waashk/dblp huggingface dataset:
an efficient rectification method for trinocular stereovision. this paper presents an efficient and intuitive algorithm of the epipolar line rectification to trinocular stereo pairs with triangular configuration of the cameras. given the perspective projection matrices of each camera, the transformation matrices are computed by determining the new camera coordinate system in affine space and imposing some constraints on the remaining freedom degrees. the proposed method is able to make matching points lie on the identical row or column on the three images in affine coordinates.
efficient cross-validation of the complete two stages in kfd classifier formulation. this paper presents an efficient evaluation algorithm for cross-validating the two-stage approach of kfd classifiers. the proposed algorithm is of the same complexity level as the existing indirect efficient cross-validation methods but it is more reliable since it is direct and constitutes exact crossvalidation for the kfd classifier formulation. simulations demonstrate that the proposed algorithm is almost as fast as the existing fast indirect evaluation algorithm and the twostage cross-validation selects better models on most of the thirteen benchmark data sets.
ear recognition by means of a rotation invariant descriptor. iannarelli's studies showed that ear shape can be considered a biometric identifier able to authenticate people as well as more established biometrics like face or voice, for instance. however, very few researches can be found in literature about ear recognition. in most cases techniques already working in other biometric fields, such as pca (principal component analysis), are applied to ear.eigen-ears provide high recognition rate only in closely controlled conditions. indeed, even a slight amount of rotation can cause a significant drop in system performance and in unattended systems rotations occur very frequently. in this paper, we propose the use of a rotation invariant descriptor, namely gfd (generic fourier descriptor), to extract meaningful features from ear images. this descriptor results to be quite robust to both ear rotations and illumination changes. experimental results confirm the superiority of this approach evencompared to eigen-ears.
novel mathematical model for enhanced fisher's linear discriminant and its application to face recognition. in this paper, a novel mathematical model for enhanced fisher's linear discriminant is proposed, and it will be referred as efld in the following discussion. efld has two main advantages: first, it takes both the within-class scatter and the between-class scatter into account as fld dose; second, it could adaptively distinguish different variables of sample vector according to their scale in statistics. the features extracted by efld are much reliable for classification. according to the experiments on harvard face database and orl face database, efld outperforms some famous algorithms (pca, fld and ica) against large variation in lighting direction, variation in pose and facial expression. efld also has another potential contribution to classifying algorithms: there have been a number of classifying algorithms which need fld to extract classifiable features, some new algorithms could be proposed by replacing fld by efld in algorithms which use fld to extract features.
a probabilistic framework for joint head tracking and pose estimation. head tracking and pose estimation are usually considered as two sequential and separate problems: pose is estimated on the head patch provided by a tracking module. however, precision in head pose estimation is dependent on tracking accuracy which itself could benefit from the head orientation knowledge. therefore, this work considers head tracking and pose estimation as two coupled problems in a probabilistic setting. head pose models are learned and incorporated into a mixed-state particle filter framework for joint head tracking and pose estimation. experimental results on real sequences show the effectiveness of the method in estimating more stable and accurate pose values.
3d face recognition using normal sphere and general fourier descriptor. today, face figures among the most promising biometrics, allowing to identify people without requiring any physical contact. in this research field, 3d provides a significant improvement in recognition performances, but the existing approaches show limitations dealing with pose variations; indeed 3d face surfaces need to be aligned before the matching operation. this paper proposes an approach that overcomes this limitation by projecting the 3d shape information onto the 2d surface of a normal sphere, while a rotation invariant descriptor is used to extract key features from this surface. in addition, using a 2d descriptor reduces the computing time that is a typical drawback of 3d methods. experimentations have been conducted on a property face dataset, to assess the robustness of the method with respect to a large set of facial expression and pose variations.
a hybrid hmm-based speech recognizer using kernel-based discriminants as acoustic models. in this paper we propose a novel order-recursive training algorithm for kernel-based discriminants which is computationally efficient. we integrate this method in a hybrid hmm-based speech recognition system by translating the outputs of the kernel-based classifier into class-conditional probabilities and using them instead of gaussian mixtures as production probabilities of a hmm-based decoder for speech recognition. the performance of the described hybrid structure is demonstrated on the darpa resource management (rm1) corpus.
estimation of dynamic light changes in outdoor scenes without the use of calibration objects. the the work presented in this paper explores how dynamical light parameters in an outdoor environment can be estimated for use in a real-time augmented reality (ar) system. a method using existing inverse rendering techniques is used to acquire diffuse surface reflectances in an offline procedure. the reflectances are used in an on-line procedure to estimate the illumination parameters of an outdoor scene. the method presented reduces the light estimation problem of outdoor scenes to a modified phong shading model with two unknown parameters, which can be determined through the use of a linear equations system. the work presented provides an elegant method for estimating dynamically changing illumination parameters without the need for a calibration object in the scene.
appearance factorization based facial expression recognition and synthesis. facial expression interpretation, recognition and analysis is a key issue in visual communication and man to machine interaction. in this paper, we address the issues of facial expression recognition and synthesis and compare the proposed bilinear factorization based representations with previously investigated methods such as linear discriminant analysis and linear regression. we conclude that bilinear factorization outperforms these techniques in terms of correct recognition rates and synthesis photorealism especially when the number of training samples is restrained.
motion capture based on color error maps in a distributed collaborative environment. in this paper a composite framework for collaborative working is presented. the framework includes real-time motion tracking based on computer vision from standard webcams situated at different locations, data transmission and real-time animation of 3d avatars in a virtual world. motion tracking is obtained without using markers, with weak constraints on users' clothes and environment lighting. it is based on a model fitting process that compares the 2d processed images supplied by cameras with a set of artificially generated views of a human model.
combining dichotomizers for map field classification. a new method for combining dichotomizers like svms is proposed for classifying multi-class pattern fields. the novelty lies in the estimation of the styleconstrained posterior field class probabilities from the frequencies of the training patterns in the regions of the feature space engendered by the pairwise decision boundaries of the dichotomizers. we show that on simulated data, this non-parametric field classifier is nearly optimal. on scanned printed digits, its accuracy is comparable to that of state-of-the-art style classifiers.
a robust block-based image watermarking scheme using fast hadamard transform and singular value decomposition. we present a new approach for transparent and high rate embedding of watermarks into digital images using fast hadamard transform (fht) and singular value decomposition (svd). the proposed algorithm consists of three main steps: dividing the cover image into small blocks, applying the fht to each block, and distributing the singular values of the visual watermark image over the transformed cover blocks. the main attractive features of this approach are simplicity, flexibility in data embedding capacity, and real-time implementation. the experimental results show the much improved performance of the proposed method in comparison with existing techniques, and also its robustness against the most common attacks.
comparative classifier aggregation. comparative neural networks are a new kind of neural networks that can be used to compare two or more items given a set of context features. they compare two items at a time indicating the one that matches the context features better. consequently, any sorting algorithm, coupled with such a neural comparator, can sort any set of items. although applications include ink segmentation (for handwriting recognition purposes) and web page ranking, our emphasis on this paper is on classifier aggregation and, in particular, the integration of our standard handwriting recognizers with a user personalization database that consists of user samples.
modelling crowd scenes for event detection. this work presents an automatic technique for detection of abnormal events in crowds. crowd behaviour is difficult to predict and might not be easily semantically translated. moreover it is difficulty to track individuals in the crowd using state of the art tracking algorithms. therefore we characterise crowd behaviour by observing the crowd optical flow and use unsupervised feature extraction to encode normal crowd behaviour. the unsupervised feature extraction applies spectral clustering to find the optimal number of models to represent normal motion patterns. the motion models are hmms to cope with the variable number of motion samples that might be present in each observation window. the results on simulated crowds demonstrate the effectiveness of the approach for detecting crowd emergency scenarios.
a new hierarchical image segmentation method. image segmentation is a popular topic in computer vision and image processing. as a region-based approach, the mumford and shah (ms) model is a powerful and robust segmentation technique as compared to local based methods. however, there are also some difficulties with the ms model. in this paper, we present a piecewise linear approximation for the ms model to adapt to the image intensities distribution inside the segmented regions. we also modify the ms model to detect roof edges. because the ms functional is not convex, the result is often trapped in a local minimum and depends on the initial conditions. to overcome this problem, we present a new hierarchical strategy that takes into account both the local information at the pixel level and the global information of the ms model. the results indicate that our approach is effective in many applications.
automatic/interactive interpretation of color map images. in this paper, a combined automatic/interactive technology and algorithms for interpretation of color maps is proposed. main stages of technology are: color image binarization, binary image vectorization and vectorized image object interpretation. special scheme for automatic image vectorization as well as automatic object recognition techniques performed under operator control are proposed. practical experience in realising this technology is shown.
image renaissance using discrete optimization. in this paper we propose a novel technique to image completion that addresses image renaissance through a graph-based matching process. to this end, a number of candidate seeds with content similar to the one of the area to be inpainted are considered. they are selected through a particle filter method and then positioned over the missing area. markov random fields are used to formalize inpainting as a labeling estimation problem while a combinatorial approach is used to recover the optimal partition of patches that completes the missing area with the á-expansion process. promising results in image and texture completion demonstrate the potentials of the proposed method.
thresholding video images for text detection. thresholdging video images is very challenging due to the fact that image background generally has low resolution and is also more complicated and highly distorted than document images. as a result, thresholding methods that work well for document images may not work effectively for video images in some applications. this paper investigates the issue of thresholding video images for text detection and further develops a relative entropy-based thresholding approach that can effectively extract text from complicated video images. in order to demonstrate its performance a comparative study is conducted among the proposedthresholding method and several thresholding techniques which are widely used for document and gray scale images. the experimental results show that thresholdging video images is far more difficult than thresholding document images and simple histogram-based methods generally do not perform well.
from cell image segmentation to differential diagnosis of thyroid cancer. an approach for cytologic diagnosis of thyroid cancer with the help of automatic morphometry is proposed. this approach is based on a developed computer analyser of images that is aimed at: automated processing and binarization of colour images; automatic raster-to-vector transformation and formation of biological objects; morphometric assessment of biological objects by quantitative parameters characterizing the changes of cell nuclei; building of anexpert system to aid the diagnosis of thyroid cancer.
hidden markov models for optical flow analysis in crowds. this paper presents an event detector for emergencies in crowds. assuming a single camera and a dense crowd we rely on optical flow instead of tracking statistics as a feature to extract information from the crowd video data. the optical flow features are encoded with hidden markov models to allow for the detection of emergency or abnormal events in the crowd. in order to increase the detection sensitivity a local modelling approach is used. the results with simulated crowds show the effectiveness of the proposed approach on detecting abnormalities in dense crowds.
the mean shift algorithm and the unified framework. this paper considers two classes of algorithms for the representation of data points using centroids: the unified framework and the mean shift algorithm. the relationship between both approaches is presented showing that the mean shift algorithm fits within the unified framework being equivalent to snake with cohen potential. however it does not use competitive learning as the other methods considered in the unified framework. the advantages of both types of techniques are exemplified through examples.
correlation based image defect detection. the defect inspection that used image sensing such as automated pattern inspection is a useful solution to automatize the visual check, not limit to factory automation field. mostly such defect inspection is using the models of defect that described by primitive features. this paper proposes a new defect detection method that is the non-model based approach. in this approach, the method extracts the image description rule from local regions.it is useful for the defect inspection problems that cannot prepare a defect model such as scratch or superimpose detection, texture image analysis, etc. in the experiment, i tried the defect detection to the landscape picture which several types of superimpose were added. from these results, it was confirmed that the proposed method has high ability to detect the defected regions independently with the texture type.furthermore, i attempted the application to a scene image.therefrom, the possibility to apply the figure-ground separation of the image understanding basic problem was confirmed.
interacting active rectangles for estimation of intervertebral disk orientation. this paper presents a fast and efficient method to determine intervertebral disk orientation in a magnetic resonance (mr) image of the spine. the algorithm originates from active contour theory and enforces a shape constraint to avoid leaks through weak or non-existent boundaries. the method represents a vertebra as a rectangle, modeled as a semi-affine transformation applied to the unit square. a regional flow integrated along the rectangle's perimeter updates the rectangle's transformation to achieve the segmentation. further constraints are added so that adjacent rectangles have similar orientation and scale. the orientation of a disk is then inferred from its adjacent vertebrae. experiments show that the method is fast and effective in detecting the correct intervertebral disk orientation, which is used for transverse image planning.
enhancement of annual rings on industrial ct images of logs. many researchers have shown the ability of x-ray computer tomography (ct) of acquiring knowledge about the internal structure of logs. contrarily to medical ct scanners, industrial ct scanners should satisfy more extreme constraints (in terms of speed, duty cycle, radiation safety, reconstruction circle and aperture size) in order to be used in a sawmill environment. all these factors lower the quality of industrial ct images while automatic methods for analyzing data are required. to ensure good performances and robustness, an automatic system should adapt to the quality of the input images. this paper focuses on an algorithm which can adaptively improve the contrast of the ridge and valley structure of annual rings in ct images of wooden logs. we also show that this algorithm does not only help in segmenting annual rings but also gives with little adaptation a good approximation of the localization of the pith.
integration of an on-line handwriting recognition system in a smart phone device. this paper presents the evolving of our academic development to a technology driven application: the integration of an unconstraint cursive on-line handwritten characters into a smart phone device. the ultimate goal of this work is to implement an accuracy handwritingrecognizer into mobile devices with limited computing and memory resources. a hierarchical fuzzy modeling is used to obtain a compact and robust knowledge representation and the decision process is based on an adapted fuzzy inference system to reduce computing without decreasing the peiformances. we describe in this paper the basic architecture of the recognition system called "resifcar", its practical adaptation to the mobile device constraintsand the recognition rates both on cursive isolated letters (91.9%) and on isolated digits (92.3%) in a writer independent context based on 100 different writers.
a bayesian approach for 3d models retrieval based on characteristic views. the management of big databases of three-dimensional models (used in cad applications, visualization, games, etc.) is a very important domain. the ability to characterize and easily retrieve models are a key issues for the designers and the final users. in this frame, two main approaches exist: search by example of a three-dimensional model, and search by a 2d view. in this paper we focus on the characterization of a 3d model by a set of views (called characteristic views), and on the indexing process of these models with a bayesian probabilistic approach using the characteristic views. we illustrate our results using a collection of three-dimensional models supplied by renault group.
reliability index of optical flow that considers error margin of matches and stabilizes camera movement estimation. a reliability index that evaluates the quality of optical flow is needed because the accuracy of estimation of camera movement or the shape of the environment depends on the accuracy of optical flow. a reliability index using spatial brightness gradient information has already been proposed, and its effectiveness has been confirmed. in this paper, we proposes a reliability index that considers the error margin of matches in addition to the spatial brightness gradient information. the result of experiments showed that considering the error margin of matches increases the performance of the reliability index. it was also found that the camera movement can be estimated from optical flow in a synthetic image to which the camera movement was known beforehand. then, it was confirmed that rejecting using the reliability index improves the accuracy of estimation. moreover, in an experiment in witch changing of noise was added to the image sequence, it was shown that moderate noise stabilized the movement estimation.
flexible text recovery from degraded typewritten historical documents. the conversion of large collections of historical typewritten documents into digital libraries and archives is met with significant challenges that standard recognition techniques cannot address. the condition and individual nature of characters in these degraded documents necessitate a departure from existing thresholding approaches. this paper presents a flexible approach designed to overcome the difficulties presented by such documents by flexibly analysing each individual character and cautiously repairing it. the main sources of ocr errors are successfully addressed and reliable corrective actions are taken.
uncalibrated visual servoing from projective reconstruction of control values. the visual servoing technique has been studied extensively as a robust method for navigating robots toward goal positions and orientations reliably. unfortunately, the existing visual servoing methods require the calibration of cameras and robots, which is time-consuming. thus, in this paper, we propose a visual servoing method which does not require the calibration of cameras and robots. in particular, we show that we can navigate uncalibrated robots to goal positions properly by using the projective reconstruction based on the abstract projection of control values.
a markov random field approach to microarray image gridding. the paper reports a novel approach for the problem of automatic gridding in microarray images. the solution is modeled as a bayesian random field with a gibbs prior possibly containing first order cliques (1-clique). on the contrary of previously published contributions, this paper does not assume second order cliques, instead it relies on a two step procedure to locate microarray spots. first a set of guide spots are used to interpolate a reference grid. the final grid is then produced by an a-posteriori maximization which takes into account the reference rectangular grid and local deformations. the algorithm is completely automatic and no human intervention is required, the only critical parameter being the range of the radius of the guide spots.