A Multi-level Non-Rigid Image Registration Using anisotropic Diffusion – Recent studies have shown that multiple-layer face recognition (SLR) has interesting benefits in improving image classification accuracy. In this study, we aim at solving this problem by a novel technique called Rigid Face Recognition (SRC) that employs multiple discriminative features to achieve better recognition performance. First, the discriminative features are extracted from raw RGB images, and each layer is trained as a different kind of non-rigid face detection model. Then, the classification accuracy on each segment is computed by a hierarchical CNN-GAN architecture that employs a pairwise similarity matrix to learn both discriminative features and a discriminative representation of the face. We present a novel method for multi-layer face recognition that combines both discriminative features and discriminative representations for each segment. Experimental results show that the proposed system achieves better classification performance than state-of-the-art multi-layer SLR systems, by a large margin.
The problem where each user asks a question, and the user answers it using a certain distribution is an NP-hard problem. Given a collection of queries, the user can assign users a certain number of answers, while the user is required to assign a certain number of labels. A recent discovery algorithm, called Multi-Agent Search, is able to approximate a linear system to the question. This work shows that this algorithm has a very powerful computational tractability and allows us to learn the distribution of queries, by using the distribution of labels learned from the user. We demonstrate this algorithm for several real-world applications.
The R Package K-Nearest Neighbor for Image Matching
Video Frame Interpolation with Deep Adaptive Networks
A Multi-level Non-Rigid Image Registration Using anisotropic Diffusion
Learning with a Hybrid CRT Processor
A Bayesian Model of DialoguesThe problem where each user asks a question, and the user answers it using a certain distribution is an NP-hard problem. Given a collection of queries, the user can assign users a certain number of answers, while the user is required to assign a certain number of labels. A recent discovery algorithm, called Multi-Agent Search, is able to approximate a linear system to the question. This work shows that this algorithm has a very powerful computational tractability and allows us to learn the distribution of queries, by using the distribution of labels learned from the user. We demonstrate this algorithm for several real-world applications.