Image Segmentation using Deep Learning and Computer Vision

How GANs or Generative Adversarial Networks Work










What is Image Segmentation?

Image segmentation is a computer vision and deep learning technique that involves dividing a digital image into segments to analyze the image's content at a pixel level.



Image analysis consists of three levels

Analysis of images by class, such as "animals," "buildings," "people."

Detection of specific objects in an image. For example, a house or a car.

Segmentation of the visual input by parts to understand which segments make the object.



Object detection and classification start from image segmentation techniques that simplify the analysis of visual input.

At BroutonLab Data Science Company, we use deep learning and computer vision to develop image segmentation models to process and analyze the visual input.




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open source scripts












Applications of Image Segmentation

Here are a few examples of current applications of image segmentation

Photo editing software

For example, to automatically separate foreground and background, cut out segmented objects, create portrait modes, blur the background to focus on the foreground, create a green screen effect, add artistic filters, etc.

Traffic control systems

Image processing and segmentation techniques using deep learning can accurately estimate traffic density and collect real-time information about vehicles' movement. Developments in artificial intelligence offer more advantages over traditional surveillance systems for regulation and management of traffic.

Medical Imaging

Computer vision and deep learning techniques can segment medical images into different tissue types, symptoms, or organs. Image segmentation significantly reduces the time to run diagnostics.









Example of Image
Segmentation Software

Data Scientists at BroutonLab created a Sky segmentation algorithm based on the application of deep learning segmentation methods. The algorithm successfully processes images with trees, buildings, and small objects. The accuracy of the model remains consistent in various lighting conditions and can run on mobile devices with low-resolution cameras.

Problem

Image segmentation, mainly Sky segmentation, is widely used in mobile app development when creating photo editing apps.


The challenge is to perform processing and analysis of low-quality images on mobile devices in different lighting conditions. There are multiple image segmentation models available in open source with OpenCV and Python. However, they lack accuracy and consistency.

Solution

Our Data Scientists experimented with several image segmentation approaches, including open-source models. After researching various algorithms for image segmentation, we created our solution using our expertise in artificial intelligence and neural networks. Our model is based on U-Net with ResNet Encoder.


We applied a post-processing algorithm to increase the quality and accuracy of the results. To train the neural networks, we manually labeled 5000 photos from iPhone and Android devices.





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