waashk/dblp huggingface dataset:
continuous-discrete filtering for cardiac kinematics estimation under spatio-temporal biomechanical constrains. a continuous-discrete filtering strategy is proposed for cardiac kinematics estimation from periodic medical image sequences. stochastic multi-frame filtering frameworks are constructed to deal with the parameter uncertainty of the biomechanical constraining model and the noisy nature of the imaging data coordinately. for the system with continuous dynamics and discrete measurements, the state estimates are predicted according to the continuous-time state equation between observation time points, and updatedwith the new measurements obtained at discrete time instants, yielding physically more meaningful and more accurate estimation results for the continuously evolving cardiac dynamics. the strategy is validated through synthetic data experiments to illustrate its advantages and on canine mr phase contrast images to show its clinical relevance.
multiresolution mesh reconstruction from noisy 3d point sets. we augment the tensor voting framework with a datadriven multiscale scheme for reconstructing a multiresolution mesh from a noisy 3d point set. the augmentations are effective, automatic but very simple, consisting of surface saliency inference, scale segmentation, and data normalization. these data analysis steps enable tensor voting to operate at a single scale in each normalized data segment, by decoupling scale and smoothness control. they also guide tensor voting to reconstruct at optimal resolutions subject to the sampling theory. the output is a multiresolution mesh that captures large and small scale features faithfully, without using the maximum resolution everywhere in the domain. the augmented methodology is very robust in the presence of noisy and irregular samples, and non-trivial holes that cover large areas involving multiple-scale features.
evolutionary optimization of feature representation for 3d point-based. in this paper, we introduce a new approach for the classification of point-based 3d computer graphics models. we propose a new representation for 3d point cloud models based on a set of principal projection axes. the point set is then projected on to each of these axes, and a suitable summary statistics of the projected point set along each axis is calculated. the complete set of statistics is then adopted as the feature representation of the point set. based on this representation, we need to search for the optimal set of projection axes which can best distinguish the different classes of point cloud models in the database. in general, this optimization problem is difficult due to the size of the search space. as a result, we propose to adopt evolutionary strategy (es)[3] as the optimization technique. this is in view of the capability of es to explore many regions of the search space in parallel. our experiment results indicate that the proposed optimized feature representation based on only the point set can attain a classification accuracy which is comparable to alternative feature representations which require the availability of the original polygonal representation.
facial feature tracking using a multi-state hierarchical shape model under varying face pose and facial expression. this paper presents a multi-state hierarchical approach for facial feature tracking. a hierarchical formulation of statistical shape models is proposed to characterize both global shape constraints of human faces and local structural details of facial components. gabor wavelets and gray level profiles are integrated for effective and efficient representation of feature points. furthermore, multi-state local shape models are presented to deal with shape variations of facial components. meanwhile, face pose estimation helps improve shape constraints for the feature search. both facial component states and feature point positions are dynamically estimated using a multi-modal tracking approach. experimental results demonstrate that the proposed method accurately and robustly tracks facial features under different facial expressions and pose variations.
local identification and removal of scatter artefacts based on the temporal information in dynamic spect images. scatter in spect images may mask decreseased uptake of radiopharmaceuticals in the left ventricle of the heart which can alter the diagnostic outcome of the study. the newly developed dynamic spect (dspect) method, which reconstructs 4d images from a standard acquisition protocol, provides additional temporal information which may be helpful to recognise such artefacts. each voxel carries a time signature, which is different for different organs. in this paper, we investigate whether this signature can be used to detect and remove scatter. time activity curves (tacs) from segmented data are tested for their potential to locally identify scatter according to a simple model. the investigation is carried out on artificial artefacts in real patient data as well as on existing scatter. tests on the artificial artefacts showed that scatter can indeed be detected and removed while tests on real data revealed that the simplified model may suffice to remove the majority of local scatter.
adaptive clustering ensembles. clustering ensembles combine multiple partitions of the given data into a single clustering solution of better quality. inspired by the success of supervised boosting algorithms, we devise an adaptive scheme for integration of multiple non-independent clusterings. individual partitions in the ensemble are sequentially generated by clustering specially selected subsamples of the given data set. the sampling probability for each data point dynamically depends on the consistency of its previous assignments in the ensemble. new subsamples are drawn to increasingly focus on the problematic regions of the input feature space. a measure of a data point's clustering consistency is defined to guide this adaptation. an empirical study compares the performance of adaptive and regular clustering ensembles using different consensus functions on a number of data sets. experimental results demonstrate improved accuracy for some clustering structures.
discriminative features for document classification. document representation using the bag-of-words approach may require bringing the dimensionality of the representation down in order to be able to make effective use of various statistical classification methods. latent semantic indexing (lsi) is one such method that is based on eigendecomposition of the covariance of the document-term matrix. another often used approach is to select a small number of most important features out of the whole set according to some relevant criterion. this paper points out that lsi ignores discrimination while concentrating on representation. furthermore, selection methods fail to produce a feature set that jointly optimizes class discrimination. as a remedy, we suggest supervised linear discriminative transforms, and report good classification results applying these to the reuters-21578 database.
shape-space from tree-union. in this paper we investigate how to construct a shape space for sets of shock trees. to do this we construct a super-tree to span the union of the set of shock trees. this super-tree is constructed so that it both minimizes the total tree edit distance and preserves edge consistency constraints. each node of the super-tree corresponds to a dimension of the pattern space. individual such trees are mapped to vectors in this pattern space.
four metrics for efficiently comparing attributed trees. we address the problem of comparing attributed trees and propose four novel distance metrics centered around the notion of a maximal similarity common subtree, and hence can be computed in polynomial time. we experimentally validate the usefulness of our metrics on shape matching tasks, and compare them with edit-distance.
spontaneous handwriting recognition and classification. finite-state models are used to implement a handwritten text recognition and classification system for a real application entailing casual, spontaneous writing with large vocabulary.handwritten short paragraphs are to be classified into a small number of predefined classes.the paragraphs involve a wide variety of writing styles and contain many non-textual artifacts.hmms and n-grams are used for text recognition and n-grams are also used for text classification. experimental results are reported which, given the extreme difficulty of the task, are encouraging.
detection of moving shadows using mean shift clustering and a significance test. an algorithm that discriminates moving objects from their shadows is presented. starting from the change mask of an image sequence, first of all the changed area is devided into subregions consisting of pixels with similar colour properties. this is done using the mean shift algorithm which is very powerful in non-parametric clustering of data. in a second step a significance test is performed to classify each image pixel inside the change mask into one of the classes foreground or shadow. to do this a straight-forward image model is used where the grey-level of a foreground pixel covered by a shadow is given by the product of the corresponding background pixels' grey-level and a constant value. assuming that fore- and background images are corrupted by gaussian white noise, a significance test is derived which classifies all pixels inside the change mask. in the third step global and local information from the first and second steps are combined. for each region inside the change mask it is examined if the majority of pixels survived the second step. if this is the case, the whole region is kept for the final moving object mask, if not the region is set to zero.
vision-based augmentation of a sentient computing world model. this paper presents work which integrates computer vision information obtained from calibrated cameras with location events from an office-based ultrasonic location system. bayesian networks are used to model dependencies and reliabilities of the multi-modal variables and perform fusion. context is represented using a world model which incorporates aspects of both the static and dynamic environment. information from the sentient computing system is used to guide and constrain the computer vision components, which in turn enhance the accuracy and capabilities of the world model.
image mosaicing from a set of images without configuration information. we proposed a method for creating image mosaics automatically under the condition that arrangement of partial images or camera movement is unknown. in this problem, the process can be divided into two steps. the first step is to search rough arrangements of partial images. the next step is to adjust detailed positioning using rough arrangements. in the first step, it is difficult to check up all combinations of arrangements because the number of them is huge. the proposed method utilizes a genetic algorithm (ga) for the first step. the second one for detailed positioning is performed by local search.
symbol recognition of printed piano scores with touching symbols. to build a music database efficiently, an automatic score recognition system is a critical component. many previous methods are applicable only to some simple music scores. in case of complex music scores it becomes difficult to detect symbols correctly because of noise and connection between symbols included in the scores. in this paper, we propose a score recognition method which is applicable to the complex music scores. symbol candidates are detected by template matching. from these candidates correct symbols are selected by considering their relative positions and mutual connections. under the presence of noise and connected symbols, the proposed method outperformed "score maker which is an optical music score recognition software.
a study of symbol segmentation method for handwritten mathematical formula recognition using mathematical structure information. symbol segmentation is very important in handwritten mathematical formula recognition, since it is the very first portion of the recognition process. this paper proposes a new symbol segmentation method using mathematical structure information. the base technique of symbol segmentation employed in the existing methods is dynamic programming which optimizes the overall results of individual symbol recognition. the new method we propose here improves symbol recognition performance by using correction values together with evaluation values of symbol recognition. these correction values are calculated from the relations among handwritten stroke positions and mathematical structure. there is no report which takes account of mathematical structure information for symbol segmentation in the handwritten mathematical formula recognition. our experiments have proven that the recognition rate of symbol segmentation by existing methods is between 90.2% and 93.3%, while our proposed method gives correct recognition rate of 97.1%.
a probabilistic model with parsinomious representation for sensor fusion in recognizing activity in pervasive environment. to tackle the problem of increasing numbers of state transition parameters when the number of sensors increases, we present a probabilistic model together with several parsinomious representations for sensor fusion. these include context specific independence (csi), mixtures of smaller multinomials and softmax function representations to compactly represent the state transitions of a large number of sensors. the model is evaluated on real-world data acquired through ubiquitous sensors in recognizing daily morning activities. the results show that the combination of csi and mixtures of smaller multinomials achieves comparable performance with much fewer parameters.
a fast multiscale edge detection algorithm based on a new edge preserving pde resolution scheme. in this communication we present a new explicit numerical scheme to approximate the solution of the linear diffusion filtering. it allows to introduce a new edge preserving scheme which is fast, stable, easy to program and applicable to any dimensions. our diffusion scheme is then put into a simple and original multi-scale edge detection algorithm. some experimental results of the proposed approach for the multi-scale detection of edges in greyscale images are presented, as well as a comparison with other diffusion filtering schemes.
optimizing nearest neighbour in random subspaces using a multi-objective genetic algorithm. in this work, the authors have evaluated almost 20 millions ensembles of classifiers generated by several methods. trying to optimize those ensembles based on the nearest neighbours and the random subspaces paradigms, we found that the use of a diversity metric called "ambiguity" had no better positive impact than plain stochastic search.
novel dct and dwt based watermarking techniques for digital images. two digital image watermarking techniques that have higher level of security compared to most of the existing algorithms have been proposed. the proposed digital watermarking scheme uses the properties of discrete cosine transform (dct) and discrete wavelet transforms (dwt) to achieve almost zero visible distortion in the watermarked images. these techniques use a unique method for spreading, embedding and extracting the watermark. embedding using a linear relation between the transform coefficients of the watermark and a security matrix has been proposed with satisfactory results. it has been shown that instead of adding the watermark to the source image, as is normally done, multiplying the watermark with the transformed source could be a preferred technique, as it would retain the original bit rate to a large extent. the watermarked images were tested subject to image cropping.
using evolution to learn how to perform interest point detection. the performance of high-level computer vision applications is tightly coupled with the low-level vision operations that are commonly required. thus, it is advantageous to have low-level feature extractors that are optimal with respect to a desired performance criteria. this paper presents a novel approach that uses genetic programming as a learning framework that generates a specific type of low-level feature extractor: interest point detector. the learning process is posed as an optimization problem. the optimization criterion is designed to promote the emergence of the detectors' geometric stability under different types of image transformations and global separability between detected points. this concept is represented by the operators repeatability rate [11]. results prove that our approach is effective at automatically generating low-level feature extractors. this paper presents two different evolved operators: ipgp1 and ipgp2. their performance is comparable with the harris [5] operator given their excellent repeatability rate. furthermore, the learning process was able to rediscover the det corner detector proposed by beaudet.
background removal system for object movies. in this paper, we present an interactive system for removing the backgrounds from object movies. our system automatically extracts initial segmentation results based on observed characteristics of object movies. these characteristics are (1) the distribution of background color is gaussian, (2) the color difference between foreground and background is distinct, and (3) the background of the images set with the same tilt angle is static. the user can modify misclassified pixels in only a few frames. the corrected result is propagated to all frames through spatial and temporal coherence. after user manipulation, the alpha estimation process is performed to obtain the alpha values for pixels that are composed of both background and foreground. our automatic process for obtaining initial segmentation results extracts most foreground and background pixels, and thus more accurate results are obtained with little user intervention.
multiple human objects tracking in crowded scenes. this paper introduces a multiple human objects tracking system to detect and track multiple objects in the crowded scene in which occlusions occur. our method assign each pixel to different human object based on its relative distance to that object and the corresponding color model. if no occlusion, we easily track each object independently based on each segmented object region and optical flow. with occlusion, we analyze the color distribution of the occlusion group to differentiate each object in the group. by calculating the distances between objects, we can determine whether an object is separated from the occlusion group and to be tracked individually afterwards.
independent component analysis based filter design for defect detection in low-contrast textured images. in this paper, we propose a convolution filtering scheme for detecting defects in low-contrast textured surface images and, especially, focus on the application for glass substrates in liquid crystal display (lcd) manufacturing. a defect embedded in a low-contrast surface image shows no distinct intensity from its surrounding region, and even worse, the sensed image may present uneven brightness on the surface. all these make the defect detection in lowcontrast surface images extremely difficult. in this study, a constrained ica (independent component analysis) model is proposed to design an optimal filter with the objective that the convolution filter will generate the most representative source intensity of the background surface without noise. the prior constraint incorporated in the ica model confines the source values of all training image patches of a defect-free image within a small interval of control limits. in the inspection process, the same control parameter used in the constraint is also applied to set up the thresholds that make impulse responses of all pixels in faultless regions within the control limits, and those in defective regions outside the control limits. a stochastic evolutionary computation algorithm, particle swarm optimization (pso), is applied to solve for the constrained ica model. experimental results have shown that the proposed method can effectively detect defects in textured lcd glass substrate images.
classification of segmented regions in brightfield microscope images. the subcellular localisation of proteins in living cells is an important step to determine their function. a common method is the evaluation of fluorescence images. the position of marked proteins, visible as bright spots, enables conclusions concerning their function. in order to determine the subcellular localisation, it is crucial to know the exact positions of the considered cells within an image. these are provided by the segmentation of a corresponding brightfield microscope image. as the resulting segments do not exclusively comprise cells, they have to be classified. therefore, we propose an approach for the classification of the resulting segments in 'cells' and 'non-cells', which is an essential step of the automatic recognition of cells and thus of the automatic subcellular localisation of proteins in living cells.
evaluating feature importance for object classification in visual surveillance. feature-based object classification, which distinguish a moving object to human or vehicle, is important in visual surveillance. in order to improve classification performance, in addition to choosing between the classification (such as svm, ann etc), we have to pay attention to which subset of features to employ in the classifier. this paper describes a method to evaluate the relative importance of various features for object type classification. starting with a given set of features, we apply the adaboost method and then we compute a metric which enables us to choose a good subset of the features. we apply our method to the task of distinguishing whether an image blob is a vehicle, a single human, a human group, or a bike, and we determine that shape-based feature, texture-based feature, and motion-based feature are reliable for this classification task. we validate our method by comparing with performance of ann-based classification.
metric measurement on arbitrary planes in 2 images using the conformal point. in 2001, richard hartley et al. [visual navigation in a plane using the conformal pint]described a novel entity known as the conformal point p which enables direct measurement of angles in the image. in spite of its convenience, p is plane-dependent. if the angle lies on another plane, the vanishing line changes and hence, the conformal point moves too. sometimes the angle lies on a virtual plane where the vanishing line is not easy to discover. this paper proposes a novel algorithm to address this problem. after ideal plane stabilization, the angle is marked in both images. through mapping the angle lines from one image to the target image, the location of the vanishing line in the target image can be determined. with the camera calibrated, the conformal point can be directly determined. the angle can then be computed as in [visual navigation in a plane using the conformal pint]. real experimentsshow that an angle in the 3d world can be computed quite accurately using the proposed method.
real-time sound source localization based on audiovisual frequency integration. we propse a pixelwise sound source localization algorithm based on audiovisual frequency integration. the localization is realized by detecting the common vibration dynamics of sound sources in the audio and the brightness signal. in order to detect the common vibration dynamics, temporal correlation values between the two signals are calculated in the algorithm. several experimental results are shown for vibrated objects, and the pixelwise sound source localization images are obtained.