No abstract

2D histogram-based thresholding methods, in which the histogram is computed from local image features, have better performance than 1D histogram-based methods, but they take much more computation time. In this paper, we present a Rényi entropic multilevel thresholding (REMT) method based on a 2D direction histogram constructed from pixel values and local directional features. In addition to presenting a fast recursive method for REMT, we propose the Rényi entropic artificial bee colony multilevel thresholding (REABCMT) method to quickly find the optimal threshold values. In order to demonstrate the efficacy of REABCMT, three versions of this method are compared in terms of computation time and optimal threshold values. In addition, the segmentation performance of REABCMT is also evaluated by comparing it with two other methods to show its effectiveness. Moreover, in order to evaluate the efficiency and stability of using the ABC algorithm in the search for threshold values, genetic algorithm (GA) and particle swarm optimization (PSO), two common optimization algorithms, are also compared with it.

Abstract-A mechanism to generate asymmetry wing flapping amplitude for the flapping-type Micro Air Vehicle (MAV) system was proposed in this paper. The mechanism concept was inspired from the flapping motion of an insect: Diptera. The insects conduct the asymmetric wing flapping amplitude to generate rotational torque to control flight orientation without using a tail rudder. The mechanism using Bio-Metal Fiber actuator was developed and evaluated experimentally.

[abstFig src='/00280003/16.jpg' width=""155"" text='Overview of a small size underwater robot' ] In abalone fishing with spearing, size of abalone which was allowed to catch is strictly controlled in Japanese law. Therefore, fisherman usually judge size by observing from a boat. However, this is difficult, due to aging. Therefore, we have been developing an underwater robot to assist fisherman in determining abalone size by using a stereo camera. The robot is small size and uses 6 thrusters to achieve 5 DOF motion in an underwater environment.

With regard to the improvement of image quality, image enhancement is an important process to assist human with better perception. This paper presents an automatic image enhancement method based on Artificial Bee Colony (ABC) algorithm. In this method, ABC algorithm is applied to find the optimum parameters of a transformation function, which is used in the enhancement by utilizing the local and global information of the image. In order to solve the optimization problem by ABC algorithm, an objective criterion in terms of the entropy and edge information is introduced to measure the image quality to make the enhancement as an automatic process. Several images are utilized in experiments to make a comparison with other enhancement methods, which are genetic algorithm-based and particle swarm optimization algorithm-based image enhancement methods.