Shape Matching Using the Included Angle Histogram of Vectors

Zhang, Chao; Prinet, V.

doi:10.1109/ccpr.2010.5659315

Cited by 3 publications

(3 citation statements)

References 15 publications

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“…An angle based histogram method developed by Zhang et al [40] includes measurement of angle between the local maximal curvature and the centroid, where the shape toning process describes the global shape in terms of the local descriptors and their combinations. Liu et al [41] proposed a distance angle pixel measure as a feature vector containing principal angle which later found to be susceptible to geometrical invariance.…”

Section: Introductionmentioning

confidence: 99%

Design of a Novel Shape Signature by Farthest Point Angle for Object Recognition

Mani¹,

Varma²,

D.M.³

et al. 2014

IJIGSP

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An overview of state of art in computerized object recognition techniques regarding digital images is revised. Advantages of shape based techniques are discussed. Importance of -Fourier Descriptor‖ (FD) for the shape based object representation is described. A survey for the available shape signature assignment methods with Fourier descriptors is presented. Details for the design of shape signature containing the crucial information of corners of the object are depicted. A novel shape signature is designed basing on the Farthest Point Angle (FPA) which corresponds to the contour point. FPA signature considers the computation of the angle between the line drawn from each contour point and the line drawn from the farthest corner point. Histogram for each 15o angle conceiving the information of the object is constructed. FPA signature is evaluated for three standard databases; viz., two in Kimia {K-99, K-216} and one in MPEG CE-1 Set B. The performance of the present FPA method estimated through recognition rate, time and degree of matching and is found to be higher.

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Section: Introductionmentioning

confidence: 99%

Design of a Novel Shape Signature by Farthest Point Angle for Object Recognition

Mani¹,

Varma²,

D.M.³

et al. 2014

IJIGSP

View full text Add to dashboard Cite

show abstract

“…In addition to the Mahalanobis distance, the shape matching method is also introduced here to determine whether two scans are associated. Originally designed for shape matching in image processing, Angle Histogram of Vectors in [15] can directly be used for shape matching between two sonar scans as we expected. Two scans, S i and S j , can be represented…”

Section: W Imentioning

confidence: 99%

Pose-based GraphSLAM algorithm for robotic fish with a mechanical scanning sonar

Chen

Wang

2013

2013 IEEE International Conference on Robotics and Biomimetics (ROBIO)

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Abstract-This paper proposes a pose-based GraphSLAM algorithm for robotic fish equipped with a Mechanical Scanning Sonar (MSS) that has a low frequency of range readings. The main contribution of this paper is the construction of a pose graph as the front-end part of the normal GraphSLAM algorithm. The proposed algorithm has three stages as follows: 1) scan generation which incorporates a novel Extended Kalman Filter (EKF) based algorithm that takes the fish motion into account; 2) data association which is based on Mahanalobis distance and shape matching for determining loop closures; 3) scan matching which is for constraints calculation and pose graph construction. The constructed pose graph is then fed into a back-end optimizer -g2o for finding the optimal position of robotic fish. The viability and the accuracy of the proposed algorithm are verified by extensive simulations, compared with the dead reckoning and scan matching approaches.

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“…In addition to the Mahalanobis distance, the shape matching method is also introduced here to determine whether two scans are associated. Originally designed for shape matching in image processing, Angle Histogram of Vectors in [27] can directly be used for shape matching between two sonar scans. Two scans, S i and S j , can be represented by…”

Section: B Scan Generationmentioning

confidence: 99%

Improving Localization Accuracy for an Underwater Robot With a Slow-Sampling Sonar Through Graph Optimization

Chen

Wang

et al. 2015

IEEE Sensors J.

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Abstract-This paper proposes a novel localization algorithm for an autonomous underwater vehicle equipped with a mechanical scanning sonar that has a slow frequency of data sampling. The proposed approach incrementally constructs a pose graph and conducts graph optimization to correct the robot poses. The construction of a pose graph has three stages: 1) scan generation which incorporates an extended Kalman filter-based dead reckoning algorithm that takes the robot motion into account while eliminating the sonar scan distortion caused by the motion; 2) data association which is based on Mahanalobis distance and shape matching for determining loop closures; and 3) scan matching which calculates constraints constructs pose graph. The constructed pose graph is then fed into a graph optimizer to find the optimal poses corresponding to each scan. A trajectory correction module uses these optimized poses to correct intermediate poses during the process of scan generation. Both simulation and practical experiments are conducted to verify the viability and accuracy of the proposed algorithm.

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Shape Matching Using the Included Angle Histogram of Vectors

Cited by 3 publications

References 15 publications

Design of a Novel Shape Signature by Farthest Point Angle for Object Recognition

Design of a Novel Shape Signature by Farthest Point Angle for Object Recognition

Pose-based GraphSLAM algorithm for robotic fish with a mechanical scanning sonar

Improving Localization Accuracy for an Underwater Robot With a Slow-Sampling Sonar Through Graph Optimization

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