Applied Mechanics and Materials Vols. 701-702

Abstract: Ocean front is a narrow transitional zone that the penetration of sea is obviously different between two or more waters there. It is an important feature of geophysical turbulence which plays an important role in ocean dynamics. Ocean fronts become visible on radar images because they are associated with a variable surface current which modulates the sea surface roughness and thus the backscattered radar power. This paper propose a new integrated method to extract ocean fronts based on two-dimensional Empirical Mode Decomposition (EMD), image edge detection and mathematical morphology processing. Experimental results show that this integrated method can be effective in ocean front feature extraction.

Abstract: The contourlet transform is a new two-dimensional extension of the wavelet transform using multiscale and directional filter banks and has better approximation precision and better sparse description. Firstly, the magnitude of polarization image and angle of polarization image can be decomposed into low-frequency coefficients and high-frequency coefficients with multi-scales and multi-directions using the contourlet transform. For the low-frequency coefficients, the average fusion method is used. For the each directional high frequency sub-band coefficients, the larger value of region teager energy information measurement is used to select the better coefficients for fusion. At last the fused image can be obtained by utilizing inverse transform for fused contourlet coefficients. Experimental results show that the proposed algorithm works better in preserving the edges and texture information compared with the traditional image fusion algorithms.

Abstract: Image segmentation plays an important role in computer vision and image processing to interpret and analyze an acquired image. Separation of objects or image regions is usually required for high-level image comprehension in practical applications involving visual inspection. In this paper, a novel automatic image segmentation method is proposed. To extract the foreground of the image automatically, we combine saliency model based on superpixels with the affinity propagation clustering algorithm in an unsupervised manner, and use graph cut method to obtain the segmentation results.

Abstract: Because of the special geometric shape and narrow space of exploration tunnel, field geological logging in exploration tunnels has always been staying at manual drafting stage with low efficiency and low level of informatization. This paper studies the existing issues of photographic geological logging in exploration tunnel, including the control method, image acquisition method, geometric correction of original images and the generation of display images, etc. This paper realizes fast acquisition and processing of the image data of geological logging, meeting the accuracy requirement of photographic geological logging in exploration tunnels, filling the vacancy of photographic geological logging technologies in exploration tunnels, improving the efficiency, level of automation and informatization of geological logging in exploration tunnels.

Abstract: Median filtering is an important approach in digital image processing for noise elimination. An improved median filtering algorithm (IMFA) is proposed which can be implemented with only 17 comparisons and 6 clocks delay for 3×3 median filtering mathematical model based on field programmable gate array (FPGA). The algorithm benefits from the parallel processing and pipelining structure of FPGA hardware. At first, the characteristics, basic operational principle and computing process of the IMFA are presented. And then the algorithm using modular technique and top-down design flow methodology with Verilog HDL are programed. At last, some simulation verifications for the algorithm by ModelSim and experimental verification on FPGA hardware platform are carried out. The IMFA can get a large number of data throughput and more quickly processing speed and less hardware resources than similar filtering algorithms.

Abstract: Image segmentation is the key step in image recognition,the result of segmentation affects the one of recognition directly.The article introduces the concept and detailed definition of the image segmentation. The segmentation algorithm of iterative threshold in detail. According to the intrinsic characteristics of weed images, just can use the iteration threshold segmentation method, and implements them by Matlab programme, then processes three weed images, respectively to obtain effective results , and establishes a good base for the pick-up of the target character.

Abstract: The article researched on edge extraction system of the laser welding image based on ARM micro controller. The system can achieve accurate extraction on the edge of the laser welding pool, and reduce the system cost effectively while satisfying the real-time constraints. With STM32f103 controller for the system as the core, the outside are image storage and display module. The image processing methods of gray-scale transformation, image smoothing, threshold segmentation and edge extraction are realized in ARM, and the processed images are displayed in LCD. The experimental results show that the system researched in this paper can extract color molten pool image edge accurately without distortion and better reflect the relationship between welding parameters and weld pool geometry size, and provide the basis for building real-time control system of the laser welding quality.

Abstract: This paper has a discussion and research on the design and realization of the speech recognition robot based on embedded system and DSP. The solution of embedded system and DSP has made the performance, cost, reconfigurable ability and extensible ability of the system to a high level. And the system has adapted the modified MFCC method to extract the feature of the speech and used the HMM model based on K segmented equalizing value to do the speech study and recognition. It has improved the transplanting and real-time ability of the arithmetic.

Abstract: In recent years, saliency detection has been gaining increasing attention since it could significantly boost many content-based multimedia applications. In this paper, we propose a visual saliency detection algorithm based on multi-scale superpixel and dictionary learning . Firstly, in each scale space, we extract the boundaries as the training samples to learn a dictionary through sparse coding and dictionary learning methods. Then, according to reconstruction error of each superpixel, the saliency map is generated for each scale of superpixel. Finally, some saliency maps from different scale spaces are fused together to generate the final saliency map. The experimental results show that the proposed algorithm can highlight the salient regions uniformly and performs better compared with the other five methods.

Abstract: A new method based on image patch reordering for removing salt-and-pepper noise from corrupted images is presented. Firstly, the problem of salt-and-pepper noise removal can be turned into the problem of image in-painting. Then, we can use the image patch reordering method to recover the missing pixels and fulfill the salt-and-pepper noise removal. Experimental results demonstrate that the proposed method obtain much better performance in terms of both qualitative and quantitative assessment. Especially, the proposed method provides the improvement in the performance of noise suppression and detail preservation.