Local binary features for texture classification

TL;DR: A comprehensive survey of algorithms proposed for binary neural networks, mainly categorized into the native solutions directly conducting binarization, and the optimized ones using techniques like minimizing the quantization error, improving the network loss function, and reducing the gradient error are presented.

TL;DR: Zhang et al. as mentioned in this paper proposed a convolutional neural network (CNN) based denoiser that can exploit the multi-scale redundancies of natural images and leverages the prior of the observation model.

TL;DR: In this paper, a survey of instance segmentation, its background, issues, techniques, evolution, popular datasets, related work up to the state of the art and future scope have been discussed.

TL;DR: This survey provides a comprehensive survey of the texture feature extraction methods and identifies two classes of methods that deserve attention in the future, as their performances seem interesting, but their thorough study is not performed yet.

TL;DR: The state-of-the-art performance of CNNs was achieved by Deep Convolutional Neural Networks (DCNNs) as discussed by the authors, which consists of five convolutional layers, some of which are followed by max-pooling layers, and three fully-connected layers with a final 1000-way softmax.

TL;DR: These results indicate that the easily computable textural features based on gray-tone spatial dependancies probably have a general applicability for a wide variety of image-classification applications.

TL;DR: A generalized gray-scale and rotation invariant operator presentation that allows for detecting the "uniform" patterns for any quantization of the angular space and for any spatial resolution and presents a method for combining multiple operators for multiresolution analysis.

TL;DR: This work considers the problem of automatically recognizing human faces from frontal views with varying expression and illumination, as well as occlusion and disguise, and proposes a general classification algorithm for (image-based) object recognition based on a sparse representation computed by C1-minimization.