A New Sparse Representation Algorithm for 3D Human Pose Estimation

This paper addresses the problem of recovering 3D human pose from single 2D images using Sparse Representation. While recent Sparse Representation (SR) based 3D human pose estimation methods have attained promising results estimating human poses from single images, their performance depends on the availability of large labeled datasets. However, in many real world applications, accessing to sufficient labeled data may be expensive and/or time consuming, but it is relatively easy to acquire a large amount of unlabeled data. Moreover, all SR based 3D pose estimation methods only consider the information of the input feature space and they cannot utilize the information of the pose space. In this paper, we propose a new framework based on sparse representation for 3D human pose estimation which uses both the labeled and unlabeled data. Furthermore, the proposed method can exploit the information of the pose space to improve the pose estimation accuracy. Experimental results show that the performance of the proposed method is significantly better than the state of the art 3D human pose estimation methods

Employing Multi Attributes Decision Making Techniques for Rating the Supply Chain Risk Factors (Case Study: the field of information technology in small and medium enterprises)

Supply chain is a network of independent organizations that cooperate with each other, in order to control, manage and improve material and information flow from suppliers to final consumers to meet customer satisfaction. In Industry, especially industries that are moving towards the longer supply chain, the issue of supply chain risk management is important. The risk management process focuses on the identification of risks and reduction of theirs adverse effects. Risk management process in supply chain includes four-phase identification, assessment, control or management and tracking of risky events. The aim of risk assessment is risk measurement based on various attributes. A key part of this process is risk rating; in this article first, with study of multi criteria decision making, it was tried to recognize the appropriate model for ranking of risk factors in order to determine their priority and allocate resources to deal with each of them. More, the field of information technology in small and medium enterprises was studied and after recognizing of risk factors of field of information technology in small and medium enterprises, Electre, Topsis and Taxonomy techniques to rank these factors were used. More and also with calculating the Spearman correlation coefficient for detecting convergence of ratings, using the mean method, the final ranking risk factors in the supply chain field were acquired