| Abstrakt: |
Object recognition is a challenging task in image processing and computer vision. In this paper, segmentation, feature extraction and classification methods are done for elephant recognition. Thresholding based segmentation technique is used for image segmentation and k-NN classifier is used for object recognition based on the shape features of the segmented image. Infrared elephant images are considered for experimentation. The database created by us for this type of object recognition includes elephant, bear, horse, pig, tiger, and cow and lion images. The recognition rate is calculated for performance evaluation. However, implementing such algorithms on software consumes more time as image sizes and bit depths grow larger. Hence this paper aims at hardware implementation of elephant recognition to reduce the computational time. The proposed hardware is prototyped inVirtex-4 xc4vlx25 FPGA using Xilinx System Generator (XSG) tool. The hardware/software co-simulation feature allows the input and output to be displayed on Matlab window while the processing is done through FPGA. The results indicate that when the category is elephant and if the recognition status is “yes”, recognition rate is 100%. If the category is not an elephant and if the recognition status is “no”, recognition rate is still 100% also indicates, the approach is successful in elephant recognition and the computation of segmentation algorithm and shape feature extraction (area, centroid, equivdiameter) in hardware reduces the computational time of elephant recognition by 89.65% as compared to software computation. |
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