Computer Vision Technique and Its Application on Moving Object Tracking
Computer vision is the science that develops the theoretical and algorithmic basis by which useful information about the world can be automatically extracted and analysed from an observed image, image set, or image sequence from computations made by special-purpose or general purpose computers (notes). The main goal of computer vision application is to produce automated recognition systems that can equivalent or eventually better than human performance. Computer vision can be used to allow new relations techniques and tie both the physical and virtual worlds. There are some techniques of computer vision that are used to produce the better quality of computer vision application.
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2. Local (neighborhood) operations: The output at a pixel depends on the input values in a neighborhood of that pixel. This more focus on a region of interest in the image. Some examples are edge detection, smoothing filters (e.g., the averaging filter and the median filter), and sharpening filters (e.g., the Laplacian filter and the gradient filter). This operation can be adaptive because results depend on the particular pixel values encountered in each image region.
3. Geometric operations: The output at a pixel depends only on the input levels at some other pixels defined by geometric transformations. Geometric operations are different from global operations, such that the input is only from some specific pixels based on geometric transformation. They do not require the input from all the pixels to make its transformation.
4. Global operations: The output at a pixel depends on all the pixels in an image. To finds the optical flow pattern in the images, the apparent velocity of the brightness pattern varies smoothly in the image. It may be independent of the pixel values in an image, or it may reflect statistics calculated for all the pixels, but not a local subset of pixels. A popular distance transformation of an image, which assigns to each object pixel the