Current Issue : July - September Volume : 2014 Issue Number : 3 Articles : 5 Articles
This study was focused on the multicolor space which provides a better specification of the color and size of the apple in an image.\nIn the study, a real-time machine vision system classifying apples into four categories with respect to color and size was designed.\nIn the analysis, different color spaces were used. As a result, 97% identification success for the red fields of the apple was obtained\ndepending on the values of the parameter ââ?¬Å?aââ?¬Â of CIE L*a*b*color space. Similarly, 94% identification success for the yellow fields\nwas obtained depending on the values of the parameter Y of CIE XYZ color space.With the designed system, three kinds of apples\n(Golden, Starking, and Jonagold) were investigated by classifying them into four groups with respect to two parameters, color and\nsize. Finally, 99% success rate was achieved in the analyses conducted for 595 apples....
Image saliency detection has become increasingly important with the development of intelligent identification and machine vision\ntechnology. This process is essential for many image processing algorithms such as image retrieval, image segmentation, image\nrecognition, and adaptive image compression. We propose a salient region detection algorithm for full-resolution images. This\nalgorithm analyzes the randomness and correlation of image pixels and pixel-to-region saliency computation mechanism. The\nalgorithm first obtains points withmore saliency probability by using the improved smallest univalue segment assimilating nucleus\noperator. It then reconstructs the entire saliency region detection by taking these points as reference and combining them with\nimage spatial color distribution, as well as regional and global contrasts. The results for subjective and objective image saliency\ndetection show that the proposed algorithm exhibits outstanding performance in terms of technology indices such as precision and\nrecall rates....
This paper describes a hardware architecture for real-time image component labeling and the computation of image component\nfeature descriptors. These descriptors are object related properties used to describe each image component. Embedded machine\nvision systems demand a robust performance and power efficiency as well as minimum area utilization, depending on the deployed\napplication. In the proposed architecture, the hardware modules for component labeling and feature calculation run in parallel.\nA CMOS image sensor (MT9V032), operating at a maximum clock frequency of 27MHz, was used to capture the images. The\narchitecture was synthesized and implemented on a Xilinx Spartan-6 FPGA. The developed architecture is capable of processing\n390 video frames per second of size 640 Ã?â?? 480 pixels. Dynamic power consumption is 13mWat 86 frames per second....
Computer-based sensors and actuators such as global positioning systems, machine vision, and laser-based sensors have\nprogressively been incorporated into mobile robots with the aim of configuring autonomous systems capable of shifting operator\nactivities in agricultural tasks. However, the incorporation of many electronic systems into a robot impairs its reliability and\nincreases its cost. Hardware minimization, as well as software minimization and ease of integration, is essential to obtain feasible\nrobotic systems. A step forward in the application of automatic equipment in agriculture is the use of fleets of robots, in which\na number of specialized robots collaborate to accomplish one or several agricultural tasks. This paper strives to develop a system\narchitecture for both individual robots and robots working in fleets to improve reliability, decrease complexity and costs, and permit\nthe integration of software from different developers. Several solutions are studied, from a fully distributed to a whole integrated\narchitecture in which a central computer runs all processes. This work also studies diverse topologies for controlling fleets of robots\nand advances other prospective topologies. The architecture presented in this paper is being successfully applied in the RHEA fleet,\nwhich comprises three ground mobile units based on a commercial tractor chassis....
In order to find the moldy maize kernels quickly, a method based on machine vision was proposed in this paper. Firstly, images of\nmaize kernels were taken by the moldy maize sorting equipment, and three parts of every kernel, that is, moldy plaques, healthy\nendospermand healthy embryo, were selected fromthese images. Then a threshold was set in R channel by analyzing color features\nof those three parts in RGB model. In this method, moldy plaques can be identified roughly. After that the location of the moldy\nplaques on the kernels was studied, a circle, whose centre was approximately the centroid of amaize kernel and diameter was about\nthe width of embryos, was set to exclude the interference caused by shadow. This method, with the accuracy of 92.1%, laid a good\nfoundation for the further study of moldy maize sorting equipment....
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