waashk/dblp huggingface dataset:
fractional component analysis (fca) for mixed signals. this paper proposes the fractional component analysis (fca), whose goal is to decompose the observed signal into component signals and recover their fractions. the uniqueness of our idea in comparison with other similar methods is the concept of the virtual pdf (probability distribution function) that models signal mixing on the sensor. in this paper, we derive the virtual pdf based on positivity constraint, unity constraint, and randomness assumption, and we then build it into the mixture density model. in order to learn parameters of this model from data using em (expectation-maximization) algorithm, the key point is to derive the approximation of the virtual pdf using its cumulants. finally we illustrate experimental results on synthetic data to show the unique decision boundary obtained from our method.
a method for automated extraction of aorta and pulmonary artery in the mediastinum using medial line models from 3d chest x-ray ct images without contrast materials. this paper proposes a new method of automated extraction of aorta and pulmonary artery (pa) areas in the mediastinum from uncontrasted 3d chest x-ray ct images. the proposed method does not extract contours of these blood vessels directly, but extracts the medial line of each vessel and recovers each vessel area. first, the process performs edge detection based on the local standard deviation to get edge areas of vessels. second, the euclidean distance transformation is applied for non-edge areas and the likelihood image of the center of vessels is obtained. medial line models are deformed basing upon the likelihood image so as to be fit to the center of each artery. the aorta and the pa areas are obtained by applying the reverse distance transformation to medial lines extracted above. we appliedthe proposed method to seven cases of uncontrasted 3d chest x-ray ct images. the experimental results showed that the aorta and the pa areas could be extracted satisfactorily.
recognition of lung lobes and its application to the bronchial structure analysis. this paper describes a method for recognizing the lung lobes and its application to analysis of the bronchial structure. analysis of the lung structure is one of important functions in a computer aided diagnosis system for chest ct data. since the lung is composed of five lobes, analysis of the lung requires recognition of each lobe area. thin membranes, called interlobar pleura, exist between lobes. their ct values are higher than those of the lung parenchyma on ct images. therefore, the proposed method extracts interlobar pleura regions and interpolates the regions by fitting quadratic surfaces. then, lung regions are divided into lobes using fitted surfaces. from the obtained lung lobe regions and the bronchial tree data extracted beforehand, each bronchial branch is classified into the lobe to which it belongs. the proposed method was applied to fourteen cases of 3d chest ct images. the experimental results showed that lung regions were satisfactorily divided into lobes and that most bronchi were classified into lobes to which they belong.
the effect of texture representations on aam performance. the active appearance model (aam) algorithm matches statistical models of shape and texture to images rapidly by assuming a linear relationship between the texture residual and changes in the model parameters. when the texture is represented as raw intensity values, this has been shown to be a reasonable approximation in many cases. however, models built on them are sensitive to changes in illumination conditions. this paper examines the effect of using different representations of image texture to improve the accuracy and robustness of the aam search. we show that normalising the gradient images by non-linear techniques can give much improved matching with higher accuracy and a wider effective range of convergence.
segment-based stereo matching using belief propagation and a self-adapting dissimilarity measure. a novel stereo matching algorithm is proposed that utilizes color segmentation on the reference image and a selfadapting matching score that maximizes the number of reliable correspondences. the scene structure is modeled by a set of planar surface patches which are estimated using a new technique that is more robust to outliers. instead of assigning a disparity value to each pixel, a disparity plane is assigned to each segment. the optimal disparity plane labeling is approximated by applying belief propagation. experimental results using the middlebury stereo test bed demonstrate the superior performance of the proposed method.
switche may solve adjacency problems. this paper presents a new and simple method for defining grid-point adjacencies, called the switch approach. it is discussed how it relates to connectedness definition in multi-valued images. the paper illustrates how the method can be used, and provides a few experimental data illustrating the relevance and simplicity of the approach.
svm-based salient region(s) extraction method for image retrieval. in region-based image retrieval, not all the regions are important for retrieving similar images and rather, the user is often interested in performing a query on only salient regions. therefore, we propose a new method for extraction of salient regions using support vector machines (svm) and a method for importance score learning according to the user's interaction. once an image is segmented, our algorithm permits the attention window (aw) according to the variation of an image and selects salient regions by using the pre-defined feature vector and svm within the aw. by using svm, we do not need to determine the heuristic feature parameters and produce more reasonable results. the distance values from svm are used for initial importance scores of salient regions and our proposed updating algorithm using relevance feedback updates them automatically. through performance comparison with parametric salient extraction method, our proposed method shows better performance as well as semantic query interface for object-level image retrieval.
improved n-division output coding for multiclass learning problems. the output coding for multiclass learning problems is a generalization of one-per-class, all-pairs, and error correcting output codes. although, the prevailing concepts of output coding has been error correcting properties, the one-per-class and all-pairs are still considered to be one of the state-of-art methods. however, these two methods are contrary to each other in the aspect of producing complex dichotomies and the problem of nonsense outputs. in additions, they all perform a prior decomposition without regards to the properties of a given training data set. in this paper, we propose a new data-driven output coding method that is the generalized form of one-per-class and all-pairs. we present the properties of the proposed method. from experimental results on both a toy problem and real benchmark datasets, we present that our proposed method achieves a comparable performance with good properties.
a new objective function for ensemble selection in random subspaces. most works based on diversity suggest that there exists only weak correlation between diversity and ensemble accuracy. we show that by combining the diversities with the classification accuracy of each individual classifier, we can achieve a strong correlation between the combined diversities and the ensemble accuracy in random subspaces.
a new image segmentation method for removing background of object movies by learning shape priors. this paper proposes a new object movie (om) segmentation method that incorporates shape priors into the segmentation algorithm. the shape prior introduced into every image of the om is learned from the 3d model reconstructed by the volumetric graph cuts. here, the constraint derived from the discrete medial axis is used to improve the reconstruction algorithm. our segmentation method requires only a small amount of user intervention, which is to select a subset of acceptable segmentations of the om after the initial segmentation process. compared to other techniques, our method provides not only the better segmentation result but also the better 3d reconstruction result.
action and simultaneous multiple-person identification using cubic higher-order local auto-correlation. we propose a new method - cubic higher-order local auto-correlation (chlac) - to address three-way data analysis. this method is a natural extension of higher-order local auto-correlation (hlac) [a new scheme for practical flexible and intelligent vision systems], which deals only with two-way data. both methods use "correlation" to summarize relative positions or motions within a local data region, and these can be calculated simply with a low computational load. moreover, our new method (chlac) offers several preferable properties as well as hlac: shift-invariance to data (rendering the method segmentation-free), additivity for data, and robustness to noise in data. in this study, we applied this method to action and simultaneous multiple-person identification from a motion-image sequence through the property of data additivity. experimental results showed that this method performed well.
determining optimal filters for binarization of degraded characters in color using genetic algorithms. this paper proposes a new binalization technique of characters in color using genetic algorithms (ga) to search for an optimal sequence of filters through a filter bank. the filter bank contains simple image processing filters as applied to one of the rgb color planes and logical/arithmetic operations between two color planes. first, we classify images of degraded characters extracted from the public icdar 2003 robust ocr dataset into several groups according to degradation categories. then, in the learning stage, by selecting training samples from each degradation category we apply ga to the combinatorial optimization problem of determining a filter sequence that maximizes the average fitness value calculated between the filtered training samples and their respective target images ideally binarized by humans. finally, in the testing stage, we apply the optimal filter sequence to binarization of remaining test samples. experimental results show the promising ability of the proposed method against a variety of image degradation causes.
asymmetric kernel method and its application to fisher's discriminant. in this paper, we propose the asymmetric kernel method. furthermore, we apply it to fisher's discriminant and provide an kernel fisher's discriminant with variable kernel parameters. we also provide the experimental result of the existing and the new kernel fisher's discriminants by using several standard datasets and show the advantage of our method.
rotated complex wavelet based texture features for content based image retrieval. in this paper we have proposed a novel approach of extracting texture features for content-based image retrieval. a new set of two-dimensional (2-d) rotated complex wavelet filters (rcwf) is designed with complex wavelet filter coefficients. 2-d rcwf are nonseparable and oriented, which improves characterization of oriented textures. dual-tree rotated complex wavelet filter (dt-rcwf) and dual-tree complex wavelet transform (dt-cwt) are used jointly for texture analysis in twelve different directions. texture features are obtained by computing the energy and standard deviation of each subband. retrieval results obtained using each individual method and in combination are presented. retrieval performance obtained with the combined filterbank is superior relative to the performance obtained using the other existing methods. new method also retains comparable levels of computational complexity.
multiresolution polygonal approximation of digital curves. we propose optimal split algorithm for multiresolution polygonal approximation of digital curves. instead of using a sequence of heuristic split steps as in the previous methods, we apply optimal approximation to obtain the next (higher) resolution levels using the previous (lower) resolution level as starting point. we compare the proposed approach against fast heuristic multiresolution algorithm based on a merge strategy under the l¿ error measure.
enhancements for local feature based image classification. using local features with nearest neighbor search and direct voting obtains excellent results for various image classification tasks. in this work we decompose the method into its basic steps which are investigated in detail. different feature extraction techniques, distance measures, and probability models are proposed and evaluated. we show that improvements are possible for each of the investigated enhancements. this shows that the important aspect of the framework is the decomposition of the training images into sets of local features for each class.
analysis of rotational robustness of hand detection with a viola-jones detector. the research described in this paper analyzes the in-plane rotational robustness of the viola-jones object detection method when used for hand appearance detection. we determine the rotational bounds for training and detection for achieving undiminished performance without an increase in classifier complexity. the result - up to 15° total - differs from the method's performance on faces (30° total). we found that randomly rotating the training data within these bounds allows for detection rates about one order of magnitude better than those trained on strictly aligned data. the implications of the results effect both savings in training costs as well as increased naturalness and comfort of vision-based hand gesture interfaces.
a viewpoint invariant approach for crowd counting. this paper describes a viewpoint invariant learningbased method for counting people in crowds from a single camera. our method takes into account feature normalization to deal with perspective projection and different camera orientation. the training features include edge orientation and blob size histograms resulted from edge detection and background subtraction. a density map that measures the relative size of individuals and a global scale measuring camera orientation are estimated and used for feature normalization. the relationship between the feature histograms and the number of pedestrians in the crowds is learned from labeled training data. experimental results from different sites with different camera orientation demonstrate the performance and the potential of our method.
a conditional random field model for video super-resolution. in this paper, we propose a learning-based method for video super-resolution. there are two main contributions of the proposed method. first, information from cameras with different spatial-temporal resolutions is combined in our framework. this is achieved by constructing training dictionary using the high resolution images captured by still camera and the low resolution video is enhanced via searching in this customized database. second, we enforce the spatio-temporal constraints using the conditional random field (crf) and the problem of video super-resolution is posed as finding the high resolution video that maximizes the conditional probability. we apply the algorithm to video sequences taken from different scenes using cameras with different qualities and promising results are presented.
coplanar light sweep-surface supported uncalibrated photometric stereo. in lambertian uncalibrated photometric stereo (ups), the object surface albedo and normals, the lighting directions and intensities are determined up to an arbitrary invertible matrix. in this paper, a novel method is proposed to reduce such an ambiguity. with the support of a coplanar light sweep-surface (clss), some key normals are determined relative to a fiducial normal which is estimated by the clss. thus the arbitrary transformation is reduced to invertible matrix up to a rotation and a scale. the rotation can be solved by controlling the direction of camera, the resulting ambiguity transformation can be finally determined up to a global scale.
competitive coding scheme for palmprint verification. there is increasing interest in the development of reliable, rapid and non-intrusive security control systems. among the many approaches, biometrics such as palmprints provide highly effective automatic mechanisms for use in personal identification. this paper presents a new method for extracting features from palmprints using the competitive coding scheme and angular matching. the competitive coding scheme uses multiple 2-d gabor filters to extract orientation information from palm lines. this information is then stored in a feature vector called the competitive code. the angular matching with an effective implementation is then defined for comparing the proposed codes, which can make over 9,000 comparisons within 1s. in our testing database of 7,752 palmprint samples from 386 palms, we can achieve a high genuine acceptance rate of 98.4% and a low false acceptance rate of 3 x 10^{-6} %. the execution time for the whole process of verification, including preprocessing, feature extraction and final matching, is 1s.
an anatomy of iriscode for precise phase representation. iriscode, a widely deployed iris recognition algorithm, developed in 1993 and continuously modified by daugman has attracted considerable attentions. iriscode using a coarse phase representation has number of properties such as rapid matching, binomial imposter distribution and predictable false acceptance rate. although many similar coding methods have been developed for irises and palmprints based on iriscode, a theoretical analysis of iriscode has not been provided. in this paper, we aim at studying (1) the nature of iriscode, (2) the property of the phase of gabor function, (3) the extension of bitwise hamming distance and (4) the theoretical foundation of the binomial imposter distribution and extending the coarse phase representation to a precise phase representation. precisely, we demonstrate that iriscode is a clustering algorithm with four prototypes; the locus of a gabor function is a two-dimensional ellipse with respect to the phase parameter and bitwise hamming can be regarded as angular distance. using these properties, we provide a precise phase representation for iriscode with an effective implementation for filtering and matching. practically, the imposter distribution of iriscode follows binomial distribution. however, the theoretical evidence is incomplete according to our analysis.
detection of spiralwaves in video. this paper presents novel video-based algorithms for detection and tracking of spiral waves in a spinning disk reactor. the algorithms are based on spiral wave model and their performance is compared with results predicted by the computational fluid dynamics algorithms for fluid flow. in each frame points on the top of waves are detected and a spiral model fitted to the points. using experimental video data, the developed models and algorithms allow investigators to estimate the characteristics of wave regimes such as wavelength and inclination angles. results computed from video data are compared with number predicted by the theoretical model.
global localization and relative pose estimation based on scale-invariant features. the capability of maintaining the pose of the mobile robot is central for basic navigation and map building tasks. in this paper we describe a vision-based hybrid localization scheme based on scale-invariant keypoints. in the first stage the topological localization is accomplished by matching the keypoints detected in the current view with the database of model views. once the best match has been found, the relative pose between the model view and the current image is recovered. we demonstrate the efficiency of the location recognition approach and present a closed form solution to the relative pose recovery for the case of planar motion and unknown focal length of the camera. the approach is demonstrated on several examples of indoors environments.
ok-quantization theory - a mathematical theory of quantization -. a mathematical basis for the digitization of gray value of an image is proposed. this was called oteru-koshimizu quantization theorem (ok-qt), on the analogy of the shannon sampling theorem (shannon-st) for the digitization of the shape of the image. inspired by the fact that the shannon-st is the reconstruction theorem of the analog image from the discrete image, ok-qt was modeled as the reconstruction theorem of the shape of the probability density function of gray values of an image. this is a novel and unique mathematical basis for the digitization of the gray scale of an image. this paper outlines this theorem and also shows some experimental results to demonstrate its practical applicability. through this, the ok-qt gives a clue to the mathematical paradigm for the complete basis for digitization, together with shannon st.
entropy-based measures for clustering and som topology preservation applied to content-based image indexing and retrieval. content-based image retrieval (cbir) addresses the problem of finding images relevant to the usersý information needs, based principally on low-level visual features for which automatic extraction methods are available. for the development of cbir applications, an important issue is to have efficient and objective performance assessment methods for different features and techniques. in this paper, we study the efficiency of clustering methods for image indexing with entropy-based measures. furthermore, the self-organizing map (som) as an indexing method is discussed further and an analysis method which takes into account also the spatial configuration of the data on the somis presented. the proposed methods enable computationally light measurement of indexing and retrieval performance for individual image features.
adaptive variational sinogram interpolation of sparsely sampled ct data. we present various kinds of variational pde based methods to interpolate missing sinogram data for tomographic image reconstruction. using the observed sinogram data we inpaint the projection data by diffusion. to overcome the problem of contour blurring we consider nonlinear and anisotropic diffusion based regularizers and include optical flow information in order to preserve the sinuodal traces corresponding to object contours in the reconstructed image. we compare our results to a spectral deconvolution based interpolation and show that the method can easily be extended to 3d.
efficient coding of stroke-rendered paintings. there are more and more applications of non-photorealistic rendered images, sketches and drawings. several techniques for generating such imagery are widely known. the stochastic painting-based painterly image (and video) generation presented herein is a multi-purpose image rendering and representation method, suitable for many purposes: painterly rendering, storing, compression or indexing. it incorporates many new features like multiscale edge following, stroke-set optimizations, templates, color morphology, etc. we will demonstrate that the presented technique (called enhanced stochastic paintbrush transformation or espt) is suitable for fast high quality painterly rendering, providing good lossless painted compression ratios and features that make it suitable for many applications. one of these we wish to emphasize is the suitability to code painted images in a way that does not introduce any coding artifacts (blockiness, ringings, etc.) but provides a compact form of representation that still retains the main property of a painting: that it is a painting after all.