Constraint-Based, Minimum Description Length Computation and Total Sampling for Efficient Constraint Problems – Non-parametric sparse coding (NSCC) is an efficient sparse coding algorithm for sparse coding which has been extensively studied in the literature. Although NSCC works well for many real-world problems, its simplicity and high computational complexity makes it difficult to learn the code to solve these problems. In this paper, we demonstrate that NSCC, using a sparse coding algorithm, can be solved to the best of our knowledge without any sparsity and by a single sparse coding algorithm in two steps of learning. Moreover, we prove that the problem of learning a sparse coding algorithm to solve non-parametric sparse coding is NP-hard. The results show the effectiveness of NSCC, and we hope that this has not hampered the other methods to solve non-parametric sparse coding.

Automatic camera tracking is essential in many applications, such as surveillance of pedestrians. To address this problem, recently several methods have been proposed by Koehler, who is well known for having developed a computer vision framework. The purpose of this paper is to examine the accuracy of this framework and propose a novel algorithm for detecting the shape and dynamics of a simulated object from three frames taken by an artificial camera. Based on this framework, we propose a new approach based on the three dimensional data in the object space. The experimental results show that the proposed method is able to track the object, provide a consistent view over its motion, and accurately discriminate an object from a human. In addition, it is the first approach that is capable of tracking objects from different angles with a view from within the 3D space.

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Predicting the shape and dynamics of objects in the natural environment via cascaded clusteringAutomatic camera tracking is essential in many applications, such as surveillance of pedestrians. To address this problem, recently several methods have been proposed by Koehler, who is well known for having developed a computer vision framework. The purpose of this paper is to examine the accuracy of this framework and propose a novel algorithm for detecting the shape and dynamics of a simulated object from three frames taken by an artificial camera. Based on this framework, we propose a new approach based on the three dimensional data in the object space. The experimental results show that the proposed method is able to track the object, provide a consistent view over its motion, and accurately discriminate an object from a human. In addition, it is the first approach that is capable of tracking objects from different angles with a view from within the 3D space.