外文文獻(xiàn)及翻譯-fpga實(shí)現(xiàn)實(shí)時(shí)適應(yīng)圖像閾值-文庫吧資料
2025-05-22 12:19本頁面
【正文】 s. Each category is applicable to a specific domain and proposing a general neural work to solve all problems seems to be impossible. One of the proposed solutions, which is applicable to the classification and image segmentation problems, is Unsupervised Competitive Learning [19]. 。 region interiors should be without artifacts。 Engineering, York University ABSTRACT： A general purpose FPGA architecture for realtime thresholding is proposed in this paper. The hardware architecture is based on a weightbased clustering threshold algorithm thattakes the thresholding as a problem of clustering background and foreground pixels. This method employs the clustering capability of a twoweight neural work to find the centriods of the two pixel groups. The image threshold is the average of these two centriods. The proposed method is an adaptive thresholding technique because for every input pixel the closest weight is selected for updating. Updating is based on the difference between the input pixel gray level and the associated weight, scaled by a learning rate factor. The hardware system is implemented on a FPGA platform and consists of two functional blocks. The first block is obtaining the threshold value for the image frame, another block applies the threshold value to the frame. This parallelism and the simple hardware ponent of both blocks make this approach suitable for realtime applications, while the performance remains parable with the Otsu technique frequently used in offline threshold determination. Results from the proposed algorithm are presented for numerous examples, both from simulations and experimentally using the FPGA. Although the primary application of this work is to centroiding of laser spots, its use in other applications will be discussed. Keywords: Real time thresholding, Adaptive thresholding, FPGA implementation, neural work. 1 INTRODUCTION Image binarization is one of the principal problems of the image processing applications. For extracting useful information from an image we need to divide it into distinctive ponents . background and foreground objects for further analyses. Often the gray level pixels of the foreground objects are quite different from background. Several superior methods for image binarization have been reported [1]. The main goal of most of these is high efficiency in term of performance rather than speed. However for some applications, especially those involving customized hardware and real time applications, the speed is the key requirement. The implementation of a fast and simple thresholding technique has many applications in practical imaging systems. For example, onchip image processing integrated with CMOS imager sensors is prevalent in a variety of imaging system. In such systems the realtime processing and related information are vital. Applications employing realtime thresholding include robotics, automobiles, object tracking, and laser range finding. In laser range finding 5 where the range of an object in motion is determined, the captured image is binarized. The thresholding technique is applied to separate the laser spot from the background and to locate the spot centroid. This application is the sc
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