Authors: Yong Sheng Wang, Jun Li Li, Yang Lou

Abstract: This paper proposed the concept of centroid in particle swarm optimization which is similar to physical centroid properties of objects. Similarly, we may think of a particle swarm as a discrete system of particles and find the centroid representing the entire population. Usually, it has a more promising position than worse particles among the population. In order to verify the role of centroid which can speed up the convergence rate of the algorithm, and prevent the algorithm from being trapped into a local solution early as far as possible at the same time, A Novel Centroid Particle Swarm Optimization Algorithm Based on Two Subpopulations(CPSO) is proposed. Numerical simulation experiments show that CPSO by testing some benchmark functions is better than Linear Decreasing Weight PSO (LDWPSO) in convergence speed in the same accuracy of solution case.

929

Authors: Chun Feng Liu, Xiao Li Meng, Huan Cheng Zhang

Abstract: By giving a centroid algorithm to extract pixel coordinates of each circle in the center of a circle under the coordinates, through the coordinate transformation the image coordinates can be obtained. Based on the data obtained from above to verify the model, from the specific data of the relative error, absolute error and error propagation theory to discuss the algorithm accuracy and stability. Finally using the least square method, according to the mean square error criteria for the establishment of a minimum of three-dimensional coordinate system fixed relative position of two cameras, binocular positioning mathematical models and by discussing the nature of matrix and the relationship between the relative position of the camera to calibrate the binocular digital camera.

473

Abstract: Large margin GMMs have many parallels to large margin nearest neighbors (LMNN), but with classes modeled by ellipsoids instead of each input data and its target neighbors by hyper-ellipsoid. Large margin GMMs naturally scales to large problems in multi-way classification. Based on large margin GMM classification, we develop a new classification method, i.e., energy based large margin classification of Gaussian mixture mode (ELM-GMM). Experiment shows that this new approach outperforms the large margin GMMs.

1601

Authors: Guang Yu Zhu, Jin Wang, Guo Dong Lu

Abstract: Models recommendation is always a key part in part-composition system based on sketch. In this paper, proper models are suggested via three steps. Firstly, the possible type of model parts is suggested according to the topological features. Then toy parts that have similar profile types with the inputted sketches are clustered and recommended by lineage clustering method. Finally, based on the promoted $1 recognizer, the suggested parts are sorted according to the shape similarity with the inputted sketch. Experimental results show that the models can be recommended quickly and properly via our method.

145

Authors: Zhao Xia Fu, Li Ming Wang

Abstract: As one of the crucial issues of computer vision, video object tracking is widely used in many applications, such as visual surveillance, human-computer interaction, visual transportation, visual navigation of robots, military guidance, etc. The existing object tracking algorithms in engineering applications have the huge amount of computation, which can not meet the needs of real-time system applications, and the tracking accuracy is not high. So a simple and practical video object tracking algorithm is proposed in this paper. The Otsu algorithm is used for image binarization to filter the background, and the object edge is further processed based on mathematical morphology, and thus the tracking object is more clearly. The centroid weighted method determines the location of the center of the object only by one step calculation, which makes the location more accurate. The experimental results show that the algorithm of the paper is effective for detecting and tracking of a moving object in a static scene and it has a low complexity.

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