Anisotropic diffusion for effective shape corner point detection

Pedrosa, Glauco Vitor; Barcelos, Célia A. Zorzo

doi:10.1016/j.patrec.2010.05.013

Cited by 37 publications

(24 citation statements)

References 27 publications

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“…This section presents the experiments and performance evaluation of the proposed technique for dominant point detection in comparison to other contour-based methods as shape salience point detection [23], curvature space-scale analysis [20], and wavelet transform modulus maxima [14] approaches. The results were obtained by running the methods for a set of shapes of different sizes and roughness, which belong to a dataset of 104 binary images from MPEG7 Part B [13].…”

Section: Simulation Resultsmentioning

confidence: 99%

“…In this section, we present three methods available in the literature and named as Pedrosa and Barcelos [23], CSS [17] and Lee et al [14]. The main reason for choosing these methods in this study is their multiscale nature, so that we can fairly assess the proposed corner detector.…”

Section: Related Methodsmentioning

confidence: 99%

“…Moreover, we introduce a novel procedure that searches for multiscale algorithm parameters, in a particular database, and combine it to the idea underlying inter-scale correlation to detect dominant points in planar shapes. The proposed method is also assessed and compared with three methods [14,20,23] including the well known curvature scale-space (CSS). Furthermore, the performance evaluation of the algorithms rely on ground-truth based measures.…”

Section: Introductionmentioning

confidence: 99%

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Multiscale Corner Detection in Planar Shapes

Paula

Medeiros

Bezerra³

et al. 2012

J Math Imaging Vis

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This paper presents a multiscale corner detection method in planar shapes, which applies an undecimated Mexican hat wavelet decomposition of the angulation signal to identify significant points on a shape contour. The advantage of using this wavelet is that it is well suited for detecting singularities as corners and contours due to its excellent selectivity in position. Thus, this wavelet plays an important role in our approach because it identifies changes in non-stationary angulation signals, and it can be extended to multidimensional approaches in an efficient way when approximating this wavelet by difference of Gaussian. The proposed algorithm detects peaks on a correlation signal which is generated from different wavelet scales and retains relevant points on the decomposed angulation signal while discards poor information. Our approach assumes that only peaks which persist through several scales correspond to corners. Furthermore, we introduce a novel procedure to tune parameters for the corner detection algorithms that corresponds to the best relation between Precision and Recall measures. This technique guides the parameter adjustment of the algorithms according to the image database and it improves their performance with regard to true corner detection. Concerning the performance assessment of the algorithms, we compare the proposed one to other corner detectors by using Precision and Recall measures which are based on ground-truth information. Tests were carried out using more than a hundred images from a non-homogenous database that contains noisy and non-noisy binary shapes 1

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Section: Simulation Resultsmentioning

confidence: 99%

Section: Related Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Multiscale Corner Detection in Planar Shapes

Paula

Medeiros

Bezerra³

et al. 2012

J Math Imaging Vis

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“…To extract the salience points from the shape contour, we utilize the corner detector proposed in [5]. This detector is based on a nonlinear equation diffusion to smooth the curvature value of the shape contour points.…”

Section: Saliences Detectionmentioning

confidence: 99%

“…To extract the salience points we use the technique proposed in [5], which is based on an evolutionary anisotropic filter robust to noise of the shape contour. Then, each salience point is represented by a multi-scale method that we will present in this paper using the curvature values computed on the shape contour.…”

Section: Introductionmentioning

confidence: 99%

Shape description based on bag of salience points

Pedrosa

Traina

Barcelos

2015

Proceedings of the 30th Annual ACM Symposium on Applied Computing

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Salient points are very important for image description because they are related to the visually most important parts of the image, leading to a compact and more discriminative representation close to human perception. Based on these promising features, in this paper we propose a new shape descriptor, namely Bag-ofSalience-Points (BoSP), using the shape salience points combined with the Bag-of-Visual-Words modeling approach. Each salience point, after extracted from the shape contour, is represented by its curvature value using a multi-scale procedure proposed in this work. Taking advantage of this representation, each salience is assigned to a visual word according to a Dictionary of Curvatures. The final shape representation is given by computing a histogram of visual words detected in the shape, combined with a spatial pooling approach that encodes the distance distribution of the visual words in relation the shape centroid. This proposed new shape description allows to analyze the dissimilarity between shapes using fast distance functions, such as the City-block distance, even if two shapes have different number of salience points. This is a powerful asset to reduce the computational complexity when retrieving images. Compared to other shape descriptors, the BoSP descriptor has the advantage of proving a powerful shape description with high recognition accuracy, a compact representation invariant to geometric transformations while demanding a low computational cost to measure the dissimilarity of shapes.

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