order to guarantee the success of your project, we examine in this article the most important competencies you should seek in computer vision engineers.

Key Expertise That Will Drive Your Computer Vision Projects to Success

Proficiency in Programming Languages

Proficiency in the appropriate programming languages should be the first qualification you look for in computer vision developers. Python and C++ are the most widely used programming languages for computer vision development. While C++ is utilized for applications requiring low latency and high performance, Python is preferred because of its ease of use, large library (including OpenCV, TensorFlow, and PyTorch), and robust community support. 

Because computer vision applications depend heavily on speed optimization, developers should also be conversant with CUDA for GPU-accelerated processing. As complicated image processing algorithms demand high speed and optimization, developers should have the know-how to construct scalable and efficient code as part of their technological toolbox.

Strong Understanding of Image Processing Techniques

It's crucial that when you hire computer vision developers they have a thorough understanding of image processing methods. The advancement of computer vision encompasses more than just picture analysis; it also includes problems like image segmentation, noise reduction, edge detection, and feature extraction. A developer should be knowledgeable about the different image processing and enhancement algorithms, such as thresholding, hough transforms, and histogram equalization.

These methods are essential for enhancing image data quality and guaranteeing the precision and effectiveness of your computer vision applications.

Expertise in Machine Learning and Deep Learning

The proficiency of computer vision developers in machine learning and deep learning is another crucial consideration. In order to identify patterns, categorize objects, and forecast results, many contemporary computer vision applications rely on model training. Developing scalable computer vision applications requires the ability to work with convolutional neural networks (CNNs) and other cutting-edge machine learning techniques.

Seek out developers with expertise in well-known deep learning frameworks like PyTorch, Keras, or TensorFlow. Building precise and dependable machine learning models for computer vision requires a solid foundation in these fields.

Experience with Computer Vision Libraries and Frameworks

Popular libraries and frameworks, which greatly accelerate development, should also be known to computer vision engineers. One of the most popular libraries for processing and analyzing images in real time is OpenCV. For more complicated tasks like object identification and classification, developers need to also be familiar with deep learning frameworks like TensorFlow and PyTorch, as well as dlib and scikit-image.

Developers can save time and money by using these frameworks to rapidly prototype and implement computer vision solutions without having to start from scratch.

Problem-Solving and Algorithm Development

Computer vision engineers should be adept at solving problems when you hire them. Developing computer vision frequently entails resolving particular issues with illumination, occlusions (when objects are partially obscured), and image quality. To develop novel solutions that can address issues in the real world, developers need to be able to think critically and creatively.

Seek out developers who can show that they can create and refine algorithms for certain tasks, such as motion tracking, facial recognition, or autonomous car navigation.

Experience in Working with Large Datasets

Working with massive datasets is frequently necessary for computer vision projects, and processing and analyzing enormous volumes of data is a critical skill. To effectively train machine learning models, developers should be able to manage massive amounts of picture and video data.

This also requires familiarity with data augmentation, which is a technique for growing datasets and enhancing model resilience.

Collaboration Skills

Lastly, it's critical to hire AI engineers who can collaborate with other teams, including data scientists, back-end developers, and product managers. Many stakeholders must contribute to computer vision projects, and good teamwork and communication are essential to the project's success.

Hire the Right Computer Vision Developer for Your Needs

In conclusion, seek out candidates who have a strong foundation in programming languages, image processing methods, machine learning, and deep learning when hiring computer vision developers. Make sure they are skilled in creating scalable algorithms and have worked with computer vision libraries before. They will also be a great benefit to your project because of their capacity to work with different teams and manage huge databases.

Hiring machine learning engineers or artificial intelligence engineers in addition to qualified computer vision experts can greatly increase the success of initiatives aimed at expanding into new markets.

[academiceurope]

Job - Alert 💡

🧠🔬 Passionate about AI & biomedical imaging?

Join the Leibniz-Institut für Analytische Wissenschaften – ISAS – e.V. in Dortmund as a PhD Candidate (m/f/d) and help shape the future of precision medicine!

In the AMBIOM project, you’ll develop next-gen machine learning and computer vision solutions to analyze biomedical microscopy images – from object tracking to foundation model integration and AI on edge devices.

🚀 Work at the intersection of AI, health research & life sciences in an international, interdisciplinary environment.

📍 Location: Dortmund

🕒 Application deadline: July 31, 2025

🔗  https://www.academiceurope.com/ads/phd-candidate-m-f-d-analysis-of-microscopic-biomedical-images-ambiom/

💡 Excellent support, cutting-edge infrastructure, and a strong scientific community await you. Apply now and bring your machine learning skills to life-changing applications in biomedicine!

[freddynossa]

Sistemas de Recomendación y Visión por Computadora: Las IAs que Transforman Nuestra Experiencia Digital

Sistemas de Recomendación: ¿Qué son y para qué sirven? Los sistemas de recomendación son tecnologías basadas en inteligencia artificial diseñadas para predecir y sugerir elementos (productos, contenidos, servicios) que podrían interesar a un usuario específico. Estos sistemas analizan patrones de comportamiento, preferencias pasadas y similitudes entre usuarios para ofrecer recomendaciones…

[voidskippa]

How did I get this far not knowing about the hough transform

[hienpt31]

Giới thiệu chi tiết về học tăng cường: Phương pháp và ứng dụng

🔍 Học tăng cường (Reinforcement Learning) không còn là khái niệm xa lạ với những người yêu thích công nghệ AI. Đây là một phương pháp học tập cho phép máy móc tự khám phá và hoàn thiện kỹ năng thông qua việc thử nghiệm và phản hồi từ môi trường. 🧠✨

💡 Bạn có biết? Học tăng cường đã tạo nên những bước tiến vượt bậc, từ việc giúp AI đánh bại con người trong các trò chơi phức tạp 🎮 như cờ vây, đến việc tối ưu hóa các hệ thống thực tế như quản lý năng lượng 🌱, xe tự hành 🚗, và thậm chí cả dịch vụ khách hàng 📞!

📈 Phương pháp này ho���t động dựa trên nguyên lý học hỏi từ phần thưởng và hình phạt 🔄, giúp mô hình AI không chỉ ra quyết định mà còn đưa ra những chiến lược tối ưu nhất để đạt mục tiêu. Điều này mở ra tiềm năng vô hạn trong các lĩnh vực như tài chính 💰, y tế 🏥, và giáo dục 📚.

👉 Bạn muốn tìm hiểu sâu hơn về cách thức hoạt động, các ứng dụng nổi bật và lý do tại sao học tăng cường lại được xem là tương lai của trí tuệ nhân tạo? Hãy đọc bài viết chi tiết tại đây: Giới thiệu chi tiết về học tăng cường: Phương pháp và ứng dụng

📢 Đừng quên chia sẻ bài viết này để bạn bè của bạn cũng cập nhật thêm kiến thức thú vị về AI nhé! 🤝💬

[kamalkafir-blog]

Lead Java Software Engineer

Job title: Lead Java Software Engineer Company: Morgan Stanley Job description: We’re seeking someone to join our team as a Lead Java Software Engineer in our Secured Financing trading tech area. Key…. What you’ll do: Provide technical leadership and vision for software development projects. Define technical architecture… Expected salary: Location: London Job date: Sun, 29 Jun 2025 04:57:31…

[xuongdv]

Imbalanced Dataset: Thách thức và Giải pháp trong Machine Learning

🚀 Bạn đã từng gặp tình huống khi dữ liệu của mình bị mất cân bằng giữa các lớp? Một lớp chiếm đến 90% dữ liệu, trong khi lớp khác chỉ chiếm 10% (hoặc ít hơn)? Điều này không chỉ phổ biến trong các bài toán thực tế mà còn đặt ra vô số thách thức khi xây dựng mô hình Machine Learning. 🤔

💡 Tại sao Imbalanced Dataset lại là vấn đề lớn?

📉 Mô hình có xu hướng ưu tiên lớp chiếm đa số, dẫn đến kết quả dự đoán sai lệch.

🛠️ Các chỉ số đánh giá như Accuracy dễ bị đánh lừa, không phản ánh đúng hiệu quả thực tế.

✨ Có giải pháp nào để đối phó với dữ liệu mất cân bằng không? Chắc chắn rồi! Trong bài viết này, chúng tôi sẽ cung cấp những chiến lược hiệu quả, từ: ✔ Resampling Techniques (Oversampling, Undersampling) ✔ Sử dụng các thuật toán mạnh mẽ hơn như SMOTE hoặc ADASYN ✔ Chọn metric đánh giá hợp lý như F1-score, ROC-AUC ✔ Và còn nhiều mẹo hữu ích khác để xử lý vấn đề này!

🔗 Đọc bài viết chi tiết tại đây: Imbalanced Dataset: Thách thức và giải pháp trong Machine Learning

📣 Hãy cùng chia sẻ kinh nghiệm của bạn! Bạn đã từng đối mặt với dữ liệu mất cân bằng chưa? Cách bạn xử lý là gì? Bình luận ngay bên dưới để chúng ta cùng trao đổi nhé! 💬👇

[sahargjr]

[1/100 of productivity]

Starting very slow, woke up late was tired most of the day which didn't help to achieve the goals I've set.

I have an assignment deadline for 12/03 which I'm stuck on, hope to manage to solve it in time.

In the meantime I am enjoying my last days of being home with before going back to hustling in paris.

It has been a long time since I baked something, so it felt refreshing trying recipes out again.

🎧Watched a podcast

🎵Music : nebni - lotfi bouchnak ft wmd and emp1re

🍰 Baked a cake which turned out delicious

Goals for the week :

Finish computer vision assignment

Write brain dump file about the last project I've done

Take notes from my nlp lesson

Do my 2 nlp labs

Prepare the lessons I am going to teach for next week

[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.

[haitv5]

Ứng dụng GoogleNet: Công nghệ AI đột phá trong phân loại hình ảnh

🌐 Trong thời đại công nghệ số, việc phân loại hình ảnh chính xác là yếu tố then chốt giúp cải thiện hiệu suất của nhiều lĩnh vực, từ y tế 🏥, giao thông 🚦 đến thương mại điện tử 🛍️. Bạn đã nghe về GoogleNet – một trong những mạng nơ-ron sâu tiên tiến nhất được Google phát triển? 💡

🔍 GoogleNet không chỉ nổi bật với cấu trúc Inception mang tính cách mạng, mà còn giúp giảm thiểu số lượng tham số và tăng độ chính xác vượt bậc. Điều này mở ra khả năng nhận diện và phân loại hình ảnh với tốc độ ⚡ nhanh chóng và độ chính xác cực cao 🎯.

👉 Tò mò về cách GoogleNet hoạt động và ứng dụng thực tiễn của nó? 🌟 Hãy đọc ngay bài viết chi tiết trên website của chúng tôi! 📖 Bạn sẽ hiểu rõ: ✅ Tại sao GoogleNet là "chìa khóa vàng" trong công nghệ phân loại hình ảnh. ✅ Các ngành nghề đang hưởng lợi từ ứng dụng này. ✅ Tiềm năng phát triển không giới hạn của AI và học sâu (Deep Learning).

💻 Khám phá ngay tại đây: Ứng dụng mạng GoogleNet vào phân loại hình ảnh

📢 Hãy chia sẻ bài viết nếu bạn thấy hữu ích nhé! 🤗 Đừng quên để lại bình luận 💬 và ý kiến của bạn về công nghệ AI tiên tiến này!

❤️ Cùng nhau lan tỏa tri thức và khám phá tương lai!

[xhalt29]

PyTorch trong học máy cho người mới bắt đầu

🔥 Khám Phá PyTorch: Công Cụ Đắc Lực Cho Học Máy Dành Cho Người Mới Bắt Đầu! 🔥

🌐 Bạn đang tìm hiểu về học máy (machine learning) nhưng không biết bắt đầu từ đâu? Hay bạn đã nghe đến PyTorch và tò mò tại sao nó lại là một trong những thư viện phổ biến nhất trong lĩnh vực này? 🧠💻

👉 PyTorch không chỉ mạnh mẽ mà còn cực kỳ dễ sử dụng, đặc biệt cho người mới bắt đầu! Với giao diện trực quan, linh hoạt, bạn có thể: ✅ Xây dựng và huấn luyện các mô hình học máy nhanh chóng. ✅ Tự tin thử nghiệm các ý tưởng mới trong lĩnh vực AI. ✅ Thực hành với các ví dụ thực tế từ các chuyên gia.

📖 Bài viết mới nhất của chúng tôi sẽ giúp bạn: 💡 Hiểu cơ bản về PyTorch và cách nó hoạt động. 🔧 Hướng dẫn từng bước cài đặt và sử dụng PyTorch. 🚀 Khám phá các ứng dụng thú vị của học máy qua PyTorch.

📌 Đừng bỏ lỡ cơ hội nâng cấp kỹ năng của bạn ngay hôm nay! 👉 Xem chi tiết tại đây: PyTorch trong học máy cho người mới bắt đầu

❤️ Nếu bạn thấy bài viết hữu ích, đừng quên like, share và để lại ý kiến của bạn trong phần bình luận nhé! 📲 Tag ngay bạn bè của bạn để cùng học và phát triển kỹ năng học máy nào! 🚀