Vision Deep Learning

In computer vision, deep learning has proven useful to extract patterns from images. Deep learning uses a neural network and optimization to relate features (pixels) to a desired label. As opposed to Cascade Classifiers, deep learning does not need specialized preprocessing of the image to develop application-specific features. The pixels from the image are processed through multiple linear and nonlinear layers to predict an output. Deep learning generally requires many thousands of labeled examples to learn. A Convolutional Neural Network (CNN) transforms the input image with a specialized connectivity structure. It stacks multiple stages of feature extractors. The higher stages compute more global, invariant features with a classification layer at the end. Feed-forward feature extraction convolves input with learned filters, transforms with non-linearity (sigmoid, hyperbolic tangent, rectified linear units), performs spatial pooling, and finally normalizes to create a feature map. With convolution the dep