Relevant bibliographies by topics / Curvature Scale Space

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Curvature Scale Space'

Author: Grafiati
Published: 4 June 2021
Last updated: 11 June 2025

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Journal articles on the topic "Curvature Scale Space"

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Zhong, Baojiang, Chang Li, and Zhengsheng Wang. "Curvature product corner detection in direct curvature scale space." International Journal of Computational Vision and Robotics 1, no. 2 (2010): 194. http://dx.doi.org/10.1504/ijcvr.2010.036081.

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CONG, GE, and SONGDE MA. "CORNER ENHANCEMENT IN CURVATURE SCALE SPACE." Pattern Recognition 31, no. 10 (1998): 1491–501. http://dx.doi.org/10.1016/s0031-3203(98)00003-x.

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Zhang, Xiaohong, Ming Lei, Dan Yang, Yuzhu Wang, and Litao Ma. "Multi-scale curvature product for robust image corner detection in curvature scale space." Pattern Recognition Letters 28, no. 5 (2007): 545–54. http://dx.doi.org/10.1016/j.patrec.2006.10.006.

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Liao, Bin, Hui Ying Sun, and Jun Gang Xu. "Adaptive Corner Detection Based on Direct Curvature Scale Space." Applied Mechanics and Materials 391 (September 2013): 488–92. http://dx.doi.org/10.4028/www.scientific.net/amm.391.488.

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Corner detection based on global and local curvature properties is an advanced method for detecting corners in images, which is a fundamental composition of many algorithms. However, we find that it is time-consuming for real-time applications and might detect wrong corners or lose some important corners. To alleviate these problems, we propose an improved curvature product corner detector with dynamic region of support based on Direct Curvature Scale Space (DCSS). Firstly, we use direct curvature scale space to reduce the complexity of computation instead of curvature scale space. Secondly, multi-scale curvature product with certain threshold is used to strengthen the corner detection. Finally, we check the angles of corner candidates in the dynamic region of support in order to eliminate falsely detected corners and use an adaptive curvature threshold to remove round corners from the initial list. The experimental results show that our proposed method improves the performance of corner detection both on accuracy and efficiency, and gain more stable corners at the same time.
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Zhong, Bao Jiang, and Chang Li. "Robust Image Corner Detection Using Curvature Product in Direct Curvature Scale Space." Applied Mechanics and Materials 20-23 (January 2010): 725–30. http://dx.doi.org/10.4028/www.scientific.net/amm.20-23.725.

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In this paper we propose an image corner detector based on the direct curvature scale space (DCSS) technique, referred to as the curvature product DCSS (CP-DCSS) corner detector. After the contours of interested objects are extracted from a real-world image, their curvature functions are respectively convolved with the Gaussian function as its standard deviation gradually increases. By measuring the product of the curvature values computed at several given scales, true corners on the contours can be easily detected since false or insignificant corners have been effectively suppressed. A point is declared as a corner when the absolute value of the curvature product exceeds a given threshold and is a local maximum at the mentioned point. CP-DCSS combines the advantages of two recently proposed corner detectors, namely, the DCSS corner detector and the multi-scale curvature product (MSCP) corner detector. Compared to DCSS, CP-DCSS omits a parsing process of the DCSS map, and hence it has a simpler structure. Compared to MSCP, CP-DCSS works equally well, however, at less computational cost.
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BUERS, AWUOR J., J. O. MALO, and SANT RAM. "A COMPOSITE SPACE–TIME CURVATURE MODEL." Modern Physics Letters A 13, no. 09 (1998): 677–83. http://dx.doi.org/10.1142/s0217732398000735.

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We develop a curvature specification scheme in which the constraint effects of a localized gravitational potential source on the Hubble expansion comprises the characteristics of the large scale dynamics of the entire universe. Our result is a composite curvature model which has a dynamical Euclidean horizon (of zero curvature) that provides an alternative to the event horizon in the determination of the local scale evolution of space–time.
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Zhong, Baojiang, and Wenhe Liao. "Direct Curvature Scale Space: Theory and Corner Detection." IEEE Transactions on Pattern Analysis and Machine Intelligence 29, no. 3 (2007): 508–12. http://dx.doi.org/10.1109/tpami.2007.50.

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Mokhtarian, F., and R. Suomela. "Robust image corner detection through curvature scale space." IEEE Transactions on Pattern Analysis and Machine Intelligence 20, no. 12 (1998): 1376–81. http://dx.doi.org/10.1109/34.735812.

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Abbasi, Sadegh, Farzin Mokhtarian, and Josef Kittler. "Curvature scale space image in shape similarity retrieval." Multimedia Systems 7, no. 6 (1999): 467–76. http://dx.doi.org/10.1007/s005300050147.

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Mokhtarian, F., and S. Abbasi. "Affine Curvature Scale Space with Affine Length Parametrisation." Pattern Analysis & Applications 4, no. 1 (2001): 1–8. http://dx.doi.org/10.1007/pl00010984.

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Dissertations / Theses on the topic "Curvature Scale Space"

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Abbasi, Sadegh. "Curvature scale space in shape similarity retrieval." Thesis, University of Surrey, 1999. http://epubs.surrey.ac.uk/843014/.

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This thesis is concerned with the problem of shape similarity retrieval in image databases. Curvature Scale Space (CSS) image representation is examined for this purpose. It consists of several arch shape contours representing the inflection points of the shape as it is smoothed. The maxima of these contours are used to represent a shape. In order to make the representation more reliable and also reflection invariant, the conventional matching algorithm is modified. The method is then tested on a database of 1100 images of marine creatures, where the advantages and shortcomings of the method are discovered. One of the main advantages of the method is the existence of local support. This enables us to deal with the main shortcoming of the method which appears in case of shapes with shallow concavities. Several approaches are suggested and implemented to overcome the problem of shallow concavities. The shape is segmented using its CSS image, and more information is extracted from the segmented shape which is then used to enrich the representation. The matching algorithm is also modified to accommodate the new information. Each segment of the shape corresponds to a contour and consequently a maximum of the CSS image. In one approach the normalised average curvature on each segment is used together with the maxima of the CSS image to represent the shape. In another approach the segments are examined at different levels of scale and for each segment, the level of scale where it is converted to a straight line is determined. Using this information along with the maxima of the CSS image yields to the best results. Both inflection points and corners are considered as end-points in different approaches. There is less information about the global appearance of a shape in its CSS image. A small number of global parameters are included and used for indexing to obtain even better results. The problem of evaluation of similarity retrieval methods is addressed. In order to evaluate different approaches, a set of classified shapes are introduced and the performance measure of each method is calculated using this database. In another approach, a subjective evaluation of the method is presented based on the judgements made by human subjects. The method is also tested on a real-world application where the task is to help the users find out whether an unknown leaf belongs to one of the existing varieties or whether it represents a new variety. A web-demo of the work is also prepared and is available in the below mentioned address. The boundary contours of the marine animals images can be obtained from this page.
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Mohanna, Farahnaz. "Content based video database retrieval using shape features." Thesis, University of Surrey, 2002. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.250764.

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Scheuer, Julian [Verfasser], and Claus [Akademischer Betreuer] Gerhardt. "Non-scale-invariant inverse curvature flows in hyperbolic space / Julian Scheuer ; Betreuer: Claus Gerhardt." Heidelberg : Universitätsbibliothek Heidelberg, 2013. http://d-nb.info/117992424X/34.

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Mokhtarian, Farzin. "A theory of multi-scale, curvature and torsion based shape representation for planar and space curves." Thesis, University of British Columbia, 1990. http://hdl.handle.net/2429/30740.

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This thesis presents a theory of multi-scale, curvature and torsion based shape representation for planar and space curves. The theory presented has been developed to satisfy various criteria considered useful for evaluating shape representation methods in computer vision. The criteria are: invariance, uniqueness, stability, efficiency, ease of implementation and computation of shape properties. The regular representation for planar curves is referred to as the curvature scale space image and the regular representation for space curves is referred to as the torsion scale space image. Two variants of the regular representations, referred to as the renormalized and resampled curvature and torsion scale space images, have also been proposed. A number of experiments have been carried out on the representations which show that they are very stable under severe noise conditions and very useful for tasks which call for recognition of a noisy curve of arbitrary shape at an arbitrary scale or orientation. Planar or space curves are described at varying levels of detail by convolving their parametric representations with Gaussian functions of varying standard deviations. The curvature or torsion of each such curve is then computed using mathematical equations which express curvature and torsion in terms of the convolutions of derivatives of Gaussian functions and parametric representations of the input curves. Curvature or torsion zero-crossing points of those curves are then located and combined to form one of the representations mentioned above. The process of describing a curve at increasing levels of abstraction is referred to as the evolution or arc length evolution of that curve. This thesis contains a number of theorems about evolution and arc length evolution of planar and space curves along with their proofs. Some of these theorems demonstrate that evolution and arc length evolution do not change the physical interpretation of curves as object boundaries and others are in fact statements on the global properties of planar and space curves during evolution and arc length evolution and their representations. Other theoretical results shed light on the local behavior of planar and space curves just before and just after the formation of a cusp point during evolution and arc length evolution. Together these results provide a sound theoretical foundation for the representation methods proposed in this thesis.<br>Science, Faculty of<br>Computer Science, Department of<br>Graduate
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Wilson, Dantas de Almeida Carlos. "Recuperação de imagens baseada em uma abordagem híbrida." Universidade Federal de Pernambuco, 2007. https://repositorio.ufpe.br/handle/123456789/2634.

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Made available in DSpace on 2014-06-12T15:59:48Z (GMT). No. of bitstreams: 2 arquivo5591_1.pdf: 1590353 bytes, checksum: 797ebf6670d6408c7051ea1631a937a4 (MD5) license.txt: 1748 bytes, checksum: 8a4605be74aa9ea9d79846c1fba20a33 (MD5) Previous issue date: 2007<br>Nos últimos anos, têm-se registrado um crescente interesse e popularização de imagens digitais, através de dispositivos tais como câmeras digitais, celulares, webcam ou filmadoras digitais. Com a grande quantidade de informação visual disponível, cresce a dificuldade do usuário em recuperar essas informações de forma precisa e eficiente. Atualmente, existem inúmeros mecanismos de busca baseados em descrições textuais ou keywords. No entanto, existem grandes dificuldades nessa abordagem, (i ) o trabalho manual requerido para notação das imagens e (ii ) a subjetividade para essa notação. Devido a essas e outras dificuldades, os mecanismos de busca baseado em keywords geram uma grande quantidade de respostas não relevantes. Nesse contexto, grandes esforços têm sido feito na área de recuperação de imagens baseados em conteúdo, de forma a tornar esse tipo de conteúdo mais acessível aos seus usuários. A proposta geral para a dissertação é desenvolver uma nova estratégia de recuperação de imagens baseada na forma, utilizando o descritor de forma Curvature Scale Space (CSS) e Mapas Auto-Organizáveis (SOM) para descrever, classificar, indexar e recuperar imagens. Essa nova abordagem possibilita a realização de consultas por similaridade levando em consideração a semelhança entre o contorno fechado dos objetos pesquisados. As características dos objetos são representados através de uma imagem multi-escalar CSS e pr´e-processados, constituindo em dados que serão usados como treinamento da rede SOM. Nesse estudo, avaliamos a acurácia e o tempo de busca através de uma base benchmark denominada Core Experiment (CE-1B). Utilizamos variações dessa base para analisar o desempenho sobre transformações geométricas de escala, rotação e translação. Os resultados obtidos mostram que a combinação do descritor CSS e SOM representa uma estratégia promissora para recuperação de imagens, com uma alta performance de tempo
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Tellioglu, Zafer Hasim. "Real Time 3d Surface Feature Extraction On Fpga." Master's thesis, METU, 2010. http://etd.lib.metu.edu.tr/upload/12612200/index.pdf.

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Three dimensional (3D) surface feature extractions based on mean (H) and Gaussian (K) curvature analysis of range maps, also known as depth maps, is an important tool for machine vision applications such as object detection, registration and recognition. Mean and Gaussian curvature calculation algorithms have already been implemented and examined as software. In this thesis, hardware based digital curvature processors are designed. Two types of real time surface feature extraction and classification hardware are developed which perform mean and Gaussian curvature analysis at different scale levels. The techniques use different gradient approximations. A fast square root algorithm using both LUT (look up table) and linear fitting technique is developed to calculate H and K values of the surface described by the 3D Range Map formed by fixed point numbers. The proposed methods are simulated in MatLab software and implemented on different FPGAs using VHDL hardware language. Calculation times, outputs and power analysis of these techniques are compared to CPU based 64 bit float data type calculations.
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Cerutti, Guillaume. "Segmentation et interprétation d'images naturelles pour l'identification de feuilles d'arbres sur smartphone." Thesis, Lyon 2, 2013. http://www.theses.fr/2013LYO22022/document.

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Les espèces végétales, et en particulier les espèces d'arbres, forment un cadre de choix pour un processus de reconnaissance automatique basé sur l'analyse d'images. Les critères permettant de les identifier sont en effet le plus souvent des éléments morphologiques visuels, bien décrits et référencés par la botanique, qui laissent à penser qu'une reconnaissance par la forme est envisageable. Les feuilles constituent dans ce contexte les organes végétaux discriminants les plus faciles à appréhender, et sont de ce fait les plus communément employés pour ce problème qui connaît actuellement un véritable engouement. L'identification automatique pose toutefois un certain nombre de problèmes complexes, que ce soit dans le traitement des images ou dans la difficulté même de la classification en espèces, qui en font une application de pointe en reconnaissance de formes.Cette thèse place le problème de l'identification des espèces d'arbres à partir d'images de leurs feuilles dans le contexte d'une application pour smartphones destinée au grand public. Les images sur lesquelles nous travaillons sont donc potentiellement complexes et leur acquisition peu supervisée. Nous proposons alors des méthodes d'analyse d'images dédiées, permettant la segmentation et l'interprétation des feuilles d'arbres, en se basant sur une modélisation originale de leurs formes, et sur des approches basées modèles déformables. L'introduction de connaissances a priori sur la forme des objets améliore ainsi de façon significative la qualité et la robustesse de l'information extraite de l'image. Le traitement se déroulant sur l'appareil, nous avons développé ces algorithmes en prenant en compte les contraintes matérielles liées à leur utilisation.Nous introduisons également une description spécifique des formes des feuilles, inspirée par les caractéristiques déterminantes recensées dans les ouvrages botaniques. Ces différents descripteurs fournissent des informations de haut niveau qui sont fusionnées en fin de processus pour identifier les espèces, tout en permettant une interprétation sémantique intéressante dans le cadre de l'interaction avec un utilisateur néophyte. Les performances obtenues en termes de classification, sur près de 100 espèces d'arbres, se situent par ailleurs au niveau de l'état de l'art dans le domaine, et démontrent une robustesse particulière sur les images prises en environnement naturel. Enfin, nous avons intégré l'implémentation de notre système de reconnaissance dans l'application Folia pour iPhone, qui constitue une validation de nos approches et méthodes dans un cadre réel<br>Plant species, and especially tree species, constitute a well adapted target for an automatic recognition process based on image analysis. The criteria that make their identification possible are indeed often morphological visual elements, which are well described and referenced by botany. This leads to think that a recognition through shape is worth considering. Leaves stand out in this context as the most accessible discriminative plant organs, and are subsequently the most often used for this problem recently receiving a particular attention. Automatic identification however gives rise to a fair amount of complex problems, linked with the processing of images, or in the difficult nature of the species classification itself, which make it an advanced application for pattern recognition.This thesis considers the problem of tree species identification from leaf images within the framework of a smartphone application intended for a non-specialist audience. The images on which we expect to work are then potentially very complex scenes and their acquisition rather unsupervised. We consequently propose dedicated methods for image analysis, in order to segment and interpret tree leaves, using an original shape modelling and deformable templates. The introduction on prior knowledge on the shape of objects enhances significatively the quality and the robustness of the information we extract from the image. All processing being carried out on the mobile device, we developed those algorithms with concern towards the material constraints of their exploitation. We also introduce a very specific description of leaf shapes, inspired by the determining characteristics listed in botanical references. These different descriptors constitute independent sources of high-level information that are fused at the end of the process to identify species, while providing the user with a possible semantic interpretation. The classification performance demonstrated over approximately 100 tree species are competitive with state-of-the-art methods of the domain, and show a particular robustness to difficult natural background images. Finally, we integrated the implementation of our recognition system into the \textbf{Folia} application for iPhone, which constitutes a validation of our approaches and methods in a real-world use
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Ciftci, Gunce. "Shape Analysis Using Contour-based And Region-based Approaches." Master's thesis, METU, 2004. http://etd.lib.metu.edu.tr/upload/1092121/index.pdf.

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The user of an image database often wishes to retrieve all images similar to the one (s)he already has. In this thesis, shape analysis methods for retrieving shape are investigated. Shape analysis methods can be classified in two groups as contour-based and region-based according to the shape information used. In such a classification, curvature scale space (CSS) representation and angular radial transform (ART) are promising methods for shape similarity retrieval respectively. The CSS representation operates by decomposing the shape contour into convex and concave sections. CSS descriptor is extracted by using the curvature zero-crossings behaviour of the shape boundary while smoothing the boundary with Gaussian filter. The ART descriptor decomposes the shape region into a number of orthogonal 2-D basis functions defined on a unit disk. ART descriptor is extracted using the magnitudes of ART coefficients. These methods are implemented for similarity comparison of binary images and the retrieval performances of descriptors for changing number of sampling points of boundary and order of ART coefficients are investigated. The experiments are done using 1000 images from MPEG7 Core Experiments Shape-1. Results show that for different classes of shape, different descriptors are more successful. When the choice of approach depends on the properties of the query shape, similarity retrieval performance increases.
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Akagunduz, Erdem. "3d Object Recognition Using Scale Space Of Curvatures." Phd thesis, METU, 2011. http://etd.lib.metu.edu.tr/upload/12612901/index.pdf.

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In this thesis, a generic, scale and resolution invariant method to extract 3D features from 3D surfaces, is proposed. Features are extracted with their scale (metric size and resolution) from range images using scale-space of 3D surface curvatures. Different from previous scale-space approaches<br>connected components within the classified curvature scale-space are extracted as features. Furthermore, scales of features are extracted invariant of the metric size or the sampling of the range images. Geometric hashing is used for object recognition where scaled, occluded and both scaled and occluded versions of range images from a 3D object database are tested. The experimental results under varying scale and occlusion are compared with SIFT in terms of recognition capabilities. In addition, to emphasize the importance of using scale space of curvatures, the comparative recognition results obtained with single scale features are also presented.
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Dvořák, Pavel. "Popis objektů v obraze." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2011. http://www.nusl.cz/ntk/nusl-218957.

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This thesis consider description of segments identified in image. At first there are described main methods of segmentation because it is a process contiguous before describing of objects. Next chapter is devoted to methods which focus on description identified regions. There are studied algorithms used for characterizing of different features. There are parts devoted to color, location, size, orientation, shape and topology. The end of this chapter is devoted to moments. Next chapters are focused on designing fit algorithms for segments description and XML files creating according to MPEG-7 standards and their implementation into RapidMiner. In the last chapter there are described results of the implementation.
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Books on the topic "Curvature Scale Space"

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Mokhtarian, Farzin, and Miroslaw Bober. Curvature Scale Space Representation: Theory, Applications, and MPEG-7 Standardization. Springer Netherlands, 2003. http://dx.doi.org/10.1007/978-94-017-0343-7.

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1965-, Bober Miroslaw Z., ed. Curvature scale space representation: Theory, applications, and MPEG-7 standardization. Kluwer Academic Publishers, 2003.

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Book chapters on the topic "Curvature Scale Space"

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Zang, Di, and Gerald Sommer. "The Monogenic Curvature Scale-Space." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11774938_25.

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Imiya, Atsushi, and Ulrich Eckhardt. "Discrete Mean Curvature Flow." In Scale-Space Theories in Computer Vision. Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/3-540-48236-9_46.

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Utcke, Sven. "Error-Bounds on Curvature Estimation." In Scale Space Methods in Computer Vision. Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/3-540-44935-3_46.

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Abbasi, Sadegh, and Farzin Mokhtarian. "Curvature Scale Space with Affine Length Parametrisation." In Scale-Space Theories in Computer Vision. Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/3-540-48236-9_39.

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Zhong, Baojiang, and Wenhe Liao. "Direct Curvature Scale Space in Corner Detection." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11815921_25.

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Abbasi, Sadegh, Farzin Mokhtarian, and Josef Kittler. "Reliable classification of chrysanthemum leaves through Curvature Scale Space." In Scale-Space Theory in Computer Vision. Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/3-540-63167-4_58.

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Avants, Brian, and James Gee. "Continuous Curve Matching with Scale-Space Curvature and Extrema-Based Scale Selection." In Scale Space Methods in Computer Vision. Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/3-540-44935-3_56.

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Mokhtarian, Farzin, and Miroslaw Bober. "Robust Image Corner Detection Through Curvature Scale Space." In Computational Imaging and Vision. Springer Netherlands, 2003. http://dx.doi.org/10.1007/978-94-017-0343-7_7.

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Kimmel, Ron. "Intrinsic scale space for images on surfaces: The geodesic curvature flow." In Scale-Space Theory in Computer Vision. Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/3-540-63167-4_52.

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Craizer, Marcos. "Evolution of the Critical Points in the Curvature and Affine Morphological Scale Spaces." In Scale Space Methods in Computer Vision. Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/3-540-44935-3_36.

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Conference papers on the topic "Curvature Scale Space"

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Dudek, Gregory. "Shape metrics from curvature-scale space and curvature-tuned smoothing." In San Diego, '91, San Diego, CA, edited by Baba C. Vemuri. SPIE, 1991. http://dx.doi.org/10.1117/12.49976.

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Awrangjeb, Mohammad, Guojun Lu, and Manzur Murshed. "An Affine Resilient Curvature Scale-Space Corner Detector." In 2007 IEEE International Conference on Acoustics, Speech, and Signal Processing. IEEE, 2007. http://dx.doi.org/10.1109/icassp.2007.366137.

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Mokhtarian, F. "Curvature scale space for image point feature detection." In 7th International Conference on Image Processing and its Applications. IEE, 1999. http://dx.doi.org/10.1049/cp:19990312.

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Zhou, Wen, Baojiang Zhong, and Kai-Kuang Ma. "Shape Matching Based on Rectangularized Curvature Scale-Space Maps." In 2019 IEEE International Conference on Image Processing (ICIP). IEEE, 2019. http://dx.doi.org/10.1109/icip.2019.8803495.

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Mokhtarian, F. "Convergence Properties of Curvature and Torsion Scale Space Representations." In British Machine Vision Conference 1995. British Machine Vision Association, 1995. http://dx.doi.org/10.5244/c.9.36.

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Mokhtarian, F., S. Abbasi, and J. Kittler. "Robust and Efficient Shape Indexing through Curvature Scale Space." In British Machine Vision Conference 1996. British Machine Vision Association, 1996. http://dx.doi.org/10.5244/c.10.33.

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Li, Ming, Sheng Yang, and Xian-wei Li. "A Head Detection Method Based on Curvature Scale Space." In 2009 3rd International Symposium on Intelligent Information Technology Application Workshops (IITAW 2009). IEEE, 2009. http://dx.doi.org/10.1109/iitaw.2009.108.

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Jacobson, Natan, Truong Nguyen, and Frank Crosby. "Curvature Scale Space Application to Distorted Object Recognition and Classification." In 2007 41st Asilomar conference on Signals, Systems and Computers (ACSSC). IEEE, 2007. http://dx.doi.org/10.1109/acssc.2007.4487611.

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Khoddami, Malike, and Alireza Behrad. "Farsi and Latin script identification using curvature scale space features." In 2010 10th Symposium on Neural Network Applications in Electrical Engineering (NEUREL 2010). IEEE, 2010. http://dx.doi.org/10.1109/neurel.2010.5644061.

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Giannekou, V., P. Tzouveli, Y. Avrithis, and S. Kollias. "Affine invariant curve matching using normalization and curvature scale-space." In 2008 International Workshop on Content-Based Multimedia Indexing. IEEE, 2008. http://dx.doi.org/10.1109/cbmi.2008.4564948.

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