waashk/dblp huggingface dataset:
key techniques and methods for imaging iris in focus. automated iris recognition is a promising method for noninvasive verification of identity. how to acquire an iris image in focus is a key issue in iris recognition. based on imaging properties of a simple lens, working principles of fixed focus and auto focus imaging systems are described. key techniques for imaging iris in focus are discussed in this paper, such as illumination, lens design and self-alignment to position the iris in the system's depth of field. according to the techniques, a clear iris acquisition system is developed and experimental results are presented in this paper.
robust partial volume segmentation with bias field correction in brain mri. in mr imaging, image noise, bias field, and partial volume effect are adverse phenomena that increases intertissue overlapping and hampers quantitative analysis. this study provides a powerful fully automated classification method, which combines the bias field correction and pv segmentation together. the method has been validated on simulated and real mr images for which gold standard segmentation available. the experimental results show that the proposed method is more accurate and robust than currently available models.
w-boost and its application to web image classification. when training data is not sufficient, boosting algorithms tend to overfit as more weak learners are combined to form a strong classifier. in this paper, we propose a new variant of realboost, called w-boost, which is based on a novel weight update scheme and uses changeable bin number to estimate marginal distributions in weak learner design. this new boosting procedure results in both fast convergence rate and small generalization error. experimental results on synthetic data and web image classification demonstrate the effectiveness of our approach.
face recognition with relative difference space and svm. in this paper, a new method based on relative difference space (rds) and support vector machine (svm) is proposed for multi-class recognition. first the rds transformation converts the multi-class problem to a binary-class problem, and then svm is used for the binary classification directly. compared with the traditional method of difference space (ds), rds is reversible and it overcomes the illtransformation problem. this method is applied to face recognition in yale face database b, and the recognition result demonstrates its robust performance under different illumination conditions.
iris localization via pulling and pushing. iris localization is a critical module in iris recognition because it defines the inner and outer boundaries of iris region used for feature analysis. state-of-the-art iris localization methods need to implement a bruteforce search of the large parameter space, which is time-consuming and sensitive to noises. this paper proposes a novel iris localization method based on a spring force-driven iteration scheme. first, the coarse localization of pupil is obtained by an adaboost-based iris detection method. then the radial edge points are detected in polar coordinate, which contribute a force on the circle center based on hooke's law. finally the center and radius of pupil and iris are refined according to the composition of forces from all points. after 3-4 iterations, the precise location of iris could be found. experimental results show that our method is faster and more accurate than state-of-the-art iris localization methods.
texture image retrieval using novel non-separable filter banks based on centrally symmetric matrices. though millions of images are stored in a large digital image library today, the user can not access or make full use of these image information unless the digital image library is well organized in order to allow efficient browsing, searching and retrieval. thus, research in image retrieval has been an active discipline since 70's last century. image retrieval is a typical problem of pattern recognition, consisting of two parts: extracting features (ef) and similarity measurement (sm). in this paper, we develop new non-separable filter banks based on the centrally symmetric matrixes, and apply them to extract the features of texture images. compared to tensor product wavelets, our new filter banks can capture more directional texture information, which is helpful for texture image retrieval. experiments show that our novel non-separable filter banks are satisfiable and achieve a better retrieval effectiveness than daubechies wavelets.
dense estimation of layer motions in the atmosphere. in this paper, we address the problem of estimating dense motion fields related to a stratified atmosphere which is observed through satellite imagery. estimating the evolving vertical distribution of horizontal wind fields from satellite image time series is of great importance for the study of atmospheric dynamics. because of the sparse 3-dimensional nature of observations, classical correlation-based techniques are not suited for the dense estimation of layer motion. moreover, such methods are not necessarily temporally consistent. this paper proposes a sound energy-based estimator producing dense wind fields estimates for each partially observed atmospheric layer. the energy function to be minimized is composed of a data term based on the continuity equation which cancels out the influence of undesirable layers and of a specific div curl regularization term. to preserve the temporal consistency of the estimates, the variational method is initialized by propagation of the previous estimated field according to a velocity-vorticity formulation of navier-stokes equations. the relevance of our estimator is demonstrated on meteosat image sequences.
hand gesture recognition: self-organising maps as a graphical user interface for the partitioning of large training data sets. gesture recognition is a difficult task in computer vision due to the numerous degrees of freedom of a human hand. fortunately, human gesture covers only a small part of the theoretical "configuration space" of a hand, so an appearance based representation of human gesture becomes tractable. a major problem, however, is the acquisition of appropriate labelled image data from which an appearance based representation can be built. in this paper we apply self-organising maps for a visualisation of large amounts of segmented hands performing pointing gestures. using a graphical interface, an easy labelling of the data set is facilitated. the labelled set is used to train a neural classification system, which is itself embedded in a larger architecture for the recognition of gestural reference to objects.
multi-scale autoconvolution for affine invariant pattern recognition. this paper describes a novel image transform called multi-scale autoconvolution which is invariant with respect to affine transformations of the spatial image coordinates. the transform can be applied directly to image patches without segmentation. algebraically, the transform is simple requiring only rescaling of the image and computation of two-dimensional convolutions that can be performed efficiently in the frequency domain. similar transforms can also be derived for other linear distortions of the image. the experiments performed show that classification of complex patterns can be carried out reliably with only a small set of transform coefficients.
searching for similarities in nearly periodic signals with application to ecg data compression. this paper proposes a new methodology to identify and correlate patterns on nearly periodic signal, based on signal simplification and clustering approaches. using cubic bezier curves some significant signal samples (control points), enabling to segment adequately the original signal, are extracted in a first step. next, given the correlation among extracted control points, the detection of similarities within the overall signal is then performed through a clustering technique. although the approach is useful for many types of signals, the compression of electrocardiogram signals (ecg) is here investigated. results with standard mitbih databases show promising compression ratios, in particular, high compression ratios are found for long duration signals, when the signal presents strong regularities.
robust multihypothesis discrimination of controlled i.i.d. processes. this paper describes a general framework for the robust discrimination of objects represented as a family of i.i.d. random distributions. testing is based on accumulating evidences on the discrimination between all-pairs of hypotheses by sampling the family of distributions according to an optimal control law. the optimality criterion is built on constraint satisfaction issues. an application on 2d rotation invariant shape recognition with noisy contours illustrates the approach.
affine layer segmentation and adjacency graphs for vortex detection. in this paper we review and present different methods for the detection and characterization of vortices. our algorithm works on the segmentation of the image into affine layers. these layers are computed using a parametric tensor voting and encoded in an adjacency graph. paths are computed from the adjacency graph and are used for characterizing paths' properties such as: critical points and vortices. we illustrate the proposed approach to a satellite image sequence of water vapor in the atmosphere.
detection of presynaptic terminals on dendritic spines in double labeling confocal images. for the analysis of learning processes and the underlying changes of the shape of excitatory synapses (spines), 3-d volume samples of selected dendritic segments are scanned by a confocal laser scanning microscope. the images are unsharp because of the (direction dependent) resolution limit. a deconvolution on image data is not sufficient for the resolution needed. therefore a parametric model is used to reconstruct the dendrite and the spines. the parameter estimation of model is done in a two step approach. first, rough center axes of dendrite and spines are found by a growing model which can be adjusted interactively. in a second step the model parameters are optimized during an iterative process. to estimate the deviation between the microscope image and the model, the model is sampled with the same resolution as the microscope image and convolved by the microscope point spread function (psf). the result is a accurate model of dendrite and spines. the model fitting process is comparable with a deconvolution but with a limited number of model parameters and stable results without strong distortions by psf. the associated presynaptic terminal can be detected in a second image channel inside a region of interest (roi) on spine position. morphological features of spines from geometrical model and from second channel roi are combined for statistical analysis.
differential-algebraic multiview constraints. in this paper we present a novel type of multiview constraints, called dtfferential-algebraic multiview constraints. these constraints are based on both corresponding feature points and the motion of these feature points, thus combining the discrete multiview constraints with the continuous multiview constraints. the main usage of these constraints is as a theoretical basis forjltering approaches to structure and motion recovery, enabling an update of a current rnotion estimate when a new image becomes available. one importantfeature is that the update formula becomes linear in the motion parameters in the calibrated case, which is a major inzprovement compared to the standard discrete approach. another advantage is that fewer points are needed in the update formula then when calculating the motion without using a prior estimate.
scene recognition based on relationship between human actions and objects. in this paper, we propose a novel method for scene recognition using video images through analysis of human activities. we aim at recognizing three kinds of things such as human activities, objects and environment. in the previous method, locations and orientations of objects are estimated using shape models, which are often claimed to be dependent upon individual scene. instead of shape models, we employ conceptual knowledge about function and/or usage of objects as well as that about human actions. in our method, the location and usage of objects can be identified by observing interaction of human with them.
estimation of 3d motion trajectory and velocity from monocular image sequences in the context of human gait recognition. we propose a simple method for human gait recognition from monocular color image sequences which recovers the approximate 3d motion trajectory. we provide concrete evidence that object-to-image height ratios can be used for estimating 3d motion trajectories and motion velocities with sufficient accuracy for making judgments about the states of motion. we also introduce a method for estimating the leg frequency of a walking person.
quantification of shrinkage of lung lobe from chest ct images using the 3d extended voronoi division and its application to the benign/malignant discrimination of tumor shadows. in this paper, we prop ose a method to quantify the convergence of tissue in lung caused by cancer, using the three-dimensional (3d) extended voronoi division from chest x-ray ct images. the convergence is the phenomenon that the pulmonary tumors pull tissues such as blood vessels and interstitium toward the pulmonary tumors. because the malignant tumors (especially adenocarcinoma) often accompanies the convergence, quantification of the convergence is useful for the benign/malignant discrimination.we first calculate the voronoi diagrams using the vessel regions which were classified according to the radius of vessels.then a feature was calculated using the volumes of the v oronoiregions in the whole of lung region and those of the neighborho odof tumor region. the significant correlation between the proposed feature and the existence of the convergence is shown experimentally using actual ct images.
level-set evolution with region competition: automatic 3-d segmentation of brain tumors. we develop a new method for automatic segmentation of anatomical structures from volumetric medical images. driving application is tumor segmentation from 3-d mris, which is known to be a very challenging problem due to the variability of tumor geometry and intensity patterns. level-set snakes offer significant advantages over conventional statistical classification and mathematical morphology, however snakes with constant propagation need careful initialization and can leak through weak or missing boundary parts. our region competition method overcomes these problems by modulating the propagation term with a signed local statistical force, leading to a stable solution.a pre- vs. post-contrast difference image is used to calculate probabilities for background and tumor regions, with a mixture-modelling fit of the histogram. preliminary results on five cases with significant shape and intensity variability demonstrate that the new method might become a powerful and efficient tool for the clinic. validity is demonstrated by comparison with manual expert segmentation.
group-based relevance feedback with support vector machine ensembles. support vector machines (svms) have become one of the most promising techniques for relevance feedback in content-based image retrieval (cbir). typical svm-based relevance feedback techniques simply apply the strict binary classifications: positive (relevant) class and negative (irrelevant) class. however, in a real-world relevance feedback task, it is more reasonable and practical to assume the data come from multiple positive classes and one negative class. in order to formulate an effective relevance feedback algorithm, we propose a novel group-based relevance feedback scheme constructed with the svm ensembles technique. experiments are conducted to evaluate the performance of our proposed scheme and the traditional svm-based relevance feedback technique in cbir. the experimental results show that our proposed scheme is more effective than the regular method.
application of semiparametric density estimation to classification. a density estimation approach to statistical pattern recognition is discussed. the pattern vector is split into two parts factoring a high dimensional class density function into a product of two lower dimensional density functions. the first factor, corresponding to the non-gaussian structure in the data, is modeled nonparametrically. the second factor is modeled as a multivariate gaussian conditionally on the first part of the pattern vector. exploratory data analysis based on two-dimensional scatter plots is used to examine the plausibility of the density model. the proposed method is applied to classification of handwritten digits and satellite image data.
multimodal temporal pattern mining. this paper proposes an approach for mining multimodal temporal patterns from multiple synchronous signal sequences generated by different modalities. the instances of the temporal patterns suffer from noise and non-linear temporal warping. there are non-pattern signal segments separating the instances of the temporal patterns in the whole signal sequences. hidden markov models with thresholds of supports are trained to capture the sub-patterns in each modality. the sub-patterns have overlaps and can be stitched together to form complete temporal patterns. the temporal information of the instances the patterns in different modalities is then utilized to discover the multimodal temporal patterns.
vibratory image feature extraction based on local log-polar symmetry. we propose a feature extraction method for a newly developed vibratory image sensor. the proposed method extracts a local log-polar symmetry as image feature. in this article, it is shown that log-polar symmetric patterns include well-known characteristic points such as edges, corners, peaks, and so on. the principle of our method is as follows: a periodic vibration of an image sensor modulates radial and angular components of local image pattern into separated temporal frequency components of incident light on each pixel. comb type filters after a photo detector decompose the radial component and the angular one, and accumulate their powers in a frame time. these powers are read out for calculating unevenness between them. the unevenness indicates the strength of symmetry of corresponding local image pattern. because our method modulates local image pattern using image sensor's vibration, the extracted features are free from spatial quantization errors.
fusion algorithm for locally arranged linear models. as an extension to a recently proposed local linear approximation method we present an algorithm that generates more compact solutions for supervised-learning problems. given a network of linear models each trained to approximate the target function in a local region of the input space, the algorithm reduces the number of the models significantly without diminishing the accuracy of the approximation. it fuses linear models by combining their local regions of validity to more complex, non-symmetrically shaped ones. a neighborhood graph introducing edges in a purely data-driven manner between adjacent linear models is used to determine which models should be fused. the also extended model for a region of validity allows to detect automatically data which is novel to a trained network and should be regarded as an outlier. the effectiveness of the proposed methods is shown with a benchmark test achieving a five times smaller rmse than the best competitors.
the effect of the inhibition-compensation learning scheme on n-tuple based classifier performance. the inhibition-compensation learning scheme (icls) has been proposed as a way of enhancing the performance of the moving window classifier . in this paper, the effect of icls on three n-tuple based classification techniques has been investigated. presegmented handwritten characters from the nist database have been used as the pattern data. results show that approximately 2-6% gain in classification accuracy can be achieved in the ocr task domain with no adverse effect on the classification throughput.
re-evaluating colour constancy algorithms. we present a re-evaluation of previous experimental data for five different colour constancy algorithms, based on experiments on real and synthetic images. our work is motivated by the observation that previous analysis of algorithm performance is flawed because it uses inappropriate statistical measures of performance. we discuss these flaws in detail and suggest more appropriate statistical tests. we show that using these tests conclusions as to the relative performance of algorithms are significantly changed as compared to the original analysis of the data. in particular we conclude that the performance of two algorithms: gamut mapping and color by correlation is statistically equivalent and significantly better than the three other algorithms tested (max-rgb and two versions of grey-world).
a real-time multi face detection technique using positive-negative lines-of-face template. this paper describes a real-time multi face detection technique for color video sequences. a 3d rational skin color model and a positive-negative lines-of-face template are proposed to improve signal to noise ratio (snr) in face detection. steady state genetic algorithm (ssga) is employed for lines-of-face detection from entire image. hardware architecture is optimized for high-speed operation and small hardware resources. an experimental system is developed in field programmable gate array (fpga), with only 40k gates for logic and 240k gates for memory. it detects 6 faces in real-time (30fps:every 33ms) from 320x240pixels (qvga) color video sequences. detection rate of 98% is achieved for 89 images including 205 faces from daily scenes.
comparison of support vector machines with autocorrelation kernels for invariant texture classification. support vector machines (svms) with autocorrelation kernels are applied to texture classification invariant to similarity transformations and noise. the inner product of autocorrelation functions of an arbitrary order is effectively calculated through the 2nd-order crosscorrelation of original data. texture classification experiments show that higher performance of svms is achieved by exploiting the autocorrelation kernels.
estimation of color for gray-level image by probabilistic relaxation. a color estimation method for a gray-level image is proposed by giving a few color pixels. it is known that a density value in the gray-level image will be calculated by linear combination of an rgb vector of the color image. the problem dealt with in this study can be formulated as an ill-posed problem which searches for an rgb vector from a density value as a solution. by assuming a restricted condition to minimize the total of the color difference defined among adjacent pixels, the color will be optimized by the probabilistic relaxation method. theperformance of the proposed method is verified by experiments. the proposed algorithm works very well when the solution is known with confidence in a few percents of the image.
similarity measure of labelled images. a similarity measure is developed for comparing labelled images. the proposed measure is calculated based on binary relations of arbitrary pixels in the labelled images. so, the proposed measure can evaluate the difference between two images speedy by pixel-to-pixel calculation. the measure can be used for not only spatial labelled images by area-based labelling such as region segmentation, but also colour labelled images by pixel-based labelling such as colour clustering. a few actual applications are shown in order to explain the behaviour.