Abstract
This paper aims at detecting objects via a partial shape matching in unlabeled real images. As both the scale and consistent fragment extraction are troublesome issues in computer vision, we first extract the corresponding parts of pairs of matching fragments generated by the curvature extreme points in object contours. Then, we establish the scale-calculable shape descriptor to keep that the partial shape matching algorithm is scale and rotation invariant. In detection stage, a weighted voting scheme is used to locate candidate object centers and followed by a refinement process to obtain the precise object boundaries. Experiments on ETHZ shape category database validate that using single model shape without training for each category can match (or exceed) the performance of state-of-the-art object detection algorithms.
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Acknowledgments
This work has been funded by Natural Science Foundation of China under Grant Nos. 61401455, 61333019 and 61375014.
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Appendix
Appendix
Here we prove Theorem 1. Figure 3 shows two arbitrary straight lines start from the origin O and intersect two aligned fragments \(\mathbf s ^\prime _i\) and \(\mathbf t ^\prime _j\) at points \(A_1\), \(A_j\), \(B_1\) and \(B_j\). \(C_1\) and \(C_2\) are object centers. We prove that if \(\mathbf s ^\prime _i\) and \(\mathbf t ^\prime _j\) are similar and the scale is s, then
We first prove \(C_2\), \(C_1\) and O are co-linearity. Suppose \(\tilde{C_1}\) instead of \(C_1\) is the object center, since the alignment, which is a congruent transformation, will not alter the relationships of corresponding points on object, i.e., \(\angle XO\tilde{C_1} = \angle XOC_2\), \(\Rightarrow C_2\), \(\tilde{C_1}\) and O are co-linearity.
\(\angle XOA_1 = \angle XOB_1, \Rightarrow A_1\) and \(B_1\) are corresponding points.
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Fan, H., Cong, Y. & Tang, Y. Object detection based on scale-invariant partial shape matching. Machine Vision and Applications 26, 711–721 (2015). https://doi.org/10.1007/s00138-015-0693-y
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DOI: https://doi.org/10.1007/s00138-015-0693-y