Line segments are ordinary in industrial scene, accurate line segments matching is a key step for many applications, such as 3-D reconstruction. A matching method based on epipolar-line constraint and line segment features is proposed. Firstly, the points on line segments between image pairs are matched by epipolar-line constraint. Secondly, geometric descriptor and gray value descriptor are used to describe the line segment features, then the two descriptors are combined into a feature vector, and Euclidean distance between vectors is used to achieve fine match. Experiment results show that the proposed method is accurate and fast

This paper introduces a new shape representation and retrieval method called distance autocorrelogram. Firstly, distance autocorrelogram is obtained under the premise of getting the contour’ centroidal distances. Then, we apply this shape descriptor to content-based image retrieval (CBIR). This feature depends on the centroidal distances and correlation between neighboring edges, so it can express the edge’ spatial distributing information. This scheme is effective and robust to translation, scaling and rotation. Experimental results and algorithm analysis demonstrate the efficiency and feasibility of this shape-based image retrieval approach. Beside, it has better performance compared to traditional distance histograms.

Important structures in a large area of a damaged image cannot be satisfactorily repaired by traditional inpainting algorithms. Here, an image completion algorithm based on structure reconstruction and constraint (SRC) is presented to improve the structural coherence of the damaged image. First, the damaged structure of the target image is detected and located. Then, different missing structures are respectively reconstructed. The edge structures are reconstructed by the Euler spiral, which satisfies energy minimization. The corner structure is reconstructed using the intersection of two extended Euler spirals. Finally, the reconstructed structure is used as a constraint condition to modify the priority of the image completion and to guide the texture propagation within the damaged part. The proposed method thereby resolves the problem of the corner structure being unable to be adequately repaired for current image inpainting methods. In addition to preserving the structural continuity, it also effectively avoids texture inconsistency. Experimental results show that the peak-signal-to-noise-ratio values of images recovered by the proposed method increased 3.43 to 12.85 dB. Moreover, compared to the content-aware fill algorithm and Criminisi algorithm, its mean square values decrease by 42.16% to 94.61%. The structure consistency, neighbor texture information coherence, neighbor and visual effect are better than those of the other algorithms. The presented algorithm is thus suitable to repair minor damage, such as a straight line or curving scratch, as well as large area damage, such as object removal in nature scenes and cultural relic images.

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Abstract. The parameters of traditional particle swarm optimization (PSO) methods are unchangeable, which may lead to the iterations have slow convergence speed or unable to converge to global optimum. In this paper, improved PSO algorithm is applied to the coverage of video sensor network. Through improving inertia factors of PSO, the algorithm has great local search ability thus can avoid converging to local optimum value, and it can converge quickly to the global optimum value. Experiment results show that the proposed method has faster convergence speed and better coverage rate than traditional method based on PSO.