iccv iccv2013 iccv2013-283 knowledge-graph by maker-knowledge-mining
Source: pdf
Author: Yi Wu, Yoshihisa Ijiri, Ming-Hsuan Yang
Abstract: Detecting and registering nonrigid surfaces are two important research problems for computer vision. Much work has been done with the assumption that there exists only one instance in the image. In this work, we propose an algorithm that detects and registers multiple nonrigid instances of given objects in a cluttered image. Specifically, after we use low level feature points to obtain the initial matches between templates and the input image, a novel high-order affinity graph is constructed to model the consistency of local topology. A hierarchical clustering approach is then used to locate the nonrigid surfaces. To remove the outliers in the cluster, we propose a deterministic annealing approach based on the Thin Plate Spline (TPS) model. The proposed method achieves high accuracy even when the number of outliers is nineteen times larger than the inliers. As the matches may appear sparsely in each instance, we propose a TPS based match growing approach to propagate the matches. Finally, an approach that fuses feature and appearance information is proposed to register each nonrigid surface. Extensive experiments and evaluations demonstrate that the proposed algorithm achieves promis- ing results in detecting and registering multiple non-rigid surfaces in a cluttered scene.
Reference: text
sentIndex sentText sentNum sentScore
1 edu Abstract Detecting and registering nonrigid surfaces are two important research problems for computer vision. [sent-7, score-0.255]
2 Specifically, after we use low level feature points to obtain the initial matches between templates and the input image, a novel high-order affinity graph is constructed to model the consistency of local topology. [sent-10, score-0.445]
3 A hierarchical clustering approach is then used to locate the nonrigid surfaces. [sent-11, score-0.256]
4 To remove the outliers in the cluster, we propose a deterministic annealing approach based on the Thin Plate Spline (TPS) model. [sent-12, score-0.532]
5 As the matches may appear sparsely in each instance, we propose a TPS based match growing approach to propagate the matches. [sent-14, score-0.409]
6 Finally, an approach that fuses feature and appearance information is proposed to register each nonrigid surface. [sent-15, score-0.328]
7 This is a difficult problem since the appearance of imaged surfaces varies due to many factors such as camera pose, surface deformation and lighting conditions. [sent-19, score-0.36]
8 When multiple deformed instances of an object appear in an image, this makes the detection and registration task even more challenging. [sent-21, score-0.348]
9 In this paper, we address the multiple non-rigid surface detection and registration problem (See Figure 1). [sent-29, score-0.317]
10 Given the template image representing the surface of interest in a canonical shape, the goal is to locate the deformed instances of the surface and estimate the deformation parameters of each instance. [sent-30, score-0.681]
11 , SIFT [18]) to obtain the feature points in both template and input image, and then the correspondences between these two sets are constructed based on the similarity of local appearance around feature points. [sent-33, score-0.501]
12 After rejecting outliers in each correspondence cluster, the sparsely distributed matches are propagated by a Thin Plate Spline (TPS) based match growing algorithm. [sent-35, score-0.68]
13 The deformation parameters of each nonrigid surface are estimated by an approach that fuses feature and appearance information based on the TPS model. [sent-36, score-0.506]
14 (a) Two templates; (b) The detection and registration results overlaid on the input image, where the estimated non-rigid warps are illustrated in deformable meshes with different colors for different objects. [sent-40, score-0.34]
15 The feature-based approaches rely on establishing correspondences between features of a template and an input image. [sent-44, score-0.286]
16 On the other hand, the appearance-based approaches do not rely on features and directly minimize the intensity discrepancy between the template and an input image warped back onto the coordinate frame of the template, which could achieve better registration accuracy. [sent-46, score-0.421]
17 The feature-based approaches first establish correspondences by feature matching, then after eliminating outliers the transformation is estimated. [sent-49, score-0.413]
18 With 25% of outliers and 100 transformation parameters, which is a common case for nonrigid surface registration, a RANSAC approach requires 1012 samples to guarantee that, with high probability, at least one sample is included without outliers [15]. [sent-55, score-0.681]
19 [23] adopt a robust estimator to measure the outliers and employ an iterative semi-implicit optimization scheme to refine the matches. [sent-57, score-0.279]
20 In [10], a robust point set matching approach, TPS-RPM, is proposed to match points and remove outlier points iteratively. [sent-60, score-0.456]
21 The TPS-RPM method solves the joint optimization problem iteratively by deterministic annealing and soft assignment with one-toone correspondence assumption, which is computationally expensive. [sent-61, score-0.397]
22 In this paper, we adopt the deterministic annealing approach, but use it only to remove spurious matches. [sent-62, score-0.364]
23 Recent feature matching methods formulate visual correspondence as a graph matching problem by considering pairwise geometric distortion of objects between im- ages [16, 4, 25]. [sent-65, score-0.278]
24 A novel high-order affinity graph is constructed to model the consistency of local topology, and a hierarchical clustering approach is used to locate the nonrigid surfaces. [sent-79, score-0.38]
25 Both approaches depend on the affine transformation from the affine feature detector [20] for match growing. [sent-84, score-0.355]
26 In this work, to deal with the case that the matches are too sparsely distributed, we propose an approach to propagate matches locally by using the TPS model. [sent-85, score-0.435]
27 Matthews and Baker [21] improves the active appearance model with an inverse composition algorithm [1] in which triangulation meshes are used and piecewise affine warps are estimated. [sent-91, score-0.269]
28 The piecewise affine warp is used to map points, where the mapping of one point only considers three surrounding control points. [sent-95, score-0.335]
29 In this paper, we propose a fusion approach using the TPS warp which maps the points in a holistic way. [sent-96, score-0.349]
30 The fusion approach can deal with large distortions in which correct feature correspondences are difficult to obtain. [sent-97, score-0.274]
31 The initial feature correspondences between a template and an input image are obtained by comparing the SIFT descriptors based on Euclidean distance using the k nearest-neighbor algorithm. [sent-100, score-0.325]
32 Note that each feature point in the template may be matched to multiple points in the input image. [sent-101, score-0.288]
33 As it requires at least three correspondences to determine one affine transformation, we construct a high order graph, G = (V, E), of triple correspondences. [sent-112, score-0.246]
34 We can also consider G as a graph of affine transformations and each node is an affine matrix. [sent-114, score-0.261]
35 However, most of these CTPs contain false correspondences and we propose a filter based on local topology to reject most outlier CTPs before constructing the high order graph. [sent-117, score-0.393]
36 We compute the attribute by using the affine matrix to transform each triangle in the template to the input image plane and compute the Euclidean distances between the transformed points and the corresponding points in the input image. [sent-130, score-0.425]
37 A few trivial clusters th,at C co=nta {inM small number) o}f matches are classified as outliers, and others correspond to instances of the template object. [sent-150, score-0.388]
38 As the correspondences in a cluster may still contain some outliers, we propose a fast determinative annealing method based on the TPS deformation model to further refine the matches. [sent-153, score-0.525]
39 In the correspondence cluster i,the sum of squared residuals of Ni correspondences based on the TPS model is: Ef=N1in? [sent-255, score-0.271]
40 We propose an approach based on the deterministic annealing algorithm [27] to gradually reduce λs. [sent-285, score-0.35]
41 At the beginning of the annealing process, λs is large, which greatly limits the range of deformation and avoids overfitting due to the outliers. [sent-286, score-0.387]
42 Furthermore, during the annealing process, the threshold, d, to determine the outliers is also gradually decreased. [sent-288, score-0.468]
43 er I wf dhere d is also gradually reduced via the annealing procedure by setting d = T · d0. [sent-303, score-0.264]
44 The non-rigid warps estimated by feature-based approach and fusion-based approach based on the feature matches shown in (a) are shown in (b) and (c), respectively. [sent-307, score-0.307]
45 In (d) the matched feature points after match growing are marked as red points. [sent-308, score-0.293]
46 The warps estimated by feature-based and fusion-based approaches based on the feature matches are shown in (e) and (f), respectively. [sent-309, score-0.307]
47 In the beginning, Tinit = T0, and then the temperature parameter T is gradually reduced Tnew = Told · rT, where rT ∈ (0, 1) is the annealing rate. [sent-310, score-0.298]
48 s F sumrtahlel amndor teh,e d correspondence set contains too many outliers, the initially estimated deformation parameters are far from the ground truth and the threshold d is also small. [sent-313, score-0.253]
49 Thus a large amount of points will be classified to be outliers which would include many inliers. [sent-314, score-0.27]
50 Therefore, in the beginning we set the maximum percentage of points that are allowed to be classified to outliers so that few inliers are falsely classified. [sent-315, score-0.485]
51 In the experiments, we show that with this bootstrap process, the recall of inliers is highly improved. [sent-317, score-0.316]
52 After all the outliers are removed, we obtain good estimates of the deformation parameters h via (11). [sent-320, score-0.353]
53 Fusion Optimization For regions lacking texture or having large deformations, the correspondences may not be well established and thus deformation parameters may not be well estimated by (11). [sent-323, score-0.284]
54 In this paper, we propose a fusion approach based on the TPS warp by exploiting both appearance and local features. [sent-324, score-0.319]
55 Match Growing As shown in the blue bounding box of Figure 2(a), the feature matches marked as red are concentrated in small regions which significantly degrades the registration accuracy as illustrated in Figure 2(b)-(c). [sent-360, score-0.4]
56 We propose a match growing approach based on the TPS model, which propagates the matches locally to establish more correspondences. [sent-362, score-0.338]
57 In the selected group, we use the matches to estimate the local TPS warp (9 control points are used) based on the feature-based approach. [sent-368, score-0.472]
58 Four matches estimated by W in each cell are added to the correspondence set. [sent-371, score-0.286]
59 In case that the propagated matches contain some outliers, 11999966 after obtaining the propagated matches, the proposed outlier rejection algorithm is used again to refine the matches. [sent-373, score-0.523]
60 Finally, we use the feature-based approach to estimate the TPS warp (100 control points are used) for the deformed instance, which is further refined by the fusion approach. [sent-374, score-0.466]
61 As shown in Figure 2(d) the matched feature points after match growing are uniformly distributed. [sent-375, score-0.293]
62 Thus, the estimated warp based on feature-based approach (Figure 2(e)) is better than that without growing (Figure 2(b)). [sent-376, score-0.28]
63 When the initial warp is far from the ground truth (Figure 2(b)), the refined warp obtained by the fusion approach is still not accurate (Figure 2(c)). [sent-377, score-0.466]
64 On the other hand, if the initial warp is close to the ground truth (Figure 2(e)), the fusion approach facilitates obtaining a more accurate warp (Figure 2(f)). [sent-378, score-0.466]
65 Experiments We present the detection and registration results on both synthetic and real images. [sent-380, score-0.26]
66 The outlier rejection is performed with T0= 10, Tfinal=2, rT=0. [sent-385, score-0.307]
67 For the quantitative evaluation of non-rigid surface detection and outlier rejection, the synthetic deformed surfaces are generated by TPS warp with 900 control points and these correspondences are considered as the ground truth. [sent-387, score-0.939]
68 We randomly sample 100 points in the template from the ground truth as the feature points denoted by Ft. [sent-395, score-0.322]
69 We further sample Nm points from Ft to construct the inlier matches for each instance. [sent-396, score-0.3]
70 Denote the number of instances by Ns and thus the total number of inlier matches is Nt = Ns Nm. [sent-397, score-0.289]
71 Let the ratio of outliers to inliers be ro and thus the ×nu Nmber of outliers is No = ro Nt. [sent-398, score-0.626]
72 To generate these outlier matches, we randomly sample Ft for No times, and the corresponding points in the input image are randomly sampled to construct spurious correspondences. [sent-399, score-0.284]
73 We compute the precision Pd and recall Rd of the surfaces to measure the detection performance. [sent-400, score-0.247]
74 Moreover, we also measure the precision Pm and recall Rm of the matches in the detected truth surfaces, which is critical for the registration task. [sent-401, score-0.495]
75 With the increase of outliers, the precision and recall of both detection and matching do not vary much. [sent-407, score-0.255]
76 However, the recall of matches decreases significantly when the number inliers is decreased (Figure 3(d)), which can be addressed by the proposed match growing algorithm. [sent-410, score-0.635]
77 In this experiment, we perform a comparative evaluation on the outlier rejection algorithms. [sent-412, score-0.307]
78 To evaluate the performance dependence on inliers, the number of inliers is varied from 20 to 110 with interval of 10. [sent-416, score-0.26]
79 The number of control points is set to be the same as the inliers but the locations are different. [sent-417, score-0.291]
80 In order to assess the robustness to the noise levels, the outlier correspondences are incrementally added. [sent-418, score-0.317]
81 The points in the template are randomly generated and the corresponding points in the synthetic images are also randomly generated. [sent-419, score-0.326]
82 We use the precision and recall of matches to measure the performance. [sent-424, score-0.316]
83 The precision and recall rates are monotonously increased with the increase of inliers, and the recall is always less than the precision. [sent-426, score-0.247]
84 The increases of precision and recall are similar when the number of inliers is small (less than 50). [sent-427, score-0.318]
85 However, when the number of inliers is further increased, the increase rate of recall is a little higher than precision. [sent-428, score-0.297]
86 The proposed algorithm is more robust to outliers when more inliers are obtained. [sent-431, score-0.388]
87 We compare our approach with the outlier rejection algorithm proposed in [24], LPOR (Local Topology based Outlier Rejection) for short, with the provided MATLAB implementation (http://isit. [sent-432, score-0.307]
88 Its performance decreases significantly with increase of outliers and its precision is always lower than recall. [sent-439, score-0.295]
89 to the fact that more outliers in the template will degrade the effect of the local topology structure assumed by the LPOR method. [sent-455, score-0.431]
90 On the contrary, the performance of our approach is only slightly decreased with increase of outlier level. [sent-456, score-0.252]
91 Even when the number of outliers is 19 times more than inlier, the precision is still above 0. [sent-457, score-0.26]
92 One example of matches before and after outlier rejection is shown in the Figure 4(e-f). [sent-464, score-0.489]
93 05 seconds to process the correspondence set with 110 inliers and 2200 outliers, and we refer it as fast AOR (FAOR for short). [sent-467, score-0.288]
94 For comparison, we use it for the outlier rejection problem by initializing its assignment matrix in accordance with the correspondences. [sent-477, score-0.307]
95 Our approach successfully detects all the deformed surfaces of the given templates and we also get good registration results. [sent-484, score-0.364]
96 Conclusions In this paper, we present an algorithm to detect and register all the nonrigid instances of given templates from noisy observations. [sent-489, score-0.298]
97 After obtaining the initial matches between the template and the input image, a novel high-order affinity graph is constructed to model the local topology consistency and then a hierarchical clustering approach is used to detect the nonrigid instances. [sent-490, score-0.739]
98 We propose a deterministic annealing approach based on the TPS model to remove the spurious matches in each cluster. [sent-491, score-0.546]
99 The proposed fusion approach exploits both appearance information and local features to register each nonrigid instance to the corresponding template. [sent-492, score-0.345]
100 To improve the registration performance, a TPS based match growing scheme is developed to propagate the matches. [sent-493, score-0.372]
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