waashk/dblp huggingface dataset:
finding correspondences of patches by means of affine transformations. in this paper we present a novel method for finding the optimal affine transformation for matching of images. the method requires no feature points, does not rely on normalization of images and can be tuned to highlight interesting parts in the images. furthermore, the method does not need any derivatives for obtaining the affine transformation and it has a computational cost proportional to n2 logn for n×n images.the problem of finding the optimal affine transformation is solved by an iterative algorithm. in each step a global optimization is performed by the use of fft. this global characteristic helps the algorithm from getting trapped in a local optimum. novel theoretical results are presented that show under what restrictions the algorithm can be expected to work properly.its intended primary use is for reconstruction problems in computer vision. these rely heavily on the establishment of point correspondences in the images. since the method makes no assumptions on the images it can be used when feature points are difficult to detect. experiments on real images are included and it is shown that the algorithm is robust and performs well even in difficult situations, with occlusions. the acm portal is published by the association for computing machinery. copyright © 2010 acm, inc. terms of usage privacy policy code of ethics contact us useful downloads: adobe acrobat quicktime windows media player real player
shape parameter optimization for adaboosted active shape model. active shape model (asm) has been shown to be a powerful tool to aid the interpretation of images, especially in face alignment. asm local appearance model parameter estimation is based on the assumption that residuals between model fit and data have a gaussian distribution. moreover, to generate an allowable face shape, asm truncates coefficients of shape principal components into the bounds determined by eigenvalues. in this paper, an algorithm of modeling local appearances, called adaboosted asm, and a shape parameter optimization method are proposed. in the algorithm of modeling the local appearances, we describe our novel modeling method by using adaboosted histogram classifiers, in which the assumption of the gaussian distribution is not necessary. in the shape parameter optimization, we describe that there is an inadequacy on controlling shape parameters in asm, and our novel method on how to solve it. experimental results demonstrate that the adaboosted histogram classifiers improve robustness of landmark displacement greatly, and the shape parameter optimization solves the inadequacy problem of asm on shape constraint effectively.
3d shape reconstruction using volume intersection techniques. volume intersection algorithms are used to reconstruct incomplete objects from their silhouettes. an imagined light source is moved about the data and the cumulative amount of "light" seen at each point in space is interpreted as indicating the likelihood that the point is inside the object. the object data need not be uniformly distributed nor exclusively surface data. explicit distinction between noise, surface and interior data is avoided. the novel concept of a localised viewing region is introduced to overcome the inherent inability of volume intersection algorithms to reconstruct concave surfaces. algorithms for 2d pixel and3d voxel data are described and applied to 3d ultra-sound data.
a chromaticity space for specularity-, illumination color- and illumination pose-invariant 3-d object recognition. most of the recent color recognition/indexing approaches concentr ate on establishing invariance to illumination color to improve the utility of color recognition. however, other effects caused by illumination pose and specularity on three-dimensional object surfaces have not received notable attention. we present a chromaticity recognition method that discounts the effects of illumination pose, illumination color and specularity. it utilizes a chromaticity space based on log-ratio of sensor responses for illumination pose and color invariance. a model-based specularity detection/rejection algorithm can be used to improve the chromaticity recognition and illumination estimation for objects including specular reflections.
geodesic active contours. a novel scheme for the detection of object boundaries is presented. the technique is based on active contours deforming according to intrinsic geometric measures of the image. the evolving contours naturally split and merge, allowing the simultaneous detection of several objects and both interior and exterior boundaries. the proposed approach is based on the relation between active contours and the computation of geodesics or minimal distance curves. the minimal distance curve lays in a riemannian space whose metric as defined by the image content. this geodesic approach for object segmentation allows to connect classical "snakes" based on energy minimization and geometric active contours based on the theory of curve evolution. previous models of geometric active contours are improved as showed by a number of examples. formal results concerning existence, uniqueness, stability, and correctness of the evolution are presented as well.
fast object recognition in noisy images using simulated annealing. a fast simulated annealing algorithm is developed for automatic object recognition. the object recognition problem is addressed as the problem of best describing a match between a hypothesized object and an image. the normalized correlation coefficient is used as a measure of the match. templates are generated on-line during the search by transforming model images. simulated annealing reduces the search time by orders of magnitude with respect to an exhaustive search. the algorithm is applied to the problem of how landmarks, e.g., traffic signs, can be recognized by a navigating robot. we illustrate the performance of our algorithm with real-world images of complicated scenes with traffic signs. false positive matches occur only for templates with very small information content. to avoid false positive matches, we propose a method to select model images for robust object recognition by measuring the information content of the model images. the algorithm works well in noisy images for model images with high information content.
information-conserving object recognition. following the theory of statistical estimation, the problem of recognizing objects imaged in complex real-world scenes is examined from a parametric perspective. a scalar measure of an objects's complexity, which is invariant under affine transformation and changes in image noise level, is extracted from the object's fisher information. the volume of fisher information is shown to provide an overall statistical measure of the object's recognizability in a particular image, while the complexity provides an intrinsically physical measure that characterizes the object in any image. an information-conserving method is then developed for recognizing an object imaged in a complex scene. here the term information-conserving means that the method uses al the measured data pertinent to the object's recognizability, attains the theoretical lower bound on estimation error for any unbiased estimate, and therefore is statistically optimal. this method is then successfully applied to finding objects imaged in thousands of complex real-world scenes.
3d point distribution models of the cortical sulci. in this paper we present the first steps in the development of a statistical shape model, specifically a point distribution model (pdm), of the cortical surface of the brain. this will ultimately be used to locate, label, and describe the cortex, for visualisation, diagnosis, andquantification. in order to produce the model it was necessary to find and label the sulcal fissures on a series of mr images. due to the complexity of the surface, an automated method was developed to facilitate development of a full surface model. automating the marking process introduced the problem of identifying correspondences between examples, the knowledge of which is essential to the development of a pdm. various methods were investigated to solve this problem including simple point matching and more complex curve matching. each is outlined and discussed. the models obtained so far provide interesting insights into the shape and cortical pattern variations over a group of normal subjects.
face recognition from one example view. to create a pose-invariant face recognizer, one strategy is the view-based approach, which uses a set of real example views at different poses. but what if we only have one real view available, such as a scanned passport photo-can we still recognize faces under different poses? given one real view at a known pose, it is still possible to use the view-based approach by exploiting prior knowledge of faces to generate virtual views, or views of the face as seen from different poses. to represent prior knowledge, we use 2d example views of prototype faces under different rotations. we develop example-based techniques for applying the rotation seen in the prototypes to essentially "rotate" the single real view which is available. next, the combined set of one real and multiple virtual views is used as example views for a view-based, pose-invariant face recognizer. oar experiments suggest that among the techniques for expressing prior knowledge of faces, 2d example-based approaches should be considered alongside the more standard 3d modeling techniques.
on multi-feature integration for deformable boundary finding. precise segmentation of underlying objects in an image is very important especially for biomedical image analysis. we present an integrated approach for boundary finding using region and curvature information along with the gradient. unlike the previous methods, where smoothing is enforced by penalizing curvature, here the grey level curvature is used as an extra source of information. however, information fusion may not be useful unless used properly. to address that, we present results that highlight the pros and cons of using the various sources of information and indicate when one should get precedence over the others.
robust path-based spectral clustering with application to image segmentation. spectral clustering and path-based clustering are two recently developed clustering approaches that have delivered impressive results in a number of challenging clustering tasks. however, they are not robust enough against noise and outliers in the data. in this paper, based on m-estimation from robust statistics, we develop a robust path-based spectral clustering method by defining a robust path-based similarity measure for spectral clustering. our method is significantly more robust than spectral clustering and path-based clustering. we have performed experiments based on both synthetic and real-world data, comparing our method with some other methods. in particular, color images from the berkeley segmentation dataset and benchmark are used in the image segmentation experiments. experimental results show that our method consistently outperforms other methods due to its higher robustness.
unsupervised image classification with a hierarchical em algorithm. this work takes place in the context of hierarchical stochastic models for the resolution of discrete inverse problems from low level vision. some of these models lie on the nodes of a quad-tree which leads to non-iterative inference procedures. nevertheless, if they circumvent the algorithmic drawbacks of grid-based models (computational load and/or great dependence on the initialization), they admit modeling shortcomings (cumbersome and somehow artificial). we investigate a new hierarchical stochastic model which takes benefit from both the spatial and the hierarchical prior modelings. the independence graph is based on a tree which has been pollarded with the nodes at the coarsest resolution exhibiting a grid-based interaction structure. for this class of models, we address the critical problem of parameter estimation. to this end, we derive an em algorithm on the hybrid structure which mixes an exact em algorithm on each subtrees and a low cost gibbsian em algorithm on the coarse spatial grid. experiments on a synthetic image and on multi-spectral satellite images are reported.
image statistics based on diffeomorphic matching. we propose a new approach to deal with the first and second order statistics of a set of images. these statistics take into account the images characteristic deformations and their variations in intensity. the central algorithm is based on non-supervised diffeomorphic image matching (without landmarks or human intervention). as they convey the notion of the mean shape and colors of an object and the one of its common variations, such statistics of sets of images may be relevant in the context of object recognition, both in the segmentation of any of its representations and in the classification of them. the proposed approach has been tested on a small database of face images to compute a mean face and second order statistics. the results are very encouraging since, wheras the algorithm does not need any human intervention and is not specific to face image databases, the mean image looks like a real face and the characteristic modes of variation (deformation and intensity changes) are sensible.
designing spatially coherent minimizing flows for variational problems based on active contours. this paper tackles an important aspect of the variational problems involving active contours, which has been largely overlooked so far: the optimization by gradient flows. classically, the definition of a gradient depends directly on the choice of an inner product structure. this consideration is largely absent from the active contours literature. most authors, overtly or covertly, assume that the space of admissible deformations is ruled by the canonical l2 inner product. the classical gradient flows reported in the literature are relative to this particular choice. in this paper, we investigate the relevance of using other inner products, yielding other gradient descents, and some other minimizing flows not deriving from any inner product. in particular, we show how to induce different degrees of spatial coherence into the minimizing flow, in order to decrease the probability of getting trapped into irrelevant local minima. we show with some numerical experiments that the sensitivity of the active contours method to initial conditions, which seriously limits its applicability and its efficiency, is alleviated by our application-specific spatially coherent minimizing flows.
lighting normalization with generic intrinsic illumination subspace for face recognition. in this paper, we introduce the concept of intrinsic illumination subspace which is based on the intrinsic images. this intrinsic illumination subspace enables an analytic generation of the illumination images under varying lighting conditions. when objects of the same class are concerned, our method allows a class-based generic intrinsic illumination subspace to be constructed in advance. we propose a lighting normalization method based on the generic intrinsic illumination subspace, which is used as a bootstrap subspace for novel images. face recognition experiments are performed to demonstrate the effectiveness of our method.
using prior shape and intensity profile in medical image segmentation. in this note we present a coupled optimization model forboundary determination. one part of the model incorporatesa prior shape into a geometric active contour modelwith a fixed parameter. the second part determines the'best' parameter used in the first part by maximizing the mutualinformation of the image geometry between the priorand an aligned novel image over all the alignments, thatare the solutions of the first part corresponding to differentparameters. we also present an alternative method, whichgenerates an intensity model formed as the average of a setof aligned training images. experimental results on cardiacultrasound images are presented. these results indicatethat the proposed model provides close agreement withexpert traced borders, and the parameter determined in thismodel for one image can be used for images with similarproperties. the existence of a solution to the proposed minimizationproblem is also discussed.
learning effective image metrics from few pairwise examples. we present a new approach to learning image metrics. the main advantage of our method lies in a formulation that requires only a few pairwise examples. apparently, based on the little amount of side-information, it would take a very effective learning scheme to yield a useful image metric. our algorithm achieves this goal by addressing two key issues. first, we establish a global-local (glocal) image representation that induces two structure-meaningful vector spaces to respectively describe the global and the local image properties. second, we develop a metric optimization framework that finds an optimal bilinear transform to best explain the given side-information. we emphasize it is the glocal image representation that makes the use of bilinear transform more powerful. experimental results on classifications of face images and visual tracking are included to demonstrate the contributions of the proposed method.
robust regression with projection based m-estimators. the robust regression techniques in the ransac family arepopular today in computer vision, but their performance dependson a user supplied threshold. we eliminate this draw-backof ransac by reformulating another robust method,the m-estimator, as a projection pursuit optimization problem.the projection based pbm-estimator automatically derivesthe threshold from univariate kernel density estimates.nevertheless, the performance of the pbm-estimator equalsor exceeds that of ransac techniques tuned to the optimalthreshold, a value which is never available in practice.experiments were performed both with synthetic and realdata in the affine motion and fundamental matrix estimationtasks.
mutual information regularized bayesian framework for multiple image restoration. bayesian methods have been extensively used in various applications. however, there are two intrinsic issues rarely addressed, namely generalization and validity. in the context of multiple image restoration, we show that traditional bayesian methods are sensitive to model errors and cannot guarantee valid results satisfying the underlying prior knowledge, e.g. independent noise property. to improve the bayesian framework¿s generalization, we propose to explicitly enforce the validity of the result. independent noise prior is very important but largely under-utilized in previous literature. in this paper, we use mutual information (mi) to explicitly enforce the independence. efficient approximations based on taylor expansion are proposed to adapt mi into standard energy forms to regularize the bayesian methods. the new regularized bayesian framework effectively utilizes the traditional generative signal/noise models but is much more robust to various model errors, as demonstrated in experiments on some demanding imaging applications.
face detection by fuzzy pattern matching. the paper describes an approach to detect faces whose size and position are unknown in an image with a complex background. the candidates of faces are detected by finding out "face like" regions in the input image using the fuzzy pattern matching method. the perceptually uniform color space is used in our research in order to obtain reliable results. the skin color that is used to detect face like regions, is represented by a model developed by us called skin color distribution function. the skin color regions are then extracted by estimating a measure that describes how well the color of a pixel looks like the skin color for each pixel in the input image. the faces which appear in images are modeled as several 2 dimensional patterns. the face like regions are extracted by a fuzzy pattern matching approach using these face models. the face candidates are then verified by estimating how well the extracted facial features fit a face model which describes the geometrical relations among facial features.
calibration of a hybrid camera network. visual surveillance using a camera network has imposed new challenges to camera calibration. an essential problem is that a large number of cameras may not have a common field of view or even be synchronized well. we propose to use a hybrid camera network that consists of catadioptric and perspective cameras for a visual surveillance task. the relations between multiple views of a scene captured from different cameras can be then calibrated under the catadioptric camera's coordinate system. this paper addresses the important issue of how to calibrate the hybrid camera network. we calibrate the hybrid camera network in three steps. first, we calibrate the catadioptric camera using only the vanishing points. in order to reduce computational complexity, we calibrate the camera without the mirror first and then calibrate the catadioptric camera system. second, we determine 3d positions of some points usingas few as two spatial parallel lines and some equidistance points. finally, we calibrate other perspective cameras based on these known spatial points.
a bidirectional matching algorithm for deformable pattern detection with application to handwritten word retrieval. a bayesian framework for deformable pattern classification has been proposed in [1] with promising results for isolated handwritten character recognition. its performance, however, degrades significantly when it is applied to detect deformable patterns in complex scenes, where the amount of outliers due to other neighboring objects or the background is usually large. also, the fact that the associated evidence measure does not penalize models resting on white space results in a high false alarm rate.in this paper, another bayesian framework for deformable pattern detection is proposed. the framework possesses the intrinsic property of matching with only part of an image (segmentation) and its associated evidence measure can penalize white space implicitly. however, limited data exploration capability is the major trade-off. by properly combining the two frameworks, a new matching algorithm called bi-directional matching is proposed.this combined approach possesses the advantages of the two frameworks and gives robust results for non-rigid shape extraction. to evaluate the performance of the proposed approach, we have applied it to shape-based handwritten word retrieval. using a subset of the bb data-set in the cedar database, we can achieve a recall rate of 59% and a precision rate of 43%.
registration of multimodal fluorescein images sequence of the retina. in this study we present a y-feature extraction method for registering color and fluorescein angiograms of the retina. the registration of multimodal fluorescein imagery requires the identification of strong geometric features in the retinal images that are invariant across modalities and to temporal grey level variations due to the propagation of the dye in the vessels. the most informative features, invariant across the considered modalities, are the locations of vessels¿ ramification: the so-called y-features. we propose a y-feature extraction method based on the local classification of image gradient information and an articulated model. an appropriate cost function is proposed for fitting the model using a gradient-based approach. the fitted y-features are subsequently matched across the images for registering the color and fluorescein images. experimental results obtained on a large database validate the proposed method.