Current Issue : January - March Volume : 2013 Issue Number : 1 Articles : 4 Articles
Over the last several years there has been a renewed interest in the automation of harvesting of\r\nfruits and vegetables. The two major challenges in the automation of harvesting are the\r\nrecognition of the fruit and its detachment from the tree. This paper deals with fruit recognition\r\nand it presents the development of a machine vision algorithm for the recognition of orange\r\nfruits. The algorithm consists of segmentation, region labeling, size filtering, perimeter extraction\r\nand perimeter-based detection. In the segmentation of the fruit, the orange was enhanced by\r\nusing the red chromaticity coefficient which enabled adaptive segmentation under variable\r\noutdoor illumination. The algorithm also included detection of fruits which are in clusters by\r\nusing shape analysis techniques. Evaluation of the algorithm included images taken inside the\r\ncanopy (varying lighting condition) and on the canopy surface. Results showed that more than\r\n90% of the fruits visually recognized in the images were detected in the 110 images tested with a\r\nfalse detection rate of 4%. The proposed segmentation was able to deal with varying lighting\r\ncondition and the perimeter-based detection method proved to be effective in detecting fruits in\r\nclusters. The development of this algorithm with its capability of detecting fruits in varying\r\nlighting condition and occlusion would enhance the overall performance of robotic fruit\r\nharvesting....
A new high-speed foreign fiber detection system with machine vision is proposed for removing\r\nforeign fibers from raw cotton using optimal hardware components and appropriate algorithms\r\ndesigning. Starting from a specialized lens of 3-charged couple device CCD camera, the system\r\napplied digital signal processor DSP and field-programmable gate array FPGA on image\r\nacquisition and processing illuminated by ultraviolet light, so as to identify transparent objects\r\nsuch as polyethylene and polypropylene fabric from cotton tuft flow by virtue of the fluorescent\r\neffect, until all foreign fibers that have been blown away safely by compressed air quality can\r\nbe achieved. An image segmentation algorithm based on fast wavelet transform is proposed\r\nto identify block-like foreign fibers, and an improved canny detector is also developed to\r\nsegment wire-like foreign fibers from raw cotton. The procedure naturally provides color image\r\nsegmentation method with region growing algorithm for better adaptability. Experiments on a\r\nvariety of images show that the proposed algorithms can effectively segment foreign fibers from\r\ntest images under various circumstances....
The evaluation of yield-related traits is an essential step in rice breeding, genetic research and functional genomics\r\nresearch. A new, automatic, and labor-free facility to automatically thresh rice panicles, evaluate rice yield traits, and\r\nsubsequently pack filled spikelets is presented in this paper. Tests showed that the facility was capable of\r\nevaluating yield-related traits with a mean absolute percentage error of less than 5% and an efficiency of 1440\r\nplants per continuous 24 h workday....
We propose a new dense local stereo matching framework for gray-level images based on an adaptive local segmentation using a\r\ndynamic threshold.We define a new validity domain of the frontoparallel assumption based on the local intensity variations in the\r\n4 neighborhoods of the matching pixel. The preprocessing step smoothes low-textured areas and sharpens texture edges, whereas\r\nthe postprocessing step detects and recovers occluded and unreliable disparities. The algorithm achieves high stereo reconstruction\r\nquality in regions with uniform intensities as well as in textured regions. The algorithm is robust against local radiometrical\r\ndifferences and successfully recovers disparities around the objects edges, disparities of thin objects, and the disparities of the\r\noccluded region.Moreover, our algorithm intrinsically prevents errors caused by occlusion to propagate into nonoccluded regions.\r\nIt has only a small number of parameters. The performance of our algorithm is evaluated on theMiddlebury test bed stereo images.\r\nIt ranks highly on the evaluation list outperforming many local and global stereo algorithms using color images. Among the local\r\nalgorithms relying on the frontoparallel assumption, our algorithm is the best-ranked algorithm. We also demonstrate that our\r\nalgorithm is working well on practical examples as for disparity estimation of a tomato seedling and a 3D reconstruction of a face....
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