waashk/dblp huggingface dataset:
biologically inspired hierarchical model for feature extraction and localization. some of the most important problems of computer vision are feature extraction and subsequent localization of those features in a new image. since it is computationally prohibitive to search for the features over all possible locations and scales, it is necessary to design an algorithm that can selectively focus on and process information from only some regions within the image. in this work we present such an algorithm that is biologically inspired and performs a hierarchical search, from coarse to fine, in order to minimize the computational costs. the algorithm is very robust to non-linear image transformations such as changes in scale, rotation, skew, addition of noise, and changes in brightness and contrast. we demonstrate the computational efficiency as well as the effectiveness of the algorithm on several real world images.
object removal by cross isophotes exemplar-based inpainting. exemplar-based inpainting algorithm is a new model used for large region removal. it combines the advantages of both inpainting and texture synthesis. the crucial part of this model is data term which is used to preserve linear structure. in this paper, basing on anisotropic diffusion, deeper mathematical analysis of along isophotes exemplar- based inpainting (aiei) is given, and it is also proved the data term is the strength of isophotes. a novel cross isophotes exemplar-based inpainting (ciei) model is proposed, which determines the data term cross the isophotes. data term in ciei considers the extent of edge, so ciei has better linear structure preserving property, and it can fill the target region of natural scene more plausibly. both theoretical analysis and experiments have verified the validity of the new ciei model proposed in this paper.
a regression model in tensorpca subspace for face image super-resolution reconstruction. a regression model in the tensorpca subspace is proposed in this paper for face super-resolution reconstruction. an approximate conditional probability model is used for the tensor subspace coefficients and maximum-likelihood estimator gives a linear regression model. the approximation is corrected by adding non-linear component from a rbf-type regressor. experiments on face images from feret database validate the algorithm. although each projection coefficient is estimated by a local estimator, tensorpca subspace analysis is still a global descriptor, which makes the algorithm have certain ability to deal with partially occluded images.
high frequency component compensation based super-resolution algorithm for face video enhancement. this paper proposes a video-based high-frequency component compensation (hfcc) super-resolution algorithm. the lost high-frequency information is estimated by local map criteria, using the registered frames. by compensating the high frequency component iteratively, the high-resolution images are recovered. the algorithm has lower computational cost than the alternatives. experimental evaluation verified the usefulness of the algorithm.
resolution enhancement by adaboost. this paper proposes a learning scheme based still image super-resolution reconstruction algorithm. super-resolution reconstruction is proposed as a binary classification problem and can be solved by conditional class probability estimation. assuming the probability takes the form of additive logistic regression function, adaboost algorithm is used to predict the probability. experiments on face images validate the algorithm.
regularized image restoration based on adaptively selecting parameter and operator. regularization has been widely used in image restoration. however, selection of the regularization parameter and the regularization operator is not solved completely and is still the main difficulty for adaptively regularized image restoration. this paper presents a new approach to select the local regularization parameter and the local regularization operator. the local regularization parameter is selected according to the distribution of local noise value in degraded images, and the local regularization operator is selected according to anisotropic properties. experiment results show that the proposed methods work well in the presence of many types noise.
fuzzy directional element energy feature (fdeef) based palmprint identification. palmprint is a novel biometric method to identify a person. generally, there are two types of features in palmprint, i.e. structural features and statistical features. structural features, such as lines, can characterize a palm exactly, but are difficult to be extracted and represented.contrarily, statistical features can be extracted and represented easily, but are unable to reflect the structural information of a palmprint. the fact that the principal features of both chinese character and palmprint are lines motivates us to try some methods of chinese character recognition to identify palmprint. in this paper, we use the idea of an efficient chinese character recognition method, directional element feature (def), to define a novel palm-print feature, named fuzzy directional element energy feature (fdeef) which is a statistical feature containing some line structural information about palmprints. it can be extracted and represented easily and, at the same time, has a strong ability to distinguish palms. two other low-dimensional features: global fuzzy directional element energy feature (gfdeef) and block edge energy feature (beef) are also derived from fdeef in this paper. the experimental results demonstrate the power of this method.
palmprint recognition using directional line energy feature. palm-lines, including the principal lines and wrinkles, can describe a palmprint clearly. this paper presents a novel approach of line feature extraction for palmprint recognition called the directional line energy feature (dlef). the directional lines in different directions are first extracted using a set of directional line detectors. then each directional line magnitude image is divided into several overlapped small grids and the magnitudes of the line points in these grids are used to compute the dlef. a template-matching method based on euclidean distance is adopted to measure the similarity of two dlefs. best results have been obtained when dlefs with 6 different directions were employed. accuracies of 97.92% and 97.5% are obtained by using the proposed approach in one-against-one matching and one-against-320 matching, respectively.
optimal gabor filters for high speed face identification. this paper describes a fast face identification method with gabor filters. two efforts are made to achieve the acceptable processing speed: 1) we design the optimal gabor filters with the arrangement theory that uses a few directions and layers. 2) the transformation with gabor filters (as called gabor transformation) is only done over the regions around the facial feature points, not the whole input image. the facial feature points extraction is performed by detecting the facial organ regions with color information and edge information, followed by the corner detection in each detected facial organ region with the susan operator.
relevant linear feature extraction using side-information and unlabeled data. "learning with side-information" is attracting more and more attention in machine learning problems. in this paper, we propose a general iterative framework for relevant linear feature extraction. it efficiently utilizes both the side-information and unlabeled data to enhance gradually algorithms' performance and robustness. both good relevant feature extraction and reasonable similarity matrix estima-tion can be realized. specifically, we adopt relevant component analysis (rca) under this framework and get the derived iterative self-enhanced relevant component analysis (iserca) algorithm. the experimental results on several data sets show that iserca outperforms rca.
a neural network approach for hand gesture recognition in virtual reality driving training system of spg. the recognition of hand gestures is a challenging task for the high degrees of freedom of hand motion. we develop a virtual reality based driving training system of self-propelled gun (spg). for this system, a dataglove with 18 sensors is employed to perform some driving tasks such as pressing switches, manipulating steering wheel, changing gears, etc. to accomplish these tasks, some hand gestures must be defined from the dataglove sensors data. a feedforward neural network can represent an arbitrary functional mapping so it is possible to map raw data directly to the required hand gestures. this paper uses bp neural network to recognize the hand patterns which exist in the raw sensor data of the dataglove. a pattern set of 300 hand gestures is used to train and test the neural network. the recognition system achieves good performance. it can be effectively used in our virtual reality training system of spg to perform various manipulating tasks in a more fast, precise, and natural way.
topological segmentation of discrete human body shapes in various postures based on geodesic distance. this paper extends our previous reeb graph approach [a discrete reeb graph approach to the segmentation of human body scans] based on a new morse function, namely geodesic distance, to segment whole body scan data into primarybody parts in various postures. because of the bending invariance of geodesic distance, the resulting reeb graph can remain stable in a large range of postures. consequently, the approach is capable of segmenting data within the posture range. the application of geodesic distance also brings the independence of coordinate frame selection. we present a number of experiments conducted on both real body 3d scan samples and simulated datasets to demonstrate the validity of the approach.
a multi-object tracking system for surveillance video analysis. in this paper we present a novel and robust clustering based multi-object tracking system for surveillance video analysis. it is designed to extract the trajectory data of vehicles in crowded traffic scenes and can be extended to other applications of surveillance and sports video analysis. in our system, a fast accurate fuzzy clustering algorithm is employed, and the feature space is constructed by extracting the position, color and velocity information of foreground pixels. by using growing and predictive adaptation, fixed linkages are expected between meaningful targets and corresponding active cluster centroids. in this way the motion classifier and tracker are combined seamlessly. experimental results suggest the efficiency and robustness of the proposed method with severe occlusions and clutter effect.
speech animation using coupled hidden markov models. we present a novel speech animation approach using coupled hidden markov models (chmms). different from the conventional hmms that use a single state chain to model the audio-visual speech with tight inter-modal synchronization, we use the chmms to model the asynchrony, different discriminative abilities, and temporal coupling between the audio speech and the visual speech, which are important factors for animations looking natural. based on the audio-visual chmms, visual animation parameters are predicted from audio through an em-based audio to visual conversion algorithm. experiments on the jewel av database show that compared with the conventional hmms, the chmms can output visual parameters that are much closer to the actual ones. explicit modelling of audio-visual speech is promising in speech animation.
improved two-stage wiener filter for robust speaker identification. in order to solve the problem of robustness in textindependent speaker identification, two-stage wiener filter presented in the etsi standard is used in this paper. however, the performance of the voice activity detection (vad) block in the standard is not so satisfying under lower snr. thus constrainedclustering vector quantization is presented to improve the block in the wiener filter method. experiments based on the msra's mandarin speech corpora show the good robustness of the improved method to several common additive noises.
bootstrap methods for reject rules of fisher lda. when there are uncertainties in pattern recognition it may be better to introduce a rejection option to reduce the total costs, and two rules have been proposed: chow's rule based on posterior probabilities and tortorella's rule based on roc curves. however, both have shortcomings for the application in practice: first, it is extremely difficult to obtain the exact posterior probability for each example to be recognized; second, for small data size, the associated roc curves may have very little number of convex points, resulting in the ineffectiveness of tortorella's rule. this paper proposes a new bootstrap algorithm for obtaining sampling distributions of test example scores produced by fisher lda. these distributions can not only convert the scores into posterior probabilities but also generate a roc curve with a lot of convex points. thus, this bootstrap method can improve the effectiveness of chow's rule and tortorella's rule in the real applications.
ndft-based audio watermarking scheme with high security. uniform discrete fourier transform (dft) has the drawback of public frequency points. on the other hand, nonuniform discrete fourier transform (ndft) could set up random sampling points in frequency domain as desired rather than fixed frequency points in dft. that is dft is the special condition of ndft. this paper utilizes ndft to instead uniform dft in the algorithm of audio watermarking scheme with the purpose of providing the probability of hidden embedding positions. moreover, to further improve the systematic security, in our method, we use coupledchaotic sequence to randomly select the ndft-domain frequency points used for embedding watermark, which overcomes the flaws brought by single chaotic map which is finite word length effect and vulnerable to repeated group attack. good experimental results have shown the proposed scheme possesses higher systematic security than dft-based watermarking scheme.
a multibit geometrically robust image watermark based on zernike moments. in image watermarking, the watermark robustness to geometric transformations is still an open problem. using invariant image features to carry the watermark is an effective approach to addressing this problem. in this paper, a multibit geometrically robust image watermarking algorithm using zernike moments is proposed. some zernike moments of an image are seleted, and their magnitudes are dither-modulated to embed an array of bits. the watermarked image is obtained via reconstruction from the modified moments and those left intact. in watermark extraction, the embedded bits are estimated from the invariant magnitudes of the zernike moments using a minimum distance decoder. simulation results show that the hidden message can be decoded at low error rates, robust against image rotation, scaling and flipping, and as well, a variety of other distortions such as lossy compression.
statistical landscape features for texture classification. this paper proposes the use of information derived from the graph of a texture image function for texture description. the graph of an image function is a rumpled surface in the three-dimensional space that appears like a landscape. four novel texture feature curves are used to characterize the texture. this method is named statistical landscape features (slf). slf achieves a very high correct classification rate of 94.53% on the entire brodatz set. besides the very good performance, another remarkable advantage of the proposed method is that it has no parameter to tune.
multiscale blob features for gray scale, rotation and spatial scale invariant texture classification. this paper proposes to apply a series of flexible threshold planes to the textured image and then use the topological and geometrical attributes of the blobs in the obtained binary images to describe image texture. the proposed multiscale blob features (mbf) is invariant to linear gray-level scaling and rotation, and is insensitive to uniform spatial scaling. the experiment results show that mbf offers very low error rate on the entire brodatz texture database, and confirm its invariance properties.
delta-mse dissimilarity in suboptimal k-means clustering. k-means clustering is a well-known partition-based technique in unsupervised learning to construct pattern models. the main difficulty, however, is that its performance is highly susceptible to the initialized partition. to attack this problem, a suboptimal k-means algorithm is briefly reviewed by applying dynamic programming over the principal component direction. in particular, a heuristic clustering dissimilarity, the delta-mse function, is incorporated into the suboptimal k-means algorithm. the delta-mse function is derived by calculating the difference of within-class variance before and after moving a given data sample from one cluster to another. experimental results show that the suboptimal k-means algorithm that uses the delta-mse dissimilarity generally outperforms the original l¿ distance based suboptimal algorithm and a specific kd-tree clustering algorithm.
segmentation and recognition of vocalized outlines in pitman shorthand. there is a wish to be able to enter text into mobile computing devices at the speed of speech. only handwritten shorthand schemes can achieve this data recording rate. a novel approach to the recognition of vocalized outlines in pitmanýs shorthand is proposed in this paper. in this approach, the recognition process is divided into two steps. the consonant outline is first recognized by a two-stage (segmentation and classification) approach. afterwards surrounding vowel and diphthong symbols are classified and their positions in relation to an associated consonant stroke are determined. promising results of over 70% recognition accuracy have been obtained.
high accuracy classification of eeg signal. improving classification accuracy is a key issue to advancing brain computer interface (bci) research from laboratory to real world applications. this article presents a high accuracy eeg signal classification method using single trial eeg signal to detect left and right finger movement. we apply an optimal temporal filter to remove irrelevant signal and subsequently extract key features from spatial patterns of eeg signal to perform classification. specifically, the proposed method transforms the original eeg signal into a spatial pattern and applies the rbf feature selection method to generate robust feature. classification is performed by the svm and our experimental result shows that the classification accuracy of the proposed method reaches 90% as compared to the current reported best accuracy of 84%.
active learning based pedestrian detection in real scenes. this work presents an active learning based method for pedestrian detection in complicated real-world scenes. through analyzing the distribution of all positive and negative samples under every possible feature, a highly efficient weak classifier selection method is presented. moreover, a novel boosting architecture is given to get satisfied false positive rate (fpr) and false negative rate (fnr) with few weak classifiers. a unique characteristic of the algorithm is its ability to train special cascade classifier dynamically for each individual scene. the benefit is that the trained classifier will only focus on the differences between the positive samples and the limited negative samples of each individual scene, thus greatly reduce the complexity of classification and achieve robust detection result even with few classifiers. a real-time pedestrian detection system is developed based on the proposed algorithm. the system produces fast and robust detection results as demonstrated by extensive experiments which use video sequences under different environments.
complete two-dimensional pca for face recognition. we propose a novel method, the complete twodimensional principal component analysis (complete 2dpca), for image features extraction. compared to the original 2dpca, complete 2dpca not only gain a higher recognition rate, but also reduce the feature coefficients needed for face recognition. complete 2dpca is based on 2d image matrices. two image covariance matrices are constructed directly using the original image matrix and theirs eigenvectors are derived for image feature extraction. our experiments were performed on orl face database, and experimental results show that the proposed method has an encouraging performance.
comparison of structural variables with spatio-temporal variables concerning the identifiability of okuri class and player in japanese traditional dancing. this paper experimentally examined the characteristics of structural variables and spatio-temporal variables through the identifiability of okuri classes and players( dancers) in traditional japanese dancing. the data for the experiment were acquired using a motion capture system, which monitored dancing motions performed by a master player (dancer) followed by two expert dancers and three beginners. the discrimination of two okuri classes, feminine okuri and descriptive okuri, and that of 5 dancers was experimentally attempted using structural variables and spatio-temporal variables. the experimental result suggested that spatio-temporal variables were more advantageous than structural variables for identification of okuri classes and dancers.
3-d affine moment invariants generated by geometric primitives. 3-d affine moment invariants are derived in a convenient way in this paper. the property of volume of a tetrahedron is studied first under affine transformation. 3-d affine moment invariants are constructed then by the multiple integrals of the combinations of this kind of geometric primitive. numerical experiments of deformed models are conducted to certificate the invariance of the new 3-d affine moment invariants given in this paper.
robust clustering based on winner-population markov chain. in this paper, we propose an unsupervised genetic clustering algorithm, which produces a new chromosome without any conventional genetic operators, and instead according to the gene reproducing probabilities determined by markov chain modeling. selection of cluster centers from the dataset enables construction of a look-up table that saves the distances between all pairs of data points. the experimental results show that the proposed algorithm not only solves the premature problem to provide a more stable clustering performance in terms of number of clusters and clustering results, but also improves the time efficiency.
face recognition by expression-driven sketch graph matching. we present a novel face recognition method using automatically extracted sketch by a multi-layer grammatical face model. first, the observed face is parsed into a 3- layer (face, parts and sketch) graph. in the sketch layer, the nodes not only capture the local features (strength, orientation and profile of the edge), but also remember the global information inherited from the upper layers (i.e. the facial part they belong to and status of the part). next, a sketch graph matching is performed between the parsed graph and a pre-built reference graph database, in which each individual has a parsed sketch graph. similar to the other successful edge-based methods in the literature, the use of sketch increases the robustness of recognition under varying lighting conditions. furthermore, with high-level semantic understanding of the face, we are able to perform an intelligent recognition process driven by the status of the face, i.e. changes in expressions and poses. as shown in the experiment, our method overcomes the significant drop in accuracy under expression changes suffered by other edge-based methods.