waashk/dblp huggingface dataset:
context-sensitive bayesian classifiers and application to mouse pressure pattern classification. in this paper, we propose a new context-sensitive bayesian learning algorithm. by modeling the distributions of data locations by a mixture of gaussians, the new algorithm can utilize different classifier complexities for different contexts/locations and, at the same time, keep the optimality of bayesian solutions. this algorithm is also an online learning algorithm, efficient in training, and easy for incorporating new knowledge from data sets available in the future. we apply this algorithm to detecting computer-user mouse pressure patterns during episodes likely to be frustrating to the user. by modeling user identity as hidden context, this algorithm achieves on average 10.6% user-independent test error rate.
graph manifolds from spectral polynomials. gaph structures have proved computationally cumbersome for pattern analysis. the reason for this is that before graphs can be converted to pattern vectors, correspondences must be established between the nodes of structures which are potentially of different size. to overcome this problem, in this paper we turn to the spectral decomposition of the laplacian matrix. we show how the elements of the spectral matrix for the laplacian can be used to construct symmetric polynomials that are permutation invariants. the co-efficients of these polynomials can be used as graph-features which can be encoded in a vectorial manner. we explore whether the vectors of invariants canbe embedded in a low dimensional space using a number of alternative strategies including principal components analysis (pca), multidimensional scaling (mds) and locality preserving projection (lpp).
an efficient automatic redeye detection and correction algorithm. a fully automatic redeye detection and correction algorithm is presented to address the redeye artifacts in digital photos. the algorithm contains a redeye detection part and a correction part. the detection part is modeled as a feature based object detection problem. adaboost is used to simultaneously select features and train the classifier. a new feature set is designed to address the orientation-dependency problem associated with the haar-like features commonly used for object detection design. for each detected redeye, a correction algorithm is applied to do adaptive desaturation and darkening over the redeye region.
novel adaptive nearest neighbor classifiers based on hit-distance. in this paper, a novel idea of distance, hit-distance, was firstly introduced to generalize the representational capacity of available prototypes. novel adaptive nearest neighbor classifiers based on hit-distance were then proposed. experiments were performed on 8 benchmark datasets from the uci machine learning repository. it was shown that the proposed classifiers performed much better than the classical nearest neighbor classifier (nn) and the nearest feature line method (nfl), the nearest feature plane method (nfp), the nearest neighbor line method (nnl) and the nearest neighbor plane method (nnp).
self-calibration of a camera from video of a walking human. analysis of human activity from a video camera is simplified by the knowledge of the camera's intrinsic and extrinsic parameters. we describe a technique to estimate such parameters from image observations without requiring measurements of scene objects. we first develop a general technique for calibration using vanishing points and vanishing line. we then describe a method for estimating the needed points and line by observing the motion of a human in thescene. experimental results, including error estimates, are presented.
semantic interpretation of object activities in a surveillance system. activity analysis and semantic interpretation of tracked targets in a dynamic image sequence has recently attracted more attentions in computer vision. in this paper, a framework for semantic interpretation of vehicle and pedestrian's behaviors is proposed for practical applications in visual traffic surveillance. the trajectories recorded in the visual tracking process are analyzed using dynamic clustering and classification on which high level semantic interpretation is based. experimental results are presented to illustrate the performance of the proposed algorithm.
face detection based on hierarchical support vector machines. this paper presents a method of detecting faces based on hierarchical support vector machines (svm). the hierarchical svm classifier is composed of a combination of linear svm (clsvm) and a nonlinear svm. in training stage, the nonlinear svm is trained under theconstraint of the clsvm to select more effective non-face samples. in detection stage, the clsvm is used to fast exclude most non-/aces in images and the nonlinear svm is used to verify possible face candidates further. experimental result on several databases demonstrates the feasibility of the method.
adaptive word style classification using a gaussian mixture model. in this paper, we present a new approach to detect bold and italic words in scanned documents. under the assumption that ocr results are available, features used for classification are selected automatically using feature selection. for each scanned page, a gaussian mixture model is constructed for characters with the same character code, and word styles are determined using a weighted majority vote. we applied this method to a variety of documents and compared the results with current commercial ocr software that provides style information. the experimental results show that our method performs better.
image and feature co-clustering. the visual appearance of an image is closely associated with its low-level features. identifying the set of features that best characterizes the image is useful for tasks such as content-based image indexing and retrieval. in this paper, we present a method which simultaneously models and clusters large sets of images and their low-level visual features. a computational energy function suited for co-clustering images and their features is first constructed and a hopfield model based stochastic algorithm is then developed for its optimization. we apply the method to cluster digital color photographs and present results to demonstrate its usefulness and effectiveness.
chaining planar homographies for fast and reliable 3d plane tracking. this paper addresses the problem of tracking a 3d plane over a sequence of images acquired by a free moving camera, a task that is of central importance to a wide variety of vision tasks. a feature-based method is proposed which given a triplet of consecutive images and a plane homography between the first two of them, estimates the homography induced by the same plane between the second and third images, without requiring the plane to be segmented from the rest of the scene. thus, the proposed method operates by "chaining" (i.e. propagating) across frames the imageto- image homographies due to some 3d plane. the chaining operation represents projective space using a "plane + parallax" decomposition, which permits the combination of constraints arising from all available point matches, regardless of whether they actually lie on the tracked 3d plane or not. experimental results are also provided.
sparse bayesian regression for head pose estimation. this paper presents a high performance ........ pose estimation system based on the newly-proposed sparse bayesian regression technique (relevance vector machine, rvm) and sparse representation of facial patterns. in our system, after localizing 20 key facial points, sparse features of these points are extracted to represent facial property, and then..rvm is utilized to learn the relation between the sparse representation and yaw and pitch angle. because rvm requires only a very few kernel functions, it can guarantee better generalization, faster speed and less memory in a practical implementation. to thoroughly evaluate the performance of our system, we compare it with conventional methods such as cca, kernel cca, svr on a large database; in experiments, we also investigate the influence of the facial points localization error on pose estimation by using manually labelled results and automatically localized results separately, and the influence of different features on pose estimation such as geometrical features and texture features. these experimental results demonstrate that our system can estimate face pose more accurately, robustly and fast than those based on conventional methods.
a graph-based approach to corner matching using mutual information as a local similarity measure. corner matching constitutes a fundamental vision problem that serves as a building block of several important applications. the common approach to dealing with this problem starts by ranking potential matches according to their affinity, which is assessed with the aid of window-based intensity similarity measures. then, actual matches are established by optimizing global criteria involving all potential matches. this paper puts forward a novel approach for solving the corner matching problem that uses mutual information as a window similarity measure, combined with graph matching techniques for determining a matching of corners that is globally optimal. experimental results illustrate the effectiveness of the approach.
using b-spline curves for hand recognition. the b-spline curve and surface provide an accurate tool to record object shape. we present a biometric identification system through hand geometry measurements by using b-spline curves. we use 4 b-spline curves to fit with fingers (except thumb) from a single hand image for a single person. then we store these 4 curves as well as other geometry measurements of the hand as the "signature" of that person into the database. by computing the differences between the curves from database hand images and the curves from the query hand image using the point projection method, we are able to verify/identify the person by locating the closest database hand image to the query hand image.
evaluation of tracking reliability metrics based on information theory and normalized correlation. the efficiency of three tracking reliability metrics based on information theory and normalized correlation is examined in this paper. the two information theory tools used for the metrics construction are the mutual information and the kullback-leibler distance. the metrics are applicable to any feature-based tracking scheme. in the context of this work they are applied for comparison purposes on an object tracking scheme using multiple feature point correspondences. experimental results have shown that the information theory based metrics perform better than the normalized correlation one.
pruning the vocabulary for better context recognition. language independent 'bag-of-words' representations are surprisingly effective for text classification. the representation is high dimensional though, containing many non-consistent words for text categorization. these non-consistent words result in reduced generalization performance of sub-sequent classifiers, e.g., from ill-posed principal component transformations. in this communication our aim is to study the effect of reducing the least relevant words from the bag-of-words representation. we consider a new approach, using neural network based sensitivity maps and information gainfor determination of term relevancy, when pruning the vocabularies. with reduced vocabularies documents are classified using a latent semantic indexing representation and a probabilistic neural network classifier. reducing the bag-of-words vocabularies with 90%-98%, we find consistent classification improvement using two mid size data-sets. we also study the applicability of information gain and sensitivity maps for automated keyword generation.
joint distributions based on dfb and gaussian mixtures for evaluation of style similarity among paintings. in this paper, we studies the ability of joint statistical information of directional subbands in evaluating style similarity among chinese ink paintings by employing the gaussian mixture models with different mixture components. the optimal number of mixture components can be automatically learned from the training features by pruning the mixture models. two types of gaussian mixture models are built on two different sets of features: one is based on the high-order statistical moments of directional subbands; the other one is based on the parameters of generalized gaussian density (ggd) of the marginal distributions of directional subbands. the experimental results show that the accuracy of the model based on the parameters of ggd is better than that of the model based on the high-order statistical moments.
fast image retrieval based on equal-average equal-variance k-nearest neighbour search. this paper presents two fast schemes to speed up the retrieval process for conventional content-based image retrieval systems. the traditional features such as color and invariant histograms are extracted offline from each image to compose a feature vector. all these feature vectors construct the feature database. then the system performs the online retrieval based on this database as soon as possible. in the case of a small number of returned images, an equal-average equalvariance k nearest neighbour search (eeknns) method is used to speed up the retrieval process. in the case of a large number of returned images, an iterative eeknns (ieeknns) method is given. experimental results show that the proposed retrieval methods can largely accelerate the retrieval process while guaranteeing the same recall and precision.
shapes modeling of 3-d objects based on a hybrid representation using extended b-spline surface model. this paper presents a method for shapes modeling of 3-d objects using a b-spline surface model based on a projection expression. the surface model is locally defined on each patch that is effectively arranged depending on object shapes. in order to assure the continuity of the resultant surface, a nurbs (non-uniform rational b-splines) surface with embedding functions is employed. consequently, this model can be considered as a hybrid model that is consist of polygon patches and nurbs surfaces. in order to estimate the surface function, a regularization problem is solved by an iterative algorithm. in the algorithm, since the domainswhere the surface has to be estimated are restricted to the only error patches, the computation time can be reduced. howevel; the accuracy of estimation is never inferior sincea subdivision operation ofnurbs is employedfor the other domains.
accurate 3d scanning of swaying human body parts by one projection based on oimp technique. three-dimensional (3d) shape measurement based on spatial pattern projection (coded structured light projection) technique has been used in practical applications. however, multiple pattern projection is required for the measurement, and so application to a swaying human body part is difficult. a new technique for measuring the 3d topography of swaying human body parts or a human face using optimal intensity- modulation projection (oimp) technique is presented. since the proposed technique can correctly detect the stripe order of the projected pattern through a single pattern projection and double image capture, swaying human body parts or a human face can be measured with an accuracy of approximately 0.5 mm.
practical 3-d shape measurement using optimal intensity-modulated projection and intensity-phase analysis techniques. in the field of 3-d image measurement, a technique based on stripe pattern projection and observation image intensity analysis is expected to be able to detect several stripes by a single projection. it is necessary to increase the stripe number of the projection pattern in order to improve the measurement accuracy of depth distance. however, when the stripe number is increased, the difference of the intensity between stripes will be reduced and stripe detection will become difficult. in order to improve the detection accuracy of the stripe order and shorten the 3-d measurement time, we use the optimal intensity-modulation projection (oimp) technique, and in order to improve the depth distance measurement accuracy, we propose an intensity-phase analysis (ipa) technique. in the proposed ipa technique, the observation pattern be segmented by the intensity of the intensity-modulated stripe, and in every segmentation the depth distance of all pixels are obtained by phase analysis. by using a combination of the oimp and ipa techniques, high-speed (single projection and double image captures) and high-accuracy (error less than 0.1%) practical 3-d shape measurement can be realized.
robust head pose estimation using lgbp. in this paper, we introduce a novel discriminative feature which is efficient for pose estimation. the multi-view face representation is based on local gabor binary patterns( lgbp) and encodes the local facial characteristics in to a compact feature histogram. in lgbp, gabor filters can extract the feature of the orientation of head and local binary pattern(lbp) can extract the features of facial local orientation. to keep the spatial information of the multi-view face images, lgbp is operated on many subregions of the images. the combination of them can represent well and truly the multi-view face images. considering the derived feature space, a radial basis function(rbf) kernel svm classifier is trained to estimate pose. extensive experiments demonstrate that the facial representation can be effective for pose estimation. is a face. the experimental results show that the proposed method is promising for the detection of occluded faces.
three-dimensional model based face recognition. the performance of face recognition systems that use two-dimensional (2d) images is dependent on consistent conditions such as lighting, pose and facial expression. we are developing a multi-view face recognition system that utilizes three-dimensional (3d) information about the face to make the system more robust to these variations. this paper describes a procedure for constructing a database of 3d face models and matching this database to 2.5d face scans which are captured from different views, using coordinate system invariant properties of the facial surface. 2.5d is a simplified 3d (x, y, z) surface representation that contains at most one depth value (z direction) for every point in the (x, y) plane. a robust similarity metric is defined for matching, based on an iterative closest point (icp) registration process. results are given for matching a database of 18 3d face models with 113 2.5d face scans.
crack defect detection and localization using genetic-based inverse voting hough transform. in this paper we propose a genetic-based inverse voting hough transform (gbivht) method to detect buried crack defects in engineering structures. the method is applied to b-scan images obtained according to the ultrasonic time of flight diffraction technique. in these image representations of the ultrasound data, crack defects are characterized by multiple arcs of diffraction that can be approximated by a parabolic model. thus, the crack defect detection problem in non-destructive inspection of engineering structures is transformed into a parabola detection and localization on b-scan images. in the proposed gbivht method, the local peak detection problem of conventional ht is converted into a parameter optimization problem that operates directly on the b-scan images. the optimization task is done using the well-knowngenetic algorithms. our main goals are an accurate detection of the parabolas while circumventing the computational complexity and huge storage problem tied to conventional ht.
periodic nonlinear principal component neural networks for humanoid motion segmentation, generalization, and generation. in an experiment with a soccer playing robot, periodic temporally-constrained nonlinear principal component neural networks (nlpcnns) are shown to characterize humanoid motion effectively by exploiting fundamental sensorimotor relationships. each network learns a periodic or transitional trajectory in a phase space of possible actions, and thus abstracts a kind of protosymbol. nlpcnns can play a key role in a system that learns to imitate people, enabling a robot to recognize the behavior of others because it has grounded that behavior in terms of its own bodily movements.
camera calibration from two shadow trajectories. we introduce an efficient method for recovering the camera parameters automatically from the cast shadows of two 3d points observed over time. compared to previous related work, our method has less restrictions in the sense that object-to-shadow correspondences do not have to be available in the image. we demonstrate how the horizon line may be recovered from only shadow points, and how the camera intrinsic and extrinsic parameters are determined using the pole-polar relationship and minimizing the algebraic distance of the principal point. the approach is fully validated on both synthetic and real data, and tested against various sources of error. we finally present an application to metrology from shadows only - i.e. when the object is not visible in the image.
a graph decomposition approach to least squares attributed graph matching. in this paper, a graph decomposition model is combined with recent developments in least squares methods for matching attributed graphs. in particular, we show how this approach improves the robustness of graph matching and also reveals important structural similarities between subgraphs of target and model graphs.
segmentation of range data based on a stochastic clustering method with competitive process. in this paper, a stochastic clustering method with a competitive process is proposed to segment significantly the entire circumferential range data. the segmentation technique is utilized as the preprocessing of 3-d shape modeling so that the modeling can be more easily achieved for the object that has arbitrary topology, in which the data points are divided into the several subsets that represent the 3-d shapes of different quadric surfaces. the clustering method is implemented by evaluating a distance computed between each data point and each quadric surface. furthermore, it consists of creation and competitive processes in order to obtain the desirable clusters. consequently, since the only appropriate clusters are remaining, the segmentation can be achieved by assigning the data points to these clusters.
the bbn byblos japanese ocr system. the bbn byblos ocr system implements a script-independent methodology for ocr using hidden markov models (hmms). we have successfully ported the system to arabic, pashto, english, and chinese. in this paper, we discuss our recent effort in configuring the system to perform recognition of noisy machine printed japanese documents. the data for our experimentation was taken from the university of washington (uw-ii) japanese ocr corpus and the ldc japanese business news supplement corpus. we evaluated the performance of a whole-character configuration in which each character was modeled using a separate hmm. as in the case of our chinese ocr system [multilingual machine printed ocr], we also used a sub-character modeling approach[porting the bbn byblos ocr system to new languages] in which each japanese character was spelled using a shared set of automatically generated sub-characters. we experimentally evaluated the performance of different sub-character clusters as well as different hmm topologies to identify the best overall system configuration. on a fair test using noisy/degraded images from the uw-ii corpus, the best sub-character configuration resulted in a character error rate of 20.13%. on relatively cleaner data, consisting of scanned newspaper images, the system delivered an error rate of 7.85%. using a whole-character configuration the corresponding error rates were 11.94% and 4.55% respectively.