In this work, we propose a new edge detection scheme which is called the decision based directional edge detector (DBDED). Also a modification of Cheng's shrinking algorithm is developed for producing one point ed...
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In this work, we propose a new edge detection scheme which is called the decision based directional edge detector (DBDED). Also a modification of Cheng's shrinking algorithm is developed for producing one point edge segments. The methodology of the proposed edge detection algorithm is described in the following manner. In each of eight discrete directions, every point is analyzed in order to decide whether it is a one-dimensional (l-D) edge point in the given direction. This analysis is performed adaptively by using the calculated local directional standard deviation, local directional averages and a constant threshold. In order to prevent multiple edges, the pixels which are locally dominant in intensity are considered to be edge candidates. The true edge pixels are decided upon by eliminating some of the false edge candidates using a decision-based algorithm. It has been shown by extensive simulation work that the DBDED has satisfactory results in some preselected requirements compared with other well-known edge detection methods in the literature.
A method used for recognition and understanding of airfield based on mathematical morphology is proposed in this paper. The new approach can he divided into three steps. First, to extract the typical geometric structu...
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A method used for recognition and understanding of airfield based on mathematical morphology is proposed in this paper. The new approach can he divided into three steps. First, to extract the typical geometric structure features of airfield, a segmentation method called recursive Otsu algorithm is employed on an airfield image. Second, thinning and shrinking algorithms are utilized to obtain the contour of airfield with single pixel and to remove diffused small particles. Finally, Radon transform is adopted to extract two typical and important components, primary and secondary runways of airfield exactly. At the same time, region growing algorithm is exploited to get the other components such as parking apron and garages. The experimental results demonstrate that the proposed method gives good performance.
A simple measurement matrix construction algorithm (MM CA) within compressive sensing framework is introduced. In compressive sensing, the smaller coherence between the measurement matrix and the sparse dictionary (ba...
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ISBN:
(纸本)9781467346214
A simple measurement matrix construction algorithm (MM CA) within compressive sensing framework is introduced. In compressive sensing, the smaller coherence between the measurement matrix and the sparse dictionary (basis) can have better signal reconstruction performance. Random measurement matrices (e. g., Gaussian matrix) have been widely used because they present small coherence with almost any sparse base. However, optimizing the measurement matrix by decreasing the coherence with the fixed sparse base will improve the CS performance greatly, and the conclusion has been well proved by many prior researchers. Based on above analysis, we achieve this purpose by adopting shrinking and Singular Value Decomposition (SVD) technique iteratively. Finally, the coherence among the columns of the optimized matrix and the sparse dictionary can be decreased greatly, even close to the welch bound. In addition, we established several experiments to test the performance of the proposed algorithm and compare with the state of art algorithms. We conclude that the recovery performance of greedy algorithms (e. g., orthogonal matching pursuit) by using the proposed measurement matrix construction method outperforms the traditional random matrix algorithm, Elad's algorithm, Vahid's algorithm and optimized matrix algorithm introduced by Xu.
In this paper, a novel deterministic measurement matrix construction algorithm (DMMCA) within compressive sensing (CS) framework was introduced for gathering and reconstructing the compressive data in large scale of w...
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In this paper, a novel deterministic measurement matrix construction algorithm (DMMCA) within compressive sensing (CS) framework was introduced for gathering and reconstructing the compressive data in large scale of wireless sensor networks (WSNs). Random measurement matrix (e.g., Gaussian matrix) has been widely used because it presents small coherence with almost any sparse basis. However, decreasing the coherence between the measurement matrix and the fixed sparse basis will improve the CS performance greatly. We achieve this purpose by adopting shrinking and Singular Value Decomposition (SVD) technique iteratively. In addition, we conducted several experiments to measure the performance of the proposed algorithm and compare it with the existing algorithms. The recovery performance of greedy algorithms (e.g., orthogonal matching pursuit) with the proposed measurement matrix construction method outperforms the traditional random, Elad's [21], Abolghasemi's [25] and Xu's [26] algorithms. Finally, the practical experimental results in WSNs present the same positive results as the simulations.
Characterizations of digital “simple arcs” and “simple closed curves” are given. In particular, it is shown that the following are equivalent for sets S having more than four points: (1) S is a simple curve; (2) S...
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Characterizations of digital “simple arcs” and “simple closed curves” are given. In particular, it is shown that the following are equivalent for sets S having more than four points: (1) S is a simple curve; (2) S is connected and each point of S has exactly two neighbors in S; (3) S is connected, has exactly one hole, and has no deletable points. It follows that if a “shrinking” algorithm is applied to a connected S that has exactly one hole, it shrinks to a simple curve.
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