waashk/dblp huggingface dataset:
utilizing information theoretic diversity for svm active learn. incrementally learning from a large number of unlabeled examples continues to be an active area of research in pattern recognition. active learning has made great strides in recent years to address this problem, taking advantage of svms to develop robust learning systems. recently, diversity sampling for svm active learning has garnered much attention. in this work we propose a fundamentally motivated view of diversity for svm active learning based on an information-theoretic diversity measure. comparative testing on a database from the small-sample learning problem of image retrieval is done and thoughts for future work are presented.
3d segmentation by maximally stable volumes (msvs). this paper introduces an efficient 3d segmentation concept, which is based on extending the well-known maximally stable extremal region (mser) detector to the third dimension. the extension allows the detection of stable 3d regions, which we call the maximally stable volumes (msvs). we present a very efficient way to detect the msvs in quasi-linear time by analysis of the component tree. two applications - 3d segmentation within simulated mr brain images and analysis of the 3d fiber network within digitized paper samples - show that reasonably good segmentation results are achieved with low computational effort.
a kernel fractional-step nonlinear discriminant analysis for pattern recognition. feature extraction is one of the most significant and fundamental problems in pattern recognition (pr). this paper introduces a novel kernel fractional-step nonlinear discriminant analysis (kf-nda) for feature extraction in pr. it not only overcomes the limitation of failing for a nonlinear problem in the direct fractional-step linear discriminant analysis (df-lda), but also improves the generalization ability of traditional kernel nonlinear discriminant analysis (k-nda). it is then applied to an experiment on face recognition, and the results demonstrate that this method is more effective than the existing methods.
online appearance-based face and facial feature tracking. we propose a simple framework that utilizes online appearance models for 3d face and facial feature tracking with a deformable model. adapting the geometrical parameters for each frame adopts a steepest ascent method in the observation likelihood using a local exhaustive and directed search in the parameter space. the observation likelihood is based on the current appearance and the registered images. the developed framework is straightforward and has the following advantages. first, it does not require any a priori statistical facial texture. second, it does not require any a priori transition model for the 3d motion. video sequences featuring large head motions, large facial animations, and external illumination variations are successfully tracked, which demonstrate the efficiency of the developed framework.
integrating emd and gradient for generating primal sketch of natural images. primal sketch performs an important role in early vision. in this paper, we propose a novel method to obtain the primal sketch of natural images by integrating empirical mode decomposition (emd) techniques and image gradient. 2d emd approach can decompose the image into a finite number of intrinsic mode functions (imf), and each one represents the original image in a different scale, with the 1st imf representing the finest scale. to enhance the information represented by the imf, we multiply the 1st imf by the image gradient. this enhanced imf highlights intensity changes in the image. by linking all the maximal points in the enhanced imf, we obtain a primal sketch of the original image. compared with the existed primal sketch extraction methods, our method is fully driven by the image data, and it needs neither to choose filters nor to learn the image bases. the experiment results show that our method is fast and effective.
facial expression recognition using auto-regressive models. in this paper, we address the analysis and recognition of facial expressions in continuous videos. we introduce a view- and texture-independent approach that exploits the temporal facial action parameters estimated by an appearance-based 3d face tracker. the facial expression recognition is carried out using learned dynamical models based on auto-regressive processes. these learned models can also be utilized for the synthesis and prediction tasks. experiments demonstrated the effectiveness of the developed method.
integrating automatic and interactive brain tumor segmentation. this paper integrates automatic segmentation based on supervised learning with an interactive multi-scale watershed segmentation method. the combined method automatically provides an initial segmentation that applies the building blocks that the user can use in the interactive method. thereby the two approaches are seamlessly integrated and the combined method can be used on the full range of problems from very easy to very difficult segmentation tasks resulting in different levels of interaction needed. the method is evaluated for segmentation of brain tumors.
structural flow smoothing for shape interpolation. this paper presents a comparative study of robust diffusion algorithms when used for smoothing structural fields applied in volumetric image interpolation. the input data consists of a set of parallel and equidistant slices which are considered sparsely located along a central axis. the structural flows are constructed using the dual directional block matching algorithm (dbma). two vectorial flows are modelled in both directions along the central axis, using the correlation between blocks of pixels from successive slices. as with most block matching algorithms, this method is susceptible to noise and the resulting vector fields contain outliers. a methodology that combines diffusion and robust statistics in order to smooth the dual structural flow is proposed. consequently, new slices are interpolated in between the existing slices, according to the smoothed vector fields. the set of algorithms is applied in volumetric medical images.
a multi-cameras 3d volumetric method for outdoor scenes : a road traffic monitoring application. this paper deals with the issue of using multi-cameras for road traffic monitoring. the aim is to remove the classic monocular ambiguities and to retrieve the objects' height. an efficient and simple calibration method is presented. it relies on the geometric constraints of the road. a high speed matching procedure is introduced. it is based on an altitude planar decomposition of the road scene. the method naturally achieves two tasks due to altitudes sampling. match and reconstruction become simultaneous. finally, experimental results are presented.
sector-based diffusion filtering. in this paper, we propose a new approach devoted to the denoising and the enhancing of strongly oriented 3-d images. in particular, the paper focuses on seismic data composed of a stack of layers disturbed by noise and broken by faults. the denoising of those data is a preprocessing used to improve the detection of the faults. our method is based on an anisotropic forward and backward diffusion scheme, which takes advantage of the computation of a "regional" orientation. this approach allows the recovering of the plan which is tangent to the current layer and the corresponding normal direction. then the diffusion goes forward along the layer in order to smooth the noise, and backward along the normal to separate the layers.
metric mixtures for mutual information (m^3 i) tracking. a new method for updating the template in a feature tracking application is presented, which has minimal memory and processing overhead. the proposed method is an expectation maximisation inspired approach based on modelling the variable appearance of a template using a gaussian mixture model in a discrete metric space, termed the m^3 i tracker for short.the proposed technique is compared to various other techniques in several experiments, where it performs robustly. several comparison methods are outperformed. in addition to robust template tracking it has wider applications to advanced techniques such as aams and deformable templates.
image template matching using mutual information and np-windows. a non-parametric (np) sampling method is introduced for obtaining the joint distribution of a pair of images. this method based on np windowing and is equivalent to sampling the images at infinite resolution. unlike existing methods, arbitrary selection of kernels is not required and the spatial structure of images is used. np windowing is applied to a registration application where the mutual information (mi) between a reference image and a warped template is maximised with respect to the warp parameters. in comparisons against the current state of the art mi registration methods np windowing yielded excellent results with lower bias and improved convergence rates.
implementing image applications on fpgas. the cameron project has developed a language and compiler for mapping image-based applications to field programmable gate arrays (fpgas). this paper tests this technology on several applications and finds that fpgas are between 8 and 800 times faster than comparable pentiums for image based tasks.
real-timehumanmotion sensingbased on vision-based inverse kinematics for interactive applications. vision-based human motion sensing has a strong merit that it does not impose any physical restrictions on humans, which provides a natural way of measuring human motion. however, its real-time processing is not easy to realize, because a human body has a high degrees of freedom, whose vision-based analysis is not simple and is usually time consuming. here, we have developed a method in which human postures are analyzed from a limited number of visual cues. it is a combination of numerical analysis of inverse kinematics and visual search. our method is based on a general framework of inverse kinematics, and, therefore, we can use relatively complex human figure model, which can generates natural human motion. in our experimental studies, we show that our implemented system works in real-time on a pc-cluster.
spectral sound gap filling. we present a new method for automatically filling in gaps of textural sounds. our approach is to transform the signal to the time-frequency space, fill in the gap, and apply the inverse transform to reconstruct the result. the complex spectrogram of the signal is partitioned into separate overlapping frequency bands. each band is fragmented by segmentation of the time-frequency space and a partition of the spectrogram in time, and filled in with complex fragments by example. we demonstrate our method by filling in gaps of various types of textural sounds.
person-on-person violence detection in video data. we address the problem of detecting human violence in video, such as fist fighting , kicking, hitting with objects, etc. to detect violence we rely on motion trajectory information and on orientation information of a person's limbs. we define an acceleration measure vector (amv) composed of direction and magnitude of motion and we define jerk to be the temporal derivative of amv. we present results from several data sequences involving multiple types of violent activities.
signal discrimination using a support vector machine for genetic syndrome diagnosis. in this study, a support vector machine (svm) classifies real world data of cytogenetic signals measured from fluorescence in-situ hybridization (fish) images in order to diagnose genetic syndromes. the study implements the svm structural risk minimization concept in searching for the optimal setting of the classifier kernel and parameters. we propose thresholding the distance of tested patterns from the svm separating hyperplane as a way of rejecting a percentage of the miss-classified patterns thereby allowing reduction of the expected risk. results show accurate performance of the svm in classifying fish signals in comparison to other state-of-the-art machine learning classifiers, indicating the potential of an svm-based genetic diagnosis system.
recognizing interaction activities using dynamic bayesian network. activity recognition is significant in intelligent surveillance. in this paper, we present a novel approach to the recognition of interacting activities based on dynamic bayesian network (dbn). in this approach the features representing the object motion are divided into two classes: global features and local features, which are at two different spatial scales. global features describe object motion at a large spatial scale and relations between objects or between the object and environment, and local ones represent the motion details of objects of interest. we propose a new dbn model structure with state duration to model human interacting activities. this dbn model structure combines the global features with local ones harmoniously. the effectiveness of this novel approach is demonstrated by experiment.
reflectance estimation from motion under complex illumination. in this paper, we propose a method for recovering the reflectance properties of a moving lambertian object from an image sequence of the object taken by a fixed camera under unknown, complex illumination. our proposed method is based on the spherical-harmonic representation of lambertian reflectance under arbitrary illumination. then, by combining the geometry reconstructed by shape-from-motion (sfm), we recover the albedo of the object and the illumination distribution only from the image sequence of the moving object. the proposed method enables us to synthesize realistic images of the object in arbitrary poses under arbitrary lighting conditions by using reconstructed shape and albedo. we conducted a number of experiments by using both synthetic and real images to confirm the effectiveness of our proposed method.
robust detection of people in thermal imagery. we present a new contour analysis technique to detect people in thermal imagery. background-subtraction is first used to identify local regions-of-interest. gradient information within each region is then combined into a contour saliency map. to extract contour fragments, a watershed-based selection algorithm is used. a path-constrained a* search is employed to complete any broken contours, from which silhouettes are formed. results using thermal video sequences demonstrate the capability of the approach to robustly detect people across a wider range of environmental conditions than is possible with standard approaches.
fast tone mapping for high dynamic range images. we present a fast, effective and flexible tone reproduction method that preserves visibility and contrast impression of high dynamic range scenes in low dynamic range reproduction devices. a single parameter controls the visibility and contrast in a simple and elegant manner and at interactive speed. the new method is simple to use and is computationally highly efficient. experiments show that the technique produces good results on a variety of high dynamic range images. the method can also be used to enhance ordinary low dynamic range digital images.
analysis and recognition of walking movements. we present an approach for recognizing human walking movements using low-level motion regularities and constraints. biomechanical features for classification are automatically extracted from video sequences of walkers. a multiplicative classification rule using statistical distances is then used to determine whether an unknown motion is consistent with normal walking patterns. recognition results are shown distinguishing walking examples across multiple speeds from other non-walking locomotions.
on the classification of temporal lobe epilepsy using mr image appearance. classification of neurological diseases based on image characteristics often requires extensive modeling and use intervention. while other techniques concentrate on specific structures, the novelty of the method presented here resides in its analysis of the grey-level appearance of large, non-specific volumes of interest (voi) from t1 mri data. no manual intervention is required other than the selection of the voi. this work presents the methodological framework and preliminary results towards our aim of classifying normal subjects and patients with temporal lobe epilepsy (tle) within the medial temporal lobe. for this purpose, principal components analysis is performed on a set of normal subjects for the creation of a multi-dimensional space representative of a normal population. new data for normal and tle subjects are proposed in this space, under the assumption that the distributions of the projections are not identical and can be used for classification. it is shown that linear discriminant analysis of the eigencoordinates of the projected data can beused to classify normals vs tle with a 70% accuracy based on only 10 eigenvectors. this results can go up to 100% if all eigenvectors defining the grey-level space are used.
transforming static ct in gated pet/ct studies to multiple respiratory phases. discrepancy between ct and pet data due to motion in pet/ct studies is a significant problem. pet is acquired over a period of time whereas ct is static. thus attenuation correction of pet data with ct leads to wrong quantification and can result in wrong diagnosis and treatment. a solution based on respiratory gating and optical flow algorithms is presented. a combined local-global optical flow algorithm is used for motion estimation on pet data, the static ct is then transformed to different phases of the respiratory cycle. corresponding pet and ct data is thus obtained for better attenuation correction. the results are verified on both software phantom and real patient data.
structural graph matching with polynomial bounds on memory and on worst-case effort. a new method of structural graph matching is introduced and compared against an existing method and against the maximum common subgraph. the method is approximate with polynomial bounds on both memory and on the worst-case compute effort. methods work on arbitrary types of graphs and tests with strongly regular graphs are included. no node or edge colors are needed in the methods; the common subgraph is extracted based in structural comparisons only. monte carlo trials are benchmarked with 100% additional (clutter) nodes. results are shown to be typically within 1-2 nodes of the maximum common subgraph. over 7500 test trials are reported with graphs up to 100 nodes.
structural matching via optimal basis graphs. the 'basis graph' approach to structural matching uses a fixed set of small (4 node) graphs to characterize local structure. we compute mapping probabilities by first finding the probability of a basis graph being an induced subgraph of the input graph. the similarity of these probabilities is used to compare nodes of the input graphs. the method permits common subgraphs to be identified without the use of any node or edge coloring. we report on an improved, simpler, version of the algorithm, which has also been optimized. performance is compared with the lerp method, which is based on length-r paths. both methods are approximate with polynomial bounds on both memory and on the worst-case compute effort. these methods work on arbitrary types of undirected graphs, and tests with strongly regular graphs are included. monte carlo test trials (3000+) included up to 100% additional (noise) nodes.
an area-based alignment method for 3d urban models. the alignment method refers to the idea of superposing a model onto an image. the problem is separated in two parts, i) first find a possible transformation of the model on the image ii) then validate this transformation by aligning the model onto the image.we propose an extension of the traditional alignment method in order to deal with complex objects sustained by projective mappings. much of the work is focalized on how to cope with spurious data as long as processing time is not becoming too excessive. moreover, the robustness and simplicity of the approach make it appealing for a broader class of objects, not necessarily urban parts.
combined use of partial least squares regression and neural network for diagnosis tasks. this paper deals with a diagnosis system, based on a combined use of partial least squares regression (pls) and neural network (nn). an application concerning the french railway track/vehicle transmission system illustrates this approach. it will be shown that a reliable selection of a reduced set of relevant descriptors is made by the pls regression. moreover, the projection of the data on the first pls plane allows to highlight trajectories of the evolution of the system state between different classes. the modeling of the process state is performed by a multilayer nn. in this case, the pls algorithm provides also a suitable approach to initialize the nn weights and to determine the optimal number of hidden nodes.
regularity and complexity of human electroencephalogram dynamics: applications to diagnosis of alzheimers disease. in this paper, we evaluate the complexity and regularity of human electroencephalogram (eeg) dynamics using approximate entropy (apen), and the results are used to distinguish alzheimer's disease (ad) patients from healthy subjects. from the 10-channel eeg time series recordings of 20 healthy subjects and 14 ad patients with closed eyes, our analysis has shown that ad patients have lower apen values than healthy subjects. these results support the previous hypothesis that greater regularity corresponds to greater component autonomy and isolation in many complex systems. we believe that our effort provides a valuable complementary framework to the classical eeg analysis, and it could help revealing the complexity of the human brain functions.
fast polygonal approximation of digital curves. in this paper, we have extended the approach defined in [segmentation of discrete curves into fuzzy segments, segmentation of discrete curvesinto fuzzy segments, extented version] to a multiorder analysis. the approach is based on the arithmetical definition of discrete lines [géométrie discrète, calculs en nombre entiers et algorithmique] with variable thickness. we provide a framework to analyse a digital curve at different levels of thickness. the extremities points of a segment provided at a high resolution are tracked at lower resolution in order to refine their locations. the high resolution level is automatically defined from the stability of the number of segments between two consecutive levels. the method is threshold-free and automatically provides a partitioning of a digital curve into its meaningful parts.
principal component analysis for online handwritten character recognition. in this paper, principal component analysis (pca) is applied to the problem of online handwritten character recognition in the tamil script. the input is a temporally ordered sequence of (x,y) pen coordinates corresponding to an isolated character obtained from a digitizer. the input is converted into a feature vector of constant dimensions following smoothing and normalization. pca is used to find the basis vectors of each class subspace and the orthogonal distance to the subspaces used for classification. pre-clustering of the training data and modification of distance measure are explored to overcome some common problems in the traditional subspace method. in empirical evaluation, these pca-based classification schemes are found to compare favorably with nearest neighbour classification.
the characterization of classification problems by classifier disagreements. in this paper we try to characterize a set of classification problems. for this, we use the disagreement between a set of standard classifiers. the disagreement patterns do not only point towards different types of classification problems, but also indicate the novelty and the usefulness of a classifier with respect to a setof classification problems and classifiers. some experiments show when known classification problems become unknown after changing their feature size or their training set size.