waashk/dblp huggingface dataset:
tracking a variable number of human groups in video using probability hypothesis density. we apply a multi-target recursive bayes filter, the probability hypothesis density (phd) filter, to a visual tracking problem: tracking a variable number of human groups in video. first, we use background subtraction to detect human groups which appear as foreground blobs. the phd filter is implemented using sequential monte carlo methods; and the centroids of the foreground blobs are used as the measurements to update the phd filter. our experimental results show that when human groups appear, merge, split, and disappear in the field of view of a camera, our method can track them correctly.
content-based audio classification using support vector machines and independent component analysis. in this paper, we present a new audio classification system. first, a frame-based multiclass support vector machine (svm) for audio classification is proposed. the accuracy rate has significant improvements over conventional file-based svm audio classifier. in feature selection, this study transforms the log powers of the critical-band filters based on independent component analysis (ica). this new audio feature is combined with mel-frequency cepstral coefficients (mfccs) and five perceptual features to form an audio feature set. the superiority of the proposed system has been demonstrated via a 15-class sound database with a 91.7% accuracy rate.
incorporating prior knowledge into svm for image retrieval. svm based image retrieval suffers from the scarcity of labelled samples. in this paper, this problem is solved by incorporating prior knowledge into svm. firstly, some prior knowledge of image retrieval is discussed and constructed. after that, the knowledge is incorporated into svm optimization as a constraint, and a new knowledge-based target function is formulated. based on this, a framework of image retrieval with knowledge based svm is proposed. experimental results demonstrate that the proposed method can effectively improve the learning and retrieval performance of svm, especially when the number of labelled samples is small.
automatic sports video genre classification using pseudo-2d-hmm. building a generic content-based sports video analysis system remains a challenging problem because of the diversity in sports rules and game features which makes it difficult to discover generic low-level features or high-level modeling algorithms. one possible alternative is to first classify the sports genre and then apply specific sports domain knowledge to perform analysis. in this paper we describe a multi-level framework to automatically recognize the genre of the sports video. the system consists of a pseudo-2d-hmm classifier using low-level visual/audio features to evaluate the video clips. the experimental results are satisfactory and extension of the framework to a generic sports video analysis system is being implemented.
efficient topological localization using orientation adjacency coherence histograms. this paper describes an efficient vision-based global topological localization approach that uses a coarse-tofine strategy. orientation adjacency coherence histogram (oach), a novel image feature, is proposed to improve the coarse localization. the coarse localization results are taken as inputs for the fine localization which is carried out by matching harris-laplace interest points characterized by the sift descriptor. computation of oachs and interest points is efficient due to the fact that these features are computed in an integrated process. we have implemented and tested the localization system in real environments. the experimental results demonstrate that our approach is efficient and reliable in both indoor and outdoor environments.
level set methods, distance function and image segmentation. in the study of level set methods, several significant problems were neglected all along, such as the existence, uniqueness and singularities of level set methods. in this article we give the proof that in a neighborhood of the initial zero level set, for the level set equations with the restriction of distance function, there exists a unique solution, which must be the signed distance function with respect to the evolving surface. we also present the analysis of singular points' effect on level set evolution and give an adaptive narrow banding algorithm. the detailed numerical analysis and a simplified definition for singular points are presented. we give an adaptive narrow banding algorithm, which avoids the singular points and is proved to be robust and efficient in segmentation of ct data and synthesized images.
kernel sample space projection classifier for pattern recognition. we propose a new kernel-based method for pattern recognition. support vector machine (svm), principal component analysis (pca), and fisher discriminant have been extended to kernel based methods and they achieve better performance. in this paper, we propose kernel sample space projection classifier (ksp) for pattern recognition. in ksp, an unknown input pattern is discriminated by comparing the norms onto kernel sample spaces which are spanned by sample vectors mapped to a high dimensional feature space by mercer kernel function. in this paper, we provide a closed form of our method and show its advantages by experimental results of the recognition problem using handwritten digit database "mnist" and some two-class classification problems. finally we compare it with other methods from several points of view.
non-linear wiener filter in reproducing kernel hilbert space. wiener filters are used widely for inverse problems. from an observed signal, a wiener filter provides the best restored signal with respect to the square error averaged over the original signal and the noise among linear operators. we introduce the non-linear wiener filter, which is a kernel-based extension of the wiener filter. when the kernel method is applied to the wiener filter directly, the dimensions of the space where the calculation has to be done is very large since noise samples have to be used. we provide a realistic solution using the first order approximation. moreover, we provide the experimental results to demonstrate the advantages of this method.
planar structure based registration of multiple range images. in this paper, we describe the method for aligning multiple range images given by a range finder. especially we will use range images of inside and outside of buildings which contain many planar structures. in our method for registration of range images, we consider not only 3d positions of range data but normal vectors of planes in the scene in order to refine the icp (iterative closest point) algorithm. first, we extract planes of range images in order to calculate normal vectors of planes in the scene. then, we estimate the motion parameters that are composed of a rotation matrix and a translation vector using our refined icp algorithm. we present results that demonstrate our approach's ability to align range images more accurately and faster than the standard icp algorithm.
motion prediction using vc-generalization bounds. this paper describes a novel application of statistical learning theory (slt) for motion prediction. slt provides analytical vc-generalization bounds for model selection; these bounds relate unknown prediction risk (generalization performance) and known quantities such as the number of training samples, empirical error, and a measure of model complexity called the vc-dimension. we use the vc-generalization bounds for the problem of choosingoptimal motion models from small sets of image measurements (flow ). we present results of experiments on image sequences for motion interpolation and extrapolation; these results demonstrate the strengths of our approach.
unsupervised segmentation using gabor wavelets and statistical features in lidar data analysis. in this paper, we address issues in segmentation of remotely sensed lidar (light detection and ranging) data. the lidar data, which were captured by airborne laser scanner, contain 2.5 dimensional (2.5d) terrain surface height information, e.g. houses, vegetation, flat field, river, basin, etc. our aim in this paper is to segment ground (flat field) from non-ground (houses and high vegetation) in hilly urban areas. by projecting the 2.5d data onto a surface, we obtain a texture map as a grey-level image. based on the image, gabor wavelet filters are applied to generate gabor wavelet features. these features are then grouped into various windows. among these windows, a combination of their first and second order of statistics is used as a measure to determine the surface properties. the test results have shown that ground areas can successfully be segmented from lidar data. most buildings and high vegetation can be detected. in addition, gabor wavelet transform can partially remove hill or slope effects in the original data by tuning gabor parameters.
digital image restoration by exposure-splitting and registration. with the explosion of digital imaging systems, high image noise levels, particularly motion blur and sensor noise, limit applications of mobile cameras. in this paper, a novel restoration approach is developed. by uniformly splitting the exposure time and averaging the multiple under-exposed captures, a single image is obtained, in which both types of noise are mitigated. comprehensive experiments demonstrate successful image restoration under various exposure conditions. exposure-splitting is further optimized to fully recover the image quality under severe motion blur. this technique can be easily implemented into contemporary digital imaging systems.
fingerprint verification based on multistage minutiae matching. this paper proposes a novel and distortion-tolerant fingerprint verification technique based on multistage minutia matching. in the first stage, the local similarities of minutiae between two fingerprints are evaluated by matching their local orientation fields and local minutiae topologic structures. in the second stage, the top 5 minutia pairs obtained in the first matching stage are used as the reference minutiae pairs to align two minutia sets, and a set of matched minutia pairs is obtained by matching the aligned two minutia sets. in the third stage, the set of matched pairs obtained in the second stage are matched again by matching two global topologic structures constructed based on the set of matched pairs. the final matching score is obtained based on the set of matched pairs obtained in the second matching stage and the separate matching scores calculated in three stages. experiments on database of fvc2002 show that this fingerprint matching technique is effective.
specification of image acquisition parameters for stereo panoramas. the specification of image acquisition parameters needs to be in accordance with given constraints defined by application requirements, the architecture of the camera, and specifications of the targeted 3d scenes. this paper proposes a novel approach for specifying acquisition parameters at image acquisition time to ensure high-quality stereo panoramas. our approach satisfies commonly demanded application requirements such as: proper scene composition in resultant images; adequate sampling at a particular scene distance; and desired stereo quality (i.e. depth level resolution) over a diversity of scenes of interest. previous studies have paid great attentions on how proposed stereo panorama imaging models/methods support the epipolar geometry constraint and system realizations. the image acquisition parameter assignment problem has not yet been dealt with in these studies. the lack of guidance in specifying image acquisition parameters affects the validity ofresults for any subsequent processes.
the design of a stereo panorama camera for scenes of dynamic range. existing stereo panorama cameras do not allow controllability of pictorial/scene composition and stereo acuity (depth levels) over dynamic 3d scene ranges. we specify the design of such a camera allowing this type of flexibility. previous approaches to design panorama cameras even lack studies with respect to this important aspect, while other design issues such as epipolar geometry, optics optimization, or realization-oriented approximations have been investigated. without incorporating the controllability into stereo panorama camera design, the poor quality of produced stereo panoramas is foreseeable (e.g. incoherence,cardboard-effect, dipopia etc).the paper proposes a solution to incorporate controllability into previously discussed [3, 8, 7, 5] stereo panorama camera models. by using a stereo panorama camera equipped with the designed camera parameters according to our solution, the desired/expected pictorial composition and stereo acuity in resultant stereo panoramas can be en-sured.
robust face recognition under lighting variations. in this paper, we propose a new face recognition algorithm based on the matching of relative image gradient magnitudes between images. the recognition algorithm first uses a face localization procedure to provide rough face regions under different lighting conditions, followed by an iterative optimization procedure for precise face matching. both our face localization and matching procedures are based on matching relative image gradient to be robust against lighting variations. then a robust face similarity measure based on comparison of relative image gradients is used to determine the face recognition results. the face localization step finds some candidate poses of the face in the image through a fast k-nn search of the best match of the relative gradient features from the database of training feature vectors, which are obtained through image synthesis. after the face images are aligned, the face similarity measure is computed from the normalized correlation between the relative gradients. experimental results are shown to demonstrate its robust recognition performance under different lighting conditions.
a hybrid som-svm method for analyzing zebra fish gene expression. microarray technology can be employed to quantitatively measure the expression of thousands of genes in a single experiment. it has become one of the main tools for global gene expression analysis in molecular biology research in recent years. the large amount of expression data generated by this technology makes the study of certain complex biological problems possible, and machine learning methods are expected to play a crucial role in the analysis process. we present our results from integrating a self-organizing maps (som) and a support vector machine (svm) for the analysis of the various functions of zebra fish genes based on their expression. we discuss how som can be used as a data-filtering tool to improve the classification performance of the svm on this data set.
markov chain monte carlo data association for merge and split detection in tracking protein clusters. tagging and tracking protein molecules with the help of laser scanning confocal microscope (lscm) are a key to better understanding of proteomics in diverse aspects. one challenge of tracking multiple green fluorescent protein (gfp) clusters is how to deal with the interaction between multiple objects, namely splitting and merging. in this paper, we propose a framework to track multiple gfp clusters merge and split by using markov chain monte carlo data association (mcmcda) method combined with asymmetric region matching strategy. the experimental results show that the method is promising.
remote sensing image fusion on gradient field. this paper presents two methods to fuse a low spatial resolution multispectral image and a high spatial resolution panchromatic one to produce a new multispectral image with high spatial resolution. first, the poisson fusion method is developed based on minimizing the gradient difference between the synthesized image and the panchromatic image with boundary conditions sampled from the multispectral image. the fusion result can therefore be achieved by solving the poisson equation with dirichlit boundary conditions. secondly, an optimal fusion technique, which minimizes the gradient difference and the color difference with respect to the panchromatic and multispectral images respectively is given and the result is induced by an iterative optimization algorithm. both of them can be applied to color composites and individual bands. their advantages of the fidelity to spectral property and the spatial resolution improvement over the hsi, brovey, pca and wavelet transform are convincingly demonstrated in the experiments from visual evaluation and statistical analysis.
an information theoretic approach for next best view planning in 3-d reconstruction. we present an algorithm for optimal view point selection for 3-d reconstruction of an object using 2-d image points. since the image points are noisy, a kalman filter is used to obtain the best estimate of the object's geometry. this kalman filter allows us to efficiently predict the effect of any given camera position on the uncertainty, and therefore quality, of the estimate. by choosing a suitable optimization criterion, we are able to determine the camera positions which minimize our reconstruction error. we verify our results using two experiments with real images: one experiment uses a calibration pattern for comparison to a ground-truth state, the other reconstructs a real world object.
fast object and pose recognition through minimum entropy coding. we present a pattern recognizer to classify a variety of objects and their pose on a table from real world images. learning of weights in a linear discriminant is based on estimating the relative information contributed by a set of features to the final decision. evaluation of the discriminant is very fast, allowing for about three decisions per second on datasets without segmentation difficulties like the coil-100 database. experiments on that database yield high recognition rates and good generalisation over pose.
mixing spectral representations of graphs. generative models are well known in the domain of statistical pattern recognition. typically, they describe the probability distribution of patterns in a vector space. the individual patterns are defined by vectors and so the individual features of the pattern are well defined. in contrast, very little has been done with generative models of graphs. graphs are not naturally represented in a vector space since there is no natural labelling of the vertices of the graphs - different labellings lead to different representations of the graph structure. because of this, simple statistical quantities such as mean and variance are difficult to define for a group of graphs. while we can define statistical quantities of individual edges, it is not so straightforward to define how sets of edges in graphs are related. the spectral decomposition of a graph can be used to extract information about the relationship of edges and parts in a graph. in this paper we look at the problem of mixing graphs by using the spectral representation of a graph as an intermediate step. the spectral representation allows us to mix different structural features from each of the graphs to create new combinations. we can also define an averaging process on the spectral representations which generates a graph close to the graph median.
reconstruction of spheres using occluding contours from stereo images. this paper discusses an efficient way of reconstructing spheres from the occluding contours of two views. it takes into consideration, the geometric properties of the system and uses frontier points as additional data. this algorithm is especially useful for applications where the objects to be modelled are known to resemble spheres and where speed is of importance. it is developed for fruit sorting applications where the processing has to be done in real-time.
generalized pattern spectra sensitive to spatial information. morphological pattern spectra computed from granulometries are frequently used to classify the size classes of details in textures and images. an extension of this technique, which retains information on the spatial distribution of the details in each size class is developed. algorithms for computation of these spatial pattern spectra for a large number of granulometries on binary images are presented.
probabilistic automatic red eye detection and correction. in this paper we propose a new probabilistic approach to red eye detection and correction. it is based on stepwise refinement of a pixel-wise red eye probability map. red eye detection starts with a fast non red eye region rejection step. a classification step then adjusts the probabilities attributed to the detected red eye candidates. the correction step finally applies a soft red eye correction based on the resulting probability map. the proposed approach is fast and allows achieving an excellent correction of strong red eyes while producing a still significant correction of weaker red eyes.
alignment of multiple non-overlapping axially symmetric 3d datasets. uknown to us, an axially-symmetric surface is broken into disjoint pieces along a set of break-curves, i.e., the curves along which the surface locally breaks into two pieces. a subset of the pieces are available and for each of them we obtain noisy 3d measurements of its surface and break-curves. using the piece measurements and knowledge of which pieces share a common break-curve, we propose a stochastic method for automatically estimating the unknown axially-symmetric global surface. surface and break-curve estimation is then an alignment problem where we must estimate the unknown axially-symmetric surface and break-curves while simultaneously estimating the euclidean transformation that positions each measured piece with respect to the a-priori unknown surface. parameter estimation is implemented as maximum likelihood estimation where we seek the global pot geometry which best explains the measured fragment data. this new approach is robust, fast, and accurate. experimental results are presented which solves an application of interest, specifically the reconstruction of archaeological pots from subsets of their surface pieces.
surface sculpting with stochastic deformable 3d surfaces. this paper introduces a new stochastic surface model for deformable 3d surfaces and demonstrates its utility for the purpose of 3d sculpting. this is the problem of simple-to-use and intuitively interactive 3d free-form model building. a 3d surface is a sample of a markov random field (mrf) defined on the vertices of a 3d mesh where mrf sites coincide with mesh vertices and the mrf cliques consist of subsets of sites. each site has 3d coordinates (x,y,z) as random variables and is a member of one or more clique potentials which are functions of the vertices in a clique and describe stochastic dependencies among sites. data, which is used to deform the surface can consist of, but is not limited to, an unorganized set of 3d points and is modeled by a conditional probability distribution given the 3d surface. a deformed surface is a map (maximum a posteriori probability) estimate of the joint distribution of the mrf surface model and the data. the generality and simplicity of the mrf model provides the ability to incorporate unlimited local and global deformation properties. included in our development is the introduction of new data models, new anisotropic clique potentials, and cliques which involve sites that are spatially far apart. other applications of these models are possible, e.g., stereo reconstruction.
modelling of 2d gel electrophoresis images for proteomics databases. an image modelling technique, based on the use of hermite functions and a multiresolution image alignment algorithm, is described and applied to the problem of analysing 2d electrophoresis images of biological samples. after a brief description of the principles underlying the model, its use is illustrated on some images of samples. the model forms the core of an image analysis and database system currently being developed for proteomics.