Research On Density Based Image Processing Algorithms And Application

This paper focuses on the low level image processing: image smoothing and low level feature detection. The classical image processing algorithms are based on the signal processing theory, and using two-dimensional linear filter for smoothing image and edge detection etc. The linear filters can well process the gray-level image. But for color images, the linear mean of the colors in neighborhood maybe different from all original colors in neighborhood. To solve this problem, a new density based image analysis method is developed. The method takes the image as a distribution of image pixels in the joint space of image plane and color space, and uses the multi-dimensional filter to analysis the image, i.e. the image pixel density estimation. Different from traditional method, the new analysis method processes the information of each color respectively, so it can more accurately analyze the color images than the traditional linear filter methods. After it analyses present approaches, the paper gives the following results by the density based image analysis.(1)The density based image smoothing algorithm: Mode filter.The image smoothing is an important method to reduce the noises. After analyzing the density space of the neighborhood, two popular smoothing methods, the mean filter and median filter, all take the central tendency of distribution of the neighborhood pixels in the color space as the smoothing result. According to this property of the smoothing, a novel Mode Filter is proposed, which uses the mode of the distribution of the pixels in the neighborhood as the smoothing result. The mode filter selects the color value with most pixels as the smoothing result. The mode filter will not blend the colors as mean filter, and does not need sort the pixels, so it can preserve the original color each pixel while reducing the noise in color image. After analysis, the popular anisotropic diffusion filters, such as the Bilateral filter, mean shift smoothing and adaptive filter, are all the special mode filter which take the local mode as the smoothing result. So the mode filter not only can enhance the edges, but also can reduce the salt and pepper noise which is preserved by the anisotropic diffusion filters. The experiments shows, the mode filter is a general smoothing filter with the advantages of all existing filters.(2) The density gradient based edge detection.In the image density gradient field, the density gradients around the edges are all point away from the edges. According to this property, a new density gradient based edge detector is proposed. The new method compares the directions density gradients of adjacent pixels to locate the edges. Different from the traditional intensity (or color) gradient based methods outputting the edges of the mixture color, the density gradients based method represent each color edges respectively, and can precisely located the edges of every color regions. Since the directions of the density gradients are invariant to the detection scale, the new detector can preserve the shapes of each color regions in all detection scales, and it can be used to separate the edges of the color regions by their size. This property is not possessed by the existing edge detector.(3) The density gradient based corner detection.To overcome the limitation of traditional intensity based interest point detector which is sensitive to texture region and most detected corners gather in high contrast region, a novel density based interest point detector is proposed. In the density gradient field, the density gradients around the corners are point away in different directions. So the new detector uses the density gradient instead of the intensity gradient to build the Harris corner detector. Comparing with the intensity based method, the density based detector can reduce the noise corners in texture region, and the detected corners distribute more uniformly.The novel algorithms are used in the"short tracking skating analysis system"to match and align the race pictures taken by a rotational camera, and then realize the measurement of the skating track of the athlete. In large detection scale, the new density gradient based edge detector can not only filter out the small moving regions, but also can preserve the shape of large static region. So the edge maps detected by the new detector can be used to matching the racing images precisely and stably. The new density based corner detector is used to process the images with the imbalance illumination, and makes the corners uniformly distribute. As a result, the racing images are robustly aligned.