Linear solution to scale and rotation invariant object matching

Jiang, Hao; Yu, Stella X.

doi:10.1109/cvpr.2009.5206776

Cited by 53 publications

(89 citation statements)

References 16 publications

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“…1. Some related studies [13], [14] have reported the use of the spatial constraint. These methods, based on graph matching between 2 sets of feature points, are only applicable to the situation wherein the structure of the graph of objects changes slightly.…”

Section: Introductionmentioning

confidence: 99%

Extraction and Tracking Moving Objects in Detail Considering Visual Feature Constraint and Structure Constraint

Zhu

Tomioka

Kitazawa

2013

IEICE Trans. Inf. & Syst.

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SUMMARYDetailed tracking is required for many vision applications. A visual feature-based constraint underlies most conventional motion estimation methods. For example, optical flow methods assume that the brightness of each pixel is constant in two consecutive frames. However, it is difficult to realize accurate extraction and tracking using only visual feature information, because viewpoint changes and inconsistent illumination cause the visual features of some regions of objects to appear different in consecutive frames. A structure-based constraint of objects is also necessary for tracking. In the proposed method, both visual feature matching and structure matching are formulated as a linear assignment problem and then integrated.

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Section: Introductionmentioning

confidence: 99%

Extraction and Tracking Moving Objects in Detail Considering Visual Feature Constraint and Structure Constraint

Zhu

Tomioka

Kitazawa

2013

IEICE Trans. Inf. & Syst.

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“…one that is close enough to the correct position of the landmark). This is analogous to the point-matching problem found in algorithms that search for correspondences [15,2]. However, the human face is a non-rigid object and these point-matching algorithms are typically restricted to rigid transformations 3 .…”

Section: Contributionmentioning

confidence: 99%

3D Facial Landmark Localization Using Combinatorial Search and Shape Regression

Sukno

Waddington

Whelan

2012

Computer Vision – ECCV 2012. Workshops and Demonstrations

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Abstract. This paper presents a method for the automatic detection of facial landmarks. The algorithm receives a set of 3D candidate points for each landmark (e.g. from a feature detector) and performs combinatorial search constrained by a deformable shape model. A key assumption of our approach is that for some landmarks there might not be an accurate candidate in the input set. This is tackled by detecting partial subsets of landmarks and inferring those that are missing so that the probability of the deformable model is maximized. The ability of the model to work with incomplete information makes it possible to limit the number of candidates that need to be retained, substantially reducing the number of possible combinations to be tested with respect to the alternative of trying to always detect the complete set of landmarks. We demonstrate the accuracy of the proposed method in a set of 144 facial scans acquired by means of a hand-held laser scanner in the context of clinical craniofacial dysmorphology research. Using spin images to describe the geometry and targeting 11 facial landmarks, we obtain an average error below 3 mm, which compares favorably with other state of the art approaches based on geometric descriptors.

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“…It is affected by environmental change. Graph structure based methods [8], [9], which perform graph matching between 2 sets of feature points, are only applicable to the situations wherein the structure of the graph of objects changes slightly. These methods are not applicable if the graph structure of the target object in the scenes cannot be created.…”

Section: Introductionmentioning

confidence: 99%

Template Matching Method Based on Visual Feature Constraint and Structure Constraint

Zhu

Tomotsune

Tomioka

et al. 2012

IEICE Trans. Inf. & Syst.

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SUMMARYTemplate matching for image sequences captured with a moving camera is very important for several applications such as Robot Vision, SLAM, ITS, and video surveillance systems. However, it is difficult to realize accurate template matching using only visual feature information such as HSV histograms, edge histograms, HOG histograms, and SIFT features, because it is affected by several phenomena such as illumination change, viewpoint change, size change, and noise. In order to realize robust tracking, structure information such as the relative position of each part of the object should be considered. In this paper, we propose a method that considers both visual feature information and structure information. Experiments show that the proposed method realizes robust tracking and determine the relationships between object parts in the scenes and those in the template. key words: template matching, visual feature constraint, structure constraint, linear assignment, genetic algorithm

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Linear solution to scale and rotation invariant object matching

Cited by 53 publications

References 16 publications

Extraction and Tracking Moving Objects in Detail Considering Visual Feature Constraint and Structure Constraint

Extraction and Tracking Moving Objects in Detail Considering Visual Feature Constraint and Structure Constraint

3D Facial Landmark Localization Using Combinatorial Search and Shape Regression

Template Matching Method Based on Visual Feature Constraint and Structure Constraint

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