Introduction to Segmentation in Computer vision
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Semantic segmentation is a dense prediction task that labels each pixel of an image with a class. It has applications in autonomous vehicles, medical imaging, and surgeries. Popular architectures for semantic segmentation include U-Net, which uses an encoder-decoder structure with skip connections, and Tiramisu, which uses dense blocks. The loss function commonly used is pixel-wise cross entropy loss, which examines predictions at each pixel.
Introduction to Segmentation in Computer vision
- 2. Hello! I am Frederick Apina Machine Learning Engineer @ParrotAI I am here because I love to give presentations. 2
- 3. “When I think about strong innovations in term of automation, cognitive computing, and artificial intelligence, they will be coming a lot from Tanzania as well.” 3
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- 6. 6 Limitations Still a bit rough since we’re only drawing bounding boxes and don’t really get an accurate idea of object shape.
- 7. 7 What if!?
- 8. 8 Semantic Segmentation Semantic Segmentation is to label each pixel of an image with a corresponding class of what is being represented. ✗ commonly referred to as dense prediction.
- 15. 15 Our goal is to take either a RGB color image or a grayscale image and output a segmentation map where each pixel contains a class label represented as an integer.
- 16. 16 We create our target by one-hot encoding the class labels - essentially creating an output channel for each of the possible classes.
- 17. 17 We can easily inspect a target by overlaying it onto the observation. When we overlay a single channel of our target (or prediction), we refer to this as a mask which illuminates the regions of an image where a specific class is present.
- 20. 20 ✗ Recall that for deep convolutional networks, earlier layers tend to learn low-level concepts while later layers develop more high-level (and specialized) feature mappings. In order to maintain expressiveness, we typically need to increase the number of feature maps (channels) as we get deeper in the network.
- 21. 21 Solution?
- 22. Lucky for us.. One popular approach for image segmentation models is to follow an encoder/decoder structure.
- 23. U-Net Architecture.. Consists of a contracting path to capture context and a symmetric expa nding path that enables precise localization.
- 24. Advanced U-Net variants The standard U-Net model consists of a series of convolution operations for each "block" in the architecture. Proposed: swap out the basic stacked convolution blocks in favor of residual blocks. This residual block introduces short skip connections (within the block) alongside the existing long skip connections (between the corresponding feature maps of encoder and decoder modules) found in the standard U-Net structure.
- 25. Tiramisu: Full Convolution DenseNet Tiramisu adopts the UNet design with downsampling, bottleneck, and upsampling paths and skip connections. It replaces convolution and max pooling layers with Dense blocks from the DenseNet architecture. Dense blocks contain residual connections.
- 26. Defining loss function The most commonly used loss function for the task of image segmentation is a pixel-wise cross entropy loss. This loss examines each pixel individually, comparing the class predictions (depth-wise pixel vector) to our one-hot encoded target vector.
- 27. Deep Learning is an continuously-growing and a relatively new concept, the vast amount of resources can be a touch overwhelming for those either looking to get into the field, or those already engraved in it. A good way of cooping is to get a good general knowledge of machine learning and then find a good structured path to follow (be a project or research). 27 Conclusion
- 28. 28 Thanks! Any questions? You can find me at: ✗ [email protected]