Study Of Middle And Low Level Algorithm In Image Understanding

Image understanding is a subject about interpreting image using computers by imitating the biological systems of human beings. There are two parts involved, which are capturing the image content and establishing the relationship among objects. Since the vision theory proposed by D.Marr in the 1980s, researchers make great progress in the area of image understanding. However, it encounters two difficulties, which are (1) designing adaptive algorithms capable of effectively understanding the most common and complicated scene, and (2) completing vision task effectively and understanding images in real time. This thesis emphasizes on the study of middle and low level algorithm in image understanding, including the section of image restoration, image segmentation, character extraction, color mapping on texture images, and etc.Due to the limitation of imaging condition and the property of the object surface, there are sometimes defects of nonuniform illumination in the captured images. To correct these defects, an algorithm of restoration and enhancement based on background function fitting is developed. In the algorithm, the uniformly spaced background sample pixels are automatically selected, and then they are used in polynomial fitting for background calculation. By adjusting the factors according to the statistical parameters, image enhancement can be achieved.In the part of character extraction, the property of multi-resolution in wavelet is carefully studied and applied in two algorithms, one is character recognition, and the other is license plate extraction. In the algorithm of character recognition, the wavelet transform is firstly applied on the linear moments and the image itself, and the statistical variable is then calculated as the input of the three-lays back-propagation neural network for recognition. For the algorithm of license plate extraction, thanks to the excellent performace of wavelet analysis in edge extraction, the locating of the plate region is more accurate and robust, compared with the former algorithm utilizing vertical edge texture of the plate.Two algorithms-hierarchical histogram analysis and watershed of difference (WOD)-are presented in the area of color image segmentation. In the former algorithm, the segmentation is reached by analyzing the three one-dimensional histograms in CIELAB color space recursively, followed by the operation of region merging. In the latter one, the spatial location of pixels is considered to ensure the compactness of the regions. As there are always wide boundaries between different regions, within which the values of the pixels vary gradually from one region to another, it is difficult to classify the pixels in that boundary region. According to the homogeneous character of region, we draw a hypothesis that the difference between pixels in the same region issmaller than that in different regions. Based on this hypothesis, the algorithm called watershed of difference performs better in segmentation than the traditional algorithm of watershed of gradient (WOG).Based on the careful investigation of the inter-relationships among spatial distribution in the red, green, and blue channels of the color textural images, a computational model is developed. Because of the difference in original image information, the computational model has two modes: gray-to-color-mapping (GCM) and color-to-color-mapping (CCM). In GCM mode, only the spatial distribution of luminance is available, the algorithm needs to deduce three-dimensional information of color image from one-dimensional information; whereas in CCM mode, the information in each of the three channels is available. The model proposed in this paper gives excellent mapping results both numerically and according to the psychophysical experiment. The algorithm may offer a possible solution in the area of image compression, communication, and CAD system.In the end, a practical image analysis system-license plate recognition system-is proposed. The paper discussed the four major steps in the realization...