#image segmentation

[andranikfakirian]

Project "ML.Satellite": Image Parser

In order to speed up the "manufacturing" of the training dataset as much as possible, extreme automation is necessary. Hence, the next step was to create a semi-automatic satellite multispectral Image Parser.

Firstly, it should carve the smaller pieces from the big picture and adjust them linearly, providing radiometrical rescaling, since spectrometer produces somewhat distorted results compared to the actual radiance of the Earth's surface. These "pieces" will comprise the dataset. It was proposed to "manufacture" about 400 such "pieces" in a 500 by 500 "pixels" format.

P.S.: Below are example images of the procedure described above. (Novaya Zemlya Archipelago)

Secondly, it should calculate some remote sensing indexes. For this task, a list of empirical indexes was taken: NDVI, NDWI, MNDWI, NDSI, ANDWI (alternatively calculated NDWI), WRI and NDTI. Only several of them were useful for the project purposes.

P.S.2: The following are example images of the indexing procedure.

Lastly, it should compute the "labels" for the "pieces" describing a schematic map of the territory on image splitting this territory into several types according to the calculated indexes. To simplify segmentation, in our project, a territory can consist only of the following types: Clouds, Water (seas, oceans, rivers, lakes…), Vegetation (forests, jungles…), Snow and Land (this class includes everything else). And, of course, Parser should save the processed dataset and labels.

P.S.3: Below are sample image of a colored "piece" and a simple map based on the label assigned to this "piece". Map may seem a drop inaccurate and it's not surprising, since as far as I know, indexes are empirical and by definition cannot be precise. As a result, if it is possible to create a sufficiently accurate model that predicts analytical classification, then it may be possible to create a model that classifies optical images better than analytics.

[call-me-rucy]

Wdym there's not an implementation of this stupidly specific algorythm in this programing language that almost no one uses.

why doesnt adapthisteq work on octave? Long shot but does anyone perchance have at hand an implemantation of CLAHE that runs in Octave without errors and in less than a minute???

Cause nooooo we couldnt do this in Matlab, or R, or even python...

[datalabeler]

How MIT researchers are utilizing AI to improve Image Segmentation on Self-driving Cars

Deep learning (DL), machine learning (ML), and artificial intelligence (AI) have recently made significant strides, and this has led to a variety of new applications for these techniques. Self-driving automobiles are one such application, which is expected to have a significant and revolutionary impact on society and how people commute. These cars will represent the first significant integration of personal robots into human society, notwithstanding the early and ongoing resistance to domesticating technology.

Rise of Autonomous Vehicles 

Autonomous vehicles have arrived and will remain. Although they are not yet widely used or accepted, that day will come. The majority of the big automakers are actively investigating autonomous vehicle programme and carrying out considerable on-road testing. The advanced driver assistance systems (ADAS) are a type of technology that are already included in many new vehicles in the United States. An infrastructure for autonomous vehicles is becoming more effective as technology develops. But maintaining public acceptance of these cars will require resolving persistent issues with safety, security, and controlling public perception and expectations.

Major manufacturers’ development efforts for autonomous driving are being guided by a number of important potential benefits.

Artificial intelligence (AI) enhanced features in Autonomous Driving 

A collection of distinct technologies, AI is a primary area of attention for autonomous vehicle testing and development. Only a small number of manufacturers have produced autonomous vehicles with advanced AI technologies like personal AI assistants, radar detectors, and cameras, all of which prioritize security among other tasks. The AI-enhanced capabilities that these self-driving cars have included represent a significant improvement over their predecessors.

Deep learning enables features including speech and voice recognition, voice search, image recognition and processing, motion detection, and data analysis by simulating neuron activity.  Together, these features enable the cars to recognise other vehicles, pedestrians, and traffic lights and follow pre-planned routes.

Autopilots

Tesla recently produced self-driving electric vehicles with autopilots, which allow for automatic steering, braking, lane change, and parking activities. These automobiles also have the potential to lower emissions globally, which is a significant advancement over fuel-powered vehicles. Many of the world’s largest cities now have autonomous vehicles on the road. Even heavy-duty vehicles without drivers are now able to travel long distances while carrying cargo. The number of deadly accidents, many of which are brought on by human error, has declined along with transportation expenses. Since autonomous vehicles are lighter than conventional cars, less energy is used.

Real-time Route Optimisation

Autonomous vehicles communicate with other vehicles and the infrastructure for traffic management to include current data on traffic volumes and road conditions into route selection. Greater lane capacity is possible since autonomous vehicles can drive at higher speeds and with closer vehicle proximity.

MIT’s Innovative Driving Scene Segmentation Researchers are compiling data to quantify drivers’ actions, such as how they react to different driving conditions and carry out additional actions like eating or holding conversations while driving. The study looks at how drivers react to alarms (lane keeping, forward collision, proximity detectors, etc.) and interact with assistive and safety technologies (such as adaptive cruise control, semi-autonomous parking assistance, vehicle infotainment, and communications systems, smartphones, and more).

The purpose of Deep Lab, a cutting-edge deep learning model for semantic image segmentation, is to give semantic labels (such as person, dog, or cat) to each pixel in the input image. To use the camera input in the driving context to grasp the front driving scene semantically. This is crucial for maintaining driving safety and a prerequisite for all forms of autonomous driving. The program’s goal is to generate human-centric insights that will advance the development of automated vehicle technology and raise consumer awareness of appropriate technology use.

Data Labeling plays a great role…Data labeling improves the context, quality, and usability of data for individuals, teams, and businesses. Specifically, you can anticipate More Accurate Prognostications. Accurate data labeling improves quality control in machine learning algorithms, enabling the model to be trained and produce the desired results.

Data Labeler is an expert in providing precise, practical, personalized, accelerated, and quality-labeled datasets for machine learning and artificial intelligence projects. Do you have a scenario in mind? Contact us now!

[gracestellaerie]

My honest reaction to your teenage son's death (nothing bad ever happens to me so I don't care):

Ref under cut

[skyburger]

this was one of my fave bits from ch3

[bykalopsia]

ryukuo hand holding.... thank you ttfc special....

[torksmiths]

“the monkees, head, and the 60’s” by peter mills, pg. 116 / “closer” by dennis cooper, pg. 32

[shallowseeker]

the way that even in early usa, extended families often lived together or within walking distance

the idea that each nuclear family should have its own house, own appliances, own everything and that adult children should move out at 18 is a relatively recent post-WWII, suburbanization-era invention

and it just so happens to be highly profitable

[e-adlirez]

Almost seven years of having this book,,,

... Only to learn in the year of our Lord 2024 that this is a Taylor Swift reference

Vanilla you clout-chasing binch /lh /affectionate

[ranchstoryblog]

Marvelous Game Showcase 2024 Screengrabs

[andranikfakirian]

Project "ML.Satellite": Launch

The new semester brings to me a new ML project. A prerequisite for getting a good grade for a mandatory data analysis course is to create the beneficial neural network of medium complexity. Thus, I started a project "Machine learning model for the terrain segmentation".

The idea of project is to train a model which could divide the multispectral satellite images into parts by type of terrain (to mark where land and water are in the image, where forest and snow are, etc.) An analytical solution for this task does exist, but it appears to be empirical in nature. And it would be really nice to devise a model independent of this solution. But I haven't been able to find a useful dataset for such a task, therefore this task is getting more and more difficult since I need to create such a dataset. First of all, this dataset will be created using an analytical solution, so the goal changes from trying to beat the analytical solution to trying to catch up with the existing solution. The resulting dataset will be published by the end of the project.

The dataset will be "manufactured" using the data from the Landsat 8 satellite. It provides the images of 11 bands with an aspect size of about 5000-10000 "pixels", excluding the 8th band of doubled size.

P.S.: Below is an example of an image band reduced in size by 10 times.

[bio-hazard]

officer cicle i think about you every day ....

[creepedfinn]

cait defenders are the most annoying people in the world

[you-have-made-a-fatal-error]

ack, have finally found this tapestry that's been driving me up the wall for aaaaaaages - it's a cropped (and strangely coloured) section of the massive Imperial hunt and hawking party of Maximilian I in the Sonian Forest, which was commissioned by Emperor Maximilian I and completed in 1510.

[chibittore]

unmasked