#image segmentation

AYA-BAMBI - slic + style2paint

Another experiment from LuluXXX exploring Machine Learning visual outputs, combining black and white image colourization, slic superpixels image segmentation and style2paint manga colourization on footage of Aya-Bambi dancing:

mixing slic superpixels and style2paint. starts with version2 than version4 plus a little breakdown at the end and all 4 versions of the algorithm. music : Spettro from NadjaLind mix - https://soundcloud.com/nadjalind/nadja-lind-pres-new-lucidflow-and-sofa-sessions-meowsic-mix original footage : https://www.youtube.com/watch?v=N2YhzBjiYUg 

Image processing services for object detection by Infosearch could be the best choice for your business.

Raw images are not all in the age of computer vision and artificial intelligence. To be able to really see and comprehend the world, machines should have image processing pipes, defined sequence of operations that process incoming raw visual data into correct recognition and useful information.

This paper discusses the steps of a current image processing pipeline, its importance, and the most recent information on its increased involvement in automation.

The importance of Image Processing Pipelines.

Raw pictures are sloppy: they can be noisy, have different lighting, and be obstructed, or they might include irrelevant details in the background. Even the most modern forms of AI cannot perform without performing thorough preprocessing and feature extraction.

Well-designed pipelines:

•          Enhance recognition task accuracy.

•          Cut on the cost of computation by purifying and simplifying the input.

•          Allow scaling to high volume applications (surveillance, retail analytics or medical imaging).

Major Steps of Image Processing Pipeline.

1. Data Acquisition

•          Photos may be in form of cameras, scanners, satellites, drones, or medical imaging devices.

•          The pipeline should be able to work with different resolutions, formats and lighting environments.

•          The data in cameras and sensors in the world is estimated to increase to 181 zettabytes in 2026, according to IDC. This is a significant portion of visual data which is estimated to have increased to 79 zettabytes in 2021.

2. Preprocessing

•          Relevant artifacts are removed with noise reduction (e.g. Gaussian filters).

•          The pixel intensity is normalized to minimize changes by lighting.

•          Augmentation (rotations, flips, crops): Augmentation of a dataset increases the diversity and increases the robustness of the model.

•          A 2023 Pattern Recognition Letters study demonstrated a 1020 percent increase in classification accuracy when using preprocessing methods that use either raw input or a combination of both.

3. Segmentation & Features Extraction.

•          Segmentation isolates object of interest (i.e. parting a tumor and surrounding tissue).

•          The extraction of features is done to define edges, textures or shapes by which models classify images.

•          Deep learning now has the ability to conduct feature extraction automatically, yet preprocessing boosts reliability.

4. Model Training & Inference

The backbone is either Convolutional Neural Networks (CNNs) or Vision Transformers (ViTs).

5. Post-Processing

Filtering of probability scores Probability scores of models can be filtered.

Results can be smoothed, used with metadata, or included in bigger systems (e.g., quality control notifications, patient notifications, self-driving vehicle decision-making).

Real-World Applications

•          Healthcare: Noise reduction on the medical scans (MRI, CT) allows identifying abnormalities, which improves the diagnostic accuracy.

•          Manufacturing: Computer-controlled defect detection is based on pipelines with the capability to detect minor changes in illumination or surface feel.

•          Retail & E-commerce: Smartphone photos are turned into matches of products with the help of visual search pipelines.

•          Smart Cities: Traffic cameras: Traffic cameras process and categorize vehicles to enhance monitoring of vehicles and their safety.

Market Growth & Impact

•          Market size of the computer vision (that relies heavily on the image processing pipelines) is expected to increase to USD 41.1 billion by 2030 and continues to increase with the rate of 11.4% with respect to CAGR. (Grand View Research)

•          Besides, in India, the image recognition market by itself is projected to become USD 3.4 billion by 2033 with a CAGR of 8.17. (IMARC Group)

•          According to McKinsey, computer vision applications are likely to create USD 500 billion of economic value in industries around the world in 2030.

Challenges to Consider

•          Data diversity: Models do not work when they are trained on limited datasets.

•          Computing needs: GPUs/TPUs are needed to train deep models.

•          Favouritism and equity: Pipelines should be made to be less demographically biased.

•          Privacy: Of special concern in surveillance and health care.

The Road Ahead

Pipelines in the future will be based on:

•          Edge computing to compute data on performance.

•          Self-guided learning so as to reduce the reliance on manual labels.

•          Multimodal artificial intelligence with visual, text and audio richer recognition.

•          Elicitable AI in decision-making to render it transparent and trustworthy.

Conclusion

The silent heroes of automation in the present day are image processing pipelines. Out of raw, noisy data, to accurate recognition, every one of these steps is important to allow AI to learn about the visual world. As the visual data is exploding and the demand on automation is growing, organizations investing in the strength of pipelines are laying a groundwork towards smarter, faster and more reliable AI uses.

Split your photos into perfect grids with the Image Splitter. Create stunning Instagram 3x3 grids or prepare large images for print with our secure, browser-based tool. No uploads, total privacy.

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???

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.

One of the most important areas of computer vision & data labelling is image segmentation. It uses both learning-based and image processing-based algorithms.

Image Segmentation is not only one of the most crucial areas of computer vision & data labeling, but it is also one of the oldest problem statements. The earliest works involved simple region growing methods and optimisation strategies, which were created as early as 1970–1972.

How Data Labeling changed the Image Segmentation Game?

Image segmentation involves both localization and categorization. To determine an object’s location, the model emphasizes the object’s boundary in picture segmentation, a subset of image classification.

Unlike classifiers, which typically use a single encoder network, computer vision picture segmentation models typically use an encoder-decoder network. The encoder transforms the input into a latent space representation, which the decoder transforms into segment maps, or, more precisely, maps indicating the positions of each object in the picture.

Annotation for Image Segmentation

As with all supervised deep learning methods, supervised segmentation techniques need a significant amount of annotated, pre-processed training data.

Depending on the sort of segmentation the model performs, different types of annotations are needed, from very detailed annotations for panoptic segmentation tasks to very basic annotations for semantic segmentation tasks.

For delicate and important use cases like self-driving cars and medical imaging, auto-annotate can quickly generate high-precision segment maps.

How Does AI Image Segmentation Work?

Image Segmentation divides an image into various areas or parts according to how similar the pixels are inside each region of interest. This likeness might stem from many things, including the color, texture, or shape of the object.

Convolutional neural networks (CNNs), fully convolutional networks (FCNs), and transformers are modern sophisticated approaches used in AI picture segmentation to increase task accuracy.

Pre-processing and data collection: A complete set of labeled data is gathered at this stage. Both input and output are present in this kind of dataset.

As a result, the AI system can operate more effectively with hidden images.

Steps to Effective Image Segmentation

Training: The AI system is trained on the relevant dataset during this stage. By establishing a connection between the input and ground truth during training, the system learns patterns and representations.

Testing: After the system has been trained, it is put to the test on a new dataset to assess how well it performs.

Segmentation Generation: If the model works well during testing, it is then put into use in the real world where it builds segmentation masks on the actual images in real time. Also known as the inference phase. The Major Gains from AI Image Segmentation for Automated Image Analysis

Time-Saving: Because of the inherent features of the item, the manual analysis might take hours or even days while AI algorithms can analyze and segment photos within seconds. This increases the effectiveness of the analysis process and facilitates speedy decision-making.

Cost-Effective: Compared to manual image analysis, AI image segmentation can be more cost-effective because fewer or no human resources are required. As a result, more people can afford and have access to the analytical process.

Safety: The possibility of bias in the training data, which can provide skewed findings, is a prevalent worry. Yet the possibility of inaccuracy can be decreased with precise data labeling and testing.

Robust Application: People frequently grow weary while performing repetitious jobs. The fact that humans’ attention spans are getting shorter over time is another important worry. Inefficiency and subpar performance can be caused by these two elements. Because it was trained on a lot of data and is not fatigued by repetition, AI image segmentation is robust.

With reinforcement learning, these algorithms can also learn from their errors and gradually become more effective. This indicates that they can adjust to various types of images and function well even under trying circumstances.

Find how to segment images and video frames using Watershed method. Overview on how to use the recent web technologies for image processing and computer vision. 

The combination of HTML, JavaScript, WebAssembly, WebGL makes it possible to bring computer vision online.