waashk/dblp huggingface dataset:
semi-parametric model-based clustering for dna microarray data. various clustering methods have been proposed for the analysis of gene expression data, but conventional clustering algorithms have several critical limitations; how to set parameters such as number of clusters, initial cluster centers, and so on. in this paper, we propose a semi-parametric model-based clustering algorithm in which the underlying model is a mixture of gaussian. each gene expression data builds a gaussian kernel, and the uncertainty of microarray data is naturally integrated in the data representation. our algorithm provides a principled method to automatically determine parameters - number of components in the mixture, mean, covariance, and weight of each gaussian - by mean-shift procedure [2] and curvature fitting. after the initialization, expectation maximization (em) algorithm is employed for clustering to achieve maximum likelihood (ml). the performance of our algorithm is compared with standard em algorithm using real data as well as synthetic data.
from massively parallel image processors to fault-tolerant nanocomputers. parallel processors such as simd computers have been successfully used in various areas of high performance image and data processing. due to their characteristics of highly regular structures and mainly local interconnections, simd or simd-like architectures have been proposed for a large-scale integration of recently developed quantum and nanoelectronic devices. in this paper, we present a fault-tolerant technique suitable for an implementation in nanoelectronics, the triplicated interwoven redundancy (tir). the tir is a general class of triple modular redundancy (tmr), but implemented with random interconnections. a prototype structure for an image processor is proposed for the implementation of the tir technique and a simulation based reliability model is used to investigate its fault-tolerance. the tir is extended to higher orders, namely, the n-tuple interwoven redundancy (nir), to achieve higher system reliabilities. it is shown that the reliability of a general tir circuit is,inmost cases,comparablewiththat of anequivalent tmr circuit, and that the design and implementation of restorative devices (voters) are important for the nir (tir) structure. our study indicates that the nir (tir) is in particular suitable for an implementation by the manufacturing process of stochastically molecular assembly, and that it may be an effective fault-tolerant technique for a massively parallel architecture based on molecular or nanoelectronic devices.
video foreground segmentation based on sequential feature clustering. segmentation of videos into layers of foreground objects and background has many important applications, such as video compression, human computer interaction, and motion analysis. most existing methods work on image pixels or color segmentations which are computation expensive. some methods require extensive manual input, static cameras, and/or rigid scenes. in this paper we propose a fully automatic segmentation method based on sequential clustering of sparse image features. the sparseness makes the method computation efficient. we use both edge and corner features to capture the outline of the foreground objects. sequential linear regression is applied to the movement sequences of image features in order to compute the motion parameters for foreground objects and background layers, and consider the temporal smoothness simultaneously. foreground layer is then extracted by a pyramidal markov random field (mrf) model taking into account the spatial smoothness constraint. experimental results on videos taken by webcams are shown and discussed.
understanding inexplicit utterances using vision for helper robots. speech interfaces should have a capability of dealing with inexplicit utterances including such as ellipsis and deixis since they are common phenomena in our daily conversation. their resolution using context and a priori knowledge has been investigated in the fields of natural language and speech understanding. however, there are utterances that cannot be understood by such symbol processing alone. in this paper, we consider inexplicit utterances caused from the fact that humans have vision. if we are certain that the listeners share some visual information, we often omit or mention ambiguously things about it in our utterances. we propose a method of understanding speech with such ambiguities using computer vision. it tracks the human's gaze direction, detecting objects in the direction. it also recognizes the human's actions. based on these bits of visual information, it understands the human's inexplicit utterances. experimental results show that the method helps to realize human-friendly speech interfaces.
the morphological top-hat operator generalised to multi-channel images. the morphological top-hat operator for greyscale images is part of the basic toolbox of mathematical morphology operators. we discuss two ways of generalising the top-hat operator to multi-channel images, such as colour images. the first method presented is the use of a vectorial order in the relevant vector space. the second is based on the demonstration that the top-hat operator can be rewritten in terms of increments. these increments can be replaced by any vectorial distance function, removing the requirement to first impose an order on the vectors. we present examples of the use of the suggested top-hat operators in feature detection in colour images and defect detection in texture.
memory consistency validation in a cognitive vision system. ensuring the consistency of memory content is a key feature of cognitive vision systems. this paper presents an approach to deal with functional dependencies of hypotheses stored in a visual active memory. by means of bayesian networks a probabilistic approach is used to incorporate uncertainty of observations. furthermore, a measurement to detect inconsistencies in the memory is introduced. the benefit of this validation module as part of an integrated system is shown for the task of visual surveillance in an office scenario.
action recognition in awearable assistance system. enabling artificial systems to recognize human actions is a requisite to develop intelligent assistance systems that are able to instruct and supervise users in accomplishing tasks. in order to enable an assistance system to be wearable, head-mounted cameras allow to perceive a scene visually from a user's perspective. but realizing action recognition without any static sensors causes special challenges. the movement of the camera is directly related to the user's head motion and not controlled by the system. in this paper we present how a trajectory-based action recognition can be combined with object recognition, visual tracking, and a background motion compensation to be applicable in such a wearable assistance system. the suitability of our approach is proved by user studies in an object manipulation scenario.
color image enhancement by fuzzy intensification. a gaussian membership function to model image information in spatial domain has been proposed in this paper. we introduce a new contrast intensification operator, which involves a parameter t for enhancement of color images. by minimizing the fuzzy entropy of the image information, the parameter t is calculated globally. a visible improvement in the image quality for human contrast perception is observed, also demonstrated here by the reduction in 'index of fuzziness' and 'entropy' of the output image.
motion-based handwriting recognition for mobile interaction. this paper presents a new interaction technique for camera-enabled mobile devices. the handheld device can be used for writing just by moving the device. in our method, interframe dominant motion is estimated from images, and the discrete cosine transform is used for computing discriminating features from motion trajectories. the k-nearest neighbor rule is applied for classification. a realtime implementation of the method was developed for a mobile phone. in experiments, recognition rates ranging from 92 % to 98 % were achieved, which testifies to the practicality of our approach.
coupled shape model segmentation in pig carcasses. in this paper we are concerned with multi-object segmentation. for each object we will train a level set function based shape prior from a sample set of outlines. the outlines are aligned in a multi-resolution scheme wrt. an euclidean similarity transformation in order to maximize the overlap of the interior between all pairs of outlines. then the outlines are converted to level set functions. a shape model is constructed from the mean level set and the first few principal variations. we combine the prior model with an observation model based on the chan-vese functional assuming constant intensity levels inside the outline as well as in a narrow band outside the outline. the maximum a posteriori estimate of the outline is found by gradient descent optimization. in order to segment a group of mutually dependent objects we propose 2 procedures, 1) the objects are found sequentially by conditioning the initialization of the next search from already found objects; 2) all objects are found simultaneously and a repelling force is introduced in order to avoid overlap between outlines in the solution. the methods are applied to segmentation of cross sections of muscles in slices of ct scans of pig backs for quality assessment of bacon slices.
cluster analysis and priority sorting in huge point clouds for building reconstruction. terrestrial laser scanners produce point clouds with a huge number of points within a very limited surrounding. in built-up areas, many of the man-made objects are dominated by planar surfaces. we introduce a ransac based preprocessing technique that transforms the irregular point cloud into a set of locally delimited surface patches in order to reduce the amount of data and to achieve a higher level of abstraction. in a second step, the resulting patches are grouped to large planes while ignoring small and irrelevant structures. the approach is tested with a dataset of a builtup area which is described very well needing only a small number of geometric primitives. the grouping emphasizes man-made structures and could be used as a preclassification.
basic concepts for testing the torah code hypothesis. this is the first part of a tutorial discussing the major strategies and methodologies by which a test of the null hypothesis of no torah effect can be done. the basic concepts of equidistant letter sequence, skip specification, resonance specification, and compactness features are discussed here.
testing the torah code hypothesis: the experimental protocol. this is the second part of a tutorial discussing the experimental protocol issues in testing the torah code hypothesis. the principal concept is the test statistic which is used to do the actual hypothesis testing of the null hypothesis against a simple alternative or against a complex of alternatives. we illustrate the methodology using the data sets from the wrr[3] experiment. we use the wrr key word sets of list 1 and 2 combined. the experiment produces a p-value of less than 1/100,000 in the genesis text. we performed another experiment pairing rule based transliterations for the spellings of the names of the american presidents into hebrew with the hebrew word for president. taking into account bonferroni, the resulting p-value of the 100,000 trial experiment was less than 1/66,667.
human model for people detection in dynamic scenes. the problem of multiple people detection in monocular video streams is addressed. the proposed method involves a human model based on skin color and foreground information. robustness to local motion of background and global color changes is achieved by modeling images as fields of color distributions, and robustly estimating temporal background global variations. the estimation of the human model parameters is done via monte carlo simulations to deal with the multimodal nature of the posterior distribution, introduced by the presence of multiple people and cluttered scene. promising results are presented for transportation vehicles sequences.
recognizing hand gesture using fourier descriptors. this paper describes a novel application of fourier descriptor techniques for the recognition of hand gesture trajectories. appearance based coordinates of hand centroids and time steps are normalized to a fixed length by multirate techniques. fourier techniques are applied to the data to produce frequency domain data that is scale and translation invariant. the results of inputting the complex harmonic data to a probabilistic neural network for gesture classification are discussed. an understanding of underlying structure of gesture trajectories can be gained from modeling them as an infinite set of ellipses at given orientations. the rotation of the ellipses are visualized in the time domain as 'elliptic corkscrews'.
video shot interpretation using principles of perceptual prominence and perceptual grouping in spatio-temporal domain. we propose a computational model for generating an interpretation of a video shot based on our proposed principle of perceptual prominence. we also provide a formulation of the perceptual grouping problem in the spatio-temporal domain to identify the perceptual clusters. we illustrate our approach with experimental results.
bayesian ms lesion classification modeling regional and local spatial information. a fully automatic bayesian framework for multiple sclerosis (ms) lesion classification is presented, using posterior probability distributions and entropy values to classify normal and lesion tissue. spatial variability in intensities of multimodal mr images over the brain is explicitly modeled by building region-specific multivariate likelihood distributions. local smoothness is ensured by incorporating neighboring voxel tissue information using markov random fields. a probabilistic measure of confidence for the classification is then presented, which can also be used to assess disease burden. the method was tested on 10 patients with ms by comparing automatically classified lesions, with and without regional information, to manual classifications by five expert raters using volume count and overlap. results improve with the incorporation of spatial information, and are comparable to manual classifications. this method also enables a more accurate classification in the posterior fossa, where no other method reports success.
a hybrid tree approach for efficient image database retrieval with dynamic feedback. the need always exists for indexing mechanisms that can precisely retrieve imagery from a database, as well as maintain certain efficiencies for large-scale image database search. to achieve this, we developed a hybrid search tree called skd-metric tree. this novel approach merges the classification power of the statistical k-dimensional tree and the efficiency of computation of the metric-tree for nearest neighbor (nn) search. another feature of skd-metric tree is its flexibility to formulate a new metric function while the retrieval system utilizes user's feedback to improve accuracy. in addition, unlike traditional relevance feedback approaches that, in most cases, sequentially search the entire database to obtain new retrieval results, skd-metric tree features a fast retrieval refinement procedure that needs to update only a small portion of the database. an extensive study, based on experiments performed for evaluating retrieval precision and computational efficiencies, is presented. we have applied our approach to a large-scale medical image database. the experimental results show that skd-metric tree can achieve a high accuracy rate with dynamic relevance feedback that requires much less computation than existing techniques.
learning video processing by example. we present an algorithm that approximates the output of an arbitrary video processing algorithm based on a pair of input and output exemplars. our algorithm relies on learning the mapping between the input and output exemplars to model the processing that has taken place. we approximate the processing by observing that pixel neighborhoods similar in appearance and motion to those in the exemplar input should result in neighborhoods similar to the exemplar output. since there are not many pixel neighborhoods in the exemplars, we use techniques from texture synthesis to generalize the output of neighborhoods not observed inthe exemplars. the same algorithm is used to learn such processing as motion blur, color correction, and painting.
exploiting the geometry of gene expression patterns for unsupervised learning. typical gene expression clustering algorithms are restricted to a specific underlying pattern model while overlooking the possibility that other information carrying patterns may co-exist in the data. this may potentially lead to a large bias in the results. in this paper we discuss a new method that is able to cluster simultaneously various types of patterns. our method is based on the observation that many of the patterns that are considered significant to infer gene function and regulatory mechanisms all share the geometry of linear manifolds.
a robust method of recognizing multi-font rotated characters. this paper presents a new robust recognition method for rotated character images. we first construct an eigen sub-space for each category using the covariance matrix calculated from a sufficient number of rotated patterns averaged by several fonts. next, we can obtain a locus by projecting their rotated characters onto the eigen sub-space and interpolating between their projected points. an unknown character is also projected onto the eigen sub-space of each category. then, verification is carried out by calculating the distance between the projected point of the unknown character and the locus. in our experiment, we obtained quite good results for three fonts of 26 capital letters of the english alphabet.
electronic endoscope system for shape measurement. in endoscope observation, acquiring shape information of a lesion area is highly demanded for objective and statistical judgment. for in-vivo applications, a high-speed measurement is also considerable. therefore, we proposed an electronic endoscope system that equips an imagefiber and a laser scanner, and achieves the high-speed measurement by using the space-encoding method with laser-scanning. in our experiment, the measurement error was 1~2 % of the distance from the endoscope tip. moreover, the acquired shape information shows that can assist physician for understanding the surface topology of lesion and would be effective in computer aided diagnosis.
a fingerprint verification algorithm using the differential matching rate. we propose a fingerprint verification algorithm with template matching based on the cyclic structure of the fingerprint image. for a correlation value between a template and a sensed image, we use the differential matching rate. the differential matching rate utilize the cyclic structure observed in the local area of a fingerprint pattern, which is calculated by the maximum matching rate minus the minimum matching rate detected near the point where the matching rate is maximum. in order to extract the maximum effectiveness of the cyclic structure, we compare divided windows of a template image against a sensed image. a verification experiment confirmed that the proposed method judges whether a template imageand a sensed image are same finger's fingerprints more accurately than the conventional method using the maximum matching rate as a correlation value. we obtain the equal error rate (eer) of 3.6%, which is better than half that of the conventional method.
ecoc and boosting with multi-layer perceptrons. the combination of boosting and ecoc with multi-layer perceptron base classifiers is experimentally evaluated for a problem in pose classification. while accuracy compared with ecoc is not improved, boosted ecoc ensembles are less sensitive to tuning parameters.
outlier detection using k-nearest neighbour graph. we present an outlier detection using indegree number (odin) algorithm that utilizes k-nearest neighbour graph. improvements to existing knn distance-based method are also proposed. we compare the methods with real and synthetic datasets. the results show that the proposed method achieves resonable results with synthetic data and outperforms compared methods with real data sets with small number of observations.
visible edges thresholding: a hvs based approach. this paper presents a technique, which determines whether an edge is visible for a human eye or not. first, a threshold function is built according to the optical characteristics of the sensor and a contrast sensitivity function (csf). then, the dct coefficients of each 8 × 8 block are computed. for each one, the computed spectrum is compared with the previous threshold function, which enables to conclude about the presence of visible picture elements. finally, this information is applied to edges detection using a binarization technique. results are given on images grabbed onboard a moving vehicle.
evaluating hierarchical graph-based segmentation. using real world images, two hierarchical graph-based segmentation methods are evaluated with respect to segmentations produced by humans. global and local consistency measures do not show big differences between the two representative methods although human visual inspection of the results show advantages for one method. to a certain extent this subjective impression is captured by the new criteria of 'region size variation
age and gender estimation based on wrinkle texture and color of facial images. we are researching about an age and gender estimation based on wrinkle texture and color of facial images. preliminary questionnaire (enquete) examination how effectively the facial images could be used for gender and age estimations was executed by using 300 different faces and 21 examinees. wrinkles appeared in the face and the shape and size of the facial parts are selected to model the age and gender estimation in this research basing on thisenquete. basing on this preliminal consideration, an image processing algorithm for wrinkle modeling was proposed in this paper. in addition, a method for making relationships between facial images and their keywords was proposed by using the latent semantic indexing. an efficient interface for displaying the relationships among keywords and facial images has been introduced with 2d/3d-feature space defined by the indexing.
human identification by spatio-temporal symmetry. we describe spatio-temporal symmetry and its extraction via a generalised symmetry operator. its use in gait recognition is reinforced by the view from psychology that human gait is a symmetrical pattern of motion. we show that by including temporal information in our symmetry calculations we are not recognizing people by their body shape but also by their motion. here, the new technique is applied to a database of 28 subjects, which equals in size the largest contemporaneous gait databases. the results of the new approach agree with earlier results that the symmetrical properties of human gait appear to be unique and can indeed be used for analysis and for recognition. the results achieved so far give promising performance and higher recognition rates than those of an earlier spatial approach. performance analyses suggest that symmetry enjoys practical advantages such as ability to handle noise and occlusion, and especially when resolution is too low for other biometrics to be deployed.