Author_Institution :
R&D Dept., Istanbul Tech. Univ., Istanbul, Turkey
Abstract :
Plant growth analysis is hard to do automatic. The burden of technique makes harder to process the algorithm. Thresholding and segmentation parts are huge part of the approaches. In this study 15 different thresholding algorithms were implemented and compared with images from field for plant growth analysis. To decrease execution time, the algorithm was implemented on GPU (Graphics Processing Unit) with CUDA (Compute Unified Device Architecture) language. Also, thresh-olding methods was applied on GPU. These are Huang´s fuzzy, Intermodes, Isodata, Li´s Minimum Cross Entropy, Kapur-Sahoo-Wong (Maximum Entropy), Mean, Minimum Error, Minimum, Moments, Otsu, Percentile, RenyiEntropy, Shanbhag, Triangle, and Yen thresholding algorithms. Each method investigated the thresholds on HSV histograms to find proper color values. After all process, threshold results for dynamic and constant values were listed and compared. Moreover, performance metrics were measured.
Keywords :
agriculture; graphics processing units; image colour analysis; parallel architectures; CUDA language; GPU based parallel image processing; HSV histograms; Huang fuzzy algorithm; Isodata algorithm; Kapur-Sahoo-Wong algorithm; Li minimum cross entropy algorithm; Otsu algorithm; RenyiEntropy algorithm; Shanbhag algorithm; Yen thresholding algorithm; color value; compute unified device architecture; graphics processing unit; hue-saturation-value; intermodes algorithm; maximum entropy; mean algorithm; minimum error algorithm; moments algorithm; percentile algorithm; plant growth analysis; segmentation part; thresholding methods; thresholding part; triangle algorithm; CUDA; Dynamic thresholding; GPU programming; Image processing; Plant Growth Analysis;
