Image Recognition with Deep Neural Networks

Image recognition is about finding automatic methods to identify objects and their arrangement in an image or photo. This includes classifying the image objects and determining their position in the image. The majority of DNNs for image processing are Convolutional Neural Networks (CNN). They use layers with small receptive fields (convolutions), which are shifted over the pixel matrix of the input image. They are capable of detecting local image features. In addition, pooling layers are used to aggregate features locally. Modern CNNs contain hundreds of these layers, which can successively recognize more complex image features. Some of them make fewer image classification errors than humans. Special variants have been developed to determine the position of objects in images with pixel precision. Finally, models for estimating the inaccuracy of image classifications are presented, and the influence of image distortions and intentional image manipulation on classification accuracy is discussed.