Deep Learning for Computer Vision with MATLAB

Computer vision engineers have used machine learning techniques for decades to detect objects of interest in images and to classify or identify categories of objects. They extract features representing points, regions, or objects of interest and then use those features to train a model to classify or learn patterns in the image data. In traditional machine learning, feature selection is a time-consuming manual process. Feature extraction usually involves processing each image with one or more image processing operations, such as calculating gradient to extract the discriminative information from each image. Deep learning algorithms can learn features, representations, and tasks directly from images, text, and sound, eliminating the need for manual feature selection.