Multibiometric in Image Processing: A Survey – In this paper, we review the results of the recently proposed Convolutional Neural Networks (CNNs) for semantic segmentation problems. The CNNs have a rich set of models and tools, but the current state of the art of CNNs on this data is largely based on using the deep convolutional features (DCNNs). Convolutional CNNs represent semantic segmentation problems by embedding the semantic segmentation problem into a multi-level representation. However, most of the existing CNNs use the deep network for its full-fledged semantic segmentation. In this paper, we propose a new CNN architecture called Deep Network-CNN for recognizing semantic segmentations. Through combining the information at different levels in a CNN and using the corresponding CNN models and tools, it is able to predict semantic segmentation by using a hierarchical CNN representation, which was used in the task of recognition of the category of words. Extensive experiments on various tasks demonstrate that it is very powerful in terms of performance performance, both for semantic segmentation as well as classification.
Person recognition is a vital task in many computer-based applications, but human performance is typically too poor to be considered a benchmark. However, it’s very important to consider the role of the human to make the decisions regarding what person to recognize. This paper presents a novel approach for face recognition in action videos, which is based on a deep network. The network is trained for a multi-dimensional space (with both a facial and a visual input), which is capable to capture the human’s face attributes. Experiments show that the proposed model is capable of recognising human expressions (including the facial-expression similarity level) of human. Moreover, it makes it possible to identify people that have been described as being similar to the human. Therefore, the proposed approach may be useful to users of action-based video games.
The Generalized Stochastic Block Model and the Generalized Random Field
Learning Robust Visual Manipulation Perception for 3D Action-Visual AI
Multibiometric in Image Processing: A Survey
TernGrad: Temporal Trees that scale to the error of Measurements
Generating a Robust Multimodal Corpus for Robust Speech RecognitionPerson recognition is a vital task in many computer-based applications, but human performance is typically too poor to be considered a benchmark. However, it’s very important to consider the role of the human to make the decisions regarding what person to recognize. This paper presents a novel approach for face recognition in action videos, which is based on a deep network. The network is trained for a multi-dimensional space (with both a facial and a visual input), which is capable to capture the human’s face attributes. Experiments show that the proposed model is capable of recognising human expressions (including the facial-expression similarity level) of human. Moreover, it makes it possible to identify people that have been described as being similar to the human. Therefore, the proposed approach may be useful to users of action-based video games.