As the voids and pits image of bridge often has uneven illumination and multi-background interference problems, an image segmentation algorithm for complex voids and pits of bridge was proposed based on HSI color space and gray fluctuation. Firstly, S-component gray curve was plotted and all the potential peaks and troughs of the curve were searched, then the height differences between adjacent peaks and thoughs were calculated. Secondly, partial height differences were selected based on the standard deviation of gray pixel difference value. Finally the threshold segmentation of image was finished by searching the best threshold based on the standard deviation of partial height differences. Experimental results show that the proposed algorithm has better segmentation effect and real-time performance than OTSU, Niblack and Tsallis entropy method.

To solve the problem that Low Density Lattice Codes (LDLC) cannot be used on the constrained power communication Additive White Gaussian Noise (AWGN) channel, the shaping methods were studied. In this paper, a lower triangular Hmatrix with a special structure was constructed first, together with the hypercube and systematic shaping method, and then the average power was fixed, the position change of lattice point before and after the shaping process, and its corresponding shaping gain were analyzed. The simulation results show that the codeword is uniformly distributed within the Voronoi regions of the lattice after shaping, and these shaping methods can achieve a shaping gain of 1.31dB when Symbol Error Rate (SER) 10 -5 and code length 10000 which improves 0.31dB compared with the traditional shaping technique. Power limited lattice points were generated efficiently after shaping.

In order to solve the distortion of wool geometric dimension, resulting from image binarization with weak contour, an automatic binarization algorithm for Charge-Coupled Device （CCD） wool image was proposed with reference to a ramp-width-reduction approach based on intensity and gradient indices, using a classical global threshold method and a local one. In that algorithm, edge-pixel-seeking step was added and gray-adjusting factor was improved, with sobel operator and ramp edge model introduced, to increase processing efficiency and avoid human intervention. Besides, every sub image was processed by the mixed global and local threshold based on the analysis of Otsus and Bernsens methods to intensify edge details and decrease distortion. Compared with the traditional ways, the experimental results show that the new algorithm has good performance in automatic binarization with weak contour.

In the first place, how to capture the Windows and Linux desktop graphic images was described, using their API functions. Then a lossless encoding arithmetic based on gridding was put forward, according to the characters of desktop graphic image sequences. The experiment results show that this arithmetic can compress the desktop graphic image sequences effectively and can meet actual application demands.