waashk/dblp huggingface dataset:
3-d surface moment invariants. 3-d surface moments and surface moment invariants under similarity transformation are defined in this paper. this variation of traditional moments and moment invariants can handle the situation where the object is unclosed. 3-d surface moment invariants can be used as shape descriptors for the representation of free-form surfaces. some explicit surface moment invariants are illustrated in the experiment to describe the partial meaningful polygonal patches.
object detection with adaptive background model and margined sign cross correlation. in this paper, we propose a successive method for adaptively estimating background components using kalman filters, and a novel method for detecting objects using margined sign cross correlation (msc). msc is a natural extension of sign cross correlation, in other words, peripheral increment sign correlation. msc has a margin to deal with observation noise. by applying msc to our adaptive background model, our proposed system can robustly and accurately perform object detection. we show experimental results using real images to demonstrate the performance of the proposed system.
entropy optimized contrast stretch to enhance remote sensing imagery. this paper presents a contrast stretch (cs) method based on minimum entropy constraint to enhance images obtained in remote sensing applications such as ground penetrating radar (gpr), synthetic aperture radar, and infra-red imagery. the cs enhances contrast of the low-contrast part of an image. in remote sensing, it is usually the desirable signals that are of low contrast while interference of high contrast. the cs modifies the original image such that pixel values above and below preset boundaries are set to zero and the maximum possible pixel value and the pixel values falling between the boundaries are stretched out to enhancethe contrast of the image. using the cs we can enhance the contrast of desirable signals from, for example, a buried landmine or an object obscured by some interference. on the other hand, the cs inevitably enhances other parts of a remote sensing images, such as clutters and measurement noise. therefore there is a trade-off in using the cs. it is beneficial to find the correct "cut-off" boundaries in the cs in some optimal sense. we propose using minimum entropy as a criterion of looking for the optimal cs parameter. using field data from gpr application, we show that improved image can be obtained which makes further processing such as detection more accurate.
a charged geometric model for active contours. this paper presents a new deformable model based on charged particle dynamics and geometric contour propagation. it detects object boundaries with a charged active contour that propagates under the influence of lorentz forces in an image-based electrostatic field. we make use of level set representation to allow topological changes to be handled naturally. also, we build on the centre of divergence concept towards automatic initialisation.
a robust and accurate method for pupil features extra. precise pupil features detection is an important factor for face recognition. this paper presents a robust and accurate algorithm to precisely estimate pupil features: pupil center and pupil radius. first, a pupil parameters estimation step is taken and a weight ring mask is created. then, a weighted hough transform is used to precisely extract pupil features. because of the estimation step, the influence of eyelid and eyelash is largely eliminated. the experimental results show very good robustness and accuracy.
layered search spaces for accelerating large set character recognition. this paper describes "layered search spaces (lss) to accelerate recognition of a large category set. the basic concept is to employ pivots into a search space of character pattern prototypes. given an input pattern, it is compared only with the pivots and those close to it are selected. then, it matched with prototypes close to the selected pivots. this paper introduces multiple layers. an input pattern is compared with the top-layer pivots and those close to it are selected. then, it is compared with the 2nd-top-layer pivots close to the selected top-layer pivots. this comparison is repeated until in the base-layer and a small set of candidate prototypes are selected. we applied this method to a handwritten japanese character recognizer with the result that the coarse classification time was reduced to 47.1% and the whole recognition time was reduced to 46.2% while keeping classification and recognition rates as the original.
tree based behavior monitoring for adaptive fraud detection. the general basis for anomaly detection and fraud detection is pattern recognition. an effective online fraud detection system should be able to discover both known and new attacks as early as possible. the detection process should be self-adjustable to allow the system to deal with the constantly changing nature of online attacks. in this paper, we present an anomaly detection technique based on behavior mining and monitoring that work at both the individual and system level. frequent pattern tree is utilized to profile the normal behavior adaptively. a novel tree-based pattern matching algorithm is designed to discover individual level anomalies. an algorithm for computing tree similarity is proposed to solve the system level problems. empirical evaluations of our technique on both synthetic and real-world data show that we can accurately differentiate anomalous behaviors from the profiled normal behavior.
harmonic cut and regularized centroid transform for localization of subcellular structures. two novel computational techniques, harmonic cut and regularized centroid transform, are developed for segmentation of cells and their corresponding substructures observed with an epi-fluorescence microscope. harmonic cut detects small regions that correspond to subcellular structures. these regions also affect the accuracy of the overall segmentation. they are detected, removed, and interpolated to ensure continuity within each region. we show that interpolation within each region (subcellular compartment) is equivalent to solving the laplace equation on a multi-connected domain with irregular boundaries. the second technique, referred to as the regularized centroid transform, aims to separate touching compartments. this is achieved by adopting a quadratic model for the shape of the object and relaxing it for final segmentation.
generating omnifocus images using graph cuts and a new focus measure. in this paper, we discuss how to generate omnifocus images from a sequence of different focal setting images. we first show that the existing focus measures would encounter difficulty when detecting which frame is most focused for pixels in the regions between intensity edges and uniform areas. then we propose a new focus measure that could be used to handle this problem. in addition, after computing focus measures for every pixel in all images, we construct a three dimensional (3d) node-capacitated graph and apply a graph cut based optimization method to estimate a spatio-focus surface that minimizes the summation of the new focus measure values on this surface. an omnifocus image can be directly generated from this minimal spatio-focus surface. experimental results with simulated and real scenes are provided.
stereo computation using radial adaptive windows. while stereo algorithms using global methods produce good depth maps, local methods are still preferred for their efficiency . in this paper, we present a novel algorithm that determines the adaptive support windows by radial computations. motivated by the experiences of human vision, we use the certainties of the initial disparity distributions to determine the support window. certain other cues, such as differences of the initial disparity distributions, and colorsimilarities, are integrated to determine the weights in support window. extensive experiments have been conducted. the algorithm performs well in textureless areas, and preserving sharp disparity discontinuities.
a maximum margin discriminative learning algorithm for temporal signals. we propose a new maximum margin discriminative learning algorithm here for classification of temporal signals. it is superior to conventional hmm in the sense that it does not need prior knowledge of the data distribution. it learns the classifier by using a nonlinear discriminative procedure based on a maximum margin criterion, providing a strong generalization mechanism. this maximum margin discriminative learning method is presented together with a two-step learning algorithm. we evaluate the kernel based hiddenmarkov model by applying it to some simulation and real experiments. the preliminary results have shown significant improvement in classification accuracy.
localization of saliency through iterative voting. saliency is an important perceptual cue that occurs at different scales of resolution. important attributes of saliency are symmetry, continuity, and closure. detection of these attributes is often hindered by noise, variation in scale, and incomplete information. an iterative voting method using oriented kernels is introduced for inferring saliency as it relates to symmetry or continuity. a unique aspect of the technique is in the kernel topography, which is refined and reoriented iteratively. the technique can cluster and group nonconvex perceptual circular symmetries along the radial line or sparse features along the trangential direction. it has an excellent noise immunity, and is shown to be tolerant to perturbation in scale. applications of this approach to blobs with incomplete and noisy boundaries and to scientific images are demonstrated.
depth vs. intensity: which is more important for face recognition? both depth and intensity are important information for face recognition. in this paper, based on 3d face modelling, we extract depth features and intensity features respectively, and further compare their capacity to characterize the individuals in our database, 3dpef. the experimental results show that depth information is more important than intensity information for recognition under the same condition. this work will be a good reference to the future work on face recognition.
human-robot interaction by whole body gesture spotting and recognition. an intelligent robot is required for natural interaction with humans. visual interpretation of gestures can be useful in accomplishing natural human-robot interaction (hri). previous hri research focused on issues such as hand gesture, sign language, and command gesture recognition. automatic recognition of whole body gestures is required in order for hri to operate naturally. this presents a challenging problem, because describing and modeling meaningful gesture patterns from whole body gestures, is a complex task. this paper presents a new method for recognition of whole body key gestures in hri. a human subject is first described by a set of features, encoding the angular relationship between a dozen body parts in 3d. a feature vector is then mapped to a codeword of gesture hmms. in order to spot key gestures accurately, a sophisticated method of designing a garbage gesture model is proposed; model reduction, which merges similar states, based on data-dependent statistics and relative entropy. the proposed method has been tested with 20 persons' samples and 200 synthetic data. the proposed method achieved a reliability rate of 94.8% in spotting task and a recognition rate of 97.4% from an isolated gesture.
automatic iris segmentation based on local areas. in this paper a novel and robust method for automatic iris segmentation based on local areas is described. such method is composed of three main parts. (a) find the local rectangle region which has the minimum intensity mean and extend it to locate pupil. (b) select two small local sector areas including the outer boundaries of iris to locate outer iris. (c) translate the iris from polar coordinates into cartesian coordinates and normalize it to fixed size to compensate the stretching of the iris texture as the pupil changes in size and remove the nonconcentricity of the iris and the pupil. the method was implemented using casia iris image databases. the experimental results show that the proposed method has an encouraging result with an overall accuracy of 98.42%.
a novel pattern classification scheme: classwise non-principal component analysis (cnpca). this paper1 presents a novel pattern classification scheme: class-wise non-principal component analysis (cnpca), which utilizes the distribution characteristics of the samples in each class. the euclidean distance in the subspace spanned by the eigenvectors associated with smallest eigenvalues in each class, named cnpca distance, is adopted as the classification criterion. the number of the smallest eigenvalues is selected in such a way that the classification error in a given database is minimized. it is a constant for the database and can be determined by experiment. the cnpca classification scheme usually outperforms other classification schemes under the situations of high computational complexity (associated with high dimensionality of features and/or calculation of inverse variance matrix) or high classification error rate (e.g., owing to the scattering of between-class being less than that of within-class). the experiments have demonstrated that this method is promising in practical applications.
near-duplicate image recognition and content-based image retrieval using adaptive hierarchical geometric centroids. in this paper, we present a new feature extraction method that simultaneously captures the global and local characteristics of an image by adaptively computing hierarchical geometric centroids of the image. we show that these hierarchical centroids have some very interesting properties such as illumination invariant and insensitive to scaling. we have applied the method for near-duplicate image recognition and for content-based image retrieval. we present experimental results to show that our method works effectively in both applications.
robust feature selection by weighted fisher criterion for multiclass prediction in gene expression profiling. this paper presents a robust feature selection approach for multiclass prediction with application to microarray studies. first, individually discriminatory genes (idgs) are identified by using weighted fisher criterion (wfc). second, jointly discriminatory genes (jdgs) are selected by a sequential search method, according to their joint class separability. to combat the small size effect on feature selection, leave-one-out procedures are incorporated into both idg and jdg selection steps to improve the robustness of the approach. by applying this approach to a microarray study of small round blue cell tumors (srbcts) of childhood, we have demonstrated that our robust feature selection method can be used to successfully identify a subset of genes with superior classification performance for multiclass prediction.
detecting coarticulation in sign language using conditional random fields. coarticulation is one of the important factors that makes automatic sign language recognition a hard problem. unlike in speech recognition, coarticulation effects in sign languages are over longer durations and simultaneously impact different aspects of the sign such as the hand shape, position, and movement. due to this effect, the appearance of a sign, especially at the beginning and at the end, can be significantly different under different sentence contexts, which makes the recognition of signs in sentences hard. we advocate a two-step approach, where in the first step one segments the individual signs in a sentence and in the next step one recognizes the signs. in this work, we show how the first step, i.e. sign segmentation, can be performed effectively by using the conditional random fields (crf) to directly detect the coarticulation points. the crf approach does not make conditional independence assumptions about the observations and can be trained with fewer samples than hidden markov models (hmms). we validate our approach by demonstrating performance with american sign language (asl) sentence level data and show that the crf approach is 85% accurate in segmenting signs compared to 60% for the hmm approach at 0.1 false alarm rate.
feature selection based on the bhattacharyya distance. this paper presents a bhattacharyya distance based feature selection method, which utilizes a recursive algorithm to obtain the optimal dimension reduction matrix in terms of the minimum upper bound of classification error under normal distribution for multi-class classification problem. in our scheme, pca is incorporated as a pre-processing to reduce the intractably heavy computation burden of the recursive algorithm. the superior experimental results on the handwritten-digit recognition with the mnist database and the steganalysis applications have demonstrated the effectiveness of our proposed method.
people tracking by integrating multiple features. because a people detection system that considers only a single feature tends to be unstable, many people detection systems that consider multiple features simultaneously have been proposed. these detection systems usually integrate features using a heuristic method based on the designers' observations and induction. whenever the number of features to be considered is changed, the designer must change and adjust the integration mechanism accordingly. to avoid this tedious process, we propose a multi-modal fusion system that can detect and track people in a scalable, accurate, robust and flexible manner. each module considers a single feature and all modules operate independently at the same time. the outputs from the individual modules are integrated together and tracked using a kalman filter.
3d+2d face localization using boosting in multi-modal feature space. facial feature extraction is important in many facerelated applications, such as face alignment for recognition. recently, boosting-based methods have led to the state-of-the-art face detection and localization systems. in this paper, we propose a multi-modal boosting algorithm to integrate 3d (range) and 2d (intensity) information provided from a facial scan to detect the face and feature point (nose tip, eyes center). given a face scan, gauss and mean curvature are calculated. face, nose and eyes detectors are trained in color images and curvature maps features space using adaboost. as a result, a fully automatic multi-modal face location system is developed. the performance evaluation is conducted for the proposed feature extraction algorithm on a publicly available data-base, containing 4007 facial scans of 466 subjects.
adaptive contour construction for face regions. contours contribute much to object analysis in many applications. in this paper, an adaptive method of constructing face contours is proposed. first, a region splitting scheme is adopted to automatically generate grids over face regions. based on the grids, initial face contours are then constructed. finally, the contours are refined according to the minimum energy principle. experimental results show that the proposed method has a good performance in face contour construction.
performance prediction for handwritten word recognizers and its application to classifier combination. this paper introduces a performance prediction model for handwritten word recognizers . this model considers the factors involved in word recognition, i.e . the recognizer, input images and lexicons, and presents a quantitative formula to associate performance with these factors. it produces a direct measure of recognition difficulty by predicted performance whic h can be utilized to improve the combiation of multiple recognizers. we support the accuracy of our model by extensive experiments conducted on five word recognizers and its applications to multiple classifier systems.
iterative image restoration using a non-local regularization function and a local regularization operator. the regularization of the least-squares criterion has been established as an effective approach of solving illposed image restoration problems. unfortunately, a proper global regularization parameter is very difficult to be determined, and edges are usually smoothed by restoration process. in this paper, a new iterative regularization algorithm is presented. before restoration, we divide the pixels of the blurred and noisy image into two types of regions: flat regions and edge regions (edges and the regions near edges). a non-local adaptive regularization function is used instead of a global regularization parameter, and a local regularization operator which is determined by the orientation of pixels is employed in edge regions. experiments show that our algorithm is effective and the edge details are well preserved during the restoration process.
regression nearest neighbor in face recognition. in this paper, we introduce a regression nearest neighbor framework for general classification tasks. to alleviate potential problems caused by nonlinearity, we propose a kernel regression nearest neighbor (krnn) algorithmand its convex counterpart (ckrnn) as two specific extensions of nearest neighbor algorithm and present a fast and useful kernel selection method correspondingly. comprehensive analysis and extensive experiments are used to demonstrate the effectiveness of our methods in real face datasets
unsupervised discriminant projection analysis for feature extraction. this paper develops an unsupervised discriminant projection (udp) technique for feature extraction. udp takes the local and non-local information into account, seeking to find a projection that maximizes the non-local scatter and minimizes the local scatter simultaneously. this characteristic makes udp more intuitive and more powerful than the up-to-date methodlocality preserving projection (lpp, which considers the local information only) for classification tasks. the proposed method is applied to face biometrics and examined using the orl and feret face image databases. our experimental results show that udp consistently outperforms lpp, pca, and lda.
a modified non-negative matrix factorization algorithm for face recognition. in this paper, we propose a new variation of the nonnegative matrix factorization (nmf) for face recognition. the original nmf algorithm is distinguished from the other methods of pattern recognition by its non-negativity constraints which lead to a parts-based representation because they allow only additive combinations. however, it should be considered as an unsupervised method since class information in the training set is not used. to take advantage of more information in the training images and improve the performance for classification problem, we integrate the fisher linear discriminant analysis into the nmf algorithm, which results in a novel modified non-negativematrix factorization algorithm. our new update rule guarantees the non-negativity for all the coefficients and hence preserve the intuitive meaning for the base vectors and weight vectors while facilitating the supervised learning of within-class information. our new technique is tested on a well-known face database: the orl face database. the experimental results are very encouraging and outperformed traditional techniques including the original nmf and the eigenface method.