Relevant bibliographies by topics / Gaussian scale-space

Contents

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

Academic literature on the topic 'Gaussian scale-space'

Author: Grafiati
Published: 4 June 2021
Last updated: 8 February 2022

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Journal articles on the topic "Gaussian scale-space"

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Lindeberg, Tony. "Generalized Gaussian Scale-Space Axiomatics Comprising Linear Scale-Space, Affine Scale-Space and Spatio-Temporal Scale-Space." Journal of Mathematical Imaging and Vision 40, no. 1 (December 1, 2010): 36–81. http://dx.doi.org/10.1007/s10851-010-0242-2.

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Rey Otero, Ives, and Mauricio Delbracio. "Computing an Exact Gaussian Scale-Space." Image Processing On Line 5 (February 2, 2016): 8–26. http://dx.doi.org/10.5201/ipol.2016.117.

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Tschirsich, Martin, and Arjan Kuijper. "Notes on Discrete Gaussian Scale Space." Journal of Mathematical Imaging and Vision 51, no. 1 (May 13, 2014): 106–23. http://dx.doi.org/10.1007/s10851-014-0509-0.

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Zhang, Meng Meng, Zhihui Yang, Yang Yang, and Yan Sun. "Bifurcation Properties of Image Gaussian Scale-Space Model." Advanced Science Letters 6, no. 1 (March 15, 2012): 430–35. http://dx.doi.org/10.1166/asl.2012.2286.

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SAKAI, Tomoya, and Atsushi IMIYA. "Qualitative Descriptions of Image in the Gaussian Scale-Space." Interdisciplinary Information Sciences 11, no. 2 (2005): 157–66. http://dx.doi.org/10.4036/iis.2005.157.

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Lionnie, Regina, and Mudrik Alaydrus. "Hierarchical Gaussian Scale-Space on Androgenic Hair Pattern Recognition." TELKOMNIKA (Telecommunication Computing Electronics and Control) 15, no. 1 (March 1, 2016): 522. http://dx.doi.org/10.12928/telkomnika.v15i1.5381.

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Babaud, Jean, Andrew P. Witkin, Michel Baudin, and Richard O. Duda. "Uniqueness of the Gaussian Kernel for Scale-Space Filtering." IEEE Transactions on Pattern Analysis and Machine Intelligence PAMI-8, no. 1 (January 1986): 26–33. http://dx.doi.org/10.1109/tpami.1986.4767749.

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Kamejima, Kohji. "Laplacian-Gaussian Sub-Correlation Analysis for Scale Space Imaging." Proceedings of the ISCIE International Symposium on Stochastic Systems Theory and its Applications 2005 (May 5, 2005): 277–82. http://dx.doi.org/10.5687/sss.2005.277.

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Behrens, T., K. Schmidt, R. A. MacMillan, and R. A. Viscarra Rossel. "Multiscale contextual spatial modelling with the Gaussian scale space." Geoderma 310 (January 2018): 128–37. http://dx.doi.org/10.1016/j.geoderma.2017.09.015.

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Mahour, Milad, Valentyn Tolpekin, and Alfred Stein. "Automatic Detection of Individual Trees from VHR Satellite Images Using Scale-Space Methods." Sensors 20, no. 24 (December 15, 2020): 7194. http://dx.doi.org/10.3390/s20247194.

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This research investigates the use of scale-space theory to detect individual trees in orchards from very-high resolution (VHR) satellite images. Trees are characterized by blobs, for example, bell-shaped surfaces. Their modeling requires the identification of local maxima in Gaussian scale space, whereas location of the maxima in the scale direction provides information about the tree size. A two-step procedure relates the detected blobs to tree objects in the field. First, a Gaussian blob model identifies tree crowns in Gaussian scale space. Second, an improved tree crown model modifies this model in the scale direction. The procedures are tested on the following three representative cases: an area with vitellaria trees in Mali, an orchard with walnut trees in Iran, and one case with oil palm trees in Indonesia. The results show that the refined Gaussian blob model improves upon the traditional Gaussian blob model by effectively discriminating between false and correct detections and accurately identifying size and position of trees. A comparison with existing methods shows an improvement of 10–20% in true positive detections. We conclude that the presented two-step modeling procedure of tree crowns using Gaussian scale space is useful to automatically detect individual trees from VHR satellite images for at least three representative cases.
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Dissertations / Theses on the topic "Gaussian scale-space"

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Bosson, Alison. "Experiments with scale-space vision systems." Thesis, University of East Anglia, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.323309.

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Ijaz, Hamza. "A Hardware Architecture for Scale-space Extrema Detection." Thesis, KTH, Skolan för informations- och kommunikationsteknik (ICT), 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-116758.

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Vision based object recognition and localization have been studied widely in recent years. Often the initial step in such tasks is detection of interest points from a grey-level image. The current state-of-the-art algorithms in this domain, like Scale Invariant Feature Transform (SIFT) and Speeded Up Robust Features (SURF) suffer from low execution speeds on a GPU(graphic processing unit) based system. Generally the performance of these algorithms on a GPU is below real-time due to high computational complexity and data intensive nature and results in elevated power consumption. Since real-time performance is desirable in many vision based applications, hardware based feature detection is an emerging solution that exploits inherent parallelism in such algorithms to achieve significant speed gains. The efficient utilization of resources still remains a challenge that directly effects the cost of hardware. This work proposes a novel hardware architecture for scale-space extrema detection part of the SIFT algorithm. The implementation of proposed architecture for Xilinx Virtex-4 FPGA and its evaluation are also presented. The implementation is sufficiently generic and can be adapted to different design parameters efficiently according to the requirements of application. The achieved system performance exceeds real-time requirements (30 frames per second) on a 640 x 480 image. Synthesis results show efficient resource utilization when compared with the existing known implementations.
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Lindeberg, Tony. "Discrete Scale-Space Theory and the Scale-Space Primal Sketch." Doctoral thesis, KTH, Numerisk analys och datalogi, NADA, 1991. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-58570.

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This thesis, within the subfield of computer science known as computer vision, deals with the use of scale-space analysis in early low-level processing of visual information. The main contributions comprise the following five subjects: The formulation of a scale-space theory for discrete signals. Previously, the scale-space concept has been expressed for continuous signals only. We propose that the canonical way to construct a scale-space for discrete signals is by convolution with a kernel called the discrete analogue of the Gaussian kernel, or equivalently by solving a semi-discretized version of the diffusion equation. Both the one-dimensional and two-dimensional cases are covered. An extensive analysis of discrete smoothing kernels is carried out for one-dimensional signals and the discrete scale-space properties of the most common discretizations to the continuous theory are analysed. A representation, called the scale-space primal sketch, which gives a formal description of the hierarchical relations between structures at different levels of scale. It is aimed at making information in the scale-space representation explicit. We give a theory for its construction and an algorithm for computing it. A theory for extracting significant image structures and determining the scales of these structures from this representation in a solely bottom-up data-driven way. Examples demonstrating how such qualitative information extracted from the scale-space primal sketch can be used for guiding and simplifying other early visual processes. Applications are given to edge detection, histogram analysis and classification based on local features. Among other possible applications one can mention perceptual grouping, texture analysis, stereo matching, model matching and motion. A detailed theoretical analysis of the evolution properties of critical points and blobs in scale-space, comprising drift velocity estimates under scale-space smoothing, a classification of the possible types of generic events at bifurcation situations and estimates of how the number of local extrema in a signal can be expected to decrease as function of the scale parameter. For two-dimensional signals the generic bifurcation events are annihilations and creations of extremum-saddle point pairs. Interpreted in terms of blobs, these transitions correspond to annihilations, merges, splits and creations. Experiments on different types of real imagery demonstrate that the proposed theory gives perceptually intuitive results.

QC 20120119

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Bessinger, Zachary. "An Automatic Framework for Embryonic Localization Using Edges in a Scale Space." TopSCHOLAR®, 2013. http://digitalcommons.wku.edu/theses/1262.

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Localization of Drosophila embryos in images is a fundamental step in an automatic computational system for the exploration of gene-gene interaction on Drosophila. Contour extraction of embryonic images is challenging due to many variations in embryonic images. In the thesis work, we develop a localization framework based on the analysis of connected components of edge pixels in a scale space. We propose criteria to select optimal scales for embryonic localization. Furthermore, we propose a scale mapping strategy to compress the range of a scale space in order to improve the efficiency of the localization framework. The effectiveness of the proposed framework and the scale mapping strategy are validated in our experiments.
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Lindeberg, Tony. "Scale Selection Properties of Generalized Scale-Space Interest Point Detectors." KTH, Beräkningsbiologi, CB, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-101220.

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Scale-invariant interest points have found several highly successful applications in computer vision, in particular for image-based matching and recognition. This paper presents a theoretical analysis of the scale selection properties of a generalized framework for detecting interest points from scale-space features presented in Lindeberg (Int. J. Comput. Vis. 2010, under revision) and comprising: an enriched set of differential interest operators at a fixed scale including the Laplacian operator, the determinant of the Hessian, the new Hessian feature strength measures I and II and the rescaled level curve curvature operator, as well as an enriched set of scale selection mechanisms including scale selection based on local extrema over scale, complementary post-smoothing after the computation of non-linear differential invariants and scale selection based on weighted averaging of scale values along feature trajectories over scale. A theoretical analysis of the sensitivity to affine image deformations is presented, and it is shown that the scale estimates obtained from the determinant of the Hessian operator are affine covariant for an anisotropic Gaussian blob model. Among the other purely second-order operators, the Hessian feature strength measure I has the lowest sensitivity to non-uniform scaling transformations, followed by the Laplacian operator and the Hessian feature strength measure II. The predictions from this theoretical analysis agree with experimental results of the repeatability properties of the different interest point detectors under affine and perspective transformations of real image data. A number of less complete results are derived for the level curve curvature operator.

QC 20121003


Image descriptors and scale-space theory for spatial and spatio-temporal recognition
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Larsson, Karl. "Scale-Space Methods as a Means of Fingerprint Image Enhancement." Thesis, Linköping University, Department of Science and Technology, 2004. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-2282.

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The usage of automatic fingerprint identification systems as a means of identification and/or verification have increased substantially during the last couple of years. It is well known that small deviations may occur within a fingerprint over time, a problem referred to as template ageing. This problem, and other reasons for deviations between two images of the same fingerprint, complicates the identification/verification process, since distinct features may appear somewhat different in the two images that are matched. Commonly used to try and minimise this type of problem are different kinds of fingerprint image enhancement algorithms. This thesis tests different methods within the scale-space framework and evaluate their performance as fingerprint image enhancement methods.

The methods tested within this thesis ranges from linear scale-space filtering, where no prior information about the images is known, to scalar and tensor driven diffusion where analysis of the images precedes and controls the diffusion process.

The linear scale-space approach is shown to improve correlation values, which was anticipated since the image structure is flattened at coarser scales. There is however no increase in the number of accurate matches, since inaccurate features also tends to get higher correlation value at large scales.

The nonlinear isotropic scale-space (scalar dependent diffusion), or the edge- preservation, approach is proven to be an ill fit method for fingerprint image enhancement. This is due to the fact that the analysis of edges may be unreliable, since edge structure is often distorted in fingerprints affected by the template ageing problem.

The nonlinear anisotropic scale-space (tensor dependent diffusion), or coherence-enhancing, method does not give any overall improvements of the number of accurate matches. It is however shown that for a certain type of template ageing problem, where the deviating structure does not significantly affect the ridge orientation, the nonlinear anisotropic diffusion is able to accurately match correlation pairs that resulted in a false match before they were enhanced.

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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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Mykhalchuk, Vasyl. "Correspondance de maillages dynamiques basée sur les caractéristiques." Thesis, Strasbourg, 2015. http://www.theses.fr/2015STRAD010/document.

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Correspondance de forme est un problème fondamental dans de nombreuses disciplines de recherche, tels que la géométrie algorithmique, vision par ordinateur et l'infographie. Communément définie comme un problème de trouver injective/ multivaluée correspondance entre une source et une cible, il constitue une tâche centrale dans de nombreuses applications y compris le transfert de attributes, récupération des formes etc. Dans récupération des formes, on peut d'abord calculer la correspondance entre la forme de requête et les formes dans une base de données, puis obtenir le meilleure correspondance en utilisant une mesure de qualité de correspondance prédéfini. Il est également particulièrement avantageuse dans les applications basées sur la modélisation statistique des formes. En encapsulant les propriétés statistiques de l'anatomie du sujet dans le model de forme, comme variations géométriques, des variations de densité, etc., il est utile non seulement pour l'analyse des structures anatomiques telles que des organes ou des os et leur variations valides, mais aussi pour apprendre les modèle de déformation de la classe d'objets. Dans cette thèse, nous nous intéressons à une enquête sur une nouvelle méthode d'appariement de forme qui exploite grande redondance de l'information à partir des ensembles de données dynamiques, variables dans le temps. Récemment, une grande quantité de recherches ont été effectuées en infographie sur l'établissement de correspondances entre les mailles statiques (Anguelov, Srinivasan et al. 2005, Aiger, Mitra et al. 2008, Castellani, Cristani et al. 2008). Ces méthodes reposent sur les caractéristiques géométriques ou les propriétés extrinsèques/intrinsèques des surfaces statiques (Lipman et Funkhouser 2009, Sun, Ovsjanikov et al. 2009, Ovsjanikov, Mérigot et al. 2010, Kim, Lipman et al., 2011) pour élaguer efficacement les paires. Bien que l'utilisation de la caractéristique géométrique est encore un standard d'or, les méthodes reposant uniquement sur l'information statique de formes peuvent générer dans les résultats de correspondance grossièrement trompeurs lorsque les formes sont radicalement différentes ou ne contiennent pas suffisamment de caractéristiques géométriques. [...]
3D geometry modelling tools and 3D scanners become more enhanced and to a greater degree affordable today. Thus, development of the new algorithms in geometry processing, shape analysis and shape correspondence gather momentum in computer graphics. Those algorithms steadily extend and increasingly replace prevailing methods based on images and videos. Non-rigid shape correspondence or deformable shape matching has been a long-studied subject in computer graphics and related research fields. Not to forget, shape correspondence is of wide use in many applications such as statistical shape analysis, motion cloning, texture transfer, medical applications and many more. However, robust and efficient non-rigid shape correspondence still remains a challenging task due to fundamental variations between individual subjects, acquisition noise and the number of degrees of freedom involved in correspondence search. Although dynamic 2D/3D intra-subject shape correspondence problem has been addressed in the rich set of previous methods, dynamic inter-subject shape correspondence received much less attention. The primary purpose of our research is to develop a novel, efficient, robust deforming shape analysis and correspondence framework for animated meshes based on their dynamic and motion properties. We elaborate our method by exploiting a profitable set of motion data exhibited by deforming meshes with time-varying embedding. Our approach is based on an observation that a dynamic, deforming shape of a given subject contains much more information rather than a single static posture of it. That is different from the existing methods that rely on static shape information for shape correspondence and analysis.Our framework of deforming shape analysis and correspondence of animated meshes is comprised of several major contributions: a new dynamic feature detection technique based on multi-scale animated mesh’s deformation characteristics, novel dynamic feature descriptor, and an adaptation of a robust graph-based feature correspondence approach followed by the fine matching of the animated meshes. [...]
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Books on the topic "Gaussian scale-space"

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Sporring, Jon, Mads Nielsen, Luc Florack, and Peter Johansen, eds. Gaussian Scale-Space Theory. Dordrecht: Springer Netherlands, 1997. http://dx.doi.org/10.1007/978-94-015-8802-7.

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Sporring, Jon. Gaussian Scale-Space Theory. Dordrecht: Springer Netherlands, 1997.

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1959-, Kerckhove Michael, ed. Scale-space and morphology in computer vision: Third International Conference, Scale-Space 2001, Vancouver, Canada, July 7-8, 2001 : proceedings. Berlin: Springer, 2001.

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1969-, Sporring Jon, ed. Gaussian scale-space theory. Dordrecht: Kluwer Academic Publishers, 1997.

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Florack, Luc, Jon Sporring, and Mads Nielsen. Gaussian Scale-Space Theory. Springer, 2014.

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(Editor), Mads Nielsen, Peter Johansen (Editor), Ole F. Olsen (Editor), and Joachim Weickert (Editor), eds. Scale-Space Theories in Computer Vision: Second International Conference, Scale-Space'99, Corfu, Greece, September 26-27, 1999, Proceedings (Lecture Notes in Computer Science). Springer, 1999.

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Scale Space and Variational Methods in Computer Vision Lecture Notes in Computer Science Image Processing Comput. Springer, 2009.

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Book chapters on the topic "Gaussian scale-space"

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Kuijper, Arjan. "On Manifolds in Gaussian Scale Space." In Scale Space Methods in Computer Vision, 1–16. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/3-540-44935-3_1.

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Loog, Marco, Martin Lillholm, Mads Nielsen, and Max A. Viergever. "Gaussian Scale Space from Insufficient Image Information." In Scale Space Methods in Computer Vision, 757–69. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/3-540-44935-3_53.

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Kuijper, Arjan, and Luc Florack. "Calculations on Critical Points under Gaussian Blurring." In Scale-Space Theories in Computer Vision, 318–29. Berlin, Heidelberg: Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/3-540-48236-9_28.

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van den Boomgaard, Rein, and Leo Dorst. "The Morphological Equivalent of Gaussian Scale-Space." In Computational Imaging and Vision, 203–20. Dordrecht: Springer Netherlands, 1997. http://dx.doi.org/10.1007/978-94-015-8802-7_15.

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Crowley, James L., and Olivier Riff. "Fast Computation of Scale Normalised Gaussian Receptive Fields." In Scale Space Methods in Computer Vision, 584–98. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/3-540-44935-3_41.

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denBoomgaard, Rein, and Rik derWeij. "Gaussian Convolutions Numerical Approximations Based on Interpolation." In Scale-Space and Morphology in Computer Vision, 205–14. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-47778-0_17.

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Carreira-Perpiñán, Miguel Á., and Christopher K. I. Williams. "On the Number of Modes of a Gaussian Mixture." In Scale Space Methods in Computer Vision, 625–40. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/3-540-44935-3_44.

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Weickert, Joachim, Seiji Ishikawa, and Atsushi Imiya. "On the History of Gaussian Scale-Space Axiomatics." In Computational Imaging and Vision, 45–59. Dordrecht: Springer Netherlands, 1997. http://dx.doi.org/10.1007/978-94-015-8802-7_4.

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Morita, Satoru. "Generating stable structure using Scale-space analysis with non-uniform Gaussian kernels." In Scale-Space Theory in Computer Vision, 89–100. Berlin, Heidelberg: Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/3-540-63167-4_42.

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Felsberg, Michael, and Ullrich Köthe. "GET: The Connection Between Monogenic Scale-Space and Gaussian Derivatives." In Lecture Notes in Computer Science, 192–203. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11408031_17.

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Conference papers on the topic "Gaussian scale-space"

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Yu-Ping Wang and S. L. Lee. "From Gaussian scale-space to B-spline scale-space." In 1999 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings. ICASSP99 (Cat. No.99CH36258). IEEE, 1999. http://dx.doi.org/10.1109/icassp.1999.757582.

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Zhihui, Yang, Cui Wenjuan, and Zhang Mengmeng. "Deep Structure of Gaussian Scale Space." In 2008 International Conference on Computer Science and Software Engineering. IEEE, 2008. http://dx.doi.org/10.1109/csse.2008.454.

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Schlauweg, Mathias, Dima Pröfrock, Benedikt Zeibich, and Erika Müller. "Self-synchronizing robust texel watermarking in gaussian scale-space." In the 10th ACM workshop. New York, New York, USA: ACM Press, 2008. http://dx.doi.org/10.1145/1411328.1411339.

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Scott, Edward T., and Sheila S. Hemami. "Image utility estimation using difference-of-Gaussian scale space." In 2016 IEEE International Conference on Image Processing (ICIP). IEEE, 2016. http://dx.doi.org/10.1109/icip.2016.7532327.

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Schlauweg, Mathias, and Erika Muller. "Gaussian scale-space features for semi-fragile image authentication." In 2009 Picture Coding Symposium (PCS). IEEE, 2009. http://dx.doi.org/10.1109/pcs.2009.5167462.

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Tan, Shukun, Yunpeng Liu, and Yicui Li. "Improved kernel correlation filter tracking with Gaussian scale space." In International Symposium on Optoelectronic Technology and Application 2016. SPIE, 2016. http://dx.doi.org/10.1117/12.2247210.

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Karim, Samsul Ariffin Abdul, and Kong Voon Pang. "Data smoothing using Gaussian scale-space and Discrete Wavelet Transform." In 2011 International Conference on Electrical, Control and Computer Engineering (INECCE). IEEE, 2011. http://dx.doi.org/10.1109/inecce.2011.5953904.

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Hegenbart, Sebastian, and Andreas Uhl. "A scale-adaptive extension to methods based on LBP using scale-normalized Laplacian of Gaussian extrema in scale-space." In ICASSP 2014 - 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2014. http://dx.doi.org/10.1109/icassp.2014.6854417.

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Abdollahi, B., A. Soliman, A. C. Civelek, X. F. Li, G. Gimel'farb, and A. El-Baz. "A novel Gaussian Scale Space-based joint MGRF framework for precise lung segmentation." In 2012 19th IEEE International Conference on Image Processing (ICIP 2012). IEEE, 2012. http://dx.doi.org/10.1109/icip.2012.6467288.

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Solin, Arno, and Simo Sarkka. "Gaussian quadratures for state space approximation of scale mixtures of squared exponential covariance functions." In 2014 IEEE 24th International Workshop on Machine Learning for Signal Processing (MLSP). IEEE, 2014. http://dx.doi.org/10.1109/mlsp.2014.6958899.

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