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Accelerating Convolutional Neural Networks (CNNs) on GPUs usually involves two stages: training and inference. Traditionally, this two-stage process is deployed on high-end GPU-equipped servers. Driven by the increase in compute power of desktop and mobile GPUs, there is growing interest in performing inference on various kinds of platforms. In contrast to the requirements of high throughput and accuracy...
The calculation of binary iris codes from feature values (e.g., the result of Gabor transform) is a key step in iris recognition systems. Traditional binarization method based on the sign of feature values has achieved very promising performance. However, currently, little research focuses on a deeper insight into this binarization method to produce iris codes. In this paper, we illustrate the iris...
License Plate Recognition (LPR) is a very important research topic in computer vision of ITS. License plate location is the key step of LPR. Though numerous of techniques have been developed, most approaches work only under restricted conditions such as fixed illumination, limited vehicle license plates,and simple backgrounds. This paper attempts to use the AdaBoost algorithm to build up classifiers...
License plate locating has been the bottleneck of Automatic Vehicle Recognition System. In order to improve the speed and accuracy of license plate locating, this paper proposes a simple and practical method of license plate locating. This method uses the adaboost algorithm to locate license plate. We can first enhance the texture features in the vertical direction of the plate through image preprocessing...
For detecting rice disease early and accurately, we presented an application of image processing techniques and Support Vector Machine (SVM) for detecting rice diseases. Rice disease spots were segmented and their shape and texture features were extracted. The SVM method was employed to classify rice bacterial leaf blight, rice sheath blight and rice blast. The results showed that SVM could effectively...
The proper extraction of road seeds is the premier step of road network extraction from high resolution remote sensing images. An optimal road seed extraction algorithm is proposed. Firstly, Canny-Deriche edge detection and spatial FCM (fuzzy c means) region extraction are performed separately to detect the details.Secondly, an averaged Hausdorff distance is introduced to evaluate the difference between...
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