waashk/dblp huggingface dataset:
technology for automated morphologic analysis of cytological slides. methods and results. the information technology for automated morphologic analysis of the cytological slides, taken from patients with the lymphatic system tumors, was developed. the main components of the technology are: acquisition of cytological slides, method for segmentation of nuclei in the cytological slides, synthesis of the feature based nuclei description for subsequent classification, nuclei image analysis based on pattern recognition and scale-space techniques. the experiments confirmed efficiency of the developed technology. the discussion of the obtained results is given. the developed technology is implemented in the software system.
method for early diagnostics of lymphatic system tumors on the basis of the analysis of chromatin constitution in cell nucleus images. in this paper, a new criterion for early diagnostics of lymphatic system tumor from images of cell nuclei of lymphatic nodes is considered. a method for image analysis of chromatin structure is developed on the basis of the scale-space approach. a diagnostically important criterion is defined as a total amount of points of spatial intensity extrema in the families of blurred images generated by the given image of a cell nucleus. the procedure for calculating criterion values is presented. testing of the obtained criterion is carried out.
rapid spline-based kernel density estimation for bayesian networks. the likelihood for patterns of continuous attributes for the naive bayesian classifier (nbc) may be approximated by kernel density estimation (kde), letting every pattern influence the shape of the probability density thus leading to accurate estimation. kde suffers from computational cost making it unpractical in many real-world applications. we smooth the density using a spline thus requiring only very few coefficients for the estimation rather than the whole training set, allowing rapid implementation of the nbc without sacrificing classifier accuracy. experiments conducted over several real-world databases reveal acceleration, sometimes in several orders of magnitude, in favor of the spline approximation making the application of kde to the nbc practical.
weighted loss functions to make risk-based language identification fused decisions. making a pattern recognition decision with the maximum-likelihood rule is a particular case of the risk-based bayesian decision rule which is simplified when the loss function is zero-one symmetrical and classes are equally a priori probable. in the case the recognition system is composed of several experts, we can take into account their estimated performance at the class level as a key heuristic-like factor to weight the loss function and drive the recognition process while fusing their decisions. such indices are formally computed by applying the discriminant factor analysis method. the experiments are carried out in the automatic language identification domain with a system composed of several identification experts. fusion of expert decisions is achieved by building statistical classifiers.
structural representation of speech for phonetic classification. this paper explores the issues involved in using symbolic metric algorithms for automatic speech recognition (asr), via a structural representation of speech. this representation is based on a set of phonological distinctive features which is a linguistically well-motivated alternative to the "beads-on-a-string" view of speech that is standard in current asr systems. we report the promising results of phoneme classification experiments conducted on a standard continuous speech task.
adjustable invariant features by partial haar-integration. a very common type of a-priori knowledge in pattern analysis problems is invariance of the input data with respect to transformation groups, e.g. geometric transformations of image data like shifting, scaling etc. for enabling most general analysis techniques, this knowledge should be incorporated in the feature-extraction stage. in the present work a method for this, called haar-integration, is generalized to make it applicable to more general transformation sets, namely subsets of transformation groups. the resulting features are no longer precisely invariant, but their variability can be adjusted and quantified. experimental results demonstrate the increased separability by these features and considerably improved recognition performance on a character recognition task.
camera self-calibration: a new approach for solving the modulus constraint. in this paper, the modulus constraint is used to retrieve the scale factors that are responsible for the nonlinearity of kruppa's equations. in the case of constant intrinsic parameters, each pair of images identifies a pair of 3dpoints at infinity whose coordinates are expressed in terms of the scale factors we are looking for. by enforcing the coplanarity constraint on these points, a set of quintic bivariate equations is obtained for each triplet of images. once the scale factors are calculated, the problems of retrieving the plane at infinity and solving kruppa's equations become straightforward and linear.
unsupervised learning using locally linear embedding: experiments with face pose analysis. this paper considers a recently proposed method for unsupervised learning and dimensionalityreduction, locally linear embedding (lle). lle computes a compact representation of high-dimensional data combining the major advantages of linear methods (computational efficiency, global optimality, and flexible asymptotic convergence guarantees) with the advantages of non-linear approaches (flexibility to learn a broad of class on non-linear manifolds). we assess the performance of the lle algorithm on a real-world data (face imagesin different poses) and compare the results with those obtained with two different approaches (pca and som). extensions to the original lle algorithm are proposed and applied to the problem of pose estimation.
selecting models from videos for appearance-based face recognition. in this paper, we propose an unsupervised approach to select representative face samples (models) from raw videos and build an appearance-based face recognition system. the approach is based on representing the face manifold in a low-dimensional space using the locally linear embedding (lle) algorithm and then performing k-means clustering. we define the face models as the cluster centers. our strategy is motivated by the efficiency of lle to recover meaningful low-dimensional structures hidden in complex and high dimensional data such as face images. two other well-known unsupervised learning algorithms (isomap and som) are also considered. we compare and assess the efficiency of these different schemes on the cmu mobo database which contains 96 face sequences of 24 subjects. the results clearly show significant performance enhancements over traditional methods such as the pca-based one.
a hybrid approach to face detection under unconstrained environments. to detect faces in natural and unconstrained environments, we propose an approach which combines the advantages of both color and gray scale based methods. the idea consists of first preprocessing the images using a state-ofthe- art approach for skin modeling in order to determine the potential skin regions. thus, a scanning of the whole image when searching for faces is avoided. then, in contrast to the existing methods, we consider the fact that the skin detection step still may produce unsatisfactory results or even fail and therefore we apply an exhaustive search in and around the detected skin regions using a new gray scale based approach. the experimental results show that the proposed approach inherits the speed from the color based methods and the efficiency from the gray scale based ones.
target model estimation using particle filters for visual servoing. in this paper, we present a novel method for model estimation for visual servoing. this method employs a particle filter algorithm to estimate the depth of the image features online. a gaussian probabilistic model is employed to model the object points in the current camera frame. a set of 3d samples drawn from the model is projected into the image space in the next frame. the 3d sample that maximizes the likelihood is considered to be the most probable real-world 3d point. the variance value of the depth density function converges to very small value within a few iterations. results show accurate estimate of the depth/model and a high level of stability in the visual servoing process.
human detection in outdoor scene using spatio-temporal motion analysis. we propose an image processing algorithm for detecting human in outdoor scenes containing changeful background. in this work, regions extracted through background subtraction procedure are accurately classified into human and others by motion analysis in the three dimensional feature space constructed by the spatial uniqueness of image motion f¿, the temporal uniqueness of image motion f¿ , and the temporal motion continuity f¿. evaluation test proved that proposed algorithm can reduce the error rates of both false positive and false negative to about 1/3 compared with a conventional method. we also tested by a pc-based real-time system over two weeks in real environments, that resulted in its false negative error rate of less than 1% and false positive error number of less than 3 times per day.
adaptive control of video display for diagnostic assistance by analysis of capsule endoscopic images. in this paper, we present a method for reducing diagnostic time by adaptively controlling the frame rate in a capsule.. endoscopic image sequence. the video sequence, which was capture over 8 hours, requires from 45 minutes to two hours of extreme concentration by examining doctors to make diagnosis. effectiveness of the method is that the sequence can be played at high..speed in stable regions to save time and then decreased at rough changes that can then help ascertain suspicious findings more..conveniently. to realize such a system, the capturing conditions are classified into groups corresponding to the changing states between two frames. the.. delay time of these frames was calculated by the parametric functions. the optimal parameter set was determined from.. evaluations by medical doctors. we concluded that the average diagnostic time could be reduced from 8 hours down to about 30 minutes.
fast restoration of colour movie scratches. this paper presents a new type of scratch removal algorithm based on a causal adaptive multidimensional multitemporal prediction. the predictor use available information from the neighbourhood of a missing multispectral pixels due to spectral, temporal and spatial correlation of video data but not any information from the failed pixels themselves.
btf image space utmost compression and modelling method. the bidirectional texture function (btf) describes texture appearance variations due to varying illumination and viewing conditions. this function is acquired by large number of measurements for all possible combinations of illumination and viewing positions hence some compressed representation of these huge btf texture data spaces is obviously inevitable. in this paper we present a novel efficient probabilistic model-based method for multispectral btf texture compression which simultaneously allows its efficient modelling. this representation model is capable of seamless btf space enlargement and direct implementation inside the graphical card processing unit. the analytical step of the algorithm starts with btf texture surface estimation followed by the spatial factorization of an input multispectral texture image. single band-limited factors are independently modelled by their dedicated 3d causal autoregressive models (car). we estimate an optimal contextual neighbourhood and parameters for each car. finally the synthesized multiresolution multispectral texture pyramid is collapsed into the required size fine resolution synthetic smooth texture. resulting btf is combined in a displacement map filter of the rendering hardware using both multispectral and range information, respectively. the presented model offers immense btf texture compression ratio which cannot be achieved by any other sampling-based btf texture synthesis method.
a gaussian mixture-based colour texture model. a new method of colour texture modelling based on gaussian distribution mixtures is discussed. we estimate the local statistical properties of the monospectral version of the target texture in the form of a gaussian mixture of product components. the synthesized texture is obtained by means of a step-wise prediction of the texture image. in order to achieve a realistic colour texture image and to avoid possible loss of high-frequency details we use optimally chosen pieces of the original colour source texture in the synthesis phase. in this sense the proposed texture modelling method can be viewed as a statistically controlled sampling. by using multispectral or mutually registered btf texture pieces the method can be easily extended also for these textures.
a multiscale colour texture model. a fast recursive model-based algorithm for realistic colour texture synthesis is proposed. the algorithm starts with a colour texture image decomposition into a multiresolution grid. each band pass colour factors are independently modelled by their dedicated 3d causal autoregressive random field models (car). we estimate an optimal contextual neighbourhood and parameters for each of the car submodel. finally the synthesized multiresolution colour texture pyramid is collapsed into the required fine resolution colour texture. the benefit of the multigrid approach is the replacement of a large neighbourhood car model with a set of several simpler car models which are easy to synthesize and wider application area of these multi-grid models capable of reproducing realistic textures for enhancing realism in various texture application areas.
unsupervised texture segmentation using multispectral modelling approach. a new unsupervised multispectral texture segmentation method with unknown number of classes is presented. multispectral texture mosaics are locally represented by four causal multispectral random field models recursively evaluated for each pixel. the segmentation algorithm is based on the underlying gaussian mixture model and starts with an over segmented initial estimation which is adaptively modified until the optimal number of homogeneous texture segments is reached. the performance of the presented method is extensively tested on the prague segmentation benchmark using the commonest segmentation criteria and compares favourably with several alternative texture segmentation methods.
illumination invariant texture retrieval. two fast illumination invariant image retrieval methods for scenes comprising textured objects with variable illumination are introduced. both methods are based on texture gradient modelled by efficient set of random field models. we developed the illumination insensitive measures for textured images representation and compared them favorably with steerable pyramid and gabor features in the illumination invariant btf texture recognition.
multimodal range image segmentation by curve grouping. a fast range image segmentation method for scenes comprising general faced objects is introduced. the range segmentation is based on a recursive adaptive probabilistic detection of step discontinuities which are present at object face borders in mutually registered range and intensity data. detected face outlines guides the subsequent region growing step where the neighbouring face curves are grouped together. region growing based on curve segments instead of pixels like in the classical approaches considerably speed up the algorithm. the exploitation of multimodal data significantly improves the segmentation quality.
ontology and taxonomy collaborated framework for meeting classification. a framework for classification of meeting videos is proposed in this paper. we define our framework consisting of a four level concept hierarchy having movements, events, behavior, and genre; which is based on the meeting ontology and taxonomy. ontology is the formal specification of domain concepts and their relationships. taxonomy is the general categorization based on a class/subclass relationships. this concept hierarchy is mapped to an implementation of finite state machines (fsm) and rule-based system (rbs) to classify the meetings. events are detected by the fsms based on the movements (head and hand tracks). classification of the meetings is performed by the rbs based on the events, and behaviors of the people present in the meetings. our framework is novel and scalable, capable of adding new meeting types with no re-training. we conducted experiments on various meeting sequences and classified meetings into voting, argument, presentation, and object passing. this framework has applications in automated video surveillance, video segmentation and retrieval (multimedia), human computer interaction, and augmented reality.
estimating geospatial trajectory of a moving camera. this paper proposes a novel method for estimating the geospatial trajectory of a moving camera. the proposed method uses a set of reference images with known gps (global positioning system) locations to recover the trajectory of a moving camera using geometric constraints. the proposed method has three main steps. first, scale invariant features transform (sift) are detected and matched between the reference images and the video frames to calculate a weighted adjacency matrix (wam) based on the number of sift matches. second, using the estimated wam, the maximum matching reference image is selected for the current video frame, which is then used to estimate the relative position (rotation and translation) of the video frame using the fundamental matrix constraint. the relative position is recovered upto a scale factor and a triangulation among the video frame and two reference images is performed to resolve the scale ambiguity. third, an outlier rejection and trajectory smoothing (using b-spline) post processing step is employed. this is because the estimated camera locations may be noisy due to bad point correspondence or degenerate estimates of fundamental matrices. results of recovering camera trajectory are reported for real sequences.
a novel approach to very fast and noise robust, isolated word speech recognition. a novel very light weight approach to isolated word speech recognition is introduced. the approach uses a new simplistic feature set and a neural network recognition system. the algorithm's main processing requirements are fft computation and a simple neural network comparison, making the method a suitable solution for low price embedded devices. the proposed method is tested on single speaker and multiple speaker test sets and the results are compared with a widely used speech recognition approach, presenting very fast recognition and quite good recognition rate.
image retrieval by local evaluation of nonlinear kernel functions around salient points. feature histograms based on the evaluation of haar integrals with nonlinear kernel functions were used successfully for the purpose of invariant content based image retrieval. in addition to being invariant to rotation and translation, the features have the advantage of preserving structural information of the image. the work presented here concentrates on the idea of calculating these features by evaluating the kernel functions around a small set of preselected points. these points are called the salient points and represent, together with their neighborhood, the most important visual information in an image. the use of these salient points leads to a better representation of the image. compared to previous work, experiments show that this method gives better retrieval results without introducing extra computational overhead.
a trainable low-level feature detector. we introduce a trainable system that simultaneously filters and classifies low-level features into types specified by the user. the system operates over full colour images, and outputs a vector at each pixel indicating the probability that the pixel belongs to each feature type. we explain how common features such as edge, corner, and ridge can all be detected within a single framework, and how we combine these detectors using simple probability theory. we show its efficacy, using stereo-matching as an example.
a comparative study between decision fusion and data fusion in markovian printed character recognition. a comparison is made between several hidden markov models in the context of printed character recognition. two hmms are first compared, one dealing with columns of a character image, the other dealing with lines. these 2 hmms are then associated in a decision fusion scheme combining the log-likelihoods provided by each hmm classifier. the statistical assumptions underlying the combination formula are described and the combination formula is shown to be an approximation of a real joint log-likelihood. the last experiment consists in building a single hmm, modeling the joint flow of lines and columns. this data fusion scheme is shown to be more accurate as it highlights correlations between line and column features.
affine structure from translational motion with varying and unknown focal length. in this paper a method for obtaining affine structure from an image sequence taken by a translating camera with varying and unknown focal length is presented. a general geometric constraint, expressed using the camera matrices, is derived and this constraint is used in a least squares solution of the problem. the proposed algorithm extends the previous result of affine structure recovery from an image sequence with a translating camera with constant intrinsic parameters to the case with a camera with varying and unknown focal length and otherwise known intrinsic parameters. the noise sensitivity of the method is evaluated in simulated experiments.
color texture signatures for art-paintings vs. scene-photographs based on human visual system. efficient image filters that classify web-images as photograph of a real-scenes or as paintings are very desired in a content-based image retrieval system. the main contribution of this paper is the proposition of two feature vectors, the receptive field profiles (rfps) and the composite visual feature (cvf), that effectively discriminate art-paintings from scene-photographs. the formulation of these signatures were inspired by the model and analysis of human visual system (hvs); the rfps are approximated by a multi-channel color gabor filters which capture color texture properties; the cvf measures color uniqueness, saturation and smoothness and edge discrepancy. experimentations on a database of 20,000 images, collected from the web, with extremely variable visual contents, shown a very promising classification results (93%). we found that rfps features are larger for photographs than for painting. the boundary separation between classes in feature spaces were modeled using gaussian mixture models (gmm) and support vector machines (svm). a comparative analysis is conducted and gmm shown higher performance.