waashk/dblp huggingface dataset:
towards robust voxel-coloring: handling camera calibration errors and partial emptiness of surface voxels. in this paper, we present two new methods to reduce the effects of camera calibration errors and partial emptiness of surface voxels on voxel-coloring. both of these sources of error introduce outlier pixels in voxel projections in the input images and thus result in over-carving of the reconstructed 3d scene. the existing methods to handle these errors are either insufficient or too complex. our proposed methods are simple and can be incorporated into existing voxel-coloring algorithms easily. our experimental results show that the methods proposed in this paper have the ability to improve the results of existing algorithms.
modification table form generation system based on the form recognition. as there exist large number of both printed and electronical documents of table form, it is very important to provide their handling system. the system should provide capability of generating, modifying or filling in to the form only with logical manipulation. in this paper, we propose a system which can extract the structure of the form, and modify it logically without considering layout information, and finally generate modified form layout. the system is based on our table form analysis method and table form representation language (tfml). experimental results show that the system can generate practical layouts for the modified documents.
robust facet model for application to speckle noise removal. in this paper, a robust facet model is developed, and applied to speckle noise removal in syntehetic aperture radar (sar) images. the parameters of a facet model is usually estimated by a least-squares (ls) method under the gaussian assumption. in many applications, such as speckle removal in sar images, the noise process is not gaussian, and conventional estimators do not work. a robust estimation algorithm is developed, and applied to remove speckle noise in synthetic aperture images. conventional adaptive filtering approaches in speckle filtering smoothes the image selectively depending on the details of underlying textures, and tend to blur details after speckle removal. in the proposed approach, the image is assumed to be composed of structural and stochastic components, and the stochastic component is modeled by a robust facet model. the proposed method is applied to real synthetic aperture images to demonstrate the validity and effectiveness of the algorithm.
neural networks vs logistic regression: a comparative study on a large data set. neural networks and logistic regression have been among the most widely used ai technique in applications of pattern classification.much has been discussed about if there is any significant difference in between them but much less has been actually done with real-world applications data (large scale) to help settle this matter, with a few exceptions.this paper presents a performance comparison between these two techniques on the market application of credit risk assessment, making use of a large database from an outstanding credit bureau and financial institution (a sample of 180,000 examples).the comparison was carried out through a 30-fold stratified cross-validation process to define the confidence intervals for the performance evaluation. several metrics were applied both on the optimal decision point and along the continuous output domain.the statistical tests showed that multilayer perceptrons perform better than logistic regression at 95% confidence level, for all the metrics used.
detection of 3d-flow by characteristic of convex-concave and color. this paper describes a method for detecting 3dflow of an arbitrary point on a free-form surface from time series range images obtained by a stereovision system. the approach that positively uses surface shape data obtained from the range images is examined. especially, it proposes height-color- histogram (hch) that integrates local convexconcave shape and color texture information on the surface as feature value used for correspondence point search between frames. hch makes the feature of an object part integrate shape and color texture information. in this paper, first, the method of generating hch is explained, and effectiveness is considered. finally, it is shown that 3d-flows detection about an arbitrary point on free-form surface is possible by the proposal technique with quasi-realtime.
a system identification approach for video-based face recognition. the paper poses video-to-video face recognition as a dynamical system identification and classification problem. video-to-video means that both gallery and probe consists of videos. we model a moving face as a linear dynamical system whose appearance changes with pose. an autoregressive and moving average (arma) model is used to represent such a system. the choice of arma model is based on its ability to take care of the change in appearance while modeling the dynamics of pose, expression etc. recognition is performed using the concept of subspace angles to compute distances between probe and gallery video sequences. the results obtained are very promising given the extent of pose, expression and illumination variation in the video data used for experiments.
detection of faces of various directions in complex backgrounds. this paper describes the detection of faces in complex backgrounds where their sizes, positions and directions are arbitrary. we detect the faces by extracting face components such as eyes, a mouth and so on. we first extract face features and then calculate their likelihoods as each face component. second we detect the face features which satisfy geometrical relations of the face. in order to reduce the number of combinations of all labels for all features, we determine face candidate regions using generalized hough transform and then apply a relaxation method to each candidate region.
real-time localization in outdoor environments using stereo vision and inexpensive gps. we describe a real-time, low-cost system to localize a mobile robot in outdoor environments. our system relies on stereo vision to robustly estimate frame-to-frame motion in real time (also known as visual odometry). the motion estimation problem is formulated efficiently in the disparity space and results in accurate and robust estimates of the motion even for a small-baseline configuration. our system uses inertial measurements to fill in motion estimates when visual odometry fails. this incremental motion is then fused with a low-cost gps sensor using a kalman filter to prevent long-term drifts. experimental results are presented for outdoor localization in moderately sized environments (\geqslant 100 meters)
face set classification using maximally probable mutual modes. in this paper we consider face recognition from sets of face images and, in particular, recognition invariance to illumination. the main contribution is an algorithm based on the novel concept of maximally probable mutual modes (mmpm). specifically: (i) we discuss and derive a local manifold illumination invariant and (ii) show how the invariant naturally leads to a formulation of "common modes" of two face appearance distributions. recognition is then performed by finding the most probable mode, which is shown to be an eigenvalue problem. the effectiveness of the proposed method is demonstrated empirically on a challenging database containing the total of 700 video sequences of 100 individuals.
hmm-based human action recognition using multiview image sequences. in this paper, we present a novel method for human action recognition from any arbitrary view image sequence that uses the cartesian component of optical flow velocity and human body silhouette feature vector information. we use principal component analysis (pca) to reduce the higher dimensional silhouette feature space into lower dimensional feature space. the action region in an image frame represents q-dimensional optical flow feature vector and r-dimensional silhouette feature vector. we represent each action using a set of hidden markov models and we model each action for any viewing direction by using the combined (q + r)-dimensional features at any instant of time. we perform experiments of the proposed method by using ku gesture database and manually captured data. experimental results of different actions from any viewing direction are correctly classified by our method, which indicate the robustness of our view-independent method.
an efficient method to detect facial fiducial points for face recognition. in this paper a completely automatic face recognition system is presented. it consists of two main modules: in the first, the facial fiducial points are localized, and in the second the face is characterized applying a bank of gabor filters in correspondence to the found fiducial points. this method is an evolution of the one we have presented in [a face recognition system based on local feature analysis]: the fiducial point estimation is more efficient and self-correcting, and the face characterization modified.
colour-based model pruning for efficient arg object recognition. in this paper we address the problem of object recognition from 2d views. a new approach is proposed which combines the recognition systems based on attribute relational graph matching (arg) [2 ] and the multimodal neighbourhood signature (mns) [7 ] method. in the new system we use the mns method as a pre-matching stage to prune the number of model candidates. the arg method then identifies the best model among the candidates through a relaxation labelling process. the results of experiments show a considerable gain in the arg matching speed. interestingly, as a result of the reduction in the entropy of labelling by a virtue model pruning, the recognition rate for extreme object views also improves.
a face recognition system dealing with expression variant faces. in this paper we present the generalization of the automatic face recognition system, presented in [2], making it able to deal with different expressions and the presence of spectacles. the system behavior is measured on the frgc 1.0 and the xm2vts databases, addressing some of the current challenges highlighted by phillips and others [7]. we set up experiments having in the gallery, for each subject, both one still image and multi-still images with different expressions. besides, we investigate how the image resolution affects the performances and test the scalability of the system doubling the gallery size.
robust object segmentation using graph cut with object and background seed estimation. in this paper we propose a new robust way of extracting accurate human silhouettes indoors with an active stereo camera. we first infer the parts of object and background areas of high confidence by fusing color, stereo matching information and image segmentation methods. then the inferred areas(seeds) are incorporated in a graph cut. the experimental results were presented with image sequences taken with pan-tilt stereo camera. our proposed algorithms were evaluated with respect to the ground truth data. we proved that our algorithms can outperform other methods that are based on either color/contrast or stereo/contrast principles alone.
a new set of topology preserving removal operations in the 3d space. a new set of 3x3x3 topology preserving removal operations is introduced to compute the surface skeleton of a 3d object. we show that this set of operations can be appropriately employed in a parallel process withoutcreating disconnections, cavities, tunnels and vanishing of object components.
face recognition based on the appearance of local regions. recently, we proposed a novel facial representation for face recognition based on local binary pattern (lbp) features. we obtained excellent results when dividing the face images into several regions from which the lbp features are extracted and concatenated into an enhanced feature vector as a face descriptor. however, it was unclear whether the obtained results were due to the use of local regions (instead of a holistic approach) or to the discriminative power of lbp. in this work, we investigated this issue by adopting and comparing four different texture features when using the appearances of local regions. the experimental results clearly showed and confirmed the validity of using lbp for face description.
supervised nonparametric information theoretic classification. in this paper, supervised nonparametric information theoretic classification (itc) is introduced. its principle relies on the likelihood of a data sample of transmitting its class label to data points in its vicinity. itc's learning rule is linked to the concept of information potential and the approach is validated on ripley's data set. we show that itc may outperform classical classification algorithms, such as probabilistic neural networks and support vector machines.
the new focal point localization algorithm for fingerprint registration. the new algorithm of unique point localization for fingerprint registration is proposed. the focal point, an average center of curvature of fingerprint, is the unique and reliable reference point, suitable for fingerprint registration. in this paper, the new approach demonstrates fast and reliable reference point. moreover, the focal point can be located even it is out of original fingerprint boundary and singularity is not exist.
a subspace approach to face detection with support vector machines. we present a subspace approach to face detection with support vector machine (svms). a linear svm classifier is trained as a filter to produce a subspace in which a non-linear svm classifier with gaussian kernel is trained for face detection. this makes training easier and results in a very efficient face detection algorithm. experimental results demonstrate their promising performance compared with some well-known existing detectors.
a comparative study of several modeling approaches for large vocabulary offline recognition of handwritten chinese characters. in this paper, we compare three representative modeling approaches, namely the multiple-prototype-based template matching approach, the subspace approach and the continuous density hidden markov model approach for large vocabulary offline recognition of handwritten chinese characters. on a task of classification of 4616 handwritten chinese characters, we evaluate and compare the strength and weakness of individual approaches in terms of the classification accuracy, the memory requirement and the computational complexity. we offer recommendations for practitioners on how to make intelligent use of these modeling approaches for different purposes in different applications.
binocular hand tracking and reconstruction based on 2d shape matching. this paper presents a method for real-time 3d hand tracking in images acquired by a calibrated, possibly moving stereoscopic rig. the proposed method consists of a collection of techniques that enable the modeling and detection of hands, their temporal association in image sequences, the establishment of hand correspondences between stereo images and the 3d reconstruction of their contours. building upon our previous research on color-based, 2d skin-color tracking, the 3d hand tracker is developed through the coupling of the results of two 2d skin-color trackers that run independently on the two video streams acquired by a stereoscopic system. the proposed method runs in real time on a conventional pentium 4 processor when operating on 320x240 images. representative experimental results are also presented.
a perceptual shape descriptor. in this study, we present the two dimensional object silhouette by a one dimensional descriptor, which preserves the perceptual structure of its shape. the proposed descriptor is based on the moments of the angles between the bearings of a point on the boundary, in a set of neighborhood systems. at each point on the boundary, the angle between a pair of bearings is calculated to extract the topological information of the boundary in a given locality. the proposed method does not use any heuristic rule or empirical threshold value in the shape representation. the similarity between the patterns is measured by elastic matching of thedescriptors. the proposed shape descriptor is tested on dataset of mpeg 7 core experiments shape-1. the experiments show better results than the previous studies reported in the literature.
plane rectification using a circle and points from a single view. this paper presents a new method for planar rectification. the pattern used here contains one circle and two points with known distances to the circle. partial rectification is introduced to calculate the trace of the mapping centers from each distance constraint. the algorithm is derived by discovering three fixed points during a matrix transformation. complex coordinates are used to simplify this problem. our experiments demonstrate that this method achieves good performance.
recognition of off-line handwritten arabic words using hidden markov model approach. hidden markov models (hmm) have been used with some success in recognizing printed arabic words. in this paper, a complete scheme for totally unconstrained arabic handwritten word recognition based on a model discriminant hmm is presented. a complete system able to classify arabic-handwritten words of one hundred different writers is proposed and discussed. the system first attempts to remove some of variation in the images that do not affect the identity of the handwritten word. next, the system codes the skeleton and edge of the word so that feature information about the lines in the skeleton is extracted. then a classification process based on the hmm approach is used. the output is a word in the dictionary. a detailed experiment is carried out and successful recognition results are reported.
a fusion methodology based on dempster-shafer evidence theory for two biometric applications. different features carry more or less rich and varied pieces of information to characterize a pattern. the fusion of these different sources of information can provide an opportunity to develop more efficient biometric system compared when using a feature vector. thus a new automatic fusion methodology using different sources of information (different feature sets) is presented here. dempster-shafer evidence theory is employed for this purpose. for performance evaluation significqntly large data sets of the biometric sources signature and hand shqpe are used. the results on combining different feature vectors compared to a single vector with our approach prove the importance of a fusion process.
introducing termination probabilities to hmm. hmm is very well suited to model sequential patterns. this paper introduces a new parameter, the termination probability, to hmm. the new parameter provides a better initialization for the backward variable during the training and evaluation phases. this improves the discriminatory power of hmm by allowing the system to judge the input observation sequence based on where it is completed. experimental results show the improvement achieved by this parameter.
texture classification using curvelet statistical and co-occurrence features. texture classification has long been an important research topic in image processing. now a days classification based on wavelet transform is being very popular. wavelets are very effective in representing objects with isolated point singularities, but failed to represent line singularities. recently, ridgelet transform which deal effectively with line singularities in 2-d is introduced. but images often contain curves rather than straight lines, so curvelet transform is designed to handle it. it allows representing edges and other singularities along lines in a more efficient way when compared with other transforms. in this paper, the issue of texture classification based on curvelet transform has been analyzed. curvelet statistical features (csfs) and curvelet co-occurrence features (ccfs) are derived from the sub-bands of the curvelet decomposition and are used for classification. experimental results show that this approach allows obtaining high degree of success rate in classification.
efficient estimation of pen trajectory from off-line handwritten words. this paper presents an easy and efficient method to estimate the pen trajectory based on minimizing the pen movement. given start and end vertices, the complexity of the proposed algorithm is linear. in addition, the algorithm clearly identifies alternatives that do not affect the overall length of the pen trajectory, making enough room for other criteria, e.g. vision rules, to be applied.
rejection strategies and confidence measures for a k- nn classifier in an ocr task. in handwritten character recognition, the rejection of extraneous patterns, like image noise, strokes or corrections, can improve significantly the practical usefulness of a system. in this paper, a combination of two confidence measures defi ned for a k-nearest neighbors classifier is proposed. experiments are presented comparing the performance of the same system with and without the new rejection rules.
learning to imitate human movement to adapt to environmental changes. a model for learning human movement is proposed. the learning model generates plausible trajectories of limbs that mimic the human movement. the learning model is able to generalize these trajectories over extrinsic constraints. these constraints result from the space of start and end configuration of the human body and task-specific constraints such as obstacle avoidance. this generalization is a step forward from existing systems that can learn single gestures only. such a model is needed to develop humanoid robots that move in a human-like way in reaction to diverse changes in their environment. the model proposed to accomplish this uses a combination of principal component analysis (pca) and a special type of a topological map called the dynamic cell structure (dcs) network. experiments on a kinematic chain of 3 joints show that this model is able to successfully generalize movement using a few training samples for both free movement and obstacle avoidance.
off-line signature verification based on the modified direction feature. signature identification and verification has been a topic of interest and importance for many years in the area of biometrics. in this paper we present an effective method to perform off-line signature verification and identification. to commence the process, the signature's contour is first determined from its binary representation. unique structural features are subsequently extracted from the signature's contour through the use of a novel combination of the modified direction feature (mdf) in conjunction with additional distinguishing features to train and test two neural network-based classifiers. a resilient back propagation neural network and a radial basis function neural network were compared. using a publicly available database of 2106 signatures containing 936 genuine and 1170 forgeries, we obtained a verification rate of 91.12%.
learning mixtures of offline and online features for handwritten stroke recognition. in this paper we propose a novel scheme to combine offline and online features of handwritten strokes. the stateof- the-art methods in handwritten stroke recognition have used a pre-determined combination of these features, which is not optimal in all situations. the proposed model addresses this issue by learning mixtures of offline and online characteristics from a set of exemplars. each stroke is represented as a probabilistic sequence of substrokes with varying compositions of these features. the model adapts to any stroke and chooses the feature composition that best characterizes it. the superiority of the method is demonstrated on handwritten numeral and character strokes.