waashk/dblp huggingface dataset:
component analysis of torah code phrases. in this paper, we develop a new tool, called component analysis (ca), to study the significance of long torah code phrases. ca quantifies the relevance of such a phrase, by comparing its components (sub-phrases) to randomly constructed competitor phrases. in the process, we gain insight into how highly unusual it is to discover focused relevance among these randomly constructed competitors. under the null hypothesis of no torah codes, we would therefore not expect to find focused relevance in the torah code phrases, but our experience suggests otherwise, as reflected in the highly significant example studied here. ca lends itself well to being duplicated and verified by others, even those unfamiliar with hebrew.
serialized unsupervised classifier for adaptative color image segmentation: application to digitized ancient manuscripts. this paper presents an adaptative algorithm for the segmentation of color images suited for document image analysis. the algorithm is based on a serialization of the k-means algorithm that is applied sequentially by using a sliding window over the image. the algorithm reuses information about the clusters computed by the previous classification and automatically adjusts the clusters during the windows displacement in order to better adapt the classifier to any new local modification of the colors. for digitized documents, we propose to define several different clusters in the color feature space for the same logical class. we also reintroduce the user into the initialization step who must define the different samples of colors for each class and the number of classes. this algorithm has been tested successfully on ancient color manuscripts having heavy defects, showing lighting variation and transparency. nevertheless, the proposed algorithm is generic enough to be applied on a large variety of images using other features for different purposes like color image segmentation as well as image binarization.
towards surface regularization via medial axis transitions. the reconstruction of objects from data in practical applications often leads to surfaces with small perturbations and other artifacts which make the detection of their ridges and generalized axes difficult. we propose an approach to smoothing small structures while preserving ridges which is based on the medial axis structure of the surface. the medial axis of the surface is organized as a graph structure and the closeness of the medial axis graph to points of instability (transitions) is used to identify those structures which are most likely due to perturbations. the removal of these structures is our approach to regularizing both the medial axis and the surface. this paper focuses on a subset of medial transitions arising from protrusions and the method is illustrated for a few synthetic and real images.
bayesian marker extraction for color watershed in segmenting microscopic images. in this paper we study the ability of the cooperation of bayesian color pixel classification in extracting seeds for color watershed. using color pixel classification alone does not extract accurately enough color regions so we suggest to use a strategy based on three steps: simplification, bayesian classification and color watershed. color watershed is based on an aggregation function using local and global criteria. the strategy is performed on microscopicimages. quantitative measures are used to evaluate the resulting segmentations according to a set of reference images.
effective and generic structure from motion using angular error. generic camera modeling using raxels and associated methods was recently introduced in computer vision. the main advantage is the applicability for any camera model, which contrasts to the many specific methods designed for a single camera model. this paper introduces a bundle adjustment based on angular error for the generic structure from motion problem. experiments include automatic, robust and optimal estimation of scene structure and camera motion from a long image sequence acquired by a handheld non-central (calibrated) catadioptric camera.
full-view spherical image camera. this paper proposes a method of realizing a full-view spherical image camera, called spherical camera, and gives its calibration method. the spherical camera can acquire a full-view spherical image by one capture of a single camera. the spherical field of view is divided into two hemispherical ones. each hemispherical field of view is imaged by a fisheye lens, respectively. the both of hemispherical views are fused by a mirror so as to acquire them on a single image plane. as for the calibration of this spherical camera, we let the spherical camera observe the inside of a calibration box so that the control points for calibration can cover the whole view of each fisheye lens, and calibrate it based upon a spherical camera model. we developed the prototype of the spherical image camera and an application of monitoring the whole surrounding environment of vehicle by the spherical camera is also given.
texture-constrained shape prediction for mouth contour extraction and its state estimation. in this paper, we present an automatic mouth contour and state estimation system. an efficient mouth contour extraction algorithm is proposed under the framework of active shape model (asm). considering large mouth shape variations, we propose a textureconstrained shape prediction method for initialization. to improve accuracy and robustness of classical asm, we use classifiers trained by real adaboost to characterize the local texture model. this model is proved to have much stronger discriminative power than gaussian model of classical asm. after extracting the mouth contour, the mouth is classified into one of 4 typical states by support vector machine (svm) based on the shape parameter. experiments over a large set show that extracted mouth contours have achieved good accuracy, with an average 89.5% acceptable rate, and the mouth state estimation reaches an average 93% correct rate. this automatic system reaches a speed of about 10 frames per second on a pentium-iv 1.7ghz pc, which may have potential applications in visual speech recognition etc.
object recognition using composed receptive field histograms of higher dimensionality. recent work has shown that effective methods for recognising objects or spatio-temporal events can be constructed based on receptive field responses summarised into histograms or other histogram-like image descriptors. this paper presents a set of composed histogram features of higher dimensionality, which give significantly better recognition performance compared to the histogram descriptors of lower dimensionality that were used in the original papers by swain & ballard (1991) or schiele & crowley (2000). the use of histograms of higher dimensionality is made possible by a sparse representation for efficient computation and handling of higher-dimensional histograms. results of extensive experiments are reported, showing how the performance of histogram-based recognition schemes depend upon different combinations of cues, in terms of gaussian derivatives or differential invariants applied to either intensity information, chromatic information or both. it is shown that there exist composed higher-dimensional histogram descriptors with much better performance for recognising known objects than previously used histogram features. experiments are also reported of classifying unknown objects into visual categories.
classification using the local probabilistic centers of k-nearest neighbors. in high dimensional feature space with finite samples, severe bias can be introduced in the nearest neighbor algorithm. in this paper, we propose a new classification method, which performs classification task based on local probability center of each class. moreover, this prototypebased method classifies the query sample by using two measures, one is the distance between query and local probability centers, the other is the posterior probability of query. although both measures are effect, the experiments show the second one is the better. the investigation results prove that this method improves the classification performance of nearest neighbor algorithm substantially.
a new approach to automated retinal vessel segmentation using multiscale analysis. computer based analysis for automated segmentation of blood vessels in retinal images will help eye care specialists screen larger populations for vessel abnormalities. however, automated retinal segmentation is complicated by the fact that the width of retinal vessels can vary from very large to very small, and that the local contrast of vessels is unstable, especially in unhealthy ocular fundus. we propose a novel method that takes these facts into account. our method includes a multiscale analytical scheme using gabor filters and scale production, and a threshold probing technique utilizing the features of retinal vessel network. our method is good for detecting large and small vessels concurrently. it also offers an efficient way to denoise and enhance the responses of line filters, allowing the detection of vessels with low local contrast.
high-dimensional discriminant analysis and its application to color face images. multilinear algebra plays an important role in signal processing. in this paper, a multilinear generalization of linear discriminant analysis is discussed. similar to hosvd, an iterative algorithm is developed for solving high-dimensional linear discriminant analysis. meanwhile, a non-iterative algorithm is also proposed. in addition, hdlda provides a unified framework for classical lda and 2dlda. experimental results on color face images show the effectiveness and usefulness of the proposed method.
hybrid kernel machine ensemble for imbalanced data sets. a two-class imbalanced data problem (idp) emerges when the data from majority class are compactly clustered and the data from minority class are scattered. though a discriminative binary support vector machine (svm) can be trained by manually balancing the data, its performance is usually poor due to the inadequate representation of the minority class. a recognition-based one-class svm can be trained using the data from the well-represented class only. however, it is not highly discriminative. exploiting the complementary natures of the two types of svms in an ensemble can bring benefits from both worlds in addressing the idp. experimental results on both artificial and real benchmark data sets support the feasibility of our proposed method.
fingerprint matching using minutia polygons. fingerprint distortion changes both the geometric position and orientation of minutiae, and leads to difficulties in establishing a match among multiple impressions acquired from the same finger. in this paper, minutia polygons are used to match distorted fingerprints. a minutia polygon describes not only the minutia type and orientation but also the minutia shape. this allows the minutia polygon to be bigger than the conventional tolerance box without losing matching accuracy. in other words, a minutia polygon has a higher ability to tolerate distortion. furthermore, the proposed matching method employs an improved distortion model using a multi-quadric basis function with parameters. adjustable parameters make this model more suitable for fingerprint distortion. experimental results show the proposed method is two times faster and more accurate (especially, on fingerprints with heavy distortion) than the method in [1].
detecting abnormal regions in colonoscopic images by patch-based classifier ensemble. in this paper, a new method is proposed to detect abnormal regions in colonoscopic images by patch-based classifier ensemble. through supervised learning from image patches of various sizes, a set of basic svm classifiers is trained for each size. a diagnostic model can then be constructed based on the ensemble of basic classifiers which is then used to detect abnormal regions in colonoscopic images. the multiple sizes of patches provide multiple level representation of the image content, which can help improve detection results. several fusion criteria are explored to determine the final output of the ensemble. experimental results show promising performance of our proposed method.
galilean-diagonalized spatio-temporal interest operators. this paper presents a set of image operators for detecting regions in space-time where interesting events occur. to define such regions of interest, we compute a spatio-temporal second-moment matrix from a spatio-temporal scale-space representation, and diagonalize this matrix locally, using a local galilean transformation in space-time, optionally combined with a spatial rotation, so as to make the galilean invariant degrees of freedom explicit. from the galilean-diagonalized descriptor so obtained, we then formulate different types of space-time interest operators, and illustrate their properties on different types of image sequences.
uncalibrated two-view metrology. a method of visual metrology from uncalibrated cameras is proposed in this paper, whereby a camera, which captures two images separated by a (near) pure translation, becomes a height measurement device. a novel projective construction allows accurate affine height measurements to be made relative to a reference plane, given that the reference plane planar homography between the two views can be accurately recovered. to this end a planar homography estimation method is presented, which is highly accurate and robust and based on a novel reciprocal-polar (rp) image rectification. the absolute height of any pixel or feature above the reference plane can be obtained from this affine height once the cameraýs distance to the reference plane, or the height of a second measurement in the image is specified. results from our data show a mean absolute error of 6.9mm and with two outliers removed this falls to 1.5mm.
a multi-label front propagation approach for object segmentation. for effective image segmentation methods, speed, accuracy and smoothness of the result are essential. in this paper, an iterative object segmentation approach is proposed based on minimal path theory. each iterative step includes one morphological dilatation and one multi-label front propagation. a narrow band is obtained by dilating the current contour with the known size. a new contour is again formed by multi-label front propagation, which is based on minimal path theory. its propagation speed is decided by the local image mean values together with the edge function. the final boundary will be obtained automatically through finite iterations. this algorithm is a global optimization method. it is simple and fast with complexity o(n). the initial contour may be chosen freely. the multi-label front propagation guarantees continuity and smooth contours with the capability to handle topology changes. furthermore, it is easy to extend to the 3d case. some experimental results are also presented.
learning high-level independent components of images through a spectral representation. statistical methods, such as independent component analysis, have been successful in learning local low-level features from natural image data. here we extend these methods for learning high-level representations of whole images or scenes. we show empirically that independent component analysis is able to capture some intuitive natural image categories when applied on histograms of outputs of ordinary gabor-like filters. this can be taken as an indication that maximizing the independence or sparseness of features may be a meaningful strategy even on higher levels of image processing, for such advanced functionality as object recognition or image retrieval from databases.
camera-based document image mosaicing. in this paper we present an image mosaicing method for camera-captured documents. our method is unique in not restricting the camera position, thus allowing greater flexibility than scanner-based or fixed-camera-based approaches. to accommodate for the perspective distortions introduced by varying poses, we implement a two-step image registration process that relies on accurately computing the projectivity between any two document images with an overlapping area as small as 10%. in the overlapping area, we apply a sharpness based selection process to obtain seamless blending across the border and within. experiments show that our approach can produce a very sharp, high resolution and accurate full page mosaic from small image patches of a document.
page classification through logical labelling. in this paper, we propose an integrated approach to page classification and logical labelling. layout is represented by a fully connected attributed relational graph that is matched to the graph of an unknown document, achieving classification and labelling simultaneously. byincorporating global constraints in an integrated fashion, ambiguity at the zone level can be reduced, providing robustness to noise and variation. models are automatically trained from sample documents. experimental results show promise for the classification and labelling oftechnical article title pages, and supports the idea of a hierarchical model base.
facial feature selection based on svms by regularized risk minimization. in this paper we present a method based on svms by regularized risk minimization for the facial feature selection aiming at improving performance of the classifier by (1) using wt + kpca as filter approach to choose a set of more meaningful representatives to replace the original data for feature selection; (2) using svm rfe iterative procedure as wrapper approach to obtain the optimum feature subset; (3) using regularized risk minimization as feature selection ranking criterion. experimental results on feret face database subsets indicate that the proposed method has a significant improvement in the classification accuracy and speed.
recognition of human periodic motion - a frequency domain approach. we present a frequency domain analysis technique for modelling and recognizing human periodic movements from moving light displays (mlds). we model periodic motions by motion templates, that consist of a set of feature power vectors extracted from unidentified vertical component trajectories of feature points. motion recognition is carried out in the frequency domain, by comparing an observed motion template with prestored templates. this method contrasts with common spatio-temporal approaches. the proposed method is demonstrated by some examples of human periodic motion recognition in mlds.
a new shape transformation approach to handwritten character recognition. a new simple algorithm, based on dynamic programming is presented, for handwritten character recognition, with improved accuracy. the proposed shape transform (st) approach is based on the calculation of the cost of transforming the image of a given character into that of another, thus taking into account local geometrical similarities and differences. a large experiment is conducted on the nist database. [2], and the effectiveness of the proposed method, is compared to the karhunen loeve transform (klt) method, with which a similar experiment was contacted [1] reporting the best results in the literature. the experiments performed, show that this new approach leads to improved recognition. it is more demanding in computer time, which is becoming ever more plentiful, but it lends itself to very efficient parallel hardware implementation.
face recognition using shading-based curvature attributes. in this paper we apply shape-from-shading to face images to extract elds of surface normals and make estimates of surface curvature attributes. the attributes studied include minimum andmaximum curvature, mean and gaussian curvature, and, curvedness and shape-index. the curvature attributes are encoded as histograms and are used to perform recognition using a number of distance and similarity measures including the euclidean distance, the shannon entropy, the renyi entropy and the tsallis entropy. we compare the results obtained using the differentcurvature attributes and the different entropy measures. using precision-recallcurves, we find that the best performance is delivered when the shannonentropy is applied to histograms of shape index and curvedness.
five-point motion estimation made easy. estimating relative camera motion from two calibrated views is a classical problem in computer vision. the minimal case for such problem is the so-called five-point problem, for which the state-of-the-art solution is nistér's algorithm [1][2]. however, due to the heuristic nature of the procedures it applies, to implement it needs much effort for non-expert user. this paper provides a simpler algorithm based on the hidden variable resultant technique. instead of eliminating the unknown variables one by one (i.e, sequentially) using the gauss-elimination as in [1], our algorithm eliminates many unknowns at once. moreover, in the equation solving stage, instead of back-substituting and solve all the unknowns sequentially, we compute the minimal singular vector of the coefficient matrix, by which all the unknown parameters can be estimated simultaneously. experiments on both simulation and real images have validated the new algorithm.
extraction of 3d microtubules axes from celluar electron tomography images. microtubules are structural and motile elements. they are essential in numerous cell processes. their function will be greatly improved by our understanding of their molecular structure, which requires extracting microtubules from the cellular electron tomography images. manual segmentation and measurement of the microtubules from 3d dataset may require several man-hours of work, and the manual approach suffers from inter-observer and intra-observer variabilities.localizing microtubules axes is an important step for the successful segmentation. in this paper, a graph searching based technique for extracting central axes of microtubules from three dimensional cellular electron tomography images is presented. a tube enhancement filter is introduced in order to define a distance metric for graph searching. the results are promising, indicating that accurate mea-surements can be obtained in a semi-automatic fashion.
a coarse-to-fine strategy for vehicle motion trajectory clustering. high-level semantic understanding of vehicle motion behaviors is often based on vehicle motion trajectory clustering. in this paper, we propose an effective trajectory clustering framework in which a coarse-to-fine strategy is taken. our framework consists of four stages: trajectory smoothing, feature extraction, trajectory coarse clustering and trajectory fine clustering. wavelet decomposition is imposed on raw trajectories to reduce noise in the trajectory smoothing stage. besides the commonly used positional feature, a novel feature called trajectory directional histogram is proposed to describe the statistic directional distribution of a trajectory in the feature extraction stage. both coarse clustering and fine clustering are based on a novel graphtheoretic clustering algorithm called dominant-set clustering, but they deal with different trajectory features. experiments in our pre-labeled trajectory database demonstrate that the proposed trajectory clustering framework possesses a very high accuracy.
feature selection for linear support vector machines. feature selection is attracted much interest from researchers in many fields such as pattern recognition and data mining. in this paper, a novel algorithm for feature selection is developed. the proposed algorithm uses the standard linear svm algorithm and is performed in an iterative way. feature selection is carried out by assigning weights to features. experimental results on uci data set and face images confirm the feasibility and validation of the proposed method.