waashk/dblp huggingface dataset:
a new attempt to gait-based human identification. vision-based human identification at a distance has attracted more attention recently. this paper makes a simple but efficient attempt to gait recognition. for each image sequence, animproved background subtraction procedure is first used to accurately extract spatial silhouettes of a walker from the background; then, eigenspace transformation to time-varyingsilhouette shapes is performed to realize feature extraction; the nearest neighbor classifier using spatio-temporal correlation or the normalized euclidean distance measure is finally utilized in the lower-dimensional eigenspace for recognition, and some additional personalized physical properties are selected for the validation of final decision. experimental results on a small database show that the proposed algorithm has an encouraging recognition rate with relatively lower computational cost.
a new approach for fractal image compression on a virtual hexagonal structure. in this paper, we propose a fractal image compression method on a virtual hexagonal image structure by adopting fisher's basic method on the traditional square image structure. the modification on the definition of range block and domain block is implemented in order to utilize the enhanced image structure. the results of the proposed approach applied to testing images are analyzed and higher fidelity is obtained. the further research directions are discussed.
multi-view face detection under complex scene based on combined svms. a single face classifier has difficulty in detecting multi-view faces under real and complex scenes due to various poses, cluttering environment and small size of faces.in this paper, we propose a novel combination of svms to detect multi-view faces, using both cascading and bagging methods. in our method, the faces are divided into seven views. each of them models a typical pose under complex scenes. by the modified bootstrap method applied in our method, a cascade of svms are constructed to quickly select face candidates from image with expected accuracy.bagging of different svms can further eliminate the false detections that are difficult to handle by single svm.such combination of svms can effectively detect multi-view faces even with large rotation angles and heavy shadow.the experiment results show better accuracy and generalization performance over single classifier.
segmentation of the left ventricle from cardiac mr images based on degenerated minimal surface diffusion and shape priors. segmentation of cardiac mr images is a hot topic in the community of medical image analysis and remains one of the open problems. in this study, we address the segmentation of 2d cardiac mr images based on gradient vector flow snake and make three contributions: firstly, the degenerated minimal surface gvf is proposed, this new flow outperforms the original one referring to boundary preserving; secondly, the shape of the left ventricle is taken into account and a shape based energy for the snake model is adopted, with this energy, the snake contour can conquer the unexpected local minimum stemming from image inhomogeneity and the final results could depend much less on the initial contour. in order to extract the epicardium, a novel approach is developed to generate the desired external force. this new external forces can overcome the demerits of the gvf force directly derived from the usual edge map and maintains the epicardium boundaries even if the contrast between the myocardium and neighbor organs is very low, taking the endocardium contour as initialization, the snake can capture the epicardium fast and accurately. the proposed strategy is also validated experimentally.
a novel video caption detection approach using multi-frame integration. captions in videos often play an important role in video information indexing and retrieval. in this paper, we present a novel video caption detection approach. we first apply a new multiple frames integration (mfi) method to minimize the variation of the background of the image. a time-based minimum (or maximum)pixel value search is employed and sobel edge map is used to determine the mode of search. then block-based text detection is performed, i.e. a small window is used to scan the image and classified as text or non-text, using sobel edges as features. we use a two-level pyramid to detect various text sizes. finally, we present a new iterative text line decomposition method and accurate text bounding boxes are extracted from candidate text areas. experimental result shows that the proposed approach achieves a high precision and recall.
automatic alignment of high-resolution nmr spectra using a bayesian estimation approach. nuclear magnetic resonance (nmr) spectral analysis has recently become one of the major means for the detection and recognition of metabolic changes of disease state, physiological alteration, and natural biological variation. for the pattern recognition tasks in which two or more nmr spectra need to be compared, it is critical to properly align the spectra for the subsequent pattern recognition analysis. previous spectral alignment methods do not consider any baseline intensity variation between the spectra and disregard the effect of noise. here we formulate the spectra alignment problem in a bayesian statistical framework, which allows us to simultaneously and efficiently estimate the spectral shift and the baseline intensity variation in the existence of independent additive noise. experimental results with real high-resolution nmr spectral data from human plasma demonstrate the effectiveness and robustness of the proposed approach.
bilateral two dimensional linear discriminant analysis for stereo face recognition. a new method called two-dimensional fisher discriminant analysis (2d-fda) is proposed to deal with the small sample size (sss) problem in lda based face recognition. then appearance and depth information are combined to improve face recognition rate. different from the conventional 1d-fda (pca plus lda) approaches, 2d-fda is based on 2d image matrices rather than column vectors so the image matrix does not need to be transformed into a long vector before feature extraction. the advantage arising in this way is that the sss problem does not exist any more because the between-class and withinclass scatter matrices constructed in 2d-fda are both of full-rank. it was verified that 2d-fda outperforms 1d fda.
characteristic line of planar homography matrix and its applications in camera calibration. in this paper, we employ the concept of characteristic line to show some useful properties of planar homography matrix. these properties relate the characteristic line of a planar homography matrix with euler angles of the planar pattern. based on the characteristic line, a new method of linear camera calibration is proposed and a strategy to select poses of planar pattern during taking calibration images is suggested. this strategy can help ensure accuracy of calibration. experiment results including both simulated data and real images validate the method and strategy.
adaptive persistence utilizing motion compensation for ultrasound images. persistence, also called temporal averaging, takes the weighted average between successive image frames to reduce the ultrasound speckle noise. conventional methods apply persistence coefficients as a function of two image pixels at the same location of two successive frames that always make structure blurring during tissue movement. in this paper we apply the motion analysis technique in a way of compensating the internal tissue motions to have accurate image registration such that the persistence works at the right spatial positions without blurring the tissue structure. to meet the real-time requirement of ultrasound imaging, we use the sum-absolute-difference based block matching method for local motion estimation, followed by motion dependent persistence averaging. in vivo tests show that our method can increase signalto- noise ratio of imaging and also present better edge differentiation in structure regions.
pattern recognition and computer vision for mineral froth. this paper presents a windows based system for image analysis and computer vision of mineral froth. to make the system work efficiently both in laboratory and in the plant, the main content of the system consists of image acquisition, image processing, froth (bubble) delineation, froth analysis and modeling, and interface between different computers in use. hundreds of functions for froth analysis and statistics are included in the system, especially in the froth delineation part. a number of newly-developed algorithms can delineate bubbles with high accuracy and high speed.
topological localization based on salient regions in unknown environments. this paper presents a new topological localization system for mobile robot navigation based on salient visual regions. these salient regions are obtained by computing the opponencies of color and texture among multi-scale image spaces. then they are organized to construct the vertex of topological map using hidden markov model. so localization problem can be transformed to the evaluation problem of hmm. in our system, the topological map of environment can be created online and the robot locates itself concurrently. experiments show that higher ratio of vertex recognition, that is localization, is obtained. and our system can guarantee mobile robot navigation safely in unknown environments.
fingerprint minutiae matching based on coordinate system bank and global optimum alignment. minutiae pattern remains a widely used representation of a fingerprint. the research on minutiae matching never stops due to its complexity and intractability. in this paper, an efficient fingerprint minutiae matching algorithm is proposed. to obtain reliable reference minutiae pairs, the bank of coordinate systems is introduced. the coordinate systems bank is derived from the original minutiae features and applied to get more useful information about the minutiae. to improve the accuracy of minutiae matching, a global optimum alignment approach is developed, which is targeted on the alignment of the set of reference minutiae pairs. experimental results show that this algorithm achieves excellent performance with high matching speed and high matching reliability.
video local pattern based image matching for visual mapping. image matching plays an important role in visual mapping, a critical task of vision based mobile robot navigation. based on the observation that the visual content in these video sequences generally changes in a slow and continuous mode, a concept of "video local pattern" is proposed to model each video frame, which is defined as a set of frames that are visuallysimilar and temporally-adjacent to that frame. instead of manually labelling, a tracking based method is developed to automatically detect the local pattern for each frame. a model is then estimated from a local pattern, and the matching of images is performed by comparing these models. experimental results demonstrated its improvement over that comparing individual frames.
tensor voting toward feature space analysis. in this paper, a general technique is proposed for the analysis of multi-dimensional feature space. the basic computational module of the technique is the tensor voting theory, which was formerly used for structure inference from sparse data. we analyze the methodology of tensor voting systematically. its relation to kernel density estimation and mean shift is also established, based on what the utilities for two fundamental analyses of feature space, density estimation and mode detection, are discussed. algorithms for two low-level vision tasks, discontinuity preserving smoothing and motion layer inference, are described as applications of tensor voting. several experimental results illustrate its excellent performance.
automatic lipreading with limited training data. speech recognition solely based on visual information such as the lip shape and its movement is referred to as lipreading. this paper presents an automatic lipreading technique for speaker dependent (sd) and speaker independent (si) speech recognition tasks. since the visual features are derived according to the frame rate of the video sequence, spline representation is then employed to translate the discrete-time sampled visual features into continuous domain. the spline coefficients in the same word class are constrained to have similar expression and can be estimated from the training data by the em algorithm. in addition, an adaptive multi-model approach is proposed to overcome the variation caused by different speaking style in speaker-independent recognition task. the experiments are carried out to recognize the ten english digits and an accuracy of 96% for speaker dependent recognition and 88% for speaker independent recognition have been achieved, which shows the superiority of our approach compared with other classifiers investigated.
real-time multi-view face detection and pose estimation in video stream. technologies for real-time multi-view face detection from video streams are indispensable to video content- based retrieval systems and video surveillance systems.. in this paper, we proposed a solution for real-time multi-view face detection and pose estimation in video stream. integrating both asymmetric and symmetric rectangle features, adaboost learning algorithm and pyramid like architecture is employed. asymmetric rectangle features (arfs) are inherited from symmetric rectangle features (srf) to reasonably interpret asymmetric gray distribution in profile face image. pose estimation for multi-view faces are brought out by view-based weighting algorithm (vbwa). our primary experiments demonstrated that the system achieved high accuracy and high speed to detect both front and profile faces with their pose information from soccer video streams.
a minimum sphere covering approach to pattern classification. in this paper we present a minimum sphere covering approach to pattern classification that seeks to construct a minimum number of spheres to represent the training data and formulate it as an integer programming problem. using soft threshold functions, we further derive a linear programming problem whose solution gives rise to radial basis function (rbf) classifiers and sigmoid function classifiers. in contrast to traditional rbf and sigmoid function networks, in which the number of units is specified a priori, our method provides a new way to construct rbf and sigmoid function networks that explicitly minimizes the number of base units in the resulting classifiers. our approach is advantageous compared to svms with gaussian kernels in that it provides a natural construction of kernel matrices and it directly minimizes the number of basis functions. experiments using real-world datasets demonstrate the competitiveness of our method in terms of classification performance and sparsity of the solution.
a method for document zone content classification. this paper describes an algorithm to classify each given document zone into one of nine classes and provides a protocol for its performance evaluation. the classification scheme uses an optimized binary decision tree and viterbi algorithm for hmm to find the optimal solution. our algorithm was trained and tested on a total of 24,177 zones within the 1600 images from uwcdrom iii database. its accuracy rate is 98.45% with a mean false alarm rateof 0.50%
face recognition using optimal non-orthogonal wavelet basis evaluated by information complexity. detecting and recognizing face images automatically is a difficu lt task due to the variability of illumination, presentation angle, face expression and other common problems of machine vision. in this paper, we represent face images as combinations of 2-d gabor wavelet basis which are non-orthogonal. genetic algorithm (ga) is used to find an optimal basis derived from a combination of frequencies and orientation angles in the 2-d gabor wavelet transform. instead of using the widely used within and between class scatter evaluation as the fitness function in ga, we use entropy to measure the information complexity of the wavelet transform. compared to the well-known "eigenface" algorithm which represents face images based on an orthogonal basis, this gabor wavelet representation with optimal basis can provide a more accurate and effic ient projection scheme and therefore a better classification result.
kernel fisher discriminant analysis for palmprint recognition. in this paper, a method for palmprint recognition, kernel fisher discriminant analysis (kfda), is proposed. the method introduces kfda to represent palmprint features for palmprint recognition. in the paper, a device without fixed peg is developed to capture palmprint images. because the movement, the rotation and the stretching of hands are uncontrollable, the features extracted from these palmprint images have a little nonlinearity. classic linear feature extraction approaches, such as pca and flda, only take the 2-order statistics among palmprint image pixels into account, and are not sensitive to higher order statistics of data. therefore, kfda is used to extract higher order relations among palmprint images for future recognition. the experiment results denote that kfda have a better performance than eigenpalms and fisherpalms, especially in case of using a small quantity of training samples.
seamless video editing. this paper presents a new framework for seamless video editing in the gradient domain. the spatio-temporal gradient fields of target videos are modified or mixed to generate a new gradient field, which is usually not integrable. we propose a 3d video integration algorithm, which finds a potential function, whose gradient field is closest to the resulting gradient field in the sense of least squares. the video is reconstructed by solving a 3d poisson equation. we use a fast and accurate 3d discrete poisson solver using diagonal multigrids. a set of gradient operators are defined for user interaction. the resulting video has temporal coherency and no artifacts. we evaluate our algorithm using a variety of examples.
background subtraction based on a robust consensus method. statistical background modeling is a fundamental and important part of many visual tracking systems and of other computer vision applications. in this paper, we presents an effective and adaptive background modeling method for detecting foreground objects in both static and dynamic scenes. the proposed method computes sample consensus (sacon) of the background samples and estimates a statistical model per pixel. numerous experiments on both indoor and outdoor video sequences show that the proposed method, compared with several state-of-theart methods, can achieve very promising performance.
facial components detection with boosting and geometric constraints. an efficient framework utilizing both local features and geometrical distribution for detecting facial components is presented. first, candidate facial components are efficiently collected by cascaded boosting of haar-like features. the candidates may include false positives and multiple detections. then, geometrical distribution of facial components is imposed on the candidates to select the optimal configuration. for simplicity, we suppose full dependence between the components and model it with multivariate gaussian. the effectiveness of the framework is evaluated with experiments.
informative shape representations for human action recognition. shape and kinematics are two important cues in human movement analysis. due to real difficulties in extracting kinematics from videos accurately, this paper proposes to address the problem of human action recognition by spatiotemporal shape analysis. without explicit feature tracking and complex probabilistic modeling of human movements, we directly convert an associated sequence of human silhouettes derived from videos into two types of computationally efficient representations, i.e., average motion energy and mean motion shape, to characterize actions. supervised pattern classification techniques using various distance measures are used for recognition. the encouraging experimental results are obtained on a recent dataset including 10 different actions from 9 subjects.
efficient visual tracking by probabilistic fusion of multiple cues. it has been shown that integrating multiple cues will increase the reliability and robustness of a vision system in situations that no single cue is reliable. in this paper, we propose a method by fusing multiple cues (i.e., the color cue and the edge cue). in contrast to previous work, we propose a novel shape similarity measure which includes the spatial distribution of, the number of, and the gradient intensity of the edge points. we integrate this shape similarity measure with our recently proposed smog-based color similarity measure in the framework of particle filter (pf). experimental results demonstrate the high robustness and effectiveness of our method in handling appearance changes, cluttered background, moving camera, and occlusions.
age simulation for face recognition. in this paper, an automatic age simulation method used for robust face recognition is proposed. we first use a shape and texture vectors to represent a facial image by projecting it in the eigenspace of shape or texture. then we use age function combined with aging way classification to estimate age. and we use estimated age, typical vector creating function and the feature vector of the original test image to generate the synthesized feature vectors at target age. at last we reconstruct the shape and texture in eigenspaces and combine them to synthesize facial image at target age. experiments show that the proposed method can effectively "change the age of face images, and help to get face recognition result robust to age variation.
bayesian face recognition based on gaussian mixture models. bayesian analysis is a popular subspace based face recognition method. it casts the face recognition task into a binary classification problem with each of the two classes, intrapersonal variation and extrapersonal variation, modeled as a gaussian distribution. however, with the existence of significant transformations, such as large illumination and pose changes, the intrapersonal facial variation cannot be modeled as a single gaussian distribution, and the global linear subspace often fails to deliver good performance on the complex non-convex data set. in this paper, we extend the bayesian face recognition into gaussian mixture models. the complex intrapersonal variation manifold is learnt by a set of local linear intrapersonal subspaces and thus can be effectively reduced. the effectiveness of the novel method is demonstrated by experiments on the data set from ar face database containing 2340 face images.
improving face recognition by online image alignment. face recognition accuracy is affected by many factors. this paper studies one of the factors, and provides a reliable image alignment for face recognition. for this purpose, a performance metric is extracted from an analysis of face recognition similarity scores. the metric varies with face alignment, and has a relationship with the actual recognition accuracy. our method adjusts face alignment online by selecting an alignment candidate corresponding to the largest performance metric. the experimental results show that the presented method can improve the accuracy and robustness of current face recognition systems.