waashk/dblp huggingface dataset:
a wavelet-based edge detection method by scale multiplication. a wavelet-based multiscale edge detection scheme is presented in this paper. by multiplying the wavelet coefficients at two adjacent scales to magnify significant structures and suppress noise, we determined edges as the local maxima directly in the scale product after an efficientthresholding instead of first forming the edge maps at several scales and then synthesizing them together, which was employed in many multiscale techniques. it is shown that the scale multiplication achieves better results than either of the two scales, especially on the localization performance. experiments on natural images are compared with the laplacian of gaussian (log) and canny edge detection algorithms.
automated face pose estimation using elastic energy models. face pose estimation forms an important part in a face recognition system. however, fully automated and accurate pose determination still remains an unsolved problem in the research community. in this paper, we propose a novel elastic energy model to automatically estimate face poses. our method employs statistical energy contributions of a set of feature points, which can avoid over-trusting selected anchor points. it provides a robust solution to the feature localisation inaccuracy problem, which is inevitable in practical applications with cluttered backgrounds. as a general configuration, our model can be easily implemented and extended to other non-rigid objects. its effectiveness and robustness are revealed in our experiments.
comparing different localization approaches of the radon transform for road centerline extraction from classified satellite imagery. using a local radon transform helps improve the performance of the radon transform-based linear feature detection. in this paper, three different approaches to localize the radon transform are implemented and compared in the context of road centerline extraction from classified satellite imagery.
part based human tracking in a multiple cues fusion framework. this paper presents a real time video surveillance system which is capable of tracking multiple humans simultaneously. to better deal with various challenging issues such as occlusions, sharp motion changes and multi-person confusions, we propose an intelligent fusion framework where multiple cues are combined to seek the optimal objects state and more reliable cues have larger influences on the final decision. further, part based human tracking provides a second-level information fusion in that parts with weak observability can be compensated by tracking other more visible ones, which demonstrates its effectiveness for highly articulated objects like humans.
minimum classification error training for handwritten character recognition. in the offline handwritten character recognition, the classifier with modified quadratic discriminant function (mqdf) has achieved good performance. the parameters of the mqdf are commonly estimated using the maximum likelihood estimator, which maximizes the within-class likelihood but not directly minimizes the classification errors. to improve the mqdf performance, the mqdf parameters are revised using the discriminative training of minimum classification error (mce). our algorithm effectiveness is demonstrated by applying it to the nist handwritten numerals and handwritten chinese characters. the experimental results show that one of the highest recognition accuracies ever reported is achieved.
human silhouette extraction based on hmm. this paper presents a system that can extract regions of a person from an image sequence. the system first detects foreground regions based on a background model. after foreground regions are extracted a human model is used to identify human regions. in the human model, we adopt a hidden markov model (hmm) to model human silhouettes. to separate human and non-human regions, we represent non-human silhouettes by another hmm. the human silhouette model can extract incomplete human regions even when parts of the person are covered by background objects. experimental results show the system proposed is very effective.
ball hit detection in table tennis games based on audio analysis. as bearer of high level semantics, audio signal is being more and more used in content-based multimedia retrieval. in this paper, we investigate the ball hit detection for sports games and propose a novel approach to detect ball hits. by employing energy peak detection (epd) and mel frequency cepstral coefficient-based (mfcc-based) refinement (mbr), high precision (91%) and adequate recall (73%) of ball hit detection are achieved with a low computational complexity and an easy training process. the proposed algorithm can be applied in audio content-based highlight detection systems and provide valuable information for semantical understanding of sports games.
hand-eye calibration based on screw motions. when computer vision technique is used in robotics, robotic hand-eye calibration is a very important research task. many algorithms have been proposed for hand-eye calibration. based on these algorithms, we introduce a new hand-eye calibration algorithm in this paper, which employs the screw motion theory to establish a hand-eye matrix equation by using quaternion and gets a simultaneous result for rotation and translation by solving linear equations. the algorithm proposed in this paper has high and stable computational efficiency without non-linear minimization and can be understood easily. both simulations and real experiments show the superiority of our algorithm over the comparative algorithms.
a hierarchical object recognition system based on multi-scale principal curvature regions. this paper proposes a new generic object recognition system based on multi-scale affineinvariant image regions. image segments are obtained by a watershed transform of the principal curvature of a contrast enhanced image. each region is described by an intensity-based statistical descriptor and a pcasift descriptor. the spatial relations between regions are represented by a cluster-index distribution histogram. with these new descriptors, we develop a hierarchical object recognition system which uses an improved boosting feature selection method [9] to construct layer classifiers by automatically selecting the most discriminative features in each layer. all layer classifiers are then combined to give the final classification. this system is tested on various object recognition problems. experimental results show that the new hierarchical system outperforms the comparable solutions on most of the datasets tested.
3d tracking of human locomotion: a tracking as recognition approach. estimating mode (walking/running/standing) and phases of human locomotion is important for video understanding. we present a new "tracking as recognition" approach. a hierarchical finite state machine constructed from 3d motion capture data serves as a prior motion model. motion templates are used as the observation model. robustness is achieved by making inferences in the prior motion model which resolves the short-term ambiguity of the observations that may cause a regular tracking formulation to fail. experiments show very promising results on some difficult sequences.
detection of moving cast shadows using image orthogonal transform. in many image and computer vision applications, shadows interfere with fundamental tasks such as moving objects segmentation and tracking. in this paper, a novel method is proposed to detect the moving cast shadows in the scene. the normalized coefficients of orthogonal transform of image block are proved to be illumination invariant and are used to classify moving shadows and foreground objects. five kinds of orthogonal transform: dct, dft, haar transform, svd and hadamard transform, are utilized in our work to detect moving cast shadows. experimental results show that the proposed method succeeds in detecting moving cast shadows within indoor and outdoor environments.
local binary pattern descriptors for dynamic texture recognition. dynamic texture is an extension of texture to the temporal domain. in this paper, a new method for recognizing dynamic textures is proposed. the textures are modeled with concatenated local binary patterns in three orthonormal planes. the circular neighborhoods are generalized to elliptical sampling to fit to the space-time statistics. this is an extension of the lbp approach widely used in still texture analysis, combining the motion and appearance together. our approach has many advantages compared with the earlier approaches providing a better performance for the dyntex and mit databases.
moving cast shadows detection based on ratio edge. in this paper we propose a novel method for moving cast shadows detection. based on the analysis to the physical model of moving shadows, we prove that the ratio edge is illumination invariant. the distribution of the ratio edge is discussed and a significance test is performed to classify each image pixel into foreground object or moving shadow. experimental results on typical scenes show that the proposed method can detect moving shadows robustly.
fingerprint registration using minutia clusters and centroid structure 1. in this paper a novel distortion-tolerant fingerprint registration method based on clustering is proposed. in this method, minutiae features of the query fingerprint are divided into various clusters. several local structure transformations are estimated by local structure sets. then the global structures (centroid structures) are constructed according to the local structure transformation. the global transformation is determined by the score of local structure transformation together with the similarity level of the global structure. experimental results show that this algorithm is robust for aligning fingerprints with a small number of minutia and heavy distortions. such situations are often encountered in forensic applications.
automatic texture synthesis for face recognition from single views. one possible solution for pose- and illuminationinvariant face recognition is to employ appearancebased approaches, which rely greatly on correct facial textures. however, existing facial texture analysis algorithms are suboptimal, because they usually neglect specular reflections and require numerous training images for virtual view synthesis. this paper presents a novel texture synthesis approach from a single frontal view for face recognition. using a generic 3d face shape, facial textures are analyzed with consideration of all of the ambient, diffuse, and specular reflections. virtual views are synthesized under different poses and illuminations. the proposed approach was evaluated using the cmu-pie face database. encouraging results show that the proposed approach improves face recognition performances across pose and illumination variations.
incremental construction of neighborhood graphs for nonlinear dimensionality reduction. most nonlinear data embedding methods use bottom-up approaches for capturing underlying structures of data distributed as points on nonlinear manifolds in high dimensional spaces. these methods usually start by designating neighbor points to each point. neighbor points have to be designated in such a way that the constructed neighborhood graph is connected so that the data can be projected to a single global coordinate system. in this paper, we present an incremental method for updating neighborhood graphs. the method guarantees k-edge-connectivity of the constructed neighborhood graph. together with incremental approaches for geodesic distance estimation and multidimensional scaling, our method enables incremental embedding of high dimensional data streams. the method works even when the data are under-sampled or non-uniformly distributed. it has important applications in the processing of data streams and multimedia data.
recovering elastic property of soft tissues using 2d image sequences with limited range data. detecting abnormal material properties associated with diseased tissues from images requires accurate displacement data. we examined three affine model-based methods for measuring 3d displacements in regular image sequences. the advantage of these methods is that only one or no range image is needed for the whole sequence. we demonstrate, using images of both synthetic material and real burn patients, that the affine tracking method is suitable for many image based medical analyses.
stabilizing route panoramas. the route panorama (rp) has been proposed as a new digital medium to record and visualize cityscapes along a route. it is a compact, continuous and complete visual representation of scenes collected from a sequence of slit views with a camera moving along a smooth path. in real scene acquisition, a camera may suffer from vehicle shaking and the obtained route panoramas are jagged and waved. to improve the quality of route panoramas, we develop an algorithm to stabilize them. by referring the continuous linear features, we use median filters to smooth the 2d route panorama. by setting the slit properly with respect to the camera, we can reduce the influence from the vehicle shaking in the rp without matching consecutive video frames. we have rectified long distance route panoramas to a moderate level for virtual tours and visual navigation.
machine printed arabic character recognition using s-gcm. arabic characters are widely used in arabic countries. however, there is a little work has been done on recognition of arabic characters. this paper proposed a new method for recognition machine printed arabic characters. the proposed method employs ishii et al's chaotic neural network model, which is called globally coupled map using the symmetric map (s-gcm), for recognizing arabic characters. the proposed method is tested on two fonts, simplified arabic and arabic transparent, and 9 sizes, 8, 9, 10, 11, 12, 14, 16, 18, 20. the recognition rate is greater than 97%.
recognizing rotated faces from two orthogonal views in mugshot databases. tolerance to pose variations is one of the key remaining problems in face recognition. it is of great interest in airport surveillance systems using mugshot databases to screen travellers' faces. this paper presents a novel poseinvariant face recognition approach using two orthogonal face images from mugshot databases. virtual views under different poses are generated in two steps: shape modeling and texture synthesis. in the shape modeling step, a feature-based multilevel quadratic variation minimization approach is applied to generate smooth 3d face shapes. in the texture synthesis step, a non-lambertian reflectance model is explored to synthesize facial textures taking into account both diffuse and specular reflections. a view-based face recognizer is used to examine the feasibility and effectiveness of the proposed pose-invariant face recognition. the experimental results show that the proposed method provides a new solution to the problem of recognizing rotated faces.
regularized locality preserving learning of pre-image problem in kernel principal component analysis. in this paper, we address the pre-image problem in kernel principal component analysis (kpca). the preimage problem finds a pattern as the pre-image of a feature vector defined in the nonlinear principal component space produced by kpca. since the preimage typically seldom exists in general, an approximate solution is appreciated. by posing a novel perspective, we find the pre-image with regularized locality preserving learning. our approach achieves a unique solution, avoiding iteration and numerical instability. significant superiority of the proposed novel algorithm is demonstrated by driving two applications, namely face denoising and occluded face reconstruction, as comparing with some existing wellknown methods on pre-image learning.
control double inverted pendulum by reinforcement learning with double cmac network. to accelerate the learning of reinforcement learning, many types of function approximation are used to represent state value. however function approximation reduces the accuracy of state value, and brings difficulty in the convergence. to solve the problems of tradeoff between the generalization and accuracy in reinforcement learning, we represent state-action value by two cmac networks with different generalization parameters. the accuracy cmac network can represent values exactly, which achieves precise control in the states around target area. and the generalization cmac network can extend experiences to unknown area, and guide the learning of accuracy cmac network. the algorithm proposed in this paper can effectively avoid the dilemma of achieving tradeoff between generalization and accuracy. simulation results for the control of double inverted pendulum are presented to show effectiveness of the proposed algorithm.
kernel-based method for tracking objects with rotation and translation. this paper addresses the issue of tracking translation and rotation simultaneously. starting with a kernel-based spatial-spectral model for object representation, we define an l¿-norm similarity measure between the target object and the observation, and derive a new formulation to the tracking of translational and rotational object. based on the tracking formulation, an iterative procedure is proposed. we also develop an adaptive kernel model to cope with varying appearance. experimental results are presented for both synthetic data and real-world traffic video.
rejection strategies for offline handwritten sentence recognition. this paper investigates three different rejection strategies for offline handwritten sentence recognition. the rejection strategies are implemented as a postprocessing step of a hidden markov model based text recognition system and are based on confidence measures derived from a list of candidate sentences produced by the recognizer. the better performing confidence measures make use of the fact that the recognizer integrates a word bigram language model. experimental results on extracted sentences from the iam database validate the effectiveness of the proposed rejection strategies.
a unifying map-mrf framework for deriving new point similarity measures for intensity-based 2d-3d registration. similarity measure is one of the main factors that affect the accuracy of intensity-based 2d-3d registration of x-ray fluoroscopy to ct images. this paper presents a unifying map-mfr framework for rationally deriving point similarity measures based on bayes theorem. three new similarity measures derived from this framework are presented and evaluated using a phantom and a human cadaveric specimen. their behaviors are compared to other well-known similarity measures and the comparison results are reported. combining any one of the new similarity measures with a previously introduced spline-based multiresolution 2d-3d registration scheme, we develop a fast and accurate registration algorithm. we report their capture ranges, converging speeds, and registration accuracies.
real-time face recognition using gram-schmidt orthogonalization for lda. a real-time face recognition method using gram-schmidt orthogonalization for linear discriminant analysis (gslda) is presented in this paper. the gslda algorithm avoids the large matrices computation such as computing the inverse or diagonalization of matrices, which may be somewhat problematic in terms of computational demands and numerical accuracy. on the other hand, gslda also achieves better recognition performance than the classical linear discriminant analysis (lda) by overcoming the degenerate eigenvalue problem of lda. experimental results on real face databases have confirmed the better performance of the proposed method.
object detection based on combination of conditional random field and markov random field. many approaches for object detection are based on markov random field (mrf) and conditional random field (crf) respectively. mrf and crf have very different characteristics. this work discusses in detail their strength and weaknesses. from the discussion, a new object detection algorithm using combination of crf and mrf was derived. we utilize the algorithm to detect urban areas, and corresponding to the urban area object, we introduce a generic feature vector for each image site. the proposed algorithm was tested extensively on a large number of remote sensing images, and very promising results can be presented.
feature fusion of face and gait for human recognition at a distance in video. a new video based recognition method is presented to recognize non-cooperating individuals at a distance in video, who expose side views to the camera. information from two biometric sources, side face and gait, is utilized and integrated at feature level. for face, a high-resolution side face image is constructed from multiple video frames. for gait, gait energy image (gei), a spatio-temporal compact representation of gait in video, is used to characterize human walking properties. face features and gait features are obtained separately using principal component analysis (pca) and multiple discriminant analysis (mda) combined method from the high-resolution side face image and gait energy image (gei), respectively. the system is tested on a database of video sequences corresponding to 46 people. the results showed that the integrated face and gait features carry the most discriminating power compared to any individual biometric.
extracting lines in noisy image using directional information. detection of lines in noisy image is not easy. when using hough transform, multiple false peaks may be generated from collinear noisy edge points, which in turn may create false line segments. to overcome this problem, we introduce a method for detecting lines in noise based on directional information. orientations are generated by gabor filters to guide an anisotropic gaussian filtering process, and they are also used in the peak selection in hough transform. the experimental results show the effectiveness of this method.
multiple-exemplar discriminant analysis for face recognition. face recognition is characteristically different from regular pattern recognition and, therefore, requires a different discriminant analysis other than linear discriminant analysis (lda). lda is a single-exemplar method in the sense that each class during classification is represented by a single exemplar, i.e. the sample mean of the class. in this paper, we present a multiple-exemplar discriminant analysis (meda) where each class is represented using several exemplars or even the whole available sample set. the proposed approach produces improved classification results when tested on a subset of feret database where lda is ineffective.
weighted bayesian network for visual tracking. bayesian network has been shown to be very successful for muny computer vision applications, most of which are solved using the generative approaches. we propose a novel weighted bayesian network which relaxes the conditional independent assumption in traditional bayesian network by assigning weights to the estimations of conditional probabilities. in the weighted bayesian network, the hidden variables are estimated generatively as in the traditional graphical models, and the weights of conditional probabilities are adjusted discriminatively from the training samples. the combined generative/discriminative approach in a loop preserves the advantage of generative model to perform unsupervised learning and handle missing data while improve the model flexibility and performance by the discriminative learning of probability estimation weights. our experiments show a number of real-time examples in visual tracking where the performances are signijicantly improved with the weighted bayesian networks.