waashk/dblp huggingface dataset:
an efficient image-based rendering method. given a set of images of the same scene, we propose an efficient method for realistically synthesizing a new image seen from a new viewpoint. compared with some existing techniques which explicitly reconstruct the 3d geometry of the scene, we directly reconstruct the colour of each pixel in the new image by utilizing a weighted photoconsistency constraint. global photoconsistency constraint of one pixel is firstly utilized to generate a list of plausible colours for each rendered pixel in the new image. considering the existing of partial occlusion or the deficiencies in the image-formation model, we iteratively update the colour for each rendered pixel based on local texture statistics similar to the input images. experimental results on the generation of a new image from a new viewing position from a set of input images show that our proposed method is promising and satisfactory.
an integrated monte carlo data association framework for multi-object tracking. we propose a sequential monte carlo data association algorithm based on a two-level computational framework for tracking varying number of interacting objects in dynamic scene. firstly, we propose a hybrid measurements generation process to facilitate varying number problems, the process mixes target-oriented measurements provided by target dynamics prior model and data-oriented measurements based on discriminative model . secondly, an improved monte carlo joint data association filter is used to combat the curse of dimension problem. finally, the particle based belief propagation is used to facilitate interactions among objects. this framework integrates discriminative model learning, monte carlo joint data association filtering, and belief propagation algorithm, these methods are realized as different levels of approximation to an 'ideal' generative model of multiple visual targets tracking, and result in a novel sequential monte carlo data association algorithm. the algorithm is illustrated via tracking many pedestrians in a real video sequence.
on-line handwritten chinese word recognition based on lexicon. we proposed a new method for on-line handwritten chinese word recognition based on lexicon. a pre-segment strategy is first applied to the input strokes and merges the strokes into candidate character boxes. segmentation path generation and filtering method is then invoked to generate segmentation paths from the boxes and filter out impossible paths by the recognition results of candidate character and lexicon-based technique. the optimal segmentation path along with the best matching word in the lexicon is finally chosen by a voting strategy using both recognition and geometrical information. superiority of the proposed approach has been proven by testing it with 8550 words collected by tablet pc. the accuracy of segmentation and recognition is about 99%
evaluation of fingerprint orientation field registration algorithms. the majority of modern fingerprint registration algorithms are based on the alignment ofminutiae features. however, shortcomings of this approach are becoming apparentdue to the difficulty of extracting minutiae from noisy or low quality images. this papers explores a novel approach to fingerprint registration based on orientation field alignment. one main advantage of this method is that orientation fields can be computed reliably for poor quality images, providing a robust feature for registration. three orientation field alignment algorithms are presented, and their performance is evaluated using an fvc2002 dataset.
fast robust ga-based ellipse detection. this paper discusses a novel and effective technique for extracting multiple ellipses from an image, using a multi-population genetic algorithm (mpga). mpga evolves a number of subpopulations in parallel, each of which is clustered around an actual or perceived ellipse. it utilizes both evolution and clustering to direct the search for ellipses - full or partial. mpga is explained in detail, and compared with both the widely used randomized hough transform (rht) and the sharing genetic algorithm (sga). in thorough and fair experimental tests, utilizing both synthetic and real-world images, mpga exhibits solid advantages over rht and sga in terms of accuracy of recognition - even in the presence of noise or/and multiple imperfect ellipses, as well as speed of computation.
integrating differential evolution and condensation algorithms for license plate tracking. in this paper, we propose a novel method for position estimation and tracking of license plates in 3d from monocular camera view. given an initial estimate, we try to track the position of the license plate in the successive video frames. in our previous works, we utilized condensation algorithm for estimating the state of the object and filtering the measurements. in this work, we present decondensation algorithm, which is an integration of condensation and differential evolution (de) algorithms. the new algorithm is composed of the probability density propagation of condensation algorithm and a fine scale optimization step according to de algorithm. each sample of the de-condensation algorithm is an estimate for the license plate's position in 3d and projected to the image plane by perspective camera model for evaluation. while tracking the license plate's texture by probability density propagation, de optimization gives a fine tuning according to the license plate boundaries. de-condensation algorithm outperforms the standard condensation algorithm and reduces the number of samples significantly.
analysis and improvement of an iris identification algorithm. iris recognition using complex valued 2d gabor filters received a great success in broad applications. such method encoded the iris phase information which was extracted according to the complex valued 2d filters' response. in this paper, we theoretically analyzed the complex-valued 2d gabor filters. based on analysis, we concluded that the real part of complex-valued 2d gabor filter would reduce the whole system performance because it is not perfectly band passed filter. an improved algorithm then proposed to extract the phase information of iris image. the new method has more advantages such as lower false rate, much less space of codes and shorter time of encoding and matching. in order to reduce the disturbance of eyelashes, eyelids, specular reflections, or other noise, a matching template is designed by dilating the positions of them. experimental results show that the latter method has an encouraging performance.
on the relationship of human walking and running: automatic person identification by gait. the intimate relationship between human walking and running lies within the skeleto-muscular structure. this is expressed as a mapping that can transform computer vision derived gait signatures from running to walking and vice versa, for purposes of deployment in gait as a biometric or for animation in computer graphics. the computer vision technique can extract leg motion by temporal template matching with a model defined by forced coupled oscillators as the basis. the (biometric) signature is derived from fourier analysis of the variation in the motion of the thigh and lower leg. in fact, the mapping between these gait modes clusters better than the original signatures (of which running is the more potent) and can be used for recognition purposes alone, or to buttress both of the signatures. moreover, the two signatures can be made invariant to gait mode by using the new mapping.
iris recognition algorithm using modified log-gabor filters. in this paper, we presented an iris recognition algorithm based on modified log-gabor filters. the algorithm is similar as the method proposed by daugman in general procedure while modified log-gabor filters are adopted to extract the iris phase information instead of complex gabor filters used in daugman's method. the advantage of log- gabor filters over complex gabor filters is the former are strictly bandpass filters and the latter are not. the property of strictly bandpass makes the log-gabor filters more suitable to extract the iris phase features regardless of the background brightness. the comparison experiments between complex gabor filters based methods and the proposed method are also presented in this paper.
real time large vocabulary continuous sign language recognition based on op/viterbi algorithm. up to now, continuous sign language recognition is mainly based on statistical methods, especially hidden markov models (hmm) and viterbi-beam searching. however, the recognition speed often gets unacceptable with an increased vocabulary, which could cause a long time delay that is not fit for the real time recognition system. to speed up the recognition process, we present a method using one-pass (op) pre-searching before viterbi recognition. the experiments are processed in the large vocabulary database. results show that the average recognition speed of op/viterbi approach can get a notable raise comparing with the single frame's without reducing too much recognition accuracy.
hipprint person identification and behavior analys. it has been gathering great deal of attention to provide personalized services in the home or in the small office. as a necessary technology for it, person recognition by pressure sensors on a chair is described. it exploits the hipprints of users. such a method makes it possible to automatically login to a computer when the user sits. the recognition rate reached to 99.6% for 5 people and 98.4% for 10 people. in addition, a change of the hipprints is available to figure out roughly what he/she was doing.
systematic static shadow detection. a systematic static shadow detection algorithm for color images is presented in this paper. the image is modeled by an undirected graph and the shadow detection is achieved through maximizing the graph probability using the em algorithm. further analysis shows the connection between our model and the relaxation labeling (rl) model. experiments clearly indicate that our method is superior to a state-of-the-art shadow detection algorithm.
space-time moment invariants and recognition of non-rigid motions from arbitrary viewpoints. the geometric invariants are very useful for recognizing objects in the scene from arbitrary viewpoints. recently, it has been shown that some geometric invariants can be defined in the space with time domain, and they can be applied for recognizing dynamic motions from arbitrary viewpoints. however, the existing invariants on motions are computed from multiple points in the sequence of images, and thus accurate tracking of image features is required. as a result, if tracking of image features fails, these invariants cannot be computed. in this paper, we propose invariants on motions, which are defined by using image moments. we show that by using the proposed moment invariants on motions, we can recognize non-rigid motions from arbitrary viewpoints without tracking image features. we also show that they can reflect entire changes in object shapes and motions in image sequences.
directly modeling of correlation matrices for gmm in speaker identification. in this paper, we present a new framework to model full covariance matrices of gaussian components. in this framework, directly modeling the full correlation matrix instead of the full covariance matrix is our purpose, as the correlation matrix is the direct description of the correlation of inter-feature elements. in order to model full correlation matrices, we share linear transformations among components' full correlation matrices. thus, the full correlation matrix of each component is represented by a shared linear transformation and a componentspecific diagonal correlation matrix. the transformation is used to help the diagonal correlation matrix to model the correlation of inter feature-vector elements more precisely. we evaluate our new framework on a mandarin speaker identification task. experiments show that above 35% reduction in speaker identification error rate is achieved compared with the best diagonal covariance models. furthermore, our algorithm achieved better performance than stc does.
tracking players and a ball in video image sequence and estimating camera parameters for 3d interpretation of soccer games. to recognize and retrieve soccer game scenes, the movement of the players and the ball must be analyzed. this paper describes a method of tracking the players and the ball and estimating their 3d positions from video images recorded from tv broadcasting. our system detects the players and tracks them by predicting their motions. our system determines the ball from among ball candidates by considering the motion continuity of the ball. the camera parameters (pan, tilt, and zoom) are estimated by detecting line regions on the field in every frame and by matching them to the model. because our method uses not only straight lines but also circular ones for the matching, it can be applied to the whole area of the field. our system also estimates the 3d position of the ball by fitting a 3d physical model to the observed ball trajectory. the effectiveness of the method is shown by experiments with actual image sequences.
detecting femur fractures by texture analysis of trabeculae. 30% of women and 13% of men worldwide suffer from osteoporotic bone fractures worldwide. in large hospitals, doctors need to visually inspect a large number of x-ray images to identify the fracture cases, which typically constitute about 12% of all the x-ray images examined. automated fracture detection can help to screen for obvious cases and flag suspicious cases for closer examinations. this paper describes a method of detecting femur fractures by analyzing trabecular texture patterns. test results show that it is more accurate than an existing method based on neck-shaft angle. moreover, combining the methods further improve of the overall performance of fracture detection.
vehicle ego-motion estimation and moving object detection using a monocular camera. this paper proposes a method for estimating the egomotion of the vehicle and for detecting moving objects on roads by using a vehicle mounted monocular camera. there are two problems in ego-motion estimation. firstly, a typical road scene contains moving objects such as other vehicles. secondly, roads display fewer feature points compared to the number associated with background structures. in our approach, ego-motion is estimated from the correspondences of feature points extracted from various regions other than those in which objects are moving. after estimating the ego-motion, the three dimensional structure of the scene is reconstructed and any moving objects are detected. in our experiments, it has been shown that the proposed method is able to detect moving objects such as vehicles and pedestrians.
audio music genre classification using different classifiers and feature selection methods. we examine performance of different classifiers on different audio feature sets to determine the genre of a given music piece. for each classifier, we also evaluate performances of feature sets obtained by dimensionality reduction methods. finally, we experiment on increasing classification accuracy by combining different classifiers. using a set of different classifiers, we first obtain a test genre classification accuracy of around 79.6 ± 4.2% on 10 genre set of 1000 music pieces. this performance is better than 71.1 ± 7.3% which is the best that has been reported on this data set. we also obtain 80% classification accuracy by using dimensionality reduction or combining different classifiers. we observe that the best feature set depends on the classifier used.
character extraction from natural scene images by hierarchical classifiers. this paper proposes a method to extract character regions in natural scene images by hierarchical classifiers. the hierarchy consists of two types of classifiers : histogram based classifier and svm. on the bottom level, fast and reliable histogram based classifier is used to reject apparent non-character regions. on the next level, a non-linear svm is exploited to make a final decision. one of the drawbacks of non-linear svms is its computational cost. to reduce the computational cost, we use sparse wavelet representation. moreover, to reduce the cost further, we propose a method to approximate a svm with sparse support vectors. we experimentally show this two-step method can perform very well with respect to both the computational cost and recognition rate.
reconstructing 3d human body pose from stereo image sequences using hierarchical human body model learning. this paper presents a novel method for reconstructing a 3d human body pose using depth information based on top-down learning. the human body pose is represented by a linear combination of prototypes of 2d depth images and their corresponding 3d body models in terms of the position of a predetermined set of joints. in a 2d depth image, the optimal coefficients for a linear combination of prototypes of 2d depth images can be estimated using least square minimization. the 3d body model of the input depth image is obtained by applying the estimated coefficients to the corresponding 3d body model of prototypes. in the learning stage, the proposed method is hierarchically constructed by classifying the training data recursively into several clusters with silhouette images and depth images. in applying hierarchical human body model learning to estimate 3d human body pose, the similar pose in a silhouette image can be estimated as a different 3d human body pose. the proposed method has been tested with 20 persons' sequences. the proposed method achieved the average errors 0f 12.3 degree for all human body components.
detection of fence climbing from monocular video. this paper presents a system that detects humans climbing fences. after extracting a binary blob contour, the system models the human with an extended star-skeleton representation consisting of the highest contour point and the blob centroid as the two stars. distances between stars and contour points are computed and smoothed to detect local maximum points. the system then finds certain predicates to form a feature vector for each frame. to analyze the resulting time series, a block based discrete hidden markov model (hmm) is built with predefined action classes {walk, climb up, cross over, drop down} as the state blocks. each block contains a subset of hidden states and is trained independently to improve the model estimation accuracy with a limited number of sequences. the detection is achieved by decoding the state sequence of the block based hmm. the experiments on image sequences of human climbing fences yield excellent results.
an integrated decoding framework for audio watermark extraction. this paper proposes a blind audio watermark extraction technique that allows performing watermark decoding while installing data synchronization. the proposed decoding algorithm employs correlation techniques supported by a wavelet denoising process, thus improves the decoding performance significantly. a data adaptive nonlinear mpeg layer 1 model 1 compatible watermark encoder is designed for watermark embedding. a channel encoder is also included into the system to take the advantage of error correction. the method does not require the original audio for decoding and it is robust to channel noise, filtering as well as stereo-to-mono conversions. it allows working at very low watermark-to-signal ratios thus preserves inaudibility.
arm-pointing gesture interface using surrounded stereo cameras system. we propose an interface achieved using surrounding stereo cameras as a method of independently recognizing the intentional arm-pointing gestures of several users. the stereo cameras provide depth information maps in real time with resulting robustness against the influence of changes in lighting and users' clothing. utilizing this, we developed a method capable of sensing intentional arm-pointing gestures that ignored unconscious arm-pointing gestures. this interface does not depend on the user's position, direction, or posture. we placed four stereo cameras in four corners of the ceiling and used this method in various lighting environments. we evaluated the arm-pointing gestures of seven subjects at multiple points and obtained reliable accuracy. furthermore, we applied this method to the arm-pointing gesture interface of home electronics appliances using bluetooth.
morphological analysis of spatio-temporal patterns for the segmentation of cyclic human activities. this paper describes a new method for the temporal segmentation of human actions based on a 2d inter-frame similarity plot. this similarity matrix contains relevant information for the analysis of cyclic and symmetric human activities, where the motion performed during the first semi-cycle is repeated in the opposite direction during the second semi-cycle. thus, the pattern associated to a periodic activity in the similarity matrix is rectangular and decomposable into elementary units. we propose a morphology-based approach for the detection and analysis of activity patterns. pattern extraction is further used for the detection of the temporal boundaries of the cyclic symmetric activities. result evaluation approach is based on a statistical estimation of the ground truth segmentation and on a confidence ratio for temporal segmentations. research reported in this paper was supported by a discovery grant of the national sciences and engineering research council of canada.
support vector machine with orthogonal chebyshev kernel. an orthogonal chebyshev kernel function for support vector machine (svm) is proposed based on extensive research about the properties of kernel functions. chebyshev polynomials are firstly constructed through chebyshev formulae. then based on these polynomials chebyshev kernels are created satisfying mercer condition. as chebyshev polynomial has the best uniform proximity and its orthogonality promises the minimum data redundancy in feature space, it is possible to represent the data with less support vectors. experimental result shows that compared with other tradition support vector machines, chebyshev kernel support vector machine performs much better and has less support vectors. chebyshev kernel also has the ability of generalization. it is proved to be an excellent, widely suited and practical kernel both theoretically and experimentally.
an experimental study on automatic face gender classification. this paper presents an experimental study on automatic face gender classification by building a system that mainly consists of four parts, face detection, face alignment, texture normalization and gender classification. comparative study on the effects of different texture normalization methods including two kinds of affine mapping and one delaunay triangulation based warping as preprocesses for gender classification by svm, lda and real adaboost respectively is reported through experiments on very large sets of snapshot images.
shape reconstruction and image restoration for non-flat surfaces of documents with a stereo vision system. in this paper, we propose a shape reconstruction and image restoration method for paper documents with curved surfaces or fold lines by using a stereo vision system. characters in images of thick book's pages acquired with an image scanner are difficult to recognize because they are deformed under the influence of curved surface. therefore, 3-d shape reconstruction of the book's surface is executed from the result of the stereoscopic measurement by putting the book upward, and an image of a flat surface is recovered from the curved or folded surface. the validity of the proposed method is shown through experiments.