Thematische Bibliographien / Correspondence image matching

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Auswahl der wissenschaftlichen Literatur zum Thema „Correspondence image matching“

Autor: Grafiati

Veröffentlicht am 1. Juli 2021

Zuletzt aktualisiert am 7. Februar 2022

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Zeitschriftenartikel zum Thema "Correspondence image matching"

LEE, CHUNG-MONG, TING-CHUEN PONG und JAMES R. SLAGLE. „A KNOWLEDGE-BASED SYSTEM FOR THE IMAGE CORRESPONDENCE PROBLEM“. International Journal of Pattern Recognition and Artificial Intelligence 04, Nr. 01 (März 1990): 45–55. http://dx.doi.org/10.1142/s0218001490000046.

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The image correspondence problem has generally been considered the most difficult step in both stereo and temporal vision. Most existing approaches match area features or linear features extracted from an image pair. The approach described in this paper is novel in that it uses an expert system shell to develop an image correspondence knowledge-based system for the general image correspondence problem. The knowledge it uses consists of both physical properties and spatial relationships of the edges and regions in images for every edge or region matching. A computation network is used to represent this knowledge. It allows the computation of the likelihood of matching two edges or regions with logical and heuristic operators. Heuristics for determining the correspondences between image features and the problem of handling missing information will be discussed. The values of the individual matching results are used to direct the traversal and pruning of the global matching process. The problem of parallelizing the entire process will be discussed. Experimental results on real-world images show that all matching edges and regions have been identified correctly.

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Zhao, Zeng-Shun, Xiang Feng, Sheng-Hua Teng, Yi-Bin Li und Chang-Shui Zhang. „Multiscale Point Correspondence Using Feature Distribution and Frequency Domain Alignment“. Mathematical Problems in Engineering 2012 (2012): 1–14. http://dx.doi.org/10.1155/2012/382369.

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In this paper, a hybrid scheme is proposed to find the reliable point-correspondences between two images, which combines the distribution of invariant spatial feature description and frequency domain alignment based on two-stage coarse to fine refinement strategy. Firstly, the source and the target images are both down-sampled by the image pyramid algorithm in a hierarchical multi-scale way. The Fourier-Mellin transform is applied to obtain the transformation parameters at the coarse level between the image pairs; then, the parameters can serve as the initial coarse guess, to guide the following feature matching step at the original scale, where the correspondences are restricted in a search window determined by the deformation between the reference image and the current image; Finally, a novel matching strategy is developed to reject the false matches by validating geometrical relationships between candidate matching points. By doing so, the alignment parameters are refined, which is more accurate and more flexible than a robust fitting technique. This in return can provide a more accurate result for feature correspondence. Experiments on real and synthetic image-pairs show that our approach provides satisfactory feature matching performance.

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Fang, Bin, Kun Yu, Jie Ma und Pei An. „EMCM: A Novel Binary Edge-Feature-Based Maximum Clique Framework for Multispectral Image Matching“. Remote Sensing 11, Nr. 24 (15.12.2019): 3026. http://dx.doi.org/10.3390/rs11243026.

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Seeking reliable correspondence between multispectral images is a fundamental and important task in computer vision. To overcome the nonlinearity problem occurring in multispectral image matching, a novel, edge-feature-based maximum clique-matching frame (EMCM) is proposed, which contains three main parts: (1) a novel strong edge binary feature descriptor, (2) a new correspondence-ranking algorithm based on keypoint distinctiveness analysis algorithms in the feature space of the graph, and (3) a false match removal algorithm based on maximum clique searching in the correspondence space of the graph considering both position and angle consistency. Extensive experiments are conducted on two standard multispectral image datasets with respect to the three parts. The feature-matching experiments suggest that the proposed feature descriptor is of high descriptiveness, robustness, and efficiency. The correspondence-ranking experiments validate the superiority of our correspondences-ranking algorithm over the nearest neighbor algorithm, and the coarse registration experiments show the robustness of EMCM with varied interferences.

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Chen, Lin, Rong Sheng Lu, Yan Qiong Shi und Jian Sheng Tian. „A Differential Evolution Stereo Matching Method in Digital Image Correlation“. Key Engineering Materials 625 (August 2014): 297–304. http://dx.doi.org/10.4028/www.scientific.net/kem.625.297.

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Stereo matching is widely used in three-dimensional (3D) reconstruction, stereo machine vision and digital image correlation. The aim of stereo matching process is to solve the well-known correspondence problem, which tries to match points or features from one image with the same points or features in another image from the same 3D scene. There are two basic ways, correlation-based and feature-based, are used to find the correspondences between two images. The correlation-based way is to determine if one location in one image looks/seems like another in another image, and the feature-based way to find if a subset of features in one image is similar in the another image. In stereo matching, a simple algorithm is to compare small patches between two rectified images by correlation search. For the pair images acquired from two cameras inevitably exists some rotation transformation, the algorithm first runs a preprocessing step to rectify the images with the epipolar rectification to simplify the problem of finding matching points between images. The epipolar rectification is to determine a transformation of each image plane such that pairs of conjugate epipolar lines become collinear and parallel to one of the image axes. It will lead the loss of gray information of images. The effect is dependent on the amount of angle. When the angle is big enough, the correlation search may yield error results because of retrograded correlation effect. In order to solve the problem, the paper presents an improved stereo matching algorithm with differential evolution to solve the correspondence problem. Our method doesn’t need to runs the preprocessing step to rectify the images with the epipolar rectification. It uses a differential evolution algorithm to minimize the correlation function which contains the angle information after acquiring the epipoar geometry constraint of two image pairs. Then it utilizes a flood-fill algorithm to search correspondence sub-region in the area around the epipolar line. The flood-fill algorithm can overcome the problem of the traditional row-column scanning search method, which will encounter boundary barrier where exists concave polygons or cavities. The Experimental results show that the proposed method can be easily implemented in stereo matching without loss of information of image features with large rotation angle transformation. In the paper, we will introduce the stereo matching principle and its algorithms, including the differential evolution algorithm for finding the correspondences with large rotation transformation between stereo image pairs and the flood-fill traversal strategy for matching large area with complex concave polygons or cavities. In the end of the paper, some experimental results will be given to illustrate the method effectiveness. Keywords: digital image correlation, stereo matching algorithm, epipolar geometry, flood fill algorithm, differential evolution, rotation angle

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Hödel, M., T. Koch, L. Hoegner und U. Stilla. „MONOCULAR-DEPTH ASSISTED SEMI-GLOBAL MATCHING“. ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences IV-2/W7 (16.09.2019): 55–62. http://dx.doi.org/10.5194/isprs-annals-iv-2-w7-55-2019.

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<p><strong>Abstract.</strong> Reconstruction of dense photogrammetric point clouds is often based on depth estimation of rectified image pairs by means of pixel-wise matching. The main drawback lies in the high computational complexity compared to that of the relatively straightforward task of laser triangulation. Dense image matching needs oriented and rectified images and looks for point correspondences between them. The search for these correspondences is based on two assumptions: pixels and their local neighborhood show a similar radiometry and image scenes are mostly homogeneous, meaning that neighboring points in one image are most likely also neighbors in the second. These rules are violated, however, at depth changes in the scene. Optimization strategies tend to find the best depth estimation based on the resulting disparities in the two images. One new field in neural networks is the estimation of a depth image from a single input image through learning geometric relations in images. These networks are able to find homogeneous areas as well as depth changes, but result in a much lower geometric accuracy of the estimated depth compared to dense matching strategies. In this paper, a method is proposed extending the Semi-Global-Matching algorithm by utilizing a-priori knowledge from a monocular depth estimating neural network to improve the point correspondence search by predicting the disparity range from the single-image depth estimation (SIDE). The method also saves resources through path optimization and parallelization. The algorithm is benchmarked on Middlebury data and results are presented both quantitatively and qualitatively.</p>

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Chen, Peizhi, und Xin Li. „Effective Volumetric Feature Modeling and Coarse Correspondence via Improved 3DSIFT and Spectral Matching“. Mathematical Problems in Engineering 2014 (2014): 1–10. http://dx.doi.org/10.1155/2014/378159.

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This paper presents a nonrigid coarse correspondence computation algorithm for volumetric images. Our matching algorithm first extracts then correlates image features based on a revised and improved 3DSIFT (I3DSIFT) algorithm. With a scale-related keypoint reorientation and descriptor construction, this feature correlation is less sensitive to image rotation and scaling. Then, we present an improved spectral matching (ISM) algorithm on correlated features to obtain a one-to-one mapping between corresponded features. One can effectively extend this feature correspondence to dense correspondence between volume images. Our algorithm can benefit nonrigid volumetric image registration in many tasks such as motion modeling in medical image analysis and processing.

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KAMEYAMA, KEISUKE, KAZUO TORAICHI und YUKIO KOSUGI. „CONSTRUCTIVE RELAXATION MATCHING INVOLVING DYNAMICAL MODEL SWITCHING AND ITS APPLICATION TO SHAPE MATCHING“. International Journal of Image and Graphics 02, Nr. 04 (Oktober 2002): 655–67. http://dx.doi.org/10.1142/s0219467802000822.

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This paper introduces a novel approach for contour-based shape matching named as Constructive Relaxation Matching (CRM). Relaxation Matching (RM) introduced by Rosenfeld et al. is one of the standard method for quasi-optimally solving the local correspondences of the template and input images. RM relies on an energy-minimizing nature of the dynamical system to update the label assignment to the input objects. In image matching relying on a particular modeling method, apparently similar images can be judged as being quite distant, according to the nature of the modeling process and its outcome. In the proposed CRM, the modeling stage of a novel input image contour, conventionally done in the same procedure used for modeling the templates, will be included in the procedure of iterative relaxation matching. The model of the input will be dynamically constructed during relaxation, by unifying the pairs of objects having similar template label assignment probabilities. After describing the CRM procedures, the method is applied to simple shape matching problems demonstrating the ability to adaptively model the input image during relaxation. It is shown that the proposed CRM improves the object-label correspondence for evaluation of the image similarities in the following stages of shape matching applications.

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KUMAR, S. SRINIVAS, und B. N. CHATTERJI. „STEREO MATCHING ALGORITHMS BASED ON FUZZY APPROACH“. International Journal of Pattern Recognition and Artificial Intelligence 16, Nr. 07 (November 2002): 883–99. http://dx.doi.org/10.1142/s0218001402002040.

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Stereo matching is the central problem of stereovision paradigm. Area-based techniques provide the dense disparity maps and hence they are preferred for stereo correspondence. Normalized cross correlation (NCC), sum of squared differences (SSD) and sum of absolute differences (SAD) are the linear correlation measures generally used in the area-based techniques for stereo matching. In this paper, similarity measure for stereo matching based on fuzzy relations is used to establish the correspondence in the presence of intensity variations in stereo images. The strength of relationship of fuzzified data of two windows in the left image and the right image of stereo image pair is determined by considering the appropriate fuzzy aggregation operators. However, these measures fail to establish correspondence of the pixels in the stereo images in the presence of occluded pixels in the corresponding windows. Another stereo matching algorithm based on fuzzy relations of fuzzy data is used for stereo matching in such regions of images. This algorithm is based on weighted normalized cross correlation (WNCC) of the intensity data in the left and the right windows of stereo image pair. The properties of the similarity measures used in these algorithms are also discussed. Experiments with various real stereo images prove the superiority of these algorithms over normalized cross correlation (NCC) under nonideal conditions.

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Phillips, P. J., J. Huang und S. M. Dunn. „Computational Micrograph Registration with Sieve Processes“. Proceedings, annual meeting, Electron Microscopy Society of America 54 (11.08.1996): 440–41. http://dx.doi.org/10.1017/s0424820100164660.

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In this paper we present an efficient algorithm for automatically finding the correspondence between pairs of stereo micrographs, the key step in forming a stereo image. The computation burden in this problem is solving for the optimal mapping and transformation between the two micrographs. In this paper, we present a sieve algorithm for efficiently estimating the transformation and correspondence.In a sieve algorithm, a sequence of stages gradually reduce the number of transformations and correspondences that need to be examined, i.e., the analogy of sieving through the set of mappings with gradually finer meshes until the answer is found. The set of sieves is derived from an image model, here a planar graph that encodes the spatial organization of the features. In the sieve algorithm, the graph represents the spatial arrangement of objects in the image. The algorithm for finding the correspondence restricts its attention to the graph, with the correspondence being found by a combination of graph matchings, point set matching and geometric invariants.

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Hu, Zhi Ping, und Yuan Jun He. „A New Method on Matching Correspondence Features in Image Warping“. Applied Mechanics and Materials 20-23 (Januar 2010): 1353–58. http://dx.doi.org/10.4028/www.scientific.net/amm.20-23.1353.

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This paper introduces a robust method for automatically matching features in images corresponding to the same physical point on an object captured from two arbitrary viewpoints. Starting from detected line segments in two or more images using phase congruency, the pairs of line segments are characterized by indices that encode the relative positions and orientations of those segments. The encoding indices are invariant with respect to viewpoint changes and the corresponding line segments between the images can be found by them. The feature matching is optimized for image warping where we wish to ignore unreliable matches at the expense of reducing the number of feature matches. This approach can be applied to the image warping to realize its automatism. Results are presented on real images.

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Dissertationen zum Thema "Correspondence image matching"

Sampaio, de Rezende Rafael. „New methods for image classification, image retrieval and semantic correspondence“. Thesis, Paris Sciences et Lettres (ComUE), 2017. http://www.theses.fr/2017PSLEE068/document.

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Le problème de représentation d’image est au cœur du domaine de vision. Le choix de représentation d’une image change en fonction de la tâche que nous voulons étudier. Un problème de recherche d’image dans des grandes bases de données exige une représentation globale compressée, alors qu’un problème de segmentation sémantique nécessite une carte de partitionnement de ses pixels. Les techniques d’apprentissage statistique sont l’outil principal pour la construction de ces représentations. Dans ce manuscrit, nous abordons l’apprentissage des représentations visuels dans trois problèmes différents : la recherche d’image, la correspondance sémantique et classification d’image. Premièrement, nous étudions la représentation vectorielle de Fisher et sa dépendance sur le modèle de mélange Gaussien employé. Nous introduisons l’utilisation de plusieurs modèles de mélange Gaussien pour différents types d’arrière-plans, e.g., différentes catégories de scènes, et analyser la performance de ces représentations pour objet classification et l’impact de la catégorie de scène en tant que variable latente. Notre seconde approche propose une extension de la représentation l’exemple SVM pipeline. Nous montrons d’abord que, en remplaçant la fonction de perte de la SVM par la perte carrée, on obtient des résultats similaires à une fraction de le coût de calcul. Nous appelons ce modèle la « square-loss exemplar machine », ou SLEM en anglais. Nous introduisons une variante de SLEM à noyaux qui bénéficie des même avantages computationnelles mais affiche des performances améliorées. Nous présentons des expériences qui établissent la performance et l’efficacité de nos méthodes en utilisant une grande variété de représentations de base et de jeux de données de recherche d’images. Enfin, nous proposons un réseau neuronal profond pour le problème de l’établissement sémantique correspondance. Nous utilisons des boîtes d’objets en tant qu’éléments de correspondance pour construire une architecture qui apprend simultanément l’apparence et la cohérence géométrique. Nous proposons de nouveaux scores géométriques de cohérence adaptés à l’architecture du réseau de neurones. Notre modèle est entrainé sur des paires d’images obtenues à partir des points-clés d’un jeu de données de référence et évaluées sur plusieurs ensembles de données, surpassant les architectures d’apprentissage en profondeur récentes et méthodes antérieures basées sur des caractéristiques artisanales. Nous terminons la thèse en soulignant nos contributions et en suggérant d’éventuelles directions de recherche futures
The problem of image representation is at the heart of computer vision. The choice of feature extracted of an image changes according to the task we want to study. Large image retrieval databases demand a compressed global vector representing each image, whereas a semantic segmentation problem requires a clustering map of its pixels. The techniques of machine learning are the main tool used for the construction of these representations. In this manuscript, we address the learning of visual features for three distinct problems: Image retrieval, semantic correspondence and image classification. First, we study the dependency of a Fisher vector representation on the Gaussian mixture model used as its codewords. We introduce the use of multiple Gaussian mixture models for different backgrounds, e.g. different scene categories, and analyze the performance of these representations for object classification and the impact of scene category as a latent variable. Our second approach proposes an extension to the exemplar SVM feature encoding pipeline. We first show that, by replacing the hinge loss by the square loss in the ESVM cost function, similar results in image retrieval can be obtained at a fraction of the computational cost. We call this model square-loss exemplar machine, or SLEM. Secondly, we introduce a kernelized SLEM variant which benefits from the same computational advantages but displays improved performance. We present experiments that establish the performance and efficiency of our methods using a large array of base feature representations and standard image retrieval datasets. Finally, we propose a deep neural network for the problem of establishing semantic correspondence. We employ object proposal boxes as elements for matching and construct an architecture that simultaneously learns the appearance representation and geometric consistency. We propose new geometrical consistency scores tailored to the neural network’s architecture. Our model is trained on image pairs obtained from keypoints of a benchmark dataset and evaluated on several standard datasets, outperforming both recent deep learning architectures and previous methods based on hand-crafted features. We conclude the thesis by highlighting our contributions and suggesting possible future research directions

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Yalcin, Bayramoglu Neslihan. „Range Data Recognition: Segmentation, Matching, And Similarity Retrieval“. Phd thesis, METU, 2011. http://etd.lib.metu.edu.tr/upload/12613586/index.pdf.

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The improvements in 3D scanning technologies have led the necessity for managing range image databases. Hence, the requirement of describing and indexing this type of data arises. Up to now, rather much work is achieved on capturing, transmission and visualization
however, there is still a gap in the 3D semantic analysis between the requirements of the applications and the obtained results. In this thesis we studied 3D semantic analysis of range data. Under this broad title we address segmentation of range scenes, correspondence matching of range images and the similarity retrieval of range models. Inputs are considered as single view depth images. First, possible research topics related to 3D semantic analysis are introduced. Planar structure detection in range scenes are analyzed and some modifications on available methods are proposed. Also, a novel algorithm to segment 3D point cloud (obtained via TOF camera) into objects by using the spatial information is presented. We proposed a novel local range image matching method that combines 3D surface properties with the 2D scale invariant feature transform. Next, our proposal for retrieving similar models where the query and the database both consist of only range models is presented. Finally, analysis of heat diffusion process on range data is presented. Challenges and some experimental results are presented.

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Olofsson, Anders. „Modern Stereo Correspondence Algorithms : Investigation and Evaluation“. Thesis, Linköping University, Information Coding, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-57853.

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Many different approaches have been taken towards solving the stereo correspondence problem and great progress has been made within the field during the last decade. This is mainly thanks to newly evolved global optimization techniques and better ways to compute pixel dissimilarity between views. The most successful algorithms are based on approaches that explicitly model smoothness assumptions made about the physical world, with image segmentation and plane fitting being two frequently used techniques.

Within the project, a survey of state of the art stereo algorithms was conducted and the theory behind them is explained. Techniques found interesting were implemented for experimental trials and an algorithm aiming to achieve state of the art performance was implemented and evaluated. For several cases, state of the art performance was reached.

To keep down the computational complexity, an algorithm relying on local winner-take-all optimization, image segmentation and plane fitting was compared against minimizing a global energy function formulated on pixel level. Experiments show that the local approach in several cases can match the global approach, but that problems sometimes arise – especially when large areas that lack texture are present. Such problematic areas are better handled by the explicit modeling of smoothness in global energy minimization.

Lastly, disparity estimation for image sequences was explored and some ideas on how to use temporal information were implemented and tried. The ideas mainly relied on motion detection to determine parts that are static in a sequence of frames. Stereo correspondence for sequences is a rather new research field, and there is still a lot of work to be made.

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Fookes, Clinton Brian. „Medical Image Registration and Stereo Vision Using Mutual Information“. Queensland University of Technology, 2003. http://eprints.qut.edu.au/15876/.

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Image registration is a fundamental problem that can be found in a diverse range of fields within the research community. It is used in areas such as engineering, science, medicine, robotics, computer vision and image processing, which often require the process of developing a spatial mapping between sets of data. Registration plays a crucial role in the medical imaging field where continual advances in imaging modalities, including MRI, CT and PET, allow the generation of 3D images that explicitly outline detailed in vivo information of not only human anatomy, but also human function. Mutual Information (MI) is a popular entropy-based similarity measure which has found use in a large number of image registration applications. Stemming from information theory, this measure generally outperforms most other intensity-based measures in multimodal applications as it does not assume the existence of any specific relationship between image intensities. It only assumes a statistical dependence. The basic concept behind any approach using MI is to find a transformation, which when applied to an image, will maximise the MI between two images. This thesis presents research using MI in three major topics encompassed by the computer vision and medical imaging field: rigid image registration, stereo vision, and non-rigid image registration. In the rigid domain, a novel gradient-based registration algorithm (MIGH) is proposed that uses Parzen windows to estimate image density functions and Gauss-Hermite quadrature to estimate the image entropies. The use of this quadrature technique provides an effective and efficient way of estimating entropy while bypassing the need to draw a second sample of image intensities (a procedure required in previous Parzen-based MI registration approaches). It is possible to achieve identical results with the MIGH algorithm when compared to current state of the art MI-based techniques. These results are achieved using half the previously required sample sizes, thus doubling the statistical power of the registration algorithm. Furthermore, the MIGH technique improves algorithm complexity by up to an order of N, where N represents the number of samples extracted from the images. In stereo vision, a popular passive method of depth perception, new extensions have been pro- posed in order to increase the robustness of MI-based stereo matching algorithms. Firstly, prior probabilities are incorporated into the MI measure to considerably increase the statistical power of the matching windows. The statistical power, directly related to the number of samples, can become too low when small matching windows are utilised. These priors, which are calculated from the global joint histogram, are tuned to a two level hierarchical approach. A 2D match surface, in which the match score is computed for every possible combination of template and matching windows, is also utilised to enforce left-right consistency and uniqueness constraints. These additions to MI-based stereo matching significantly enhance the algorithms ability to detect correct matches while decreasing computation time and improving the accuracy, particularly when matching across multi-spectra stereo pairs. MI has also recently found use in the non-rigid domain due to a need to compute multimodal non-rigid transformations. The viscous fluid algorithm is perhaps the best method for re- covering large local mis-registrations between two images. However, this model can only be used on images from the same modality as it assumes similar intensity values between images. Consequently, a hybrid MI-Fluid algorithm is proposed to compute a multimodal non-rigid registration technique. MI is incorporated via the use of a block matching procedure to generate a sparse deformation field which drives the viscous fluid algorithm, This algorithm is also compared to two other popular local registration techniques, namely Gaussian convolution and the thin-plate spline warp, and is shown to produce comparable results. An improved block matching procedure is also proposed whereby a Reversible Jump Markov Chain Monte Carlo (RJMCMC) sampler is used to optimally locate grid points of interest. These grid points have a larger concentration in regions of high information and a lower concentration in regions of small information. Previous methods utilise only a uniform distribution of grid points throughout the image.

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Dahlqvist, Marcus. „Adaptive Losses for Camera Pose Supervision“. Thesis, Linköpings universitet, Datorseende, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-177339.

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This master thesis studies the learning of dense feature descriptors where camera poses are the only supervisory signal. The use of camera poses as a supervisory signal has only been published once before, and this thesis expands on this previous work by utilizing a couple of different techniques meant increase the robustness of the method, which is particularly important when not having access to ground-truth correspondences. Firstly, an adaptive robust loss is utilized to better differentiate inliers and outliers. Secondly, statistical properties during training are both enforced and adapted to, in an attempt to alleviate problems with uncertainties introduced by not having true correspondences available. These additions are shown to slightly increase performance, and also highlights some key ideas related to prediction certainty and robustness when working with camera poses as a supervisory signal. Finally, possible directions for future work are discussed.

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Setkov, Aleksandr. „IVORA (Image and Computer Vision for Augmented Reality) : Color invariance and correspondences for the definition of a camera/video-projector system“. Thesis, Université Paris-Saclay (ComUE), 2015. http://www.theses.fr/2015SACLS168/document.

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La Réalité Augmentée Spatiale (SAR) vise à superposer spatialement l'information virtuelle sur des objets physiques. Au cours des dernières décennies ce domaine a connu une grande expansion et est utilisé dans divers domaines, tels que la médecine, le prototypage, le divertissement etc. Cependant, pour obtenir des projections de bonne qualité, on doit résoudre plusieurs problèmes, dont les plus importants sont la gamme de couleurs réduite du projecteur, la lumière ambiante, la couleur du fond, et la configuration arbitraire de la surface de projection dans la scène. Ces facteurs entraînent des distorsions dans les images qui requièrent des processus de compensation complémentaires.Les projections intelligentes (smart projections) sont au cœur des applications de SAR. Composées d'un dispositif de projection et d'un dispositif d'acquisition, elles contrôlent l'aspect de la projection et effectuent des corrections à la volée pour compenser les distorsions. Bien que les méthodes actives de Lumière Structurée aient été utilisées classiquement pour résoudre ces problèmes de compensation géométrique, cette thèse propose une nouvelle approche non intrusive pour la compensation géométrique de plusieurs surfaces planes et pour la reconnaissance des objets en SAR s'appuyant uniquement sur la capture du contenu projeté.Premièrement, cette thèse étude l'usage de l'invariance couleur pour améliorer la qualité de la mise en correspondance entre primitives dans une configuration d'acquisition des images vidéoprojetées. Nous comparons la performance de la plupart des méthodes de l'état de l'art avec celle du descripteur proposé basé sur l'égalisation d'histogramme. Deuxièmement, pour mieux traiter les conditions standard des systèmes projecteur-caméra, deux ensembles de données de captures de projections réelles, ont été spécialement préparés à des fins expérimentales. La performance de tous les algorithmes considérés est analysée de façon approfondie et des propositions de recommandations sont faites sur le choix des algorithmes les mieux adaptés en fonction des conditions expérimentales (paramètres image, disposition spatiale, couleur du fond...). Troisièmement, nous considérons le problème d'ajustement multi-surface pour compenser des distorsions d'homographie dans les images acquises. Une combinaison de mise en correspondance entre les primitives et de Flux Optique est proposée afin d'obtenir une compensation géométrique plus rapide. Quatrièmement, une nouvelle application en reconnaissance d'objet à partir de captures d'images vidéo-projetées est mise en œuvre. Finalement, une implémentation GPU temps réel des algorithmes considérés ouvre des pistes pour la compensation géométrique non intrusive en SAR basée sur la mise en correspondances entre primitives
Spatial Augmented Reality (SAR) aims at spatially superposing virtual information on real-world objects. Over the last decades, it has gained a lot of success and been used in manifold applications in various domains, such as medicine, prototyping, entertainment etc. However, to obtain projections of a good quality one has to deal with multiple problems, among them the most important are the limited projector output gamut, ambient illumination, color background, and arbitrary geometric surface configurations of the projection scene. These factors result in image distortions which require additional compensation steps.Smart-projections are at the core of PAR applications. Equipped with a projection and acquisitions devices, they control the projection appearance and introduce corrections on the fly to compensate distortions. Although active structured-light techniques have been so far the de-facto method to address such problems, this PhD thesis addresses a relatively new unintrusive content-based approach for geometric compensation of multiple planar surfaces and for object recognition in SAR.Firstly, this thesis investigates the use of color-invariance for feature matching quality enhancement in projection-acquisition scenarios. The performance of most state-of-the art methods are studied along with the proposed local histogram equalization-based descriptor. Secondly, to better address the typical conditions encountered when using a projector-camera system, two datasets of real-world projections were specially prepared for experimental purposes. Through a series of evaluation frameworks, the performance of all considered algorithms is thoroughly analyzed, providing several inferences on that which algorithms are more appropriate in each condition. Thirdly, this PhD work addresses the problem of multiple-surface fitting used to compensate different homography distortions in acquired images. A combination of feature matching and Optical Flow tracking is proposed in order to achieve a more low-weight geometric compensation. Fourthly, an example of new application to object recognition from acquired projections is showed. Finally, a real-time implementation of considered methods on GPU shows prospects for the unintrusive feature matching-based geometric compensation in SAR applications

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Mišta, Petr. „Hledání objektů v obraze“. Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2009. http://www.nusl.cz/ntk/nusl-217759.

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Detection of objects based on color is not commonly used method of computer vision. There are many methods thats deals with the detection of significant points, but the color information has been omitted. The goal of this thesis is to design method of the detection significant color image areas and these areas match up with areas detected in another image. I analyzed features of detectors required to identify the reciprocal correspondence of images, defined the color significance concept, described basic color models and their properties, and a design of statistically compiled data - based method was described. Algorithms for the detection of color use color models RGB and HSV. Correspondence of areas detected in different images is performedy Kohonen neural network. The first input vector can teach Kohonen neural network and the second vector is classified by this network. To remove erroneous classifications RANSAC method is used. As a result, the method can be used for basic and rapid determination of correspondence between images, or to speed up commonly used methods for detection of significant points. At the end of the thesis are presented programs, showing functionality and options of design methods. The designed algorithms have been developed in MATLAB.

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„Bending invariant correspondence matching on 3D models with feature descriptor“. 2010. http://library.cuhk.edu.hk/record=b5896651.

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Li, Sai Man.
Thesis (M.Phil.)--Chinese University of Hong Kong, 2010.
Includes bibliographical references (leaves 91-96).
Abstracts in English and Chinese.
Abstract --- p.2
List of Figures --- p.6
Acknowledgement --- p.10
Chapter Chapter 1 --- Introduction --- p.11
Chapter 1.1 --- Problem definition --- p.11
Chapter 1.2. --- Proposed algorithm --- p.12
Chapter 1.3. --- Main features --- p.14
Chapter Chapter 2 --- Literature Review --- p.16
Chapter 2.1 --- Local Feature Matching techniques --- p.16
Chapter 2.2. --- Global Iterative alignment techniques --- p.19
Chapter 2.3 --- Other Approaches --- p.20
Chapter Chapter 3 --- Correspondence Matching --- p.21
Chapter 3.1 --- Fundamental Techniques --- p.24
Chapter 3.1.1 --- Geodesic Distance Approximation --- p.24
Chapter 3.1.1.1 --- Dijkstra ´ةs algorithm --- p.25
Chapter 3.1.1.2 --- Wavefront Propagation --- p.26
Chapter 3.1.2 --- Farthest Point Sampling --- p.27
Chapter 3.1.3 --- Curvature Estimation --- p.29
Chapter 3.1.4 --- Radial Basis Function (RBF) --- p.32
Chapter 3.1.5 --- Multi-dimensional Scaling (MDS) --- p.35
Chapter 3.1.5.1 --- Classical MDS --- p.35
Chapter 3.1.5.2 --- Fast MDS --- p.38
Chapter 3.2 --- Matching Processes --- p.40
Chapter 3.2.1 --- Posture Alignment --- p.42
Chapter 3.2.1.1 --- Sign Flip Correction --- p.43
Chapter 3.2.1.2 --- Input model Alignment --- p.49
Chapter 3.2.2 --- Surface Fitting --- p.52
Chapter 3.2.2.1 --- Optimizing Surface Fitness --- p.54
Chapter 3.2.2.2 --- Optimizing Surface Smoothness --- p.56
Chapter 3.2.3 --- Feature Matching Refinement --- p.59
Chapter 3.2.3.1 --- Feature descriptor --- p.61
Chapter 3.2.3.3 --- Feature Descriptor matching --- p.63
Chapter Chapter 4 --- Experimental Result --- p.66
Chapter 4.1 --- Result of the Fundamental Techniques --- p.66
Chapter 4.1.1 --- Geodesic Distance Approximation --- p.67
Chapter 4.1.2 --- Farthest Point Sampling (FPS) --- p.67
Chapter 4.1.3 --- Radial Basis Function (RBF) --- p.69
Chapter 4.1.4 --- Curvature Estimation --- p.70
Chapter 4.1.5 --- Multi-Dimensional Scaling (MDS) --- p.71
Chapter 4.2 --- Result of the Core Matching Processes --- p.73
Chapter 4.2.1 --- Posture Alignment Step --- p.73
Chapter 4.2.2 --- Surface Fitting Step --- p.78
Chapter 4.2.3 --- Feature Matching Refinement --- p.82
Chapter 4.2.4 --- Application of the proposed algorithm --- p.84
Chapter 4.2.4.1 --- Design Automation in Garment Industry --- p.84
Chapter 4.3 --- Analysis --- p.86
Chapter 4.3.1 --- Performance --- p.86
Chapter 4.3.2 --- Accuracy --- p.87
Chapter 4.3.3 --- Approach Comparison --- p.88
Chapter Chapter 5 --- Conclusion --- p.89
Chapter 5.1 --- Strength and contributions --- p.89
Chapter 5.2 --- Limitation and future works --- p.90
References --- p.91

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Jones, Michael J., und Tomaso Poggio. „Model-Based Matching by Linear Combinations of Prototypes“. 1996. http://hdl.handle.net/1721.1/7183.

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We describe a method for modeling object classes (such as faces) using 2D example images and an algorithm for matching a model to a novel image. The object class models are "learned'' from example images that we call prototypes. In addition to the images, the pixelwise correspondences between a reference prototype and each of the other prototypes must also be provided. Thus a model consists of a linear combination of prototypical shapes and textures. A stochastic gradient descent algorithm is used to match a model to a novel image by minimizing the error between the model and the novel image. Example models are shown as well as example matches to novel images. The robustness of the matching algorithm is also evaluated. The technique can be used for a number of applications including the computation of correspondence between novel images of a certain known class, object recognition, image synthesis and image compression.

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Taati, BABAK. „Generation and Optimization of Local Shape Descriptors for Point Matching in 3-D Surfaces“. Thesis, 2009. http://hdl.handle.net/1974/5107.

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We formulate Local Shape Descriptor selection for model-based object recognition in range data as an optimization problem and offer a platform that facilitates a solution. The goal of object recognition is to identify and localize objects of interest in an image. Recognition is often performed in three phases: point matching, where correspondences are established between points on the 3-D surfaces of the models and the range image; hypothesis generation, where rough alignments are found between the image and the visible models; and pose refinement, where the accuracy of the initial alignments is improved. The overall efficiency and reliability of a recognition system is highly influenced by the effectiveness of the point matching phase. Local Shape Descriptors are used for establishing point correspondences by way of encapsulating local shape, such that similarity between two descriptors indicates geometric similarity between their respective neighbourhoods. We present a generalized platform for constructing local shape descriptors that subsumes a large class of existing methods and allows for tuning descriptors to the geometry of specific models and to sensor characteristics. Our descriptors, termed as Variable-Dimensional Local Shape Descriptors, are constructed as multivariate observations of several local properties and are represented as histograms. The optimal set of properties, which maximizes the performance of a recognition system, depend on the geometry of the objects of interest and the noise characteristics of range image acquisition devices and is selected through pre-processing the models and sample training images. Experimental analysis confirms the superiority of optimized descriptors over generic ones in recognition tasks in LIDAR and dense stereo range images.
Thesis (Ph.D, Electrical & Computer Engineering) -- Queen's University, 2009-09-01 11:07:32.084

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Buchteile zum Thema "Correspondence image matching"

Wang, Hongfang, und Edwin R. Hancock. „Improving Correspondence Matching Using Label Consistency Constraints“. In Pattern Recognition and Image Analysis, 235–42. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11492429_29.

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Haseeb, Muhammad, und Edwin R. Hancock. „Eigenvector Sign Correction for Spectral Correspondence Matching“. In Image Analysis and Processing – ICIAP 2013, 41–50. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-41184-7_5.

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Wang, Hongfang, und Edwin R. Hancock. „Kernel Spectral Correspondence Matching Using Label Consistency Constraints“. In Image Analysis and Processing – ICIAP 2005, 503–10. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11553595_62.

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Wu, Song, und Michael S. Lew. „Image Correspondences Matching Using Multiple Features Fusion“. In Lecture Notes in Computer Science, 737–46. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-49409-8_61.

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Koschan, Andreas. „Dense stereo correspondence using polychromatic block matching“. In Computer Analysis of Images and Patterns, 538–42. Berlin, Heidelberg: Springer Berlin Heidelberg, 1993. http://dx.doi.org/10.1007/3-540-57233-3_71.

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Gilinsky, Alexandra, und Lihi Zelnik-Manor. „SIFTpack: A Compact Representation for Efficient SIFT Matching“. In Dense Image Correspondences for Computer Vision, 109–33. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-23048-1_6.

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Cortés, Xavier, Francesc Serratosa und Carlos Francisco Moreno-García. „Ground Truth Correspondence Between Nodes to Learn Graph-Matching Edit-Costs“. In Computer Analysis of Images and Patterns, 113–24. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-23192-1_10.

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Xu, Jinwei, und Jiankun Hu. „Direct Feature Point Correspondence Discovery for Multiview Images: An Alternative Solution When SIFT-Based Matching Fails“. In Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, 137–47. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-49580-4_13.

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Liu, Jianfei, HaeWon Jung und Johnny Tam. „Accurate Correspondence of Cone Photoreceptor Neurons in the Human Eye Using Graph Matching Applied to Longitudinal Adaptive Optics Images“. In Lecture Notes in Computer Science, 153–61. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-66185-8_18.

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Fysh, Matthew C. „Factors Limiting Face Matching at Passport Control and in Police Investigations“. In Forensic Face Matching, 15–37. Oxford University Press, 2021. http://dx.doi.org/10.1093/oso/9780198837749.003.0002.

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Face matching entails a comparison between two faces that are unfamiliar to an observer, who must then decide whether these depict the same person or different people. Despite the ubiquity of face matching in practical settings, such as passport control and police investigations, laboratory research has established that this task is highly error-prone, and that many of these errors derive from visual characteristics of to-be-compared face stimuli. Such characteristics include factors such as image quality, lighting, and natural changes in personal appearance, which influence the visual correspondence between face stimuli. In this chapter, factors that are likely to limit face-matching accuracy in real-world settings are reviewed, with the aim of providing insight into how these influence the accuracy of this process and how subsequent errors may be mitigated.

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Konferenzberichte zum Thema "Correspondence image matching"

Nathan, Mitchell, und Michael Magee. „Correspondence, Partial Matching And Image Understanding“. In Cambridge Symposium_Intelligent Robotics Systems, herausgegeben von David P. Casasent. SPIE, 1987. http://dx.doi.org/10.1117/12.937738.

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Liu, Yang, Jinshan Pan und Zhixun Su. „Deep feature matching for dense correspondence“. In 2017 IEEE International Conference on Image Processing (ICIP). IEEE, 2017. http://dx.doi.org/10.1109/icip.2017.8296390.

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Joung, Sunghun, Seungryong Kim, Bumsub Ham und Kwanghoon Sohn. „Unsupervised stereo matching using correspondence consistency“. In 2017 IEEE International Conference on Image Processing (ICIP). IEEE, 2017. http://dx.doi.org/10.1109/icip.2017.8296736.

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Qu, Jianqin, Leiguang Gong, Chen Huang und Ruoyu Fang. „Point correspondence by matching scaled invariants“. In 2012 International Conference on Audio, Language and Image Processing (ICALIP). IEEE, 2012. http://dx.doi.org/10.1109/icalip.2012.6376594.

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Bansal, Mayank, und Kostas Daniilidis. „Joint Spectral Correspondence for Disparate Image Matching“. In 2013 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2013. http://dx.doi.org/10.1109/cvpr.2013.361.

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Wang, Fang, und Yi Li. „Spatial matching of sketches without point correspondence“. In 2015 IEEE International Conference on Image Processing (ICIP). IEEE, 2015. http://dx.doi.org/10.1109/icip.2015.7351724.

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Lahdenoja, Olli, und Mika Laiho. „Regional image correspondence matching method for SIMD processing“. In 2009 European Conference on Circuit Theory and Design (ECCTD 2009). IEEE, 2009. http://dx.doi.org/10.1109/ecctd.2009.5275105.

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Greenspan, Dvir und Rubner. „Region correspondence for image matching via EMD flow“. In Proceedings IEEE Workshop on Content-based Access of Image and Video Libraries. IEEE, 2000. http://dx.doi.org/10.1109/ivl.2000.853835.

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Laskar, Zakaria, Iaroslav Melekhov, Hamed R. Tavakoli und Juha Ylioinas. „Geometric Image Correspondence Verification by Dense Pixel Matching“. In 2020 IEEE Winter Conference on Applications of Computer Vision (WACV). IEEE, 2020. http://dx.doi.org/10.1109/wacv45572.2020.9093482.

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Yun, Inyong, Seokhoon Boo, Joongkyu Kim und Cheolkon Jung. „Moment-Based Dense Correspondence Matching Robust to Image Variation“. In 2017 IEEE International Symposium on Multimedia (ISM). IEEE, 2017. http://dx.doi.org/10.1109/ism.2017.44.

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