Corner detection is an important step in the imageprocessing of machine vision. An improved algorithm is proposed in this paper following the analysis on the existing corner detection algorithms and on the localizati...
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Corner detection is an important step in the imageprocessing of machine vision. An improved algorithm is proposed in this paper following the analysis on the existing corner detection algorithms and on the localization precision and computation efficiency in the Harris corner detection algorithm. In this algorithm, a large number of irrelevant points are rejected by statistical analyzing the pixel gray level difference around the target pixel, and then the response function of residual points is calculated and compared with the set threshold value to certify the real corner. Finally computation program is programmed, using this program, the synthesize images of five types of corners are analyzed and calculated, which shows that the improved algorithm acquires better efficiency and accuracy in corner detection.
Fingerprint images are textural images consisting of ridges and valleys. The orientation of textures can be determined by orientation field computation. Fingerprint orientation field is the critical basis for fingerpr...
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Fingerprint images are textural images consisting of ridges and valleys. The orientation of textures can be determined by orientation field computation. Fingerprint orientation field is the critical basis for fingerprint image segmentation, filtering enhancement and matching processes, and the fingerprint orientation field algorithm plays a very important role in the applied Automated Fingerprint Identification systems (AFIS). The available fingerprint orientation field algorithms include mainly the mask algorithm and the gradient algorithm. They are used in spatial and frequency domains, respectively, and both can offer satisfactory fingerprint orientation matrixes. Based on an experimental comparison of their effectiveness in fingerprint preprocessing, this paper analyzes in detail the performance of these two algorithms.
Evolutionary multi-objective optimization (EMO), whose main task is to deal with multi-objective optimization problems by evolutionary computation, has become a hot topic in evolutionary computation community. After s...
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In object matching and recognition it is useful to represent an image with the sets of local features such as SIFT etc. However this representation poses a challenge to the popular SVM machine learning method, since i...
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In object matching and recognition it is useful to represent an image with the sets of local features such as SIFT etc. However this representation poses a challenge to the popular SVM machine learning method, since it needs ordered and fixlength data. To solve this problem, we focus in this paper on a Max-matching context kernel, which computes Max-matching points based on the angle between two points of sets and sums its context energy to Max-matching point energy. We prove Max-matching context kernel yields a Mercer kernel. We demonstrate our algorithm on Caltech 101's object recognition, which shows that the proposed method is accurate and significantly more efficient than current other approaches.
Segmentation of Connected numeral strings plays an important role in number recognition systems. A new method to segment two connected numeral strings is proposed in the article, we define characteristic position firs...
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Segmentation of Connected numeral strings plays an important role in number recognition systems. A new method to segment two connected numeral strings is proposed in the article, we define characteristic position first, using it the graph-representation of the image is derived, based on this and using contour detecting we attain the candidate segmentation position pairs used for determining the segmentation path, then a criterion is applied to choose the best segmentation from the candidates. Finally, an experiment is conducted. The results reveal that the method proposed has high speed and good result.
As an important component of passive media forensics (PMF), content-based copy detection (CBCD) is appropriate for identifying piracy of digital content and preventing falsified evidence. In this paper, we propose a n...
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As an important component of passive media forensics (PMF), content-based copy detection (CBCD) is appropriate for identifying piracy of digital content and preventing falsified evidence. In this paper, we propose a novel and efficient CBCD scheme for identifying different altered versions of original images. We employ singular value decomposition (SVD) and block partition to improve the robustness and discriminability of image content identification. To examine the validity of proposed copy detection algorithm, 32 attacks including geometric transformation, signal processing and image manipulation, are applied to copyrighted images to generate large virtual altered images. Experimental results demonstrate the efficacy of the proposed copy detection scheme in withstanding various attacks.
It is difficult to separate object from background using conventional method when the processed image is nonuniform. A new method is proposed in the paper for nonuniform image segmentation: Firstly, grid sample method...
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It is difficult to separate object from background using conventional method when the processed image is nonuniform. A new method is proposed in the paper for nonuniform image segmentation: Firstly, grid sample method is performed on initial image to reduce data space and prepare for background estimation. Secondly, Gaussian low pass filter (GLPF) is used to reduce the intensity value of the high-frequency points that is caused by objects included in the image. Thirdly, facet model based interpolation algorithm is used to estimate the background image. Finally, object image is acquired according to the difference of initial image and background image. Experiments were performed and according to the results the validity and adaptability of our method is enhanced obviously compared with conventional image segmentation algorithms.
In this paper, a location algorithm pattern based on fuzzy identification with multi-layer synchronization and cooperative difference is proposed to eliminate location errors and system interference caused by ambiguit...
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In this paper, a location algorithm pattern based on fuzzy identification with multi-layer synchronization and cooperative difference is proposed to eliminate location errors and system interference caused by ambiguity distribution parameters. As the algorithm model is built to carry out fuzzy identification among faintness and ambiguity signal parameters, the location algorithm is provided with indubitable feasibility, validity and precision for fuzzy signal processing. Simulation results show that more precise location processing can be achieved with mass fuzzy parameters.
Vision-based registration techniques for augmented reality(AR) systems have been the subject of intensive research recently due to their potential to accurately align virtual objects with the real world. The downfall ...
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Vision-based registration techniques for augmented reality(AR) systems have been the subject of intensive research recently due to their potential to accurately align virtual objects with the real world. The downfall of these vision-based approaches, however, is their high computational cost and lack of robustness. To address these shortcomings, a robust pose estimation algorithm based on artificial planar markers is adopted. This algorithm solves the problem of camera pose ambiguities and is able to draw a unique and robust solution. Experiments show the robustness and effectiveness of this method in the context of real-time AR tracking.
As a global optimizing algorithm, genetic algorithm (GA) is applied to solve the problem of multicast more and more. GA has more powerful searching ability than traditional algorithm, however its property of ¿pre...
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As a global optimizing algorithm, genetic algorithm (GA) is applied to solve the problem of multicast more and more. GA has more powerful searching ability than traditional algorithm, however its property of ¿prematurity¿ makes it difficult to get a good multicast tree. A quantum-inspired evolutionary algorithm (QEA) to deal with multicast routing problem is presented in this paper, which saliently solves the ¿prematurity¿ problem in genetic based multicast algorithm. Furthermore, in QEA, the individuals in a population are represented by multistate gene quantum bits and this representation has a better characteristic of generating diversity in population than any other representations. In the individual's updating, the quantum rotation gate strategy is applied to accelerate convergence. The algorithm has the property of simple realization and flexible control. The simulation results show that QEA has a better performance than CS and conventional GA.
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