waashk/dblp huggingface dataset:
a new off-line signature verification method based on graph. a graph matching approach to off-line signature verification is presented. each of the signature images being compared is represented as a point set, which includes the local extremas of various types along the signature contours. graph matching involves a deformation measure and a mapping function between point sets. multiresolution signature features, as computed by gradient, structural and concavity features, are used to measure local correspondence. deformation and similarity scores are combined to make the final decision. the method performs better than previous off-line methods and is comparable to that of on-line systems.
model based object recognition by robust information fusion. given a set of 3d model features and their 2d image, model based object recognition determines the correspondences between those features and hence computes the pose of the object. to achieve good recognition results, a novel approach based on robust information fusion is put forward in this paper. in this algorithm, the property of probabilistic peaking effect is employed to generate sets of hypothesized matches between model and image points. the correct hypotheses are obtained by searching for clusters among projections of predefined 3d reference points using the pose implied by each hypothesis. to assure the robustness of clustering, a new data fusion technique that is based on the nonparametric mode search method, mean shift, is proposed. the uncertainty information of the hypotheses is also incorporated into the fusion process to adaptively determine the bandwidth of the mean shift procedure. experimental results demonstrating the satisfactory performance of this algorithm are presented.
modification of the adaboost-based detector for partially occluded faces. while face detection seems a solved problem under general conditions, most state-of-the-art systems degrade rapidly when faces are partially occluded by other objects. this paper presents a solution to detect partially occluded faces by reasonably modifying the adaboost-based face detector. our basic idea is that the weak classifiers in the adaboost-based face detector, each corresponding to a haar-like feature, are inherently a patch-based model. therefore, one can divide the whole face region into multiple patches, and map those weak classifiers to the patches. the weak classifiers belonging to each patch are re-formed to be a new classifier to determine if it is a valid face patch-without occlusion. finally, we combine all of the valid face patches by assigning the patches with different weights to make the final decision whether the input subwindow is a face. the experimental results show that the proposed method is promising for the detection of occluded faces.
simultaneous segmentation and registration for functional mr images. a simultaneous segmentation and registration model is proposed for functional mr (fmr) image alignment that is significant for minimizing motion artifacts on fmri data analysis. due to t_2^* weighted signal loss and decreased resolution, in fmr images, the images can't be aligned reliably by only using image information. our approach uses the shape of a contour pre-segmented in a high resolution image to find the unknown contour in each of the time series images, and the spatial transform that maps the interface to the given contour. this is achieved by minimizing an energy functional depending on the information of the image gradient and the shape of interest, so that the boundary of the object can be captured either by higher gradient or by the prior knowledge of its shape. in the meantime, the registration is achieved by the transformation determined in shape matching. the model has been tested both on synthetic data and fmr brain image data. the experimental results showed the effectiveness of this model in feature determination and time series image registration. the existence of the solution to the proposed model is also discussed.
texture segmentation using independent component analysis of gabor features. this paper proposes a novel method for texture segmentation using independent component analysis (ica) of gabor features (called icag). it has three distinguished aspects. (1) gabor wavelets transformation first produces distinct textural features characterized by spatial locality, scale and orientation selectivity. (2) principal component analysis (pca) then reduces the dimensionality of these features and ica finally derives independent features for texture segmentation. (3) two different frameworks for ica are discussed. framework i regards pixels as random variables and represents them as a column vector by re-shaping all the transformed images row-by-row, while framework ii treats the statistical features, viz. the mean and standard deviation of image, as random variables. the statistical features of all the transformed images construct a column vector. comparative experiment results among icag, gabor wavelets and ica indicate that icag provides the best performance and framework ii is more efficient and applicable for texture segmentation.
isomap based on the image euclidean distance. scientists find that the human perception is based on the similarity on the manifold of data set. isometric feature mapping (isomap) is one of the representative techniques of manifold. it is intuitive, well understood and produces reasonable mapping results. however, if the input data for manifold learning are corrupted with noises, the isomap algorithm is topologically unstable. in this paper, we present an improved manifold learning method when the input data are imagesthe image euclidean distance based isomap (imisomap), in which we use a new distance for images called image euclidean distance (imed). experimental results demonstrate a consistent performance improvement of the algorithm imisomap over the traditional isomap based on euclidean distance.
blind image steganalysis based on statistical analysis of empirical matrix. in this paper, a novel steganalysis method based on statistical analysis of empirical matrix (em) is proposed to detect the presence of hidden message in an image. the projection histogram ..ph.. of em is used to extract features composed of two parts: the moments of ph and the moments of the characteristic function of ph. also, features extracted from prediction-error image [7] are included to enhance performance. svm is utilized as classifier. a test database is constructed, based on which a detailed test for different categories of features and a comparison with methods in prior arts are conducted. experiments show that the features we proposed are more effective than prior arts and our steganalysis method could blindly detect the presence of data hiding for various embedding schemes with high performance.
exploiting high dimensional video features using layered gaussian mixture models. analysis of video data usually requires training classifiers in high dimensional feature spaces. this paper proposes a layered gaussian mixture model (lgmm) to exploit high dimensional features for classifying various shots in video. lgmm decomposes a high dimensional feature space by building a pyramid structure and estimating the distribution of local partitions in each layer using gaussian mixtures from the bottom of the pyramid to the top. we reduce the dimension of features in each local region at a lower layer by projecting them onto the estimated gaussian components. these projected feature vectors are then used to estimate the gaussian mixture models at a upper layer. the final dimension of the feature is adjustable by choosing the number of gaussians at the top layer of the pyramid. we demonstrate the proposed method using motion features to classify video shots. the proposed method is independent from low level features and can be extended to other classification tasks.
a new adaptive diffusion equation for image noise removal and feature preservation. anisotropic diffusion can remove noise to some extent in image processing. however the contradiction between diffusion and preservation still exists. in this paper, a new nonlinear diffusion model for image noise removal and feature preservation is presented. this model treats inhomogeneity region and image feature adaptively by discontinuity measure and local gradient information. a well balance between diffusion and preservation is also made in this new diffusion method. experiments results show that the proposed method has high performance compared to other literature methods and is an ideal edge-preserving filtering method. in addition, we use block-based noise estimation to estimate deviation in diffusion equation.
inter-subspace distance: a new method for face recognition with multiple samples. in this paper, we develop a systematic method that can cope with multiple images simultaneously for face recognition. the proposed method, referred to as inter-subspace distance, employs the minimal distance between the two subspaces formed by training and test images, respectively. the advantages of our method are that it can use temporal information (image sequences) and multiple sampling (in scales or spatial positions) for face recognition. in addition, our method can ease the burdens of face detection by dealing with inaccuracies of positions and scales of the detected faces.
edge detection and texture segmentation based on independent component analysis. in this paper, we present a new feature extraction technique based on independent component analysis (ica). we use ica to learn the basis functions of natural images and then the basis functions are used as pattern templates for feature detections. the succesful applications of the proposed method to edge detection and texture segmentation are demonstrated.
deformable model based data compression for gesture recognition. we aim at recognizing a set of dance gestures from contemporary ballet. our input data are motion trajectories followed by the joints of a dancing body provided by a motion-capture system. it is obvious that direct use of the original signals is unreliable and expensive. therefore, we propose a suitable tool for nonuniform sub-sampling of spatio-temporal signals. the key of our approach is the use of a deformable model to provide a compact and efficient representation of motion trajectories.
a complementary ordering method for class imbalanced problem. we propose a method called asymmetric bagging with vector complementary ordering (abvco) to handle the class imbalanced problem. the proposed approach is not dependent on artificially generated data or re-weighted data. it can avoid the synthesis of classifiers that favor the majority class. the method is applied to multi-modal biometric authentication for further raising the fusion classifier performances.
periodic human motion description for sports video databases. many different visual features can be used for analysis and annotation of sports video material. here we present a periodic motion feature descriptor that can discriminate between different sports types that contain periodic motion. the experimental results, using video material from the 1992 barcelona olympic games, show that the proposed periodic motion descriptor can successfully classify four sports types: sprint, long-distance running, hurdling and canoeing.
bit-pairing codification for binary pattern projection system. in a previous work, we proposed a new binary-light projection mechanism that had a much reduced system size that made it particularly suitable for 3d shape inspection of semiconductor products. the inspection speed of the mechanism was governed by the number of required images which also equaled the number of shiftings of the grating. in this paper we address how inspection speed could be gained, i.e., how the number of required images could be reduced, by the incorporation of two neighboring bits in the codification of each scene element. we provide an optimal design of such a codification strategy. a solution to the shifting strategy optimization is also proposed that is applicable to any given binary patterns. theoretical analysis and real image experiments are presented to illustrate the workability of the solutions.
a trainable hierarchical hidden markov tree model for color image annotation. in this paper we consider how to annotate or label regions of grey-level or multispectral images based upon known labels and a set of interacting hierarchical doubly stochastic processes. the proposed model extends current work on the use of hierarchical markovian models for image processing using multiscale representations. in this paper we explore a new objective up-down algorithm whereby the spatio-spectral context of specific image region signatures are encoded via different types of trainable support kernels for the upward and downward operations.
a prototypes-embedded genetic k-means algorithm. this paper presents a genetic algorithm (ga) for kmeans clustering. instead of the widely applied stringof- group-numbers encoding, we encode the prototypes of the clusters into the chromosomes. the crossover operator is designed to exchange prototypes between two chromosomes. the one-step k-means algorithm is used as the mutation operator. hence, the proposed ga is called the prototypes-embedded genetic k-means algorithm (pgka). with the inherent evolution process of evolutionary algorithms, pgka has superior performance than the classical k-means algorithm, while comparing to other ga-based approaches, pgka is more efficient and suitable for large scale data sets.
texture classification using kernel independent component analysi. we propose a novel method, kernel independent component analysis (kica), for texture features extraction. the texture images are first mapped into a higher-dimensional implicit feature space. then a set of nonlinear basis functions are learned using kica. the feature vectors are obtained by projected the texture images onto the basis functions. comparison experiments between kica and the other two classic methods: gabor filters and ica, are performed. the results indicate that the kica is an efficient approach for texture classification.
fingerprint enhancement with dyadic scale-space. fingerprint enhancement is a critical step in fingerprint identification. most of the existing enhancement uses a set of contextual filters to enhance fingerprint. the main drawback of these methods is these contextual filters based on the local information of the fingerprint, such as ridge width, orientation, curvature et al. these information are unreliable in the areas corrupted by the noise. this paper introduces the scale space theory in the computer vision to enhance the fingerprint. in the enhancement process, decompose fingerprint into a series of images and organize the images by finer to coarser scheme. thus a globe and integrate interpretation is available and it enable us to get rid of the influence of noise to the largest extent. experiments show our algorithm is fast and has excellent performance.
multi-view sampling for relevance feedback in image retrieval. labelling is a boring task for users in relevance feedback. how to maximumly reduce the labelling is crucial for relevance feedback algorithms. in spirited by active learning and co-testing, we proposed a co-svm algorithm to improve the efficiency and effectiveness of selective sampling in image retrieval. in co-svm, color and texture are looked as sufficient and uncorrelated views of an image. svm classifier is learned in color and texture feature subspaces, respectively. then the two classifiers are used to classify the unlabelled data. these unlabelled samples that disagree in the two classifiers are chose to label. the experimental results show that the proposed algorithm is beneficial to image retrieval.
boosted gabor features applied to vehicle detection. robust vehicle detection is a challenging task given vehicles with different types, and sizes, and at different distances. this paper proposes a boosted gabor features (bgf) approach for vehicle detection. the two main conventional gabor filter design approaches are a filter bank design approach with fixed parameters even for different applications and a learning approach. in contrast, the parameters of our boosted gabor filters, learned from examples, differ from application to application. moreover, our boosted approach optimizes the filter parameters for every image sub-window, and the boosted filters have a large response for sub-windows containing a part of a vehicle resulting in a greatly improved performance in vehicle detection. our vehicle detection has two basic phases in which we build a multi-resolution hypothesis-validation structure. in the vehicle hypothesis generation phase, hypothesis lists are generated for three rois with different resolutions using horizontal and vertical edges ,and following that, a hypothesis list for the whole image is obtained by combining these three lists. in the subsequent hypothesis validation phase, we validate the vehicle hypothesis list by inputting the boosted gabor feature vector into the support vector machine. in the context of vehicle detection, the resulting system yields detection rates comparable to the best previous systems while achieving a 20 frames per second real-time performance on a pentium(r)4 cpu 2.4ghz.
perceptual distance normalization for appearance detection. in this paper we develop a novel contrast-invariant appearance detection model. the goal is to classify object-specific images (e.g. face images) from generic background patches. the novel contribution of this paper is the design of a perceptual distortion measure for comparing the appearance of an object to its reconstruction from the principal subspace. we demonstrate our approach on two different datasets: separating eyes from non-eyes and classifying faces from non-faces. on the eye database, for a true detection rate of 95% we demonstrate a nine-fold improvement in the false positive rates over a previously reported detection model [robust contrast-invariant eigendetection]. we also compare our detector model with a svm classifier.
finding region correspondences for wide baseline stereo. this study addresses the problem of finding correspondences for wide baseline stereo. texture has traditionally been utilised as a single-image cue for 3d shape reconstruction (shape-from-texture); at the same time, its role in multiview scene reconstruction has been very limited. in stereo image matching, repetitive patterns are usually considered as disturbing factor since they tend to produce multiple peaks of correlation, which results in matching ambiguity. we argue that presence and proper analysis of distinct, compact periodic texture areas can facilitate wide baseline matching by providing periodic distinguished regions (pdrs) that efficiently constrain the search for correspondences. we demonstrate how pdrs can be used to find a few initial correspondences in a wide baseline stereo pair and to establish precise correspondences for building the epipolar geometry. experimental results for various wide baseline stereo pairs are shown.
the trimmed iterative closest point algorithm. the problem of geometric alignment of two roughly preregistered, partially overlapping, rigid, noisy 3d point sets is considered. a new natural and simple, robustified extension of the popular iterative closest point (icp) algorithm [1] is presented, called the trimmed icp (tricp). the new algorithm is based on the consistent use of the least trimmed squares (lts) approach in all phases of the operation. convergence is proved and an efficient implementation is discussed. tricp is fast, applicable to overlaps under 50%, robust to erroneous measurements and shape defects, and has easy-to-set parameters. icp is a specialcase of tricp when the overlap parameter is 100%. results of testing the new algorithm are shown.
a rival penalized em algorithm towards maximizing weighted likelihood for density mixture clustering with automatic model selection. how to determine the number of clusters is an intractable problem in clustering analysis. in this paper, we propose a new learning paradigm named maximum weighted likelihood (mwl), in which the weights are designable. accordingly, we develop a novel rival penalized expectation-maximization (rpem) algorithm, whose intrinsic rival penalization mechanism enables the redundant densities in the mixture to be gradually faded out during the learning. hence, the rpem can automatically select an appropriate number of densities in density mixture clustering. the experiments have shown the promising results.
semantic retrieval by spatial relationships. a novel spatial representation of image objects is presented for efficient image retrieval based on spatial relationships. using our new representation, fast indexing and matching of images by spatial relationships can be accomplished. our approach takes care of the special requirements for content-based image retrieval (cbir) as compared to existing spatial representation schemes. experiments demonstrated the feasibility of semantic spatial retrieval using the proposed spatial representation in cbir.
does eigenpalm work? a system and evaluation perspective. recently, there are keen interests in eigenpalm, which, collectively, refers to those methods that extract palmprint features directly from the appearance by means of principal component analysis (pca) for (dis)similarity matching. encouraging results have been reported with the use of eigenpalm. however, we find a different story under a system and evaluation perspective. in this paper, we would like to introduce three issues that should be considered: the effects of templates from two different sessions, the effects of identical twins and the effects of unseen subjects. they are missing in the previous studies of eigenpalm.
multiple objects tracking with multiple hypotheses graph representation. we present a novel multi-object tracking algorithm based on multiple hypotheses about the trajectories of the objects. our work is inspired by reid's multiple hypothesis tracking algorithm which is an optimal solution to the motion correspondence that occurs in multi-object tracking. unfortunately, the exponential growth of the hypotheses tree precludes practical applications. to restrict this growth, many approximations relying on a series of clustering and pruning operations have been proposed. the decisions for these operations are based solely on previous observations and are not guided by observations in later frames. we show that due to multiple splits and merges, relying solely on previous observations to guide these operations may inadvertently eliminate the correct hypothesis. consequently, this leads to poor tracking performance. to overcome this problem, we determine the validity of a hypothesis by exploiting information in later frames and relating them to previous observations. experimental results demonstrate the robustness and efficiency of our approach.
classification of line and character pixels on raster maps using discrete cosine transformation coefficients and support vector machine. raster maps are widely available on the internet. valuable information such as street lines and labels, however, are all hidden in the raster format. to utilize the information, it is important to recognize the line and character pixels for further processing. this paper presents a novel algorithm using 2-d discrete cosine transformation (dct) coefficients and support vector machines (svm) to classify the pixels of lines and characters on raster maps. the experiment results show that our algorithm achieves 98% precision and 85% recall in classifying the line pixels and 83% precision and 96% recall in classifying the character pixels on a variety of raster map sources.