CHOOSING THE RIGHT NETWORK ARCHITECTURE FOR YOUR SEGMENTATION PIPELINE

Image segmentation is the fundamental task in computer vision which  involves dividing images into multiple sub-parts or images or regions  based on their visual properties such as shapes, textures, colors, object  boundaries. Segmentation of image includes identifying and  differentiating different objects, regions inside of the image. Each segment  has different information to extract. Image helps in understanding and  interpreting images in a more detailed and meaningful way.

Types of Image Segmentation:

1. Semantic Segmentation: Semantic segmentation aims to assign  each pixel in an image to a particular class. The output of semantic  segmentation is a dense pixel-wise labeling, where pixels belonging  to the same object class are assigned the same label. For example :  Semantic segmentation labels pixels into classes like road,  pedestrian , car, tree, etc. This type of segmentation provides a high notch level understanding of scene or surrounding and is used in  applications such as autonomous driving, scene understanding and  medical image analysis.
2. Instance Segmentation: This segmentation is a level above semantic segmentation here not only labeling pixels happens but  also distinguishing individual object instances takes place. We can  say that it assigns a unique label to each distinct object instance  present in the image. For example : In an image consisting of  multiple dogs , instance segmentation will assign a separate label to  each dog for precise identification. This segmentation is helpful in various applications like object detection, robotics and video  surveillance.

Types of Segmentation Techniques:

1. Thresholding: It is a simple technique where pixels in an image are  classified as foreground or background based on a specific threshold  value. It is commonly used for segmenting images with clear  boundaries between objects and the background.

2. Edge-based segmentation: This method focuses on identifying edges  or boundaries in an image. It involves detecting abrupt changes in  pixel intensity, gradients or other features to locate object  boundaries.

3. Region-based Segmentation: This approach groups pixels into  regions based on certain criteria, such as color similarity, texture or  pixel intensity. One common algorithm used for region-based  segmentation is the watershed algorithm, which treats pixel  intensities as a topographic surface and separates regions based on  watershed lines.

4. Contour – based Segmentation: This technique involves detecting  and tracing the contours or outlines of objects in an image. It is often  used when the boundaries of objects are not well defined or when  objects overlap.

5. K-means Clustering: It is a basic approach that can be used to  partition an image into different regions based on color similarity. The K-means algorithm aims to group data points into K-clusters ,  where K is a user-defined parameter. In the context of image  segmentation, each pixel in the image is treated as a data point, and  the clustering process assigns each pixel to one of the clusters.

6. Deep Learning-Based Methods: With the advancements in deep  learning , Convolutional Neural Networks (CNN’s) have been widely used  for image segmentation . Popular architectures for this task include  U-Net, Mask R-CNN, FCN, DeepLab, PSPNet, SegNet . These models  can automatically segment objects from images by training on  large annotated datasets.

U-Net: U-Net is a widely used architecture for image  segmentation. It consists of an encoder-decoder structure , where  the encoder captures high-level features and the decoder recovers  spatial information, skip connections between corresponding  encoder and decoder layers help preserve fine details.

SegNet: It uses an encoder-decoder structure and performs  up-sampling with pooling indices from the corresponding encoder  layer. This approach helps retain spatial information during  up-sampling.

Mask R-CNN: It extends the faster R-CNN object detection  architecture by adding a segmentation branch. It enables instance level segmentation by predicting pixel-level masks for each object in  an image.

DeepLab: It uses dilated convolutions to capture multi-scale  information while maintaining computational efficiency. It utilizes  dilated convolutions at different rates to gather contextual  information at varying receptive field sizes.

PSPNet: Pyramid Scene Parsing Network uses pyramid pooling  module to aggregate contextual information at multiple scales. It  leverages different pooling sizes to capture global context and  achieve accurate image segmentation.

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-Ishwarya