waashk/dblp huggingface dataset:
recognition of screen-rendered text. the recognition of screen-rendered text is to our knowledge a yet unaddressed task. it has to be performed e.g. by translation tools which allow users to click on any text on the screen and give a translation. this often requires to capture a screenshot and to perform optical character recognition which is very challenging due to very small and smoothed fonts. this paper presents a method capable of recognizing smoothed and non-smoothed screen-rendered text of very small size which also works for colored fonts on inhomogeneous backgrounds.
an evolutionary approach for the generation of diversiform characters using a handwriting model. in pattern recognition, a large number of diversiform characters is necessary to train/test a handwritten character recognition system. however, it is not easy to collect a large number of natural samples. the artificial diversification of characters has been suggested as one means of collecting a variety of characters [1]. in this paper, we show that a handwriting model can be applied to the diversification of characters. the characters diversified by the model can be used as a database of character images for training/testing purposes. wada amp; kawato's handwriting model [2] is based on an optimal principle and the feature space of the characters includes sets of via-points extracted from actual handwritten characters. the handwriting model can be used to generate a variety of characters by changing via-point information . in this paper, we propose a method for generating a large variety of characters by changing via-point information based on a genetic algorithm and we show that the accuracy of a handwritten character recognition system that uses the characters generated by the proposedmethod as the training data, is equivalent to that of a system composed by using natural data.
adaptive normalization of handwritten characters using gat correlation and mixture models. this paper proposes an adaptive or category-dependent normalization technique for handwritten characters featuring global affine transformation (gat) correlation and mixture models. key ideas are twofold. first, we estimate a probability density function (pdf) of black pixels for each category using mixture models of gaussian distribution functions and the em algorithm. second, we determine optimal, global affine transformation that maximizes a normalized cross-correlation value between a gat-superimposed input pattern and the above-mentioned pdf by the successive iteration method. experiments using the handwritten numeral database iptp cdrom1b show that the entropy of optimally gat-superimposed test samples decreases substantially by more than 20%. we discuss the enhanced normalization ability and the computational complexity of the proposed method.
face tracking in meeting room scenarios using omnidirectional views. the robust localization and tracking of faces in video streams is a fundamental concern for many subsequent multi-modal recognition approaches. especially in meeting scenarios several independent processing queues often exist that use the position and gaze of faces, such as group action- and face recognizers. the costs for multiple camera recordings of meeting scenarios are obviously higher compared to those of a single omnidirectional camera setup. therefore it would be desirable to use these easier to acquire omnidirectional recordings. the present work presents an implementation of a robust particle filter based face-tracker using omnidirectional views. it is shown how omnidirectional images have to be unwarped before they can be processed by localization and tracking systems being invented for undistorted material. the performance of the system is evaluated on a part of the pets-icvs 2003 smart meeting room dataset.
efficient facial component extraction for detection and recognition. in this paper, we present an efficient algorithm for facial component extractions, which are eyes detection and the mouth width measuring, and pose estimation. the algorithm is based on the novel overcomplete wavelet feature template, the support vector machine (svm) classifier, and the wavelet entropy filtering to robustly detect and segment the t-shape face region. the segmented t-shape face region, which is the smallest area enclosed by the face ellipse including eyes and mouth, is used to select the corresponding viewbased classifier for face recognition. the experimental results show that the proposed method is robust against the complex scenes.
abnormal walking gait analysis using silhouette-masked flow histograms. abnormalities of gait patterns can provide telltale signs of the onset or progression of certain diseases. this paper proposes a simple but effective approach to abnormal gait analysis using computer vision techniques. the proposed method starts with the extraction of human silhouettes from input videos and the computation of frame-to-frame optical flows, then motion metrics based on histogram representations of silhouette-masked flows, and finally gait analysis with eigenspace transformation. different from current gait classification and recognition studies, the proposed method deals with another interesting problem, namely not only determining different styles of the same walking action but detecting whether or not it is deviated from usual walking pattern, which is expected as a feasible means to deduce physical conditions of people. experimental results show its promising performance.
colony delineation on image classification. this paper presents a methodology for high resolution image classification and segmentation. the size and information volume of the images, taken by a high resolution digital camera, will be tens to hundreds times as the ones taken by an ordinary ccd camera. in order to speed up the image segmentation process of the large images, we classify the images first by using a low resolution image, then, segment them by a fast segmentation algorithm. the algorithm is studied mainly based on multi-resolution technique and the fusion of edge detection result and similarity segmentation result. by use this methodology, the whole image segmentation process time is reduced by tens' times than traditional segmentation methods. and the accuracy of the image segmentation is not decreased.
online aggregate particle size measurement on a conveyor belt. in order to quickly and accurately estimate average size of densely packed aggregate particles on a moving conveyor belt, a new image processing method is studied. the method consists of two major algorithms, one is a one-pass boundary detection algorithm that is specially designed for the images of densely packed particles (the word "particle" is used in a wide sense), and the other is average size estimation based on image edge density. the algorithms are cooperative. our method has been tested experimentally for different kinds of closely packed particle images which are difficult to detect by ordinary edge detections. the new method avoids delineating and measuring every particle on an image, therefore, is suitable for realtime imaging. it is particularly applicable for a densely packed and complicated particle image sequence.
from blob metrics to posture classification to activity profiling. the development of unobtrusive monitoring systems is important to obtain informative cues of human postures and behaviours for the next generation pervasive home care environment. to this end, this paper applies a set of computationally efficient vision techniques to classify human postures, and consequently, to analyze human behaviours such as fall detection. the method starts with the extraction of human silhouettes, then blob metrics using multiple appearance representations, and finally activity profiling based on frame-by-frame posture classification. a large number of experimental results have demonstrated its validity regardless of its simplicity.
compact representation of multidimensional data using tensor rank-one decomposition. this paper presents a new approach for representing multidimensional data by a compact number of bases. we consider the multidimensional data as tensors instead of matrices or vectors, and propose a tensor rank-one decomposition (trod) algorithm by decomposing nth-order data into a collection of rank-1 tensors based on multilinear algebra. by applying this algorithm to image sequence compression, we obtain much higher quality images with the same compression ratio as principle component analysis (pca). experiments with gray-level and color video sequences are used to illustrate the validity of this approach.
multi-lingual phoneme recognition and language identification using phonotactic information. previous research indicates that automatic language identification systems based on phonotactic information produce the best results compared with other systems based on acoustic or prosodic information. this paper investigates two different approaches that use phonotactic information: parallel phoneme recognition followed by language modeling (pprlm) and multi-lingual prlm. in the pprlm approach, we have modified the system by using four different language models with different discounting methods, including the linear, absolute, good- turning and witten-bell. our results show that the modified pprlm system with the witten-bell discounting outperforms other systems and achieves 75.5% language identification accuracy for the ogits speech corpus.
automatic estimation of 3d transformations using skeletons for object alignment. an algorithm for automatic estimation of 3d transformations between two objects is presented in this paper. skeletons of the 3d objects are created using a fully parallel thinning technique, feature point pairs (land markers) are automatically extracted from skeletons, and a least squares method is applied to solve an over determined linear system to estimate the 3d transformation matrix. experiments show that this method is quite accurate when the translations and rotation angles are small, even when there is some noise in the data. the estimation process requires about 2 seconds on an intel centrino laptop with 512 mb memory, for a complex model with about 37,000 object points and 500 object points for its skeletons.
captcha challenge tradeoffs: familiarity of strings versus degradation of images. it is a well documented fact that, for human readers, familiar text is more legible than unfamiliar text. current-generation computer vision systems also are able to exploit some kinds of prior knowledge of linguistic context: for example, many ocr systems can use known lexica (word-lists, such as of commonly occurring english words) to disambiguate interpretations. it is interesting that human readers can exploit various degrees of familiarity: for example, strings of characters which, while not found in dictionaries, are similar to spelled words: e.g. "pronounceable" strings, or strings made up of frequently occurring character n-grams. in contrast to this, computer vision technologies for exploiting such poorly characterized constraints (absent an explicit, complete lexicon) are not yet well developed. this gap in ability may allow us to design stronger captchas. we measure the familiarity of challenge strings generated by four methods (described by bentley and mallows) and we use the scattertype captcha to degrade challenge images. we report the results of a human legibility trial which supports the hypothesis that more familiar strings are indeed more legible in captchas. our measurements may enable engineering captchas with a more uniform distribution of difficulty by balancing image degradations against familiarity.
performance prediction for multimodal biometrics. sensor fusion is commonly used to improve the detection and recognition performance of a pattern recognition system. in this paper we propose a prediction model to predict the performance of a sensor fusion system. in particular, we answer two questions associated with the performance prediction in a sensor fusion system: (a) given the characteristics of the individual sensors how can we predict the performance of the fusion system? (b) how good the prediction is? we provide the cramer-rao bounds for the prediction model. we carry out experiments on the publicly available database xm2vts that has speech and face data.
combining hmm-based two-pass classifiers for off-line word recognition. for off-line recognition of cursive handwritten word, the intersection between segmentation and recognition is complicated and makes the recognition problem still a challenging task. hidden markov models (hmms) have the ability to perform segmentation and recognition in a single step. in this paper we present an hmm based unsymmetric two-pass modeling approach for recognizing cursive handwritten word. the two-pass recognition approach exploits the segmentation ability of the viterbi algorithm and creates three different hmm sets and carries out two passes of recognition. a weighted voting approach is used to combine results of the two recognition passes. high recognition rate has been achieved for recognizing cursive handwritten words with a lexicon of 1120 words. experiment on nist sample hand print data of ten different writers has also been carried out. the experimental results demonstrate that the two-pass approach can achieve better recognition performance and reduce the relative error rate significantly .
robust modelling of local image structures and its application to medical imagery. a robust modelling method for detecting and measuring isotropic, linear features and bifurcations is described and applied to analysing 2d eletrophoresis and retinal images. features are modelled as a superposition of gaussian functions with the hermite expansion and estimated by a combination of a multiresolution, windowed fourier approach followed by an em type of spatial regression. a penalised likelihood test, the akakie information criteria (aic) is used to select the best model and scale for feature segments. results are shown by using samples on both gel and retinal images.
detecting video texts using spatial-temporal wavelet transform. in this paper we present a novel approach to detect texts in video frames. the approach proposes a spatio-temporal wavelet transform to integrate information of multiple frames rather than a single one. static and dynamic texts are detected separately due to their characteristics in temporal domain. sub-bands decomposed from the original image sequence are combined to form a salience map, which features are extracted from. the approach is verified by experiments with various types of videos. high average recall and precision rates confirm the effectiveness of the proposed method.
mv-map: multiresolution video visualization and summarization on maps. this paper considers visualizing and summarizing image sequences using manifold learning and multiresolution techniques. the images in a video are found usually lying on a significantly low-dimensional manifold, which provides intrinsic information on the video content and formation. the parametrization of the manifold is discovered using a nonlinear subspace method preserving underlying geometry, especially local topology, in the original space. two modes of video roadmaps have been constructed using vmaps [vmap: video visualization and summarization on embedded manifold articulation primitives]. the first discovers the landmark points signaling dramatic changes in video content in the temporal order. the second reveals the global content coherence, without the temporal ordering. to facilitate the browsing of long sequences with complicated contents and structures, we build multiresolution visualization and summarization tools on vmaps. experimental results validate the proposed method. it may find applications to video monitoring and surveillance for interactive exploitation of video contents, intrusion detection, etc.
vision-based traffic measurement system. in this paper, we present a vision-based traffic measurement system. the system automatically counts the vehicles passing through a designated segment of roadway and measures their speeds. based on the obtained number of vehicles and their speeds, a variety of traffic parameters are readily calculated. a number of experiments with the video sequences taken under different weather, illumination and traffic conditions have been conducted. the results have revealed that the proposed system could perform well under different conditions.
enhancing training set for face detection. we present a novel method to enhance training set for face detection with nonlinearly generated examples from the original data. the motivation is from support vector machines (svm) that, for classification problems, examples lying close to class boundary usually have more influence and thus are more informative than those far from the boundary. we utilize a nonlinear technique - reduced set (rs) method and a new image distance metric to generate new examples, and then add them to the original collected database to enhance it. extensive experiments show that the proposed approach has an encouraging performance.
a verification method for viewpoint invariant sign language recognition. viewpoint variance is one of the inevitable problems in vision based sign language recognition. however, most researchers avoid this problem by assuming a special view, especially the front view. in the paper, we propose a verification method for viewpoint invariant sign language recognition. in general, there are two major variances between two video sequences of the same sign: performance variance and viewpoint variance. for small performance variance, dtw can help us eliminate it. when there is only viewpoint variance between two sequences, we can consider the two sequences as obtained synchronously by a stereo vision system. thus, for the current input, we can judge whether the known template is the matched one by verifying whether the two sequences can be considered as obtained by a stereo vision system. our experiments demonstrate the efficiency of the proposed method. furthermore, such verification method can be easily extended to other recognition tasks.
hierarchical content classification and script determination for automatic document image processing. page segmentation and image content classification plays an important role in automatic document image processing with apllications to mixed-type document image compression, form and check reading, and automatic mail sorting. in this paper, we propose an enhanced backgroung-thinning based page segmentation algorithm to process document images rapidly and eliminate some small regions embedded in other regions. we then present a hierarchical approach, which combines cross correlation measure, kolmogorov complexity measure, and a neural network, to classify sub-images into halftones and texts. the approach also achieves high accuracy in text determination using a three-layer feed-forward network, where text region can be classified into chinese or alphabetic character. experimental results on a number of mixed-type document images show the efficiency and effectiveness of our approach.
a dynamic bayesian network approach to multi-cue based visual tracking. visual tracking has been an active research field of computer vision. however, robust tracking is still far from satisfactory under conditions of various background clutter, poses and occlusion in the real world. to increase reliability, this paper presents a novel dynamic bayesian networks (dbns) approach to multi-cue based visual tracking. the method first extracts multi-cue observations such as skin color, ellipse shape, face detection, and then integrates them with hidden motion states in a compact dbn model. by using particle-based inference with multiple cues, our method works well even in background clutter without the need to resort to simplified linear and gaussian assumptions. the experimental results are compared against the widely used condensation and kf approaches. our better tracking results along with ease of fusing new cues in the dbn framework suggest that this technique is a fruitful basis to build top performing visual tracking systems.
tensor discriminant analysis for view-based object recognition. in this paper, we use a general mth order tensor discriminant analysis approach [11] for view based object recognition. this method is an extension of the 2d image coding technique [10] to general mth order tensors for discriminant analysis, and has good convergence property. we demonstrate the performance advantages of this approach over existing techniques using experiments on the coil-100 and the eth-80 datasets. specifically, our experimental results on eth-80 show the particular strength of this tensor discriminant analysis method when only a small number of training samples with big intra-class variation are available.
an efficient algorithm for fingerprint matching. this paper proposes novel topology-based algorithms for fingerprint matching. three major aspects of fingerprint matching are considered: local matching, tolerance to deformation and global matching. the approach improves both the accuracy and the speed of fingerprint identification. computational geometry methods including delaunay triangulation and spatial interpolation are used. the proposed methods are able to efficiently deal with the distortions of fingerprints. experimental results confirm that the algorithms presented are effective and more efficient compared to other fingerprint matching algorithms.
palmprint identification using boosting local binary pattern. local binary pattern (lbp) is a powerful texture descriptor that is gray-scale and rotation invariant [3]. because texture is one of the most clearly observable features in low-resolution palmprint images, we think local binary pattern based features are very discriminative for palmprint identification. in this paper, we propose a palmprint identification approach using boosted local binary pattern based classifiers. the palmprint area is scanned with a scalable subwindow from which local binary pattern histograms [4] are extracted to represent the local features of a palmprin image. the multi-class problem is transformed into a two-class one of intra- and extraclass by classifying every pair of palmprint images as intra-class or extra-class ones[19]. we use the adaboost[18] algorithm to select those sub-windows that are more discriminative for classification. weak classifiers are constructed based on the chi square distance between two corresponding local binary pattern histograms. experiments on the ust-hk palmprint database show competitive performance.
probabilistic relaxation using the heat equation. in this paper a new formulation of probabilistic relaxation labeling is developed using the theory of diffusion processes on graphs. according to this picture, the label probabilities are given by the state-vector of a continuous time random walk on a support graph. the state-vector is the solution of the heat equation on the support-graph. the nodes of the support graph are the cartesian product of the object-set and label-set of the relaxation process. the compatibility functions are combined in the weight matrix of the support graph. the solution of the heat-equation is found by exponentiating the eigensystem of the laplacian matrix for the weighted support graph with time. we demonstrate the new relaxation process on a feature correspondence matching problem abstracted in terms of relational graphs.