{"id":2108,"date":"2023-03-08T05:35:27","date_gmt":"2023-03-08T04:35:27","guid":{"rendered":"https:\/\/www.architecturemaker.com\/?p=2108"},"modified":"2023-03-08T05:35:27","modified_gmt":"2023-03-08T04:35:27","slug":"a-deep-convolutional-encoder-decoder-architecture-for-image-segmentation","status":"publish","type":"post","link":"https:\/\/www.architecturemaker.com\/a-deep-convolutional-encoder-decoder-architecture-for-image-segmentation\/","title":{"rendered":"A deep convolutional encoder decoder architecture for image segmentation?"},"content":{"rendered":"

The encoder-decoder architecture for image segmentation is a deep convolutional neural network that is able to take an image as input and output a segmentation mask for that image. The network consists of two parts: an encoder whichdownsamples the image and extracts features from it, and a decoder whichupsamples the features and outputs the segmentation mask. The two parts are connected by a latent space in which the features are represented. The encoder-decoder architecture has been shown to be effective at image segmentation and can be trained end-to-end.<\/p>\n

There is no one-size-fits-all answer to this question, as the ideal deep convolutional encoder decoder architecture for image segmentation will vary depending on the specific application. However, some possible deep convolutional encoder decoder architectures that could be used for image segmentation include the U-Net architecture and the SegNet architecture.<\/p>\n

What is convolutional encoder decoder? <\/h2>\n

A convolutional encoder-decoder network is a standard network used for tasks requiring dense pixel-wise predictions. The network is composed of two main parts: the encoder and the decoder. The encoder is responsible for extracting features from the input image, while the decoder uses these features to generate the dense predictions.<\/p>\n

This type of network has been used for tasks such as semantic segmentation, computing optical flow and disparity maps, and contour detection.<\/p>\n

\"A
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According to the paper, DeepLab v3+ with Resnet18 and Sgdm performs best, FCN 32s with Sgdm takes the second, and U-Net with Adam ranks third. This paper also analyzes the segmentation strategies of the three networks in terms of feature map visualization.<\/p>\n

Why do segmentation CNNs typically have an encoder\/decoder style structure <\/h3>\n

There are a few reasons for this common structure in segmentation CNNs. First, by using an encoder-decoder architecture, the network can learn to extract features at multiple scales. This is important because different features are important at different scales for segmentation tasks – for example, small objects might only be visible at a small scale, while large objects might only be visible at a large scale.<\/p>\n

Second, the encoder-decoder structure allows for better control over the final output size. With other architectures, it can be difficult to control the size of the output, but with an encoder-decoder structure, the output size is determined by the size of the decoder.<\/p>\n

Finally, the encoder-decoder structure is common in other computer vision tasks, such as image classification and object detection. By using a common structure, segmentation CNNs can benefit from the advances made in other areas of computer vision.<\/p>\n

\"A
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The encoder-decoder architecture is a neural network architectu<\/p>\n

How does a convolutional encoder work? <\/h2>\n

Fig 25. Illustration of convolutional coding<\/p>\n

Convolutional coding is a type of error-correcting code that generates parity bits by a sliding window of k bits. The code rate is k\/n. It is used in deep space communications and satellite links where errors are common.<\/p>\n

Fig 26. Trellis diagram of a convolutional code<\/p>\n

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The trellis diagram is a graphical representation of the encoding process. It shows the states of the shift register and the input and output bits for each clock cycle.<\/p>\n

A convolutional encoder outputs N bits for every K input bits. The input can have varying multiples of K bits over a simulation. Using a MATLAB trellis structure that defines a set of generator polynomials, you can model nonsystematic, systematic feedforward, or systematic feedback convolutional codes.<\/p>\n

Can CNN be used for image segmentation? <\/h2>\n

There are two main approaches to image segmentation: convolutional neural networks (CNN) and superpixels. Superpixel is an approach that divides an image into regions (called superpixels) with similar properties, such as color, texture, and brightness.<\/p>\n

CNN architectures have two primary types: segmentations CNNs that identify regions in an image from one or more classes of semantically interpretable objects, and classification CNNs that classify each pixel into one or more classes given a set of real-world object categories.<\/p>\n

Which model is best for segmentation <\/h3>\n
\"A
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FCN is a popular algorithm for doing semantic segmentation. This model uses various blocks of convolution and max pool layers to first decompress an image to 1\/32th of its original size. It then makes a class prediction at this level of granularity.<\/p>\n

R-CNN (Regions with CNN feature) is one representative work for the region-based methods. It performs the semantic segmentation based on the object detection results. To be specific, R-CNN first utilizes selective search to extract a large quantity of object proposals and then computes CNN features for each of them.