waashk/dblp huggingface dataset:
object tracking by the mean-shift of regional color distribution combined with the particle-filter algorithm. this paper presents a method for tracking a person in a video sequence in real time. in this method the profile of color distribution characterises target's feature. it is invariant for rotation and scale changes. it's also robust to non-rigidity and partial occlusion of the target. we employ the mean-shift algorithm to track the target and to reduce the computational cost. moreover, we incorporate the particle-filter into it to cope with a temporal occlusion of the target, and largely reduce the computational cost of the original particle-filter. experiments show the availability of this method.
features for printed document image analysis. this paper presents features for text/non-text area separation in printed document images. it firstly introduces entropic discrimination (i.e., a simple separation using only one feature). then, a brief recall on existing texture and geometric discriminant parameters proposed in previous research is done. several of them are statistically examined.
an information theoretic approach for active and effective object recognitions. this paper describes the strategy of optimal action selection for recognition of the observed object among those registered in the image data base from an information theoretic point of view. the goal is to reduce the number and effort of the recognition steps to reach the final decision of the recognition. we have chosen an information theoretic framework, motivated by the fact that sensored image data is not noiseless or ideal, nor can the effect of a certain action be completely determined in advance. we present the strategy of the recognition process, and then, the results of the feasibility study by experiments.
human behavior recognition with generic exponential family duration modeling in the hidden semi-markov model. the ability to learn and recognize human activities of daily living (adls) is important in building pervasive and smart environments. in this paper, we tackle this problem using the hidden semi-markov model. we discuss the stateof- the-art duration modeling choices and then address a large class of exponential family distributions to model state durations. inference and learning are efficiently addressed by providing a graphical representation for the model in terms of a dynamic bayesian network (dbn). we investigate both discrete and continuous distributions from the exponential family (poisson and inverse gaussian respectively) for the problem of learning and recognizing adls. a full comparison between the exponential family duration models and other existing models including the traditional multinomial and the new coxian are also presented. our work thus completes a thorough investigation into the aspect of duration modeling and its application to human activities recognition in a real-world smart home surveillance scenario.
augmented reality through real-time tracking of video sequences using a panoramic view. we propose a 2d approach for augmented reality (ar) applications where the real scene is modelled as a static panorama. we adapted a sparse tracking method based on homographies to track the orientation and zooming parameters of the camera during a video sequence. ar scenarii (synthetic object insertion, real object or character extraction) can be performed in arbitrary static environments (from wide outdoor scenes to virtually augmented desktops or conference rooms).
robust segmentation of hidden layers in video sequences. in this paper, we propose a novel and robust method for extracting motion layers in video sequences. taking advantage of temporal continuity, our framework considers both the visible and the hidden parts of each layer in order to increase robustness. moreover, the hidden parts of the layers are recovered, which could be of great help in many high level vision tasks. modeling the problem as a labeling task, we state it in a mrf-optimization framework and solve it with a graph-cut algorithm. both synthetic and real video sequences show a visible layers extraction comparable to the one usually performed by state of the art methods, as well as a novel and successful segmentation of hidden layers.
hidden markov models for couples of letters applied to handwriting recognition. this paper deals with handwritten word recognition using hidden markov models (hmm) and presents a new solution to cope with problems of segmentation resulting from image preprocessing. this first step involves cutting an image of an isolated word into letters or pieces of letters called graphems. it builds a sequence of small images described by features which are the input of hmm. the image segmentation usually produces errors and lowers the results obtained by a recognition system based on a set of hmm models corresponding to the twenty-six letters of the alphabet. this paper proposes to extend the alphabet with models of couples of letters which are often badly segmeented.
estimating the stretching characteristics of fiber bundles in microscopic images. collagen fiber bundles are important constituent parts of biological soft tissues, such as tendons, blood vessels and skin.the single collagen fibers within a bundle have a certain crimp that determines the bundle's stretching characteristic, which is of particular interest in mechanobiology. a thorough understanding of the complex interrelations between mechanical factors and the associated biological responses may help to improve diagnostics, which allow disease and injury to be treated earlier. we present a method to obtain the relative stretching characteristics (rsc) of collagen (fibrous) bundles based on the analysis of microscopic tissue images. we show, how the orientation statistics of local orientations in a bundle can be directly related to the rsc. the von mises distribution (vmd) - a circular probability distribution - is used to describe the 2d orientation statistics. the k parameter of the vmd is used to obtain the mean stretching and the probability density function (pdf) of the bundles' rsc is identified.
adaptive and smart interface for vcr remote control using hand gestures. this paper describes an adaptive and smart interface and shows its successful application for vcr remote control using hand gestures. the interface is capable of learning the user's operational habits and can offer self-help, aka wizard mode of operation; it can then monitorthe user's gestures and maintain constant vigilance in an attempt to assist her through feedback using video display and/or loud-speaker. the availability of users' profiles is used in an adaptive fashion to enhance human-computer interactions and to make them intelligent, i.e., causal. the smart interface is suitable for handicapped users and it can be used for security purposes too.
on-line handwriting recognition based on bigram co-occurrences. we propose a handwriting recognition method that utilizes the n-gram statistics of the english language. it is based on the linguistic property that very few pairs of english words share exactly the same letter bigrams. this property is exploited to bring context to the recognitionstage and to avoid segmentation. the recognition is based on detecting bigram co-occurrences. even with naive features and a limited reference set, it recognizes over 45% of lexicon words that it has never seen before in handwritten form.
local straightness: a contrast independent statistical edge measure for color and gray level images. most existing methods for edge detection rely on contrast dependent thresholds. we show that a local measurement defined by the ratio of the smallest to the largest eigenvalue of the second moment matrix of filter kernels, can be used to separate smooth, low curvature curves and straight lines from noise, independent of contrast, in both color and gray level images. this is done without applying a threshold to the gradient magnitude. the edge images are defined as zero crossings in the gradient direction. the covariance matrix can easily be computed for both gray level images and color images. further we show the potentiality of such a measure by integrating it with the hough transform to extract long straight lines in noisy color images. the method is shown to successfully extract consistent line features from color images of a scene, captured under drastically different lightening conditions.
canonical skeletons for shape matching. skeletal representations of 2-d shape, including shock graphs, have become increasingly popular for shape matching and object recognition. however, it is well known that skeletal structure can be unstable under minor boundary deformation, part articulation, and minor shape deformation (due to, for example, small changes in viewpoint). as a result, two very similar shapes may yield two significantly different skeletal representations which, in turn, will induce a large matching distance. such instability occurs both at external branches as well as internal branches of the skeleton. we present a framework for the structural simplification of a shape's skeleton which balances, in an optimization framework, the desire to reduce a skeleton's complexity by minimizing the number of branches, with the desire to maximize the skeleton's ability to accurately reconstruct the original shape. this optimization yields a canonical skeleton whose increased stability yields significantly improved recognition performance.
specular free spectral imaging using orthogonal subspace projection. specularity is an important issue in computer vision. many algorithms have been proposed to remove highlights for color images. however, to our knowledge, no work has been done so far which specifically handles highlights in spectral imaging. in this paper, we introduce a specular invariant representation for hyperspectral images based on the dichromatic model and orthogonal subspace projection. it is a simple one step algorithm which only involves pixellevel operations, thus it does not require any segmentation. nor does it require any pre/postprocessing or explicit spectral normalization. importantly, unlike the previous methods for color images, it can be theoretically extended to handle highlights caused by multicolored illuminations. experimental results demonstrate the effectiveness of our algorithm.
a strongly coupled architecture for contextual object and scene identification. the context-centered approach to object detection and recognition is based on the intuition that the contextual information of real-world scenes provides relevant information for these tasks. this intuition is supported by psychophysical experiments in human scene perception and visual search, which provide evidence that the human visual system uses the relationship between the environment and the objects to facilitate object recognition. here we use a probabilistic model to investigate the possible interactions between object class hypotheses and scene class hypotheses in a visual system. the architecture of the model is based on separate modules interacting with each other via feedforward and feedback connections. a competitive-priors structure is used to implement the feedback connections.
kalman filtering for robust identification of face images with varying expressions and lighting conditions. we propose a novel algorithm for the identification of faces from image samples. the algorithm uses the kalman filter to identify significant face features. we employ the kalmanfaces approach on a database of face images that show a variety of different expressions and were recorded under varying lighting conditions. kalmanfaces show robustness against distortion and outperform the classic eigenfaces approach in terms of identification performance and algorithm speed.
improvement of prediction accuracy using discretization and voting classifier. there are many examples of classification algorithms developed so far for data analysis, pattern recognition, scene analysis and learning from graphical models. being motivated by the works of a number of researchers, here i have tried to improve the prediction accuracy by first discretizing the real world dataset and then applying a voting classifier on the discretized dataset. in this work, continuous dataset from the raw real world dataset having missing attribute values have been generated and discretized the dataset using spid 3 algorithm. then naïve-bayesian classifier has been implemented to apply it on the continuous and discretized dataset. finally, an ensemble learner (ada-boost algorithm) has been developed where the naïve bayesian classifier has been used as the base learner of the ensemble. the extensive empirical results over the twenty real world datasets show that the prediction accuracy can be increased by the joint performance of discretization and voting classifier.
robust image registration based on markov-gibbs appearance model. a new approach to align an image of a textured object with a given prototype is proposed. visual appearance of the images, after equalizing their signals, is modeled with a markov-gibbs random field with pairwise interaction. similarity to the prototype is measured by a gibbs energy of signal co-occurrences in a characteristic subset of pixel pairs derived automatically from the prototype. an object is aligned by an affine transformation maximizing the similarity by using an automatic initialization followed by gradient search. experiments confirm that our approach aligns complex objects better than popular conventional algorithms.
probabilistic modeling of blood vessels for segmenting mra images. a new physically justified adaptive probabilistic model of blood vessels on magnetic resonance angiography (mra) images is proposed. the model accounts for both laminar (for normal subjects) and turbulent blood flow (in abnormal cases like anemia or stenosis) and results in a fast algorithm for extracting a 3d cerebrovascular system from the mra data. experiments with real data sets confirm the high accuracy of the proposed approach.
a framework for automatic segmentation of lung nodules from low dose chest ct scans. to accurately separate each pulmonary nodule from its background in a low dose computer tomography (ldct) chest image, two new adaptive probability models of the visual appearance of small 2d and large 3d pulmonary nodules are jointly used to control the evolution of the deformable model. the appearance prior is modeled with a translation and rotation invariant markov-gibbs random field of voxel intensities with pairwise interaction. the model is analytically identified from a set of training nodule images with normalized intensity ranges. both the nodules and their background in each current multi-modal chest image are also modeled with a linear combination of discrete gaussians that closely approximate the empirical marginal probability distribution of voxel intensities. experiments with real ldct chest images confirm the high accuracy of the proposed approach.
image analysis of renal dce mri for the detection of acute renal rejection. acute rejection is the most common reason of graft failure after kidney transplantation, and early detection is crucial to survive the transplanted kidney function. in this paper we introduce a new approach for the automatic classification of normal and acute rejection transplants from dynamic contrast enhanced magnetic resonance imaging (dce-mri). the proposed algorithm consists of three main steps; the first step isolates the kidney from the surrounding anatomical structures. in the second step, a novel nonrigid-registration algorithm is employed to account for the motion of the kidney due to patient breathing, and finally, the perfusion curves that show the transportation of the contrast agent into the tissue are obtained from the cortex and used in the classification of normal and acute rejection transplants. applications of the proposed approach yield promising results.
wide baseline matching through homographic transformation. this paper discusses the wide baseline matching problem where the camera parameters are known up to an error factor and the ground surface is considered planar. junctions of different shapes and orientations are detected. homographic correlation, an invariant measure to projective transformation, is utilized using reconstructed planes made of detected junctions. two variants of homographic correlation are proposed showing that this approach outperforms non-homographic correlation.
physically motivated reconstruction of fiberscopic images. flexible endoscopes are applied in modern techniques for technical inspection as well as medical diagnostic and therapy in keyhole-surgery-scenarios. their characteristic bendable image conductor consists of a limited number of coated fibers. an optical fiber consists of a core surrounded by a cladding layer. this configuration leads to imaging artifacts, called comb structures. they have a negative impact on further image processing steps, like feature detection and tracking. the intensity distribution of a cross-section of a fiber is usually modeled by a two-dimensional gaussian. we propose a preprocessing algorithm which exploits this physical property to remove the comb structure while retaining the image content. the proposed approach effectively removes of fiberscopic comb structure in real-time. it is adaptive to arbitrary endoscope and sensor combinations. the results give prospect of a large field of possible applications both for visual optimization in the clinical environment and for further digital imaging tasks.
a parallel pipelined implementation of loco-i for jpeg-ls. we describe n2c-ex, a parallel, pipelined version of a modified loco-i lossless compression algorithm used within the jpeg-ls coding scheme. this version takes into account the sequential nature of the original loco-i algorithm due to the use of context statistics in coding the residual errors of the predictive phase and uses three pipelines to carry out concurrently the encoding on independent pixels extracted from the serial stream of incoming data.
graph matching using interference of coined quantum walks. in this paper we consider how coined quantum walks can be applied to exact graph matching. the matching problem is abstracted using an auxiliary structure that connects pairs of vertices from the graphs to be matched by way of auxiliary vertices. we locate matches using coined quantum walks on this structure. we have tested the algorithm on graphs derived from the nci molecule database and found it to significantly reduce the space of possible matchings thereby allowing the graphs to be matched directly. we also perform a sensitivity analysis on the algorithm in order to examine its behaviour in the presence of noise.
an experimental comparison between consistency-based and adaptive prototype replacement schemes. an empirical characterization of a family of condensing algorithms for the 1-nn rule with regard to the different learning vector quantization schemes is presented. in particular, generalized prototype merging based on consistency on one hand and adaptive placement of a prespecified number of prototypes on the other, are considered. both families of methods have advantages and drawbacks. basically, lvq methods tend to be more robust and efficient butthey strongly depend on initialization and parameter setting while consistency-based merging methods have no initialization and parameter setting but tend to be very dependent on the particular training data.
a bayesian framework for robust human detection and occlusion handling using human shape model. one challenging aspect of automated surveillance for real environments is the occurrences of various difficult scenarios brought about by practical unconstrained settings. in this paper, we address foreground detection for automated surveillance under the following challenging situations: i) foregrounds being partially hidden due to close similarities to the background, and ii) foregrounds representing multiple objects being inseparable, forming a large contiguous blob due to occlusion. to build a robust system, we present a new foreground detection framework based on bayesian formulation, comprising both bottom-up and top-down approaches. we first propose a region-based background subtraction and a localized spatial segmentation scheme as the bottom-up steps for foreground detection. we then incorporate a human shape model as the top-down step for foreground validation and occlusion handling. segmentation is obtained when a maximum posteriori value is found, corresponding to the best description about foregrounds given by the approach. such integration of bottom-up and top-down approaches leads directly to more robust performance in handling challenging situations within hostile real environments. promising results are obtained when the algorithm is tested on real video sequences captured from a live surveillance system that operates at a public outdoor swimming pool.
panoramic stereo reconstruction using non-svp optics. omni-directional sensors are useful in obtaining a 360^o field of view with a single lens camera. omni-directional stereo imaging systems can be constructed using a single camera and a mirror consisting of two concentric, radially symmetric lobes. if the central camera-mirror axis is vertical, a stereo image containing imagery from all azimuth directions from two viewpoints can be captured within one image. a vertically oriented catadioptric optical system that employs a mirror with a radial profile other than parabolic or hyperbolic cannot maintain the straightness of non-vertical lines. however, non-parabolic or hyperbolic mirror profiles are desirable for resolution distribution, sensor size, and cost. these mirror shapes, including spherical mirrors, are said to be non-central or non-single view-point (svp); without a virtual perspective point, they pose new feature extraction challenges. the projection of straight lines in the world do not map to straight lines on the pixel array for non-svp panoramic images, nor in the general case map to any conventional conic. a method for processing the imagery from a panoramic non-svp catadioptric stereo sensor to reconstruct a 3d model of polyhedral objects with horizontal and vertical line edges is introduced. horizontal line segments are extracted using the panoramic hough transform and vertical line segments are recognized as straight radial lines. these segments, and closed shapes they form, are matched between the two lobe views to estimate position in three-dimensions. a novel hough transform technique is described, and practical considerations for its successful use are explored. four general weaknesses with hough transform techniques in general are identified, and solutions to address them in each case are given. details of a complete system using the panoramic hough transform are given to demonstrate where this transform must be supplemented with other methods for robust operation. issues of reconstruction accuracy are addressed, and experimental results with synthetic and real images are presented.
automatic detection of relevant head gestures in american sign language communication. an automated system for detection of head movements is described. the goal is to label relevant head gestures in video of american sign language (asl) communication. in the system, a 3d head tracker recovers head rotation and translation parameters from monocular video. relevant head gestures are then detected by analyzing the length and frequency of the motion signal's peaks and valleys. each parameter is analyzed independently, due to the fact that a number of relevant head movements in asl are associated with major changes around one rotational axis. no explicit training of the system is necessary. currently, the system can detect "head shakes." in experimental evaluation, classification performance is compared against ground-truth labels obtained from asl linguists. initial results are promising, as the system matches the linguists' labels in a significant number of cases.