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 หัวเรื่อง:ไม่มีชื่อไทย (ชื่ออังกฤษ : Shape- and Texture-Based Fish Image Recognition System) ผู้เขียน: Chomtip Pornpanomchai, Benjamaporn Lurstwut, Pimprapai Leerasakultham, Waranat Kitiyanan สื่อสิ่งพิมพ์:pdf AbstractThis research developed a computer system capable of recognizing some fish images. The system known as the “shape- and texture-based fish image recognition system” (FIRS) consists of five subsystems—namely: 1) image acquisition, 2) image preprocessing 3) feature extraction, 4) image recognition and 5) result presentation. The experiment was conducted on 30 fish species, which consisted of 600 fish images as the training dataset and 300 fish images for testing. The system compared two recognition techniques—a Euclidean distance method (EDM) and artificial neural networks (ANN). The system was able to recognize all 30 species of the training fish images with a precision of 99.00 and 81.67% for the ANN and the EDM techniques, respectively. The average access times were 24.4 and 154.43 sec per image for the EDM and ANN techniques, respectively. |
หัวเรื่อง:ไม่มีชื่อไทย (ชื่ออังกฤษ : Thai Herb Leaf Image Recognition System (THLIRS)) ผู้เขียน:Chomtip Pornpanomchai, Supolgaj Rimdusit, Piyawan Tanasap, Chutpong Chaiyod สื่อสิ่งพิมพ์:pdf AbstractThere are many kinds of Thai herb species, so it very difficult to identify them all. The objective of this research was to build a computer system that could recognize some Thai herb leaves, using a process called the Thai herb leaf image recognition system (THLIRS). The system consisted of four main components: 1) image acquisition, 2) image preprocessing, 3) recognition and 4) display of results. In the image acquisition component, the system used a digital camera to take a leaf picture with white paper as the background. A one-baht coin was photographed beside the leaf in order to provide a scale for comparison. In the image preprocessing component, the system applied several image-processing techniques to prepare a suitable image for the recognition process. In the recognition component, the system extracted 13 features from the leaf image and used a k-nearest neighbor (k-NN) algorithm in the recognition process. In the result display component, the system displayed the results of the classification. The experiment involved 32 species of Thai herbs, with more than 1,000 leaf images. The system was trained with 656 herb leaf images and was tested using 328 leaf images for a training dataset and 30 leaf images for an untrained dataset. The precision rate of the THLIRS of the training dataset was 93.29, 5.18 and 1.53% for match, mismatch and unknown, respectively. Moreover, the precision rate of the THLIRS of the untrained data set was 0, 23.33 and 76.67% for match, mismatch and unknown, respectively. |
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 Researcherดร. ธีรศักดิ์ เอโกบล, ผู้ช่วยศาสตราจารย์ที่ทำงาน:ภาควิชาพันธุศาสตร์ คณะวิทยาศาสตร์ สาขาที่สนใจ:ชีวสารสนเทศ และโปรตีโอมิกส์ของแบคทีเรีย (Bioinformatics & Microbial Proteomics), ชีววิทยาของสัตว์จำพวกหอย(Biology of Mollusc) Resume |
 Researcherดร. ปราโมทย์ ชำนาญปืน, รองศาสตราจารย์ที่ทำงาน:ภาควิชาสัตววิทยา คณะวิทยาศาสตร์ สาขาที่สนใจ:Cell and Molecular Biology, Systems Biology, Lipid Metabolism in Eukaryotic cells, Histochemistry, Bioinformatics Resume |
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