waashk/dblp huggingface dataset:
an accurate discrete fourier transform for image processing. the classical method of numerically computing the fourier transform of digitized functions in one or in d-dimensions is the so-called discrete fourier transform (dft), efficiently implemented as fast fourier transform (fft) algorithms. in many cases the dft is not an adequate approximation of the continuous fourier transform. the method presented in this contribution provides accurate approximations of the continuous fourier transform with similar time complexity. the assumption of signal periodicity is no longer posed and allows to compute numerical fourier transforms in a broader domain of frequency than the usual half-period of thedft. in image processing this behavior is highly welcomed since it allows to obtain the fourier transform of an image without the usual interferences of the periodicity of the classical dft. the mathematical method is developed and numerical examples are presented.
a unified system for segmentation and tracking of face and hands in sign language recognition. this paper presents a unified system for segmentation and tracking of face and hands in a sign language recognition using a single camera. unlike much related work that uses colour gloves, we detect skin by combining 3 useful features: colour, motion and position. these features together, represent the skin colour pixels that are more likely to be foreground pixels and are within a predicted position range. we extend the previous research in occlusion detection to handle occlusion between any of the skin objects using a kalman filter based algorithm. the tracking improves the segmentation by reducing the search space and the segmentation enhances the overall tracking process. the algorithm is tested on several video sequences from a standard database and can provide a very low error rate.
interactive tools for pattern discovery. real-world pattern recognition problems contain rich context that must be taken into account in solution development. beyond the core classification tasks, there are several common challenges including the need to correlate non-comparable groups of variables while preserving data proximity relationships within each group, visualization of data geometry, rapid trial of many methodological alternatives, and integration with existing infrastructure. we explore the effective methods and tools to meet with these challenges. we describe a graphical software, mirage, as an experiment to address such concerns.
geometric approach for pose detection of moving human heads. this paper presents the detection of 3d pose of moving humans heads. the contribution of this work is the conjunction of an effective preprocessing method and a strong estimation kalman technique which uses less noise sensitive 3d line observations. the procedure involves the detection of the target face in any type of background using color histograms. to outline the face we use a minimization algorithm for the determination of its tangent directions. face 3d lines are coded in terms of motors (dual quaternions) which are used by a motor extended kalman filter for the estimation of the 3d pose of the head through time.
real time tracking for 3d realistic lip animation. this article deals with facial segmentation and liptracking with feedback control for real-time animation of a synthetic 3d face model. classical approaches consist in two successive steps : video analysis then synthesis. we want to build a global analysis/synthesis processing loop, where the image analysis needs the 3d synthesis and conversely. for that, we fit a generic 3d-face model on the speaker's face in our analysis algorithm for using synthesis information (like 3d information or face shape). this approach is inspired from control systems theory with feedback loops. the contribution of the paper is to use simple image processing techniques on available data, but to improve segmentation through the feedback loop. moreover, we propose a robust lip corners tracking based on estimation motion algorithm. the speaker is only asked to be in front of the camera with the mouth closed at the beginning of the video session (neutral position). this allows to do a quick initialisation step in order to fit the 3d-face model. results show that real-time (30hz) and robust performances are achievable under real-world conditions, which are two key issues for face and lip tracking applications.
kmod - a tw o-parameter svm kernel for pattern recognition. it has been shown that support vector machine theory optimizes a smoothness functional hypothesis through kernel application. we present kmod a two - parameter svm kernel with distinctive properties of good discrimination between patterns while reserving the data neighborhood information. in classification problems the experiments we carried out on the breast cancer benchmark produced better performance than rbf kernel and some stat e of the art classifiers. as well it also generated favorable results when subjected to a 10-class problem of recognizing handwritten digits in th e nist database .
blind super-resolution using a learning-based approach. the super-resolution of a single image of unknown point-spread-function (psf) is addressedby extending a learning framework using blind deconvolution with an uncertainty around the resulting psf. results indicate success in refining the estimate of the psf as well as to restoring the image. a novel disparity measure is also proposed to quantify the results.
speech music discrimination using class-specific features. in this paper the application of the class-specific features approach to classification is demonstrated for the problem of discriminating between speech and music. feature extraction is class-specific and can therefore be tailored to each class meaning that segment size, model orders and the type of features used can be different for the classes. the performance of the discriminator is evaluated and an example of how classification is possible without training is given.
probabilistic matching of image- to model-features for real-time object tracking. background clutter produces a difficult problem for edge matching within model-based object tracking approaches. the solution of matching all possible candidate image features with themodel features is computationally infeasible for real-time tracking. it is proposed to draw probabilistic samples of candidate sets based on measures for local topological constraints. line features are constraint by parallel and junction constraints. continuous measures are used for evaluation of the match of the features sets to avoid thresholds. this approach limits the number of matchings and processing time increases linearly with the number of features. experiments show the correct selection among multiple candidates for different scenarios.
morphological tagging approach in document analysis of invoices. in this paper a morphological tagging approach for document image invoice analysis is described. tokens close by their morphology and confirmed in their location within different similar contexts make apparent some parts of speech representative of the structure elements. this bottom up approach avoids the use of an priori knowledge provided that there are redundant and frequent contexts in the text. the approach is applied on the invoice body text roughly recognized by ocr and automatically segmented. the method makes possible the detection of the invoice articles and their different fields. the regularity of the article composition and its redundancy in the invoice is a good help for its structure. the recognition rate of 276 invoices and 1704 articles, is over than 91.02% for articles and 92.56% for fields.
neural network-based proper names extraction in fax images. in this paper, we are interested in the sender's name extraction in fax cover pages through a machine learning scheme. for this purpose, two analysis methods are implemented to work in parallel. the first one is based on image document analysis (ocr recognition, physical block selection), the other on text analysis (word feature extraction, local grammar rules). our main contribution consisted in introducing a neural network to find an optimal combination of the two approaches. tests carried on real fax images show that the neural network improves performance compared to an empirical combination function and to each method used separately.
a unified camera calibration using geometry and blur of feature points. although a lot of camera calibration methods have been proposed so far, most of them deal with the pinhole camera model, which neglects the defocus effects introduced by real lens systems. in this paper, we present a unified camera calibration method which determines both internal and external camera parameters from several blurred edges detected in a single image. first, we show the calibration procedure using our thin lens based camera model. then, we describe how to determine the position and the width of blurred features in an image. experiments have been done with real images and those results have shown the validity of our method.
medical image compression: study of the influence of noise on the jpeg 2000 compression performance. in this paper the eficiency of the jpeg 2ooo scheme combined with a complementary denoising process is analyzed on simulated and real denial ortho-pantomographic images, where the simulation images are perturbed by poisson noise. the case of dental radiography is investigated, because radiographic images are a combinatron between the relevant signal and a significant amount of acquisition noise, which is per definition not compressible. the noise behaves generally close to poisson statistics, which generally affects the compression rformance. the denoising process is supported by monte carlo noise modeling, which is introduced in the jpeg 2000 compression scheme to improve the compression ebciency of the medicul images in terms of compression ratio and image qualip f t f i selected images are denoised and the compression ratio, using lossless and lossy jpeg 2000, is reported and evaluated.
shape representation using concavity graphs. in this paper, a new graph data structure for 2-d shape representation is proposed. the new structure is called a concavity graph, and is an evolution from the already known "concavity tree". even though a concavity graph bears a fundamental resemblance to a concavity tree, the former is able to describe the shape of multiple objects in an image and their spatial configuration, and is hence inherently more complex.the aim of concavity graphs is two-fold: first we want to analyze the patterns in a multi-object image in a way that will (1) provide better representation of their shapes, and (2) convey useful information about how they "interact" together. second, we want our analysis technique to facilitate similarity matching between two images. this paper introduces the new structure and outlines how it can be used for shape representation as well as similarity matching.
multiple object tracking using local pca. tracking multiple interacting objects represents a challenging area in computer vision. the tracking problem in general can be formulated as the task of recovering the spatio-temporal trajectories for an unknown number of objects appearing and disappearing at arbitrary times. observations are noisy, their origin is unknown, generated by true detections or false alarms. data association and the estimation of object states are two crucial tasks to be solved in this context. this work describes a novel, computationally efficient tracking approach to generate consistent trajectories. first, trajectory segments are created by analyzing the spatio-temporal data distribution using local principal component analysis. subsequently, linking between trajectory segments is carried out relying on spatial proximity and kinematic smoothness constraints. tracking results are demonstrated in the context of human tracking and compared to results of a frame-to-frame-based tracking approach.
shape retrieval using concavity trees. concavity trees are well-known abstract structures. this paper proposes a new shape-based image retrieval method based on concavity trees. the proposed method has two main components. the first is an efficient (in terms of space and time) contour-based concavity tree extraction algorithm. the second component is a recursive concavity-tree matching algorithmthat returns a distance between two trees. we demonstrate that concavity trees are able to boost the retrieval performance of two feature sets by at least 15% when tested on a database of 625 silhoutte images.
binary image transformation using two-dimensional chaotic maps. we present an algorithm for binary image transformation using chaotic maps. because of its random-like behavior, chaos is a good candidate for encryption. we show that a two-dimensional discrete time dynamical system with one positive lyapunov exponent allows the transformation of the image in an unpredictable manner. the suggested algorithm acts on the pixel position, where the diffusion property resulting from the sensitivity to the initial states is used to accomplish the transformation in a random-like way. the suggested algorithm uses three types of keys: initial state, external parameters and the number of iterations. using the so-called henon map as an example, we show that the algorithm produces almost uncorrelated images even when the keys are slightly changed, making it an attractive and fast method for image encryption.
non-isotropic regularization of the correspondence space in stereo-vision. the correspondence problem in stereo vision is notoriously difficult. inany approaches a noisy solution is extracted fro the correspondence space. various sophisticated regularization techniques are applied then on this noisy solution. we study here the possibility to denoise the correspondence/correlation space before extracting the solution, by a non-linear and non-isotropic scheme. we showthat this methods preserves edges (depth discontinuities) welland overcomes some of the problems encountered in previous approaches.
filtering with gray-code kernels. in this paper we introduce a family of filter kernels - the gray-code kernels (gck) and demonstrate their use in image analysis. filtering an image with a sequence of gray-code kernels is highly efficient and requires only 2 operations per pixel for each filter kernel, independent of the size or dimension of the kernel. we show that the family of kernels is large and includes the walsh-hadamard kernels amongst others. the gck can also be used to approximate arbitrary kernels since a sequence of gck can form a complete representation. the efficiency of computation using a sequence of gck filters can be exploited for various real-time applications, such as, pattern detection, feature extraction, texture analysis, and more.
learning bayesian network classifiers for credit scoring using markov chain monte carlo search. in this paper, we will evaluate the power and usefulness of bayesian network classifiers (probabilistic networks) for credit scoring. various types of bayesian network classifiers will be evaluated and contrasted including unrestricted bayesian network classifiers learned using markov chain monte carlo (mcmc) search. the experiments will be carried out on three real life credit scoring data sets. it will be shown that mcmc bayesian network classifiers have a very good performance and by using the markov blanket concept, a natural form of feature selection is obtained, which results in parsimonious and powerful models for financial credit scoring.
eye tracking using markov models. we propose an eye detection and tracking method based on color and geometrical features of the human face using a monocular camera. in this method a decision is made on whether the eyes are closed or not and, using a markov chain framework to model temporal evolution, the subject's gaze is determined. the method can successfully track facial features even while the head assumes various poses, so long as the nostrils are visible to the camera. we compare our method with recently proposed techniques and results show that it provides more accurate tracking and robustness to variations in view of the face. a procedure for detecting tracking errors is employed to recover the loss of feature points in case of occlusion or very fast head movement. the method may be used in monitoring a driver's alertness and detecting drowsiness, and also in applications requiring non-contact human computer interaction.
ebem: an entropy-based em algorithm for gaussian mixture models. in this paper we address the problem of estimating the parameters of a gaussian mixture model. although the em algorithm yields the maximum-likelihood solution it requires a careful initialization of the parameters and the optimal number of kernels in the mixture may be unknown beforehand. we propose a criterion based on the entropy of the pdf (probability density function) associated to each kernel to measure the quality of a given mixture model. a novel method for estimating shannon entropy based on entropic spanning graphs is developed and a modification of the classical em algorithm to find the optimal number of kernels in the mixture is presented. we test our algorithm in probability density estimation, pattern recognition and color image segmentation.
improving evidential quality of surveillance imagery through active face tracking. we report on a system for automatically obtaining high-resolution images of surveillance targets. the system, beginning from a framed target, uses the presence of faces as a cue to initiate a real-time active face tracking process. using a single pan-tilt-zoom (ptz) camera, the system tracks the target while zooming in for a closeup image of the face. the system tracks feature points on a target face, and a simple yet reliable control strategy is used in conjunction with robust target localization to guide a ptz camera to a facial closeup of the target. we report on experiments with our system in real surveillance environments.
granulometric analysis of document images. we report on new form of multivariate granulometries based on rectangles of varying size and aspect ratio. these granulometries are used for describing visual similarity between document images. rectangular granulometries are used to probe the layout structure of document images, and the rectangular size distributions derived from them are used as descriptors for each image. feature selection is used to reduce the dimensionality and redundancy of thesize distributions, while preserving the essence of the visual appearance of a document. experimental results indicate that rectangular size distributions are an effective way tocharacterize visual similarity of document images, and provide insightful interpretation of classification results in the original image space.
vertebra edge detection using polar signature. in this paper we propose a new method of vertebrae segmentation in medical images. x-ray images of the spinal columns are analysed in order to extract a closed contour for each vertebra. to achieve this goal, we proceed by steps: the starting one is a segmentation approach based on the selection of each vertebra region. we use these regions information to identify each individual vertebra by its contour. for this task, we propose a polar signature representation of the contour using the image gradient of each region. lastly, an edge closing method exploiting polynomial fitting is used. we use the resulting extracted contours to determine vertebral mobility. for this, we proceed by the extraction of some parameters characterizing each vertebra, related to their form, position and orientation.
steerable kernels for arbitrarily-sampled spaces. this paper describes a procedure for generating steerable kernels corresponding to general image mappings (e.g. non-rigid transformations) for arbitrary image tesselations.
the chain-rule processor: optimal classification through signal processing. the chain-rule processor is a method of constructing an optimal bayes classifier from a bank of processors. each processor is a feature extractor designed to separate the given class from a class-dependent reference hypothesis, thereby avoiding the curse of dimensionality. this work builds upon prior work in optimal classifier design using class-specific features. the chain-rule processor is an improvement that recursively applies the pdf projection theorem.
object manipulation using fuzzy logic and geometric algebra. in this paper we present a method for object manipulation using a barrett hand and algorithms which involve fuzzy logic and conformal geometric algebra. the paper explains how we relate these two mathematical systems in order to implement algorithms for geometric computing under uncertainty. in the experimental part we show a task of visually-guided smooth grasping of real 3d objects.
hmms with explicit state duration applied to handwritten arabic word recognition. this paper describes an off-line segmentation-free handwritten arabic words recognition system. the described system uses discrete hmms with explicit state duration of various kinds (gauss, poisson and gamma) for the word classification purpose. after preprocessing, the word image is analyzed from right to left in order to extract from it a sequence of feature vectors. then, vector quantization is applied to this sequence and its output is submitted to a hmms classifier based on a likelihood criterion for identifying the word using the viterbi algorithm. several experiments were performed using the ifn/enit benchmark database, they showed, on the one hand, a substantial improvement in the recognition rate when hmms with explicit state duration of either discrete or continuous distribution are used instead of classical hmms (i.e. with implicit state duration), on the other hand, the gamma distribution for the state duration, that have given the best recognition rate (91.23 % in top 2), seems more suitable for the hmms based modeling of arabic handwriting..
a goal-oriented verification-based approach for target text line extraction from a document image captured by a pen scanner. in this paper, we present a goal-oriented verification-based approach for target text line extraction from a document image captured by a pen scanner. given a binary image, a series of processing steps are invoked adaptively, guided by the text line verification result in the preceding step. each step adopts a strategy that is most effective for dealing with the problem concerned. consequently, the target text line can be extracted in a more efficient and reliable way depending on the nature of the captured image. the effectiveness of the above approach is confirmed by a benchmark test.
underline detection and removal in a document image using multiple strategies. this paper presents a novel three-module approach for underline detection and removal in chinese/english ocr. the detection module uses strategies of connected component analysis and bottom edge analysis. the removal module uses different methods for different kinds of underlines. the disambiguation module is effected via recognition confidence comparison for reducing the risk of removing wrongly doubtful underlines. our approach can deal with untouched, touched, broken and slightly curved underlines. in a benchmark test using single text line images extracted from uw-i database and images captured by c-pen, we demonstrate that our approach has little negative effect on pure-text images, and can detect and remove reliably underlines in text line images with underlines.