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We address an important issue of fully low-cost and low-complex video compression for use in resource-extremely limited sensors/devices. Conventional motion estimation-based video compression or distributed video coding (DVC) techniques all rely on the high-cost mechanism, namely, sensing/sampling and compression are disjointedly performed, resulting in unnecessary consumption of resources. That is,...
We present a low resolution face recognition technique based on a Convolutional Neural Network approach. The network is trained to reconstruct a reference per subject image. In classical feature-based approaches, a first stage of features extraction is followed by a classification to perform the recognition. In classical Convolutional Neural Network approaches, features extraction stages are stacked...
This paper discusses the effectiveness of deep auto-encoder neural networks in visual reinforcement learning (RL) tasks. We propose a framework for combining the training of deep auto-encoders (for learning compact feature spaces) with recently-proposed batch-mode RL algorithms (for learning policies). An emphasis is put on the data-efficiency of this combination and on studying the properties of...
In this paper, we examine the problem of learning a representation of image transformations specific to a complex object class, such as faces. Learning such a representation for a specific object class would allow us to perform improved, pose-invariant visual verification, such as unconstrained face verification. We build off of the method of using factored higher-order Boltzmann machines to model...
Sparse representation for machine learning has been exploited in past years. Several sparse representation based classification algorithms have been developed for some applications, for example, face recognition. In this paper, we propose an improved sparse representation based classification algorithm. Firstly, for a discriminative representation, a non-negative constraint of sparse coefficient is...
This paper presents an algorithm based on the method of supervised machine learning and multi-keyframes to achieve markerless augmented reality (AR) application when there is a locally planar object in the scene. The main goal is to solve the problem of AR tracking in outdoor environment by only using vision and natural features. Instead of tracking fiducial markers, we track natural keypoints, during...
In this paper, first we discuss human motion analysis using the temporal template methodology. This methodology deals with the creation of motion history images (MHIs). Hu moment invariants are calculated from MHIs, for feature description. Two types of training datasets based on Hu moment invariants, have been developed. One training dataset is of 105times7 elements and other consists of 200times7...
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