Fast template matching based on deformable best-buddies similarity measure

Xia, Haiying; Zhao, Wenxian; Jiang, Frank; Li, Haisheng; Xin, Jing; Zhou, Zheng

doi:10.1007/s11042-018-6722-x

Cited by 10 publications

(7 citation statements)

References 23 publications

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“…Xia et al. [ 25 ] proposed the deformable best buddies similarity method based on BBS. Constraints such as multiscale combinatorial grouping; normalized cross correlation; and shape, size, and color appearance feature were introduced to discover potential target areas, and BBS was adopted to search for targets in the potential area to reduce computation.…”

Section: Methodsmentioning

confidence: 99%

Practical Tracking Method based on Best Buddies Similarity

Chen

Liu

et al. 2023

Cyborg Bionic Syst

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Visual tracking is a crucial skill for bionic robots to perceive the environment and control their movement. However, visual tracking is challenging when the target undergoes nonrigid deformation because of the perspective change from the camera mounted on the robot. In this paper, a real-time and scale-adaptive visual tracking method based on best buddies similarity (BBS) is presented, which is a state-of-the-art template matching method that can handle nonrigid deformation. The proposed method improves the original BBS in 4 aspects: (a) The caching scheme is optimized to reduce the computational overhead, (b) the effect of cluttered backgrounds on BBS is theoretically analyzed and a patch-based texture is introduced to enhance the robustness and accuracy, (c) the batch gradient descent algorithm is used to further speed up the method, and (d) a resample strategy is applied to enable the BBS to track the target in scale space. The proposed method on challenging real-world datasets is evaluated and its promising performance is demonstrated.

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Section: Methodsmentioning

confidence: 99%

Practical Tracking Method based on Best Buddies Similarity

Chen

Liu

et al. 2023

Cyborg Bionic Syst

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“…BBS [1,32] counts the mutual two-side NNs as a similarity statistic. Furthermore, there are some best-buddies similarity based methods that have been proposed to deal with a variety of problems, such as multi-source image matching [33], 3D point cloud registration [34], fast matching [35,36], occlusion [37], and tracking [38]. The DDIS [2] measures the diversity of feature matches between the two sets and is reported to outperform BBS by revealing the "deformation" of the NN field.…”

Section: Related Workmentioning

confidence: 99%

DS‐SRI: Diversity similarity measure against scaling, rotation, and illumination change for robust template matching

Zhang

Akashi

2022

IET Image Processing

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This paper presents a novel multi-scale template matching method that can be applied in unconstrained environments. The key component behind this is a general similarity measure is referred to as the diversity similarity measure against scaling, rotation, and illumination (DS-SRI). Specifically, DS-SRI exploits bidirectional diversity calculated from the nearest neighbour matches between two sets of points. Scaling and rotation changes are taken into consideration by introducing normalisation term on the scale change, and geometric consistency term with respect to the polar coordinate system. Moreover, in order to deal with the illumination change and further deformation, illumination-corrected local appearance and rank information are jointly exploited during the nearest neighbour search. All the features of DS-SRI are statistically assessed, and the extensive visual and quantitative results on both synthetic and real-world data show that DS-SRI can significantly outperform state-of-the-art methods.

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“…The features can be peaks and valleys in the image intensity, edges, and the intensity itself, as well as points of high curvature 3 (sharp turns or bends; see Yuille et al, 1992 ; Basri et al, 1998 ). Deformable templates are mainly used for object recognition and object tracking (e.g., Ravishankar et al, 2008 ; Xia et al, 2019 ; Gallardo et al, 2020 ). They support the particular relevance of critical points in modeling deformations, which could make them key to developing a robot's ability to generate its own optimal representation of a deformable object.…”

Section: Representing Shape For Deformable Objectsmentioning

confidence: 99%

Modeling of Deformable Objects for Robotic Manipulation: A Tutorial and Review

et al. 2020

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Manipulation of deformable objects has given rise to an important set of open problems in the field of robotics. Application areas include robotic surgery, household robotics, manufacturing, logistics, and agriculture, to name a few. Related research problems span modeling and estimation of an object's shape, estimation of an object's material properties, such as elasticity and plasticity, object tracking and state estimation during manipulation, and manipulation planning and control. In this survey article, we start by providing a tutorial on foundational aspects of models of shape and shape dynamics. We then use this as the basis for a review of existing work on learning and estimation of these models and on motion planning and control to achieve desired deformations. We also discuss potential future lines of work.

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Fast template matching based on deformable best-buddies similarity measure

Cited by 10 publications

References 23 publications

Practical Tracking Method based on Best Buddies Similarity

Practical Tracking Method based on Best Buddies Similarity

DS‐SRI: Diversity similarity measure against scaling, rotation, and illumination change for robust template matching

Modeling of Deformable Objects for Robotic Manipulation: A Tutorial and Review

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