waashk/dblp huggingface dataset:
breast mass segmentation based on information theory. in this study, an information based algorithm, called c-shells based deterministic annealing (csda), is proposed for breast mass segmentation on digital mammograms. csda recasts the fuzzy clustering concept into the probability framework and offers two improved features over existing clustering algorithms. first, it is a global minimization algorithm through mass constrained deterministic annealing rather than a local minimization method in the original fuzzy c-shells (fcs) approach. second, the prototype in this algorithm is shell, which is more effective in segmentation with compact or hollow spherical shells compared to the standard deterministic annealing (da) algorithm. experimental results show that the information based csda clustering algorithm is a promising image segmentation technique for digital mammographic mass detection.
self-calibration using constant camera motion. this paper investigates using constant inter-frame motion for self-calibration from an image sequence of an object rotating around a single axis with varying camera internal parameters. our approach is based on the facts that in many commercial systems rotation angles are often controlled by an electromechanical system, and the inter-frame essential matrices are invariant if the rotation angles are constant but not necessarily known. it is shown that recovering camera internal parameters is possible by making use of the equivalence of essential matrices, which relate the unknown calibration matrices to the fundamental matrices computed from the point correspondences. experimental results on both synthetic and real sequences are presented to determine the accuracy and the robustness of the proposed algorithm.
camera motion quantification and alignment. we propose a method to synchronize video sequences of distinct scenes captured by cameras undergoing similar motions. for the general camera motion and 3d scene, the camera ego-motions are featured by fundamental ratios obtained from the fundamental matrices. in the case of pure translation, translational magnitude features is used. these extracted features are invariant to the camera internal parameters, and therefore can be computed without recovering camera trajectories along the image sequences. consequently, the alignment problem reduces to matching sets of feature vectors, obtained without any knowledge of other sequences. experimental results demonstrate the accuracy and applications of the proposed method.
concurrent segmentation and recognition with shape-driven fast marching methods. we present a variational framework that integrates the statistical boundary shape models into a level set system that is capable of both segmenting and recognizing objects. since we aim to recognize objects, we trace the active contour and stop it near real object boundaries while inspecting the shape of the contour instead of enforcing the contour to get a priori shape. we get the location of character boundaries and character labels at the system output. we developed a promising local front stopping scheme based on both image and shape information for fast marching systems. a new object boundary shape signature model, based on directional gauss gradient filter responses, is also proposed. the character recognition system that employs the new boundary shape descriptor outperforms the other systems, based on well-known boundary signatures such as centroid distance, curvature etc.
synthetic fingerprint-database generation. this work complements our previous efforts in generating realistic fingerprint images for test purposes. the main variability which characterizes the acquisition of a fingerprint through an on-line sensor is modeled and a sequence of steps is defined to derive a series of impressions from the same master-fingerprint. this allows large fingerprint databases to be randomly generated according to some given parameters. the experimental results validate our technique and prove that it can be very useful for performance evaluation, learning and testing in fingerprint-based systems.
robust appearance-based object recognition using a fully connected markov random field. this paper presents a new kernel method for appearance-based object recognition, highly robust to noise and occlusion. it consists of a fully connected markov random field that integrates results of spin glass theory with gibbs probability distributions via nonlinear kernel mapping. we call this model spin glass-markov random field. we present theoretical analysis and several experiments that show its effectiveness and robustness to noise and occlusion. we obtain in both cases excellent results. particularly, we achieve a recognition rate above 93% with just 40% of visible portion of the object.
object categorization via local kernels. this paper considers the problem of multi-object categorization. we present an algorithm that combines support vector machines with local features via a new class of mercer kernels. this class of kernels allows us to perform scalar products on feature vectors consisting of local descriptors, computed around interest points (like corners); these feature vectors are generally of different lengths for different images. the resulting framework is able to recognize multi-object categories in different settings, from lab-controlled to real-world scenes. we present several experiments, on different databases, and we benchmark our results with state-of-the-art algorithms for categorization, achieving excellent results.
texture recognition through modal analysis of spectral peak patterns. in this paper we investigate how texture recognition can be achieved through the modal analysis of the pattern of peaks in the spectral density function. we commence from a texture characterisation which is based on the positions of peaks in the power spectrum. our aim is to use the modal structure of the pattern of peaks to perform texture retrieval from an image data-base. we explore two different approaches to the problem. first, we use a variant of the shapiro and brady method to perform recognition by comparing the modal structure of the proximity matrix for peak cluster centres. second, we perform latent semantic indexing on vectors representing the polar distribution of frequency peaks. we provide and experimental evaluation of these two methods on a data-base of fabric and wrapping paper patterns.
a learning model for multiple-prototype classification of strings. an iterative learning method to update labeled string prototypes for a 1-nearest prototype (1-np) classification is introduced. given a (typically reduced) set of initial string prototypes and a training set, it iteratively updates prototypes to better discriminate training samples. the update rule, which is based on the edit distance, adjusts a prototype by removing those local differences which are both frequent with respect to same-class closer training strings and infrequent with respect to different-class closer training strings. closer training strings are defined by unsupervised clustering. the process continues until prototypes converge. its main innovation is to provide a non-random local update rule to "move" a string prototype towards a number of string samples. a series of learning/classification experiments show a better 1-np performance of the updated prototypes with respect to the initial ones, that were originally selected to guarantee a good classification.
pruning local feature correspondences using shape context. we propose a novel approach to improve the distinctiveness of local image features without significantly affecting their robustness with respect to image deformations. local image features have proven to be successful in computer vision tasks involving partial occlusion, background noise, and various types of image deformations. however, the relatively high number of outliers that have to be rejected from the correspondences set, formed during the search for similar features, still plagues this approach. the task of rejecting outliers is usually based on estimating the global spatial transform suffered by the features in the correspondences set. this presents two problems: i) it cannot properly deal with non-rigid objects, and ii) it is sensitive to a high number of outliers. here, we address these problems by combining typical local features [multi-scale phase-based local features, object recognition from local scale-invariant features] with shape context [shape matching and object recognition using shape contexts]. a performance evaluation shows that this new semi-local feature generally provides higher distinctiveness and robustness to image deformations, thus potentially increasing the inlier/outlier ratio in the correspondences set. also, we show that in wide baseline stereo matching, and non-rigid motion applications, the use of the novel semi-local feature not only provides robustness to non-rigid deformations, but also produces a higher inlier/outlier ratio than the standard hough clustering of the global spatial transform of parameters.
a method for detecting artificial objects in natural environments. in this paper we will present a method for automatic detection of man-made objects in digital images representing natural environments. this method is based on statistical distribution of texture patterns in the image. this distribution is computed using zipf's law. the image is divided into sub-frames and zipf's distribution is computed for each sub-frame. then the surfaces under zipf's plots of the different sub-frames are compared in order to determine which sub-frame will contain an object. the sensitivity of this detection method to image resolution is also examined
compressed image quality evaluation using power law models. this paper presents a compressed image quality measure based on the properties of zipf law, which is a power-law model adapted from linguistic analysis. it describes the frequency distribution of image patterns, and can be used to put into evidence image details which are affected by compression. two image quality evaluation measures based on zipf law have been proposed and tested, one designed to be used on jpeg images and the other on jpeg2000 images. these measures have also been used with the tabu search algorithm for automatic compression optimization..
a combined bayesian markovian approach for behaviour recognition. numerous techniques exist which can be used for the task of behavioural analysis and recognition. common amongst these are bayesian networks and hidden markov models. although these techniques are extremely powerful and well developed, both have important limitations. by fusing these techniques together to form bayes-markov chains, the advantages of both techniques can be preserved, while reducing their limitations. the bayes-markov technique forms the basis of a common, flexible framework for supplementing markov chains with additional features. this results in improved user output, and aids in the rapid development of flexible and efficient behaviour recognition systems.
bayes information criterion for tikhonov regularization with linear constraints: application to spectral data estimation. spectral data estimation is an ill-posed problem, since (i) it is difficult to collect sufficient linear independent data and (ii) due to the integral nature of solid-state light sensors, camera outputs do not depend continuously on input signals. to solve these problems, most methods rely on exact a priori knowledge to reduce the problem's complexity (solution space). in this paper a new algorithm is introduced which does not require a priori information. the method is build upon a new extension of the bayes information criterion for ill-posed estimation problems, that is able to extract this information from the input data. the proposed solution is quite general and can readily be applied to other ill-posed problems, which are common in computer vision and image processing.
wavelet-based multiresolution stereo vision. an efficient wavelet-based multiresolution approach to the stereo vision problem is presented. a cost function is defined and iteratively minimized. the minimization is performed on the images' representation in the wavelet space. we employ the theory of representation of operators in spaces spanned by scaling functions and thereby take advantage of a simplified approximation of differentiation. examples illustrate the advantages afforded by the applicationof our algorithm over correlation-based methods.
implementation of a modular real-time feature-based architecture applied to visual face tracking. this paper presents a modular real-time feature-based visual tracking architecture where each feature of an object is tracked by one module. a data fusion stage collects the information from various modules exploiting the relationship among features to achieve robust detection and visual tracking. this architecture takes advantage of the temporal and spatial information available in a video stream. its effectiveness is demonstrated in a face tracking system that uses eyes and lips as features. in the architecture implementation, each module has a pre-processing stage that reduces the number of image regions that are candidates for eyes and lips. support vector machines are then used in the classification process, whereas a combination of kalman filters and template matching is used for tracking. the geometric relation between features is used in the data fusion stage to combine the information from different modules to improve tracking.
combining data-closeness and fourier domain integrability constraints in shape-from-shading. this paper describes a shape-from-shading algorithm that combines constraint on data-closeness from lambert's law and fourier domain integrability. the data closeness is ensured by constraining surface normals to fall on an irradiance cone, whose axis points in the light source direction and whose apex angle varies with iteration number. the integrability is ensured by projecting the non-integrable set of surface normals to the nearest integrable one by globally minimizing the distance among them in the fourier domain. the combination of both data-closeness and integrability constraints is aimed to overcome the problem of high dependency on the image irradiances. experimental results prove that the new method recovers needle maps that are both smooth and integrable and improves height surface stability.
a simple coupled statistical model for 3d face shape recovery. we focus on the problem of developing coupled statistical models that can be used to recover surface height from brightness images of faces. our approach consists on using a simple model that assumes that the height eigenmodes are identical to the intensity eigenmodes. we recover the height function directly from the best-fit intensity parameters. as a result the computations involve only a straightforward matrix-vector multiplication. experiments show that this method generate accurate height surfaces from out-of training intensity images.
a facial statistical model from complex numbers. in this paper we explore the use of complex numbers as means of representing angular statistics for surface normal data. our aim is to use the representation to construct a statistical model that can be used to describe the variations in fields of surface normals. we focus on the problem of representing facial shape. the fields of surface normals used to train the model are furnished by range images. we compare the complex representation with one based on angles, and demonstrate the advantages of the new method. once trained, we illustrate how the model can be fitted to brightness images by searching for the set of parameters that both satisfy lambert's law and minimize the integrability error.
efficient model selection for kernel logistic regression. kernel logistic regression models, like their linear counterparts, can be trained using the efficient iteratively re-weighted least-squares (irwls) algorithm. this approach suggests an approximate leave-one-out cross-validation estimator based on an existing method for exact leave-one-out cross-validation of least-squares models. results compiled over seven benchmark datasets are presented for kernel logistic regression with model selection procedures based on both conventional k-fold and approximate leave-one-out cross-validation criteria, demonstrating the proposed approach to be viable.
generic real-time tracking method on semi-dynamic scenes. this paper describes a tracking method capable of following multiple targets for a generic scene in real-time. the tracker has no a priori knowledge of the objects and does not know how many objects will be present in the scene. the tracker uses straight-forward techniques to extract, identify and track the targets within the image sequence.
integrating local and global features in automatic fingerprint verification. this paper presents a new approach for combining local and global recognition schemes for automatic fingerprint verification (afv), by using matched local features as the reference axis for generating global features. in our specific implementation, minutia-based and shape-basedtechniques were combined. the first one matches local features (minutiae) by a point-pattern matching algorithm. the second one generates global features (shape signatures) by using the matched minutiae as its frame of reference. shape signatures are then digitised to form afeature vector describing the fingerprint. finally, a lvq neural network was trained to match the fingerprints by using the difference of a pair of feature vectors. the experimental results show that the integrated system significantly outperforms the minutiae-based system in terms ofclassification accuracy and stability. this makes the new approach a promising solution for biometric applications.
trainable table location in document images. we describe an approach for table location in document images. the documents are described by means of a hierarchical representation that is based on the mxy tree. thepresence of a table is hypothesized by searching parallel lines in the mxy tree of the page. this hypothesis is afterwards verified by locating perpendicular lines or white spaces in the region included between the parallel lines. lastly,located tables can be merged on the basis of proximity and similarity criteria.the use of an optimization method,that relies on the definition of an appropriate table location index, allows us to identify the optimal values of thresholds involved in the algorithm. in this way the algorithm can be adapted to recognize tables with different features by maximizing the performance on an appropriate training set.the algorithm has been evaluated on two data-sets containing more than 1500 pages, and comparing its results with the tables identified by two commercial ocrs.
face recognition by using discriminative common vectors. in face recognition tasks, the dimension of the sample space is typically larger than the number of the samples in the training set. as a consequence, the within-class scatter matrix is singular and the linear discriminant analysis (lda) method cannot be applied directly. this problem is also known as the "small sample size" problem. in this paper, we propose a new face recognition method based on the discriminative common vectors for the small sample size case. the discriminative common vectors representing the people in the face database were found by using the null space of the within-class scatter matrix. then, these vectors were used for classification of new faces. test results show that the proposed method is superior to other methods in terms of accuracy, efficiency, and numerical stability.
automatic detection of planar contours from uncalibrated images. the present paper describes an original technique developed for detecting and matching unknown planar structures between a pair of uncalibrated images. problems related to bad segmentation or to occlusion are automatically solved in most cases. the match process works hierarchically on pertinent point which provide local consistency and on contours which ensure the global consistency. after match, contours are splited into planar contour accordingto collineations. tests made over different pairs of images demonstrate the efficiency of our approach.
unsupervised evaluation of image segmentation application to multi-spectral images. we present in this article a study of some unsupervised evaluation criteria of an image segmentation result. the goal of this work is to be able to automatically choose the parameters of a segmentation method best fitted for an image or to fusion different segmentation results. we compared six unsupervised evaluation criteria on a database composed of 100 synthetic gray-level images segmented by four methods. vinet's measure is used as an objective function to compare the behavior of the different criteria. we finally apply these criteria to evaluate segmentation results of multi-components images. we present in this article some experimental results of evaluation of gray-level and multi-components natural images.
a novel human gait recognition method by segmenting and extracting the region variance feature. existing methods of gait recognition suffer from some shortcomings, which are discussed at the beginning of the full paper. in order to suppress these shortcomings as much as possible, we proposed a new automatic gait recognition approach based on the region variance feature. firstly, the binary silhouette of a walking person is detected from each frame of the monocular image sequences. then we divide the two dimensional silhouette of the walker into three regions (head region, trunk region and legs region). next, the variance features of these regions are extracted respectively. together with the ratio of the silhouette's height and width, the gait signature vectors are constructed to identify different subjects. finally, similarity measurement based on the gait cycles and nn and knn classifiers are carried out to recognize the different subjects. experimental results show that the proposed novel method is very effective and correct recognition rates are over 92% and 97% on ucsd and cmu database, respectively.
enhancing edit distance on real sequences filters using histogram distance on fixed reference ordering. distance functions are the main tools to measure similarity of two sequences and to search the closest sequences to given query sequence. several well known distance functions, however, have asymptotical time complexity of o(mn) which cannot be fully afforded by systems that deal with large volumes of data. these distance functions, including edit distance on real sequences (edr) [5], have pruning methods to reduce execution time by dismissing false candidates as early as possible. in this paper, we propose the histogram distance on fixed reference (hdfr) ordering, with various reference histogram construction methods, to improve the filtering power of the pruning methods in edr. experiments show that a decrease in edr execution time is observed after hdfr is applied. while we base our experiments on edr, hdfr can also be applied to other distance functions with appropriate pruning methods.
towards correlation-based matching algorithms that are robust near occlusions. in the context of computer vision, matching can be done using correlation measures. this paper presents new algorithms that use two correlation measures: the zero mean normalised cross-correlation, zncc, and the smooth median absolute deviation, smad. while zncc is efficient in non-occluded areas and non-robust near occlusions, smad is non-efficient in non-occluded areas and robust near occlusions. the aim is to use the advantages of zncc and smad to deal with the problem of occlusions and to obtain dense disparity maps. the experimental results show that these algorithms are better than zncc-based algorithm and smad-based algorithm.