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Block matching (BM) motion estimation plays a very important role in video coding. In a BM approach, image frames in a video sequence are divided into blocks. For each block in the current frame, the best matching block is identified inside a region of the previous frame, aiming to minimize the sum of absolute differences (SAD). Unfortunately, the SAD evaluation is computationally expensive and represents the most consuming operation in the BM process. Therefore, BM motion estimation can be approached as an optimization problem, where the goal is to find the best matching block within a search space. The simplest available BM method is the full search algorithm (FSA) which finds the most accurate motion vector through an exhaustive computation of SAD values for all elements of the search window. Recently, several fast BM algorithms have been proposed to reduce the number of SAD operations by calculating only a fixed subset of search locations at the price of poor accuracy. In this paper, a new algorithm based on Artificial Bee Colony (ABC) optimization is proposed to reduce the number of search locations in the BM process. In our algorithm, the computation of search locations is drastically reduced by considering a fitness calculation strategy which indicates when it is feasible to calculate or only estimate new search locations. Since the proposed algorithm does not consider any fixed search pattern or any other movement assumption as most of other BM approaches do, a high probability for finding the true minimum (accurate motion vector) is expected. Conducted simulations show that the proposed method achieves the best balance over other fast BM algorithms, in terms of both estimation accuracy and computational cost., Comment: 22 Pages. arXiv admin note: substantial text overlap with arXiv:1405.4721, arXiv:1406.4484

In recent years, Artificial Intelligence techniques have emerged as useful tools for solving various engineering problems that were not possible or convenient to handle by traditional methods. AI has directly influenced many areas of computer science and becomes an important part of the engineering curriculum. However, determining the important topics for a single semester AI course is a nontrivial task, given the lack of a general methodology. AI concepts commonly overlap with many other disciplines involving a wide range of subjects, including applied approaches to more formal mathematical issues. This paper presents the use of a simple robotic platform to assist the learning of basic AI concepts. The study is guided through some simple experiments using autonomous mobile robots. The central algorithm is the Learning Automata. Using LA, each robot action is applied to an environment to be evaluated by means of a fitness value. The response of the environment is used by the automata to select its next action. This procedure holds until the goal task is reached. The proposal addresses the AI study by offering in LA a unifying context to draw together several of the topics of AI and motivating the students to learn by building some hands on laboratory exercises. The presented material has been successfully tested as AI teaching aide in the University of Guadalajara robotics group as it motivates students and increases enrolment and retention while educating better computer engineers., Comment: 11 Pages

This paper shows the potential of a Lego\c{opyright} based low-cost commercial robotic platform for learning and testing prototypes in higher education and research. The overall setup aims to explain mobile robotic issues strongly related to several fields such as Mechatronics, Robotics, and Automatic Control theory. The capabilities and limitations of LEGO robots are studied within two projects. The first one involves a robotic vehicle which is able to follow several predefined paths. The second project concerns to the classical problem of position control. Algorithms and additional tools have been fully designed, applied and documented with results shown throughout the paper. The platform is found to be suitable for teaching and researching on key issues related to the aforementioned fields., Comment: 10 Pages

The automatic detection of White Blood Cells (WBC) still remains as an unsolved issue in medical imaging. The analysis of WBC images has engaged researchers from fields of medicine and computer vision alike. Since WBC can be approximated by an ellipsoid form, an ellipse detector algorithm may be successfully applied in order to recognize them. This paper presents an algorithm for the automatic detection of WBC embedded into complicated and cluttered smear images that considers the complete process as a multi-ellipse detection problem. The approach, based on the Differential Evolution (DE) algorithm, transforms the detection task into an optimization problem where individuals emulate candidate ellipses. An objective function evaluates if such candidate ellipses are really present in the edge image of the smear. Guided by the values of such function, the set of encoded candidate ellipses (individuals) are evolved using the DE algorithm so that they can fit into the WBC enclosed within the edge-only map of the image. Experimental results from white blood cell images with a varying range of complexity are included to validate the efficiency of the proposed technique in terms of accuracy and robustness., Comment: 20 Pages. arXiv admin note: text overlap with arXiv:1405.5164