Classification of Contour Shapes Using Class Segment Sets

Sun, Ke; Super, Boaz J.

doi:10.1109/cvpr.2005.98

Cited by 55 publications

(39 citation statements)

References 13 publications

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“…By normalizing the contour parts, following the method of Sun and Super [22], we have solved the problem of scale. The images in MPEG-7 dataset have a large variation of different sizes.…”

Section: Discussionmentioning

confidence: 99%

Detection and recognition of contour parts based on shape similarity

Bai

Yang

Latecki

2008

Pattern Recognition

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Due to distortion, noise, segmentation errors, overlap, and occlusion of objects in digital images, it is usually impossible to extract complete object contours or to segment the whole objects. However, in many cases parts of contours can be correctly reconstructed either by performing edge grouping or as parts of boundaries of segmented regions. Therefore, recognition of objects based on their contour parts seems to be a promising as well as a necessary research direction.The main contribution of this paper is a system for detection and recognition of contour parts in digital images. Both detection and recognition are based on shape similarity of contour parts. For each contour part produced by contour grouping, we use shape similarity to retrieve the most similar contour parts in a database of known contour segments. A shape-based classification of the retrieved contour parts performs then a simultaneous detection and recognition.An important step in our approach is the construction of the database of known contour segments. First complete contours of known objects are decomposed into parts using discrete curve evolution. Then, their representation is constructed that is invariant to scaling, rotation, and translation. ᭧ 2008 Elsevier Ltd. All rights reserved.Keywords: Shape similarity; Parts of visual form; Detection of contour parts System overviewWe begin with an overview of the system for contour-based object recognition. Based on psychophysical evidence [1], we can derive the following stages of contour-based object recognition (see Fig. 1):We do not discuss edge detection here, since it is an obvious step in image analysis that is also known to be performed by the human visual system. We view contour grouping as grouping of edge pixels to contour parts. It is a local process based on rule of good continuation (see Ref. comparing (b) and (c) in Fig. 1, contour grouping also means contour simplification and removal of small irrelevant features in our approach. We describe it in Section 5.In the proposed system contour grouping is followed by contour detection, described in Section 6. For each contour part produced by contour grouping [32], the most similar contour parts in a database of known contour segments are retrieved using shape similarity. Therefore, contour detection also yields preliminary recognition results of contour parts, which can be viewed as recognition hypothesis. This fact is illustrated in Fig. 2. The second column shows most significant contour parts detected in images shown in first column. The contour part significance ranking is based on shape similarity to know contour parts, which are shown in columns 3-7. If we use first nearest neighbor classifier (1NN), then column 3, which shows the most similar database contour segments, illustrates the recognition results. Thus, contour part detection and recognition are based on shape similarity to know contour parts. Observe that the objects in all three query images are correctly recognized based

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“…By normalizing the contour parts, following the method of Sun and Super [22], we have solved the problem of scale. The images in MPEG-7 dataset have a large variation of different sizes.…”

Section: Discussionmentioning

confidence: 99%

Detection and recognition of contour parts based on shape similarity

Bai

Yang

Latecki

2008

Pattern Recognition

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“…1). In Sun and Super [8] proposed a three-level framework for shape classification. It requires that the query fragment be nearly identical to the target.…”

Section: The External Form Contours or Outline Of Someone Or Somethingmentioning

confidence: 99%

Object detection based on scale-invariant partial shape matching

Fan

Yang

Tang

2015

Machine Vision and Applications

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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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“…Normalization of the Part Segments: To achieve the invariance to planar transformations (2D translations, rotation, and uniform scaling), we use a similar method in [2] to normalize the part segments.…”

Section: Fig 4 Extraction Processes Of the Part Segmentsmentioning

confidence: 99%

“…Due to the high variability of objects and backgrounds in images, it is still an extremely challenging problem. With the progress in shape representation and recognition [1][2][3], researchers start to use shape information to help detecting and recognizing objects in cluttered images [5,6,19]. Different from the methods based on the shape patches [5,6], we detect and group the contour of the object by using shape similarity between edge segments extracted from the image and the learned contour parts.…”

Section: Introductionmentioning

confidence: 99%

Contour Grouping with Partial Shape Similarity

Bai

et al. 2009

Advances in Image and Video Technology

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Abstract. In this paper, a novel algorithm is introduced to group contours from clutter images by integrating high-level information (prior of part segments) and low-level information (edges of segmentations of clutter images). The partial shape similarity between these two levels of information is embedded into the particle filter framework, an effective recursively estimating model. The particles in the framework are modeled as the paths on the edges of segmentation results by Normalized Cuts. At prediction step, the paths extend along the edges of Normalized Cuts; while, at the update step, the weights of particles update according to their partial shape similarity with priors of the trained contour segments. Successful results are achieved against the noise of the testing image, the inaccuracy of the segmentation result as well as the inexactness of the similarity between the contour segment and edges segmentation. The experimental results also demonstrate robust contour grouping performance in the presence of occlusion and large texture variation within the segmented objects.

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Classification of Contour Shapes Using Class Segment Sets

Abstract: Abstract

Cited by 55 publications

References 13 publications

Detection and recognition of contour parts based on shape similarity

Detection and recognition of contour parts based on shape similarity

Object detection based on scale-invariant partial shape matching

Contour Grouping with Partial Shape Similarity

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