waashk/dblp huggingface dataset:
the role of featural and configural information in face classification a simulation of the expertise hypothesis. face recognition in adults is the product of a unique mechanism in the brain and it is based on years of experience. the goal of this paper is to analyze the role of configural and featural information for face classification and to compare the performance of bayesian network classifiers with the human performance in three experiments: similarity matching, gender, and race classification. our results show that despite the fact that the machine classification results are worse than the one of the humans, they are consistent with human classification results.
fast linear feature detection using multiple directional non-maximum suppression. linear feature detection is a very important issue in the areas of image analysis, computer vision, and pattern recognition. it has found applications in many diverse areas such as neurite outgrowth detection, compartment assay analysis, retinal vessel extraction, skin hair removal for malonoma detection, plant root analysis, and roads detection. we have developed a new algorithm for linear feature detection using multiple directional non-maximum suppression. the algorithm is very fast compared with methods in the literature. we also show a large number of application examples using our linear feature detection algorithm, and very good results have been obtained.
robust direction estimation of gradient vector field for iris recognition. as a reliable personal identification method, iris recognition has been receiving increasing attention. based on the theory of robust statistics, a novel geometry-driven method for iris recognition is presented in this paper. an iris image is considered as a 3d surface of piecewise smooth patches. the direction of the 2d vector, which is the planar projection of the normal vector of image surface, is illumination insensitive and opposite to the direction of gradient vector. so the directional information of iris imageýs gradient vector field (gvf) is used to represent iris pattern. robust direction estimation, direction diffusion followed by vector directional filtering, is performed on the gvf to extract stable iris feature. extensive experimental results demonstrate that the recognition performance of the proposed algorithm is comparable with the best method in the open literature.
evaluation of 3d facial feature selection for individual facial model identification. face recognition using 3d information has been intensively investigated in recent years. the features selected from 3d facial surfaces are invariant to pose and lighting conditions. however, they are sensitive to expression variations. in this paper, we investigate the issues on selecting good features for 3d facial shape classification, and evaluate its applicability to various types of models. based on our existing work on feature selection using a genetic algorithm, we derived a set of features from the individualized wire-frame models. we evaluate the usefulness of such features not only to the generated models from images, but also to the range data from 3d imaging systems with variable resolutions. we tested the algorithm on two types of data sets: generic model based dataset and range-scan model based dataset. experimental results show that the optimal features derived from both datasets are robust among two databases. the resolution of captured models affects the selection of optimal features; however, the combination of the optimal features improves the recognition rate.
better foreground segmentation for static cameras via new energy form and dynamic graph-cut. in this paper, we propose a new foreground segmentation method for applications using static cameras. it formulates foreground segmentation as an energy minimization problem, and produces much better results than conventional background subtraction methods. due to the integration of better likelihood term, shadow elimination term and contrast term into energy function, it also achieves more accurate segmentation than existing method of the same type. furthermore, real-time performance is made possible by employing dynamic graph-cut algorithm. quantitative and qualitative experiments on real videos demonstrate our improvements.
segmentation and probabilistic registration of articulated body models. there are different approaches to pose estimation and registration of different body parts using voxel data. we propose a general bottom-up approach in order to segment the voxels into different body parts. the voxels are first transformed into a high dimensional space which is the eigenspace of the laplacian of the neighbourhood graph. we exploit the properties of this transformation and fit splines to the voxels belonging to different body segments in eigenspace. the boundary of the splines is determined by examination of the error in spline fitting. we then use a probabilistic approach to register the segmented body segments by utilizing their connectivity and prior knowledge of the general structure of the subjects. we present results on real data, containing both simple and complex poses. while we use human subjects in our experiment, the method is fairly general and can be applied to voxel-based registration of any articulated or non-rigid object composed of primarily 1-d parts.
a real-time facial expression recognition using the staam. this paper proposes a real-time person independent facial expression recognition in two folds. one is using the stereo active appearance model (staam) fitting algorithm that uses a geometric relationship between two tightly coupled views to increases the accuracy and speed of fitting. also, a layered generalized discriminant analysis (gda) classifier combines 3d shape and appearance to improve the recognition performance of person independent facial expressions. experimental results show that the staam have a better fitting stability than the multi-view aam (mvaam), and the combination of the shape and appearance features using a layered gda classifier improves the recognition performance of facial expressions greatly.
iris recognition using collarette boundary localization. there has been a rapid increase in the need of accurate and reliable personal identification infrastructure in recent years, and biometrics has become an important technology for security. the iris recognition system consists of four-process: image acquisition, preprocessing, feature extraction and identification or verification. in this paper, we propose the methods for localizing the iris area between the inner boundary and the collarette boundary, to remove unnecessary areas and to increase the recognition rate. for finding the collarette boundary, histogram equalization and a high pass filter, after using an one-dimensional dft, are applied to the image. the collarette boundary is found using an statistical information from the image which removes low-frequencies, and, finally, the iris is localized between the inner boundary and the collarette boundary. the iris is localized by two kinds of methods, and the recognition rate were compared. the recognition rate was evaluated by using dwt and svm. these show that the iris localization by the proposed methods contains more information than the previous methods and improves the recognition rate.
super-resolution in the presence of space-variant blur. an efficient algorithm using maximum a posteriori- markov random field (map-mrf) based approach for recovering a high-resolution image from multiple sub-pixel shifted low-resolution images is proposed. the algorithm can be used for super-resolution of both space-invariant and space-variant blurred images. we prove an important theorem that the posterior is also markov and derive the exact posterior neighborhood structure in the presence of warping, blurring and down-sampling operations. the posterior being markov enables us to perform all matrix operations as local image domain operations thereby resulting in a considerable speedup. experimental results are given to demonstrate the effectiveness of our method.
automatic local effect of window/level on 3-d scale-space ellipsoidal filtering on run-off-arteries from white blood magnetic resonanc angiography. pre-filtering is a critical step in 3-d segmentation of the blood vessel and its display. this paper presents the local effect of window/level over the 3-d scale-space approach for filtering the white blood angiographic volumes and its implementation issues. the raw mr angiographic volume is first converted to an isotropic volume, and then the window/level is automatically adjusted slice by slice and a composite volume is generated. then, 3-d edges are generated using the separable gaussian derivative convolution with known scales. the edge volume is then run by the directional processor at each voxel where the eigenvalues of the 3-d ellipsoid are computed. the vessel score per voxel is then estimated based on these three eigenvalues which suppress the non-vasculature and background structures, yielding the filtered volume. the filtered volume is ray-cast to generate the maximum intensity projection images for display. the performance of the system is evaluated by computing the mean, variance, snr and cnr images. we compare the filtering results with and without the usage of the local effect of window/level over 3-d scale-space ellipsoidal filtering. we show that the automatic window/level is effective in detecting small vessels which are otherwise difcult to extrapolate them. the system was run over 20 patient studies from different parts of the body such as: brain, abdomen, kidney and knee/ankle. the computer program takes around 150 seconds of processing time per study for study for a data size of 512 × 512 × 194 which includes the complete performance evaluation.
a comparison of state-of-the-art diffusion imaging techniques for smoothing medical/non-medical image data. partial differential equations (pde's) have dominated image processing research recently (see suri et al. [1], [3], [5], [4] and haker [6].the three main reasons for their success are: (1) their ability to transform a segmentation modeling problem into a partial differential equation framework and their ability to embed and integrate different regularizers into these models; (2) their ability to solve pde's in the level set framework using finite difference methods; and (3) their easy extension to a higher dimensional space.this paper is an attempt to summarize p e's and their solutions applied to image diffusion. the paper first presents the fundamental diffusion equation. next, the multi-channel anisotropic diffusion imaging is presented, followed by tensor non-linear anisotropic diffusion. we also present the anisotropic diffusion based on p e and the tukey/huber weight function for image noise removal. the paper also covers the recent growth of image denoising using the curve evolution approach and image denoising using histogram modification based on p e. finally, the paper presents the non-linear image denoising. examples covering both synthetic and real world images are presented.
mutual information based evaluation of 3d building models. this paper presents a metric based on information theory principles that compares 3d object models to images. the metric is based on the formulation of the mutual information between the model and the images. the technique does not require a priori information about the surface properties of the object and is robust with respect to variations of illumination. as result the method is quite general and may be used in a wide variety of applications.experiments are presented that demonstrate the approach by evaluating reconstructed 3d building models in aerial images.
using a fuzzy framework for delineation and decomposition of immunoglobulin g in cryo electron tomographic images. in structural studies of proteins, the first task is to identify the different parts of the protein. we present a robust method using a fuzzy framework for delineating a protein and to identify its parts. the method is used in a study of the immunoglobulin g antibody, individually imaged using cryo electron tomography, with satisfactory results.
order-preserving clustering and its application to gene expression data. clustering of ordered data sets is a common problem faced in many pattern recognition tasks. existing clustering methods either fail to capture the data or use restrictive models such as hmms or ar models to model the data. in this paper, we present a general order-preserving clustering algorithm that allows arbitrary patterns of data evolution by representing each ordered set as a curve. clustering of the data then reduces to grouping curves based on shape similarity. we develop a novel measure of shape similarity between curves using scale-space distance. shape similarity or dis-similarity is judged by composing higher-dimensional curves from constituent curves and noting the additional twists and turns in such curves that can be attributed to shape differences. an algorithm analogous to k-means clustering is then developed that uses prototypical curves for representing clusters. results are demonstrated on ordered gene expression data sets obtained from gene chips.
technical symbols recognition using the two-dimensional radon transform. we introduce a new method to generate feature vectors to be used in symbol recognition. we propose a new exploitation of the radon transform to generate relevant features. the radon transform is essentially a transformation of an image into a transform plane (p, ¿) represented by an accumulator in the discrete case. from the accumulator array we extract a signature (r-signature) which provides a global information of a binary shape whatever its type and its form. the signature allows to keep fundamental geometrical transformation like scale, translation and rotation.
recognition of symbols in grey level line-drawings from an adaptation of the radon transform. an adaptation of the radon transform is proposed to recognize grey level symbols. we directly process on the grey level document in order to improve the recognition step. a tridimensional signature is defined which takes into account the variations of photometry and the shape of a symbol. the proposed signature also allows to keep fundamental geometric transformations (scale, translation and rotation). experimental results on technical documents show the promising aspect of our approach.
retrieving images by content from strong relational graph matching. a new method is proposed to retrieve images by content. the approach is based on the matching of strong graphs defined by relational signatures computed between clusters of a fuzzy partition. such a representation combines color and spatial information between the regions and have nice geometric properties w.r.t scale factor, rotation and translation.
binarization of color images from an adaptation of possibilistic c-means algorithm. a color image binarization is presented in this paper. the iterative possibilistic c-means algorithm is adapted by adding a fuzzy entropy criterion to split the membership function in two clusters (background and object). such an improvement allows to perform a threshold free color binarization. experimental results show the promising aspect of our approach.
fast visual search using simplified pruning rules - streamlined active search. object detection in an image has a lot of applications. it has been reported that color information has advantages in detecting objects. in this paper, a high-speed visual search strategy using color histograms by introducing simplified pruning rules and global pruning is proposed. experimental results show that the proposed method outperforms traditional methods.
feature selection using tabu search for improving the classification rate of prostate needle biopsies. the introduction of multispectral imaging in pathology problems such as the identification of prostatic cancer is recent. unlike conventional rgb color space, it allows the acquisition of large number of spectral bands within the visible spectrum. this results in a feature vector of size greater than 100. for such high dimensionality problems, pattern recognition techniques suffer from the well-known curse-of-dimensionality problem. the two well known techniques to solve this problem are feature extraction and feature selection. in this paper, a feature selection technique using tabu search with an intermediate-term memory is proposed. the cost of a feature subset is measured by leave-one-out correct-classification rate of a nearest-neighbor (1-nn) classifier. experiments have been carried out on textured multispectral images taken at 16 spectral channels and the results have been compared with a reported classical feature extraction technique.
simultaneous image denoising and compression by multiscale 2d tensor voting. in this paper we propose a method that simultaneously performs image denoising and compression by using multiscale tensor voting. given a real color image, the pixels are first converted into a set of tokens to be grouped by tensor voting, where optimal scales are automatically selected among others for perceptual grouping and faithful reconstruction. tensor voting at multiple scales are performed at all input tokens to infer the feature grouping attributes such as region-ness, curve-ness, and junction-ness with their optimal scales. we perform experiments on complex real images to demonstrate the robustness of our method.
planning of multiple camera arrangement for object recognition in parametric eigenspace. when objects are recognized by using multiple cameras, recognition rates strongly depend on the camera arrangement. in this paper, we propose a new method for planning a multiple camera arrangement for accurate recognition. we use a parametric eigenspace method for the recognition framework in which objects are represented as manifolds in an eigenspace. the proposed method evaluates the adequacy of camera arrangement according to the relations between the manifolds in the eigenspace. in the experiments, we defined a function that measures relations by the distances between manifolds. the experimental results show the effectiveness of the proposed method.
high quality isosurface generation from volumetric data and its application to visualization of medical ct data. we propose a method for generating an isosurface from volumetric data sampled with a face-centered cubic lattice. the display quality of the isosurface obtained by our method is greatly enhanced because it generates many good aspect ratio triangle patches. we applied the method to visualization of a colonic wall from medical data. we experimentally compared the resulting surface of our method with those of existing methods, showing the effectiveness of our method.
dvhmm: variable length text recognition error model. this paper proposes a text recognition error model called the dual variable length output hidden markov model (dvhmm) and gives a parameter estimation algorithm based on the em algorithm. although existing probabilistic error models are limited to substitution (1,1), insertion (1,0), and deletion (0,1) errors, the dvhmm can handle error patterns of any pair (i, j) of lengths including substitution, insertion, and deletion.
object contour detection using spatio-temporal self-sim. a novel contour detector that refines a rough boundary between an object and a background to a precise boundary in moving pictures robustly is proposed. to estimate boundaries of objects, the proposed method uses self-similar block matching (ssbm) in spatio-temporal 3-d space. ssbm, which searches a larger similar block for each block placed near a boundary, estimates contours correctly. in this paper, it is shown analytically that the robustness of spatiotemporal ssbm is superior to that of conventional 2-d ssbm. since ssbm does not assume contour smoothness, the proposed algorithm can detect sharp corners more accurately than the methods using smooth constraints such as snake. experimental results show that the proposed method is effective for estimating precise regions of objects even if pictures are noisy.
3d reconstruction and virtual forming in rotationally symmetric space. for reconstructing 3d objects and for generating arbitrary views of the scene, accurate calibration of multiple cameras is very important. in this paper, we introduce rotationally symmetric space, and show that in rotationally symmetric space, cameras can be calibrated and 3d points can be reconstructed just from a single basis line reliably. the proposed method is applied for deforming virtual clay in rotationally symmetric space and for generating augmented reality images of virtual pottery.
a study on character recognition error correction at higher level recognition step for mathematical formulae understanding. in this paper we propose a method for correcting character recognition errors at the higher level recognition step in an understanding system for mathematical formulae. the system consists of two-level recognition steps: the low level recognition including character recognition, and the higher level recognition including layout recognition. we use the layout information recognized in the latter step to correct the character recognition errors by using two sources of information. one is based on some keywords such as mathematical function names, and the other is based on a cost tree and co-occurrence probabilities between symbols. the efficacy of the proposed method is verified by some experimental results, and the character recognition rate increased from 80.2% to 89.2%.
surface reconstruction from stereovision data using a 3-d mrf of discrete object models. in the present paper, we propose a method for reconstructing the surfaces of objects from stereovision data. both the fitness of stereo data to surfaces and interrelation between the surfaces are defined in the framework of a three-dimensional (3-d) markov random field (mrf) model. the surface reconstruction is accomplished by searching for the most likely state of the mrf model. an experimental result is shown for a real scene.
recognition of lung nodules from x-ray ct images using 3d markov random field models. in this paper, we propose a new recognition method of lung nodules from x-ray ct images using 3d markov random field(mrf) models. pathological shadow candidates are detected by a mathematical morphology filter, and volume of interest(voi) areas which include the shadow candidates are extracted. the probabilities of the hypotheses that the voi areas come from nodules(which are candidates of cancers) and blood vessels are calculated using nodule andblood vessel models evaluating the relations between these object models by 3d mrf models. if the probabilities for the nodule models are higher, the shadow candidates are determined to be abnormal. by applying this new recognition method to actual 38 ct images, good results has been acquired.
applying the conjugate gradient method for text document categorization. in this paper, we investigate the effectiveness of two different methods to solve the linear least squares fit (llsf) problem for document categorization.the first method is the singular value decomposition (svd) method that has been previously used to solve the document categorization problem.the second method is the conjugate gradient (cg) method that is one of the most effective algorithms for solving a linear equation problem.however, up to our knowledge, the cg method has never been applied to handle the document classification problem.therefore, we compare the effectiveness of these two llsf methods to categorize text documents.in addition, we examine the effect of using different term weighting schemes on their performance for document classification.lastly, we compare the performance of the llsf classifiers agaisnt the neighborhood-based dt-knn classifier, our best variant of the knn classifier integrated with a dynamic threshold scheme, on the reuters 21578 dataset.besides being the first proposal to use the cg method for document classification, our work opens up many exciting directions for future investigation.