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A Novel Passive Contour Model for Visual Tracking – We propose an extension of the standard Active Contour Model (ACM) for tracking, where the target point is the target of a visual tracking system as well as a background object. The objective of the ACM is to provide a better and more accurate tracking of […]

Semi-supervised learning in Bayesian networks – We propose a deep reinforcement learning (RL) approach to online learning (EL), specifically, deep reinforcement learning (RL). For RL, we propose a learning algorithm, which learns a model of an agent by learning the state of the agent. At the end of this model, the agent is able to […]

A Deep Generative Model for 3D Object Recognition with Densely Convolutional Neural Networks – We present a new approach to deep learning that combines a learned representation of the problem with a supervised learning method. We propose a novel learning method that relies on supervised deep generative models to learn to represent a model in […]

Identifying What You Are Looking For: Task-oriented Attentional Features for Video Object Segmentation – Recent work has shown that deep neural network (DNN) can be used to generate more informative visual information than traditional DNNs. However, these CNNs cannot be applied to a vision task due to their limited spatial scale. In this paper, we […]

Learning Hierarchical Features of Human Action Context with Convolutional Networks – Recently, deep neural networks have been applied to a wide variety of tasks, mostly in the context of supervised learning of sequential decision-making. We describe a model-based approach for the task of sequence summarization that is able to model the decision processes of a […]

Multi-view Segmentation of 3D Biomedical Objects – This paper proposes a new method for detecting high-level 3D objects using optical coherence tomography (OC) and an imaging filter (Filters). In addition, we have recently conducted experiments with 3D CT scans with a novel technique for detection of high-level 3D objects using an optical flow and an […]

The Spatial Proximal Projection for Kernelized Linear Discriminant Analysis – Proximal matrix functions in the form of a vector-valued matrix are considered to be a fundamental dimension in a variety of fields. The use of a polynomial point (PP) matrix for solving polynomial-time problem solving (PCS) has been explored as a possible solution within an […]

Learning a Human-Level Auditory Processing Unit – We propose a deep generative model that can learn to produce a variety of emotions. Our model includes an external representation of the emotions of a given scene in which the emotions of human beings are encoded. To learn a complex emotion representation for the scenes, we combine […]

Mixture of DAG-causal patterns and conditional probability in trainable Bayesian networks – The Bayesian network has the opportunity of having a fundamental role in many problems in finance. With the interest of finance, there has been a large effort in applying Bayesian networks in the financial sector. In this paper, we describe a new algorithm […]

Learning the Structure of Concept Networks with a Sandwiching Process – In this paper, a new structure of knowledge representation is proposed for this system. One of the main challenges in this system is to model semantic interactions among multiple objects with no human-annotated knowledge. One of the main tasks in this system is to […]