ACADSTAFF UGM
| Title | : | Plant Recognition Using Stereo Leaf Image Using Gray-Level Co-occurrence Matrix |
| Author | : |
HERMAWAN SYAHPUTRA (1) Prof. Drs. Agus Harjoko, M.Sc., Ph.D. (2) Prof. Drs. Retantyo Wardoyo, M.Sc., Ph.D. (3) Prof. Dr.-Ing. Mhd. Reza M. I. Pulungan, S.Si., M.Sc. (4) |
| Date | : | 0 2014 |
| Keyword | : | Disparity, Plant Recognition, Stereo Vision, GLCM Disparity, Plant Recognition, Stereo Vision, GLCM |
| Abstract | : | Adequate knowledge, such as information about the unique characteristics of each plant, is necessary to identify plant. Researchers have made plant recognition based on leaf characteristics. The leaf image-based plant recognition in view of different angles is a new challenge. In this study, the research on the plant recognition was conducted based on leaf images resulted from 3D stereo camera. The 3D images are very influential in the development of computer vision theory, which can provide more detailed information of an object. One of the information that can be obtained is about the position of the object in its image with the background as well as of the camera. One of the ways used to obtain such information is to calculate the disparity. However, this method will only tell the position of the object compared to other objects without that of range. Sum Absolute Different (SAD) is a method that can be used to find the disparity value. The SAD method does not require heavy computations and long process. Before calculating the disparity, all the images should be previously segmented. The objective of this segmentation is to separate all the objects from the background. Furthermore, filtering and polynomial transformation at the results of disparity is necessary to improve the quality of resultant images. Furthermore, 22 features were extracted using GLCM features (second order statistics) of images resulted from disparity improvement. The highest accuracy of match in the recognition of plant varieties was obtained at 50 cm distance and in the recognition of three plant varieties was 83.3%. |
| Group of Knowledge | : | Ilmu Komputer |
| Original Language | : | English |
| Level | : | Internasional |
| Status | : |
Published
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