State of the Art in Shape Matching

Veltkamp, Remco C.; Hagedoorn, Michiel

doi:10.1007/978-1-4471-3702-3_4

Cited by 336 publications

(188 citation statements)

References 59 publications

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“…A distance measure is then used to estimate their similarity. The best known distances are the Euclidean distance, the Chamfer distance, the HD and the Mahalanobis distance [14]. Since the comparison is carried out between the extracted features, the distance measures are globally calculated.…”

Section: Image Retrievalmentioning

confidence: 99%

“…The most common measures are based on the Euclidean distance, on the 1-1 correspondence distances and on the HD. The measures based on a 1-1 correspondence comprise the bottleneck distance, minimum weight matching, uniform matching and minimum deviation matching [14]. They are used in graph theory and imply to find a correspondence between the points of the two images.…”

Section: Image Retrievalmentioning

confidence: 99%

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Binary-image comparison with local-dissimilarity quantification

Baudrier

Nicolier

Millon

et al. 2008

Pattern Recognition

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In this paper, we present a method for binary image comparison. For binary images, intensity information is poor and shape extraction is often difficult. Therefore binary images have to be compared without using feature extraction. Due to the fact that different scene patterns can be present in the images, we propose a modified Hausdorff distance (HD) locally measured in an adaptive way. The resulting set of measures is richer than a single global measure. The local HD measures result in a local-dissimilarity map (LDMap) including the dissimilarity spatial layout. A classification of the images in function of their similarity is carried out on the LDMaps using a support vector machine. The proposed method is tested on a medieval illustration database and compared with other methods to show its efficiency. For the first approach, conspicuous features must be captured in the signature of each image in order to be as discriminating as possible in some user defined way [6]. The choice of the signature attributes is not always easy and depends on the processed images [5,1]. In case that there are several patterns in the images, a segmentation is often necessary to compare locally the attributes. For binary images, object shapes cannot always be precisely identified, so it is difficult to find the features related to shapes. Moreover, the texture attribute is also difficult to extract (binary images are not always * Corresponding author. textured), and the color attribute is poor (only black and white colors). Thus, the second approach, a straight image comparison, seems adapted in the case of binary images. In our work, the measure is windowed and the window size is adjusted so as to measure exclusively the local dissimilarity. The obtained result is composed of a set of local measures covering the image. The following state of the art gives a general presentation of dissimilarity measures and tends to choose the Hausdorff distance (HD). The computation of windowed measure all over the images is time consuming, nevertheless, for the HD, the algorithm comes down to a formula based on the distance transform (DT) of each image which accelerates the computation.The paper is organized as follows: firstly we present an overview of the image retrieval in Section 1.1 and then focus on the HD in Section 1.2. Secondly the notion of local HD is introduced and its properties are exposed in Sections 2 and 3. This allows us to determine automatically the size of the local HD's window in function of the dissimilarity, as shown in Section 4. Then it is shown how the algorithm may be reduced to a formula based on the DT. The map of local HD defined without parameters is presented in Sections 4 and 5. Finally, qualitative results and an application to image classification are presented in Section 6 before concluding.Please cite this article as:Ḃaudrier, et al., Binary-image comparison with local-dissimilarity quantification, Pattern Recognition (2007),

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Section: Image Retrievalmentioning

confidence: 99%

Section: Image Retrievalmentioning

confidence: 99%

Binary-image comparison with local-dissimilarity quantification

Baudrier

Nicolier

Millon

et al. 2008

Pattern Recognition

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“…All these difficulties make shape matching a formidable task. To overcome these problems, different methods have been proposed [2], which can be classified as those based on local search and those based on global search.…”

Section: Introductionmentioning

confidence: 99%

Rotation Invariant Non-rigid Shape Matching in Cluttered Scenes

Lian

Zhang

2010

Computer Vision – ECCV 2010

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Abstract. This paper presents a novel and efficient method for locating deformable shapes in cluttered scenes. The shapes to be detected may undergo arbitrary translational and rotational changes, and they can be non-rigidly deformed, occluded and corrupted by clutters. All these problems make the accurate and robust shape matching very difficult. By using a new shape representation, which involves a powerful feature descriptor, the proposed method can overcome the above difficulties successfully, and it possesses the property of global optimality. The experiments on both synthetic and real data validated that the proposed algorithm is robust to various types of disturbances. It can robustly detect the desired shapes in complex and highly cluttered scenes.

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“…This problem was studied extensively via template matching techniques, see [17,21,22,26,40,41,44] and references therein. When dealing with contours, registration via template matching is equivalent to the Hough Transform [37].…”

Section: Introductionmentioning

confidence: 99%

Prior-based Segmentation and Shape Registration in the Presence of Perspective Distortion

Riklin-Raviv

Kiryati

Sochen

2006

Int J Comput Vision

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Challenging object detection and segmentation tasks can be facilitated by the availability of a reference object. However, accounting for possible transformations between the different object views, as part of the segmentation process, remains difficult. Recent statistical methods address this problem by using comprehensive training data. Other techniques can only accommodate similarity transformations.We suggest a novel variational approach to prior-based segmentation, using a single reference object, that accounts for planar projective transformation. Generalizing the Chan-Vese level set framework, we introduce a novel shape-similarity measure and embed the projective homography between the prior shape and the image to segment within a region-based segmentation functional. The proposed algorithm detects the object of interest, extracts its boundaries, and concurrently carries out the registration to the prior shape.We demonstrate prior-based segmentation on a variety of images and verify the accuracy of the recovered transformation parameters.

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State of the Art in Shape Matching

Cited by 336 publications

References 59 publications

Binary-image comparison with local-dissimilarity quantification

Binary-image comparison with local-dissimilarity quantification

Rotation Invariant Non-rigid Shape Matching in Cluttered Scenes

Prior-based Segmentation and Shape Registration in the Presence of Perspective Distortion

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