waashk/dblp huggingface dataset:
face sketch synthesis and recognition. in this paper, we propose a novel face photo retrievalsystem using sketch drawings. by transforming a photoimage into a sketch, we reduce the difference betweenphoto and sketch significantly, thus allow effectivematching between the two. to improve the synthesisperformance, we separate shape and texture informationin a face photo, and conduct transformation on themrespectively. finally a bayesian classifier is used torecognize the probing sketch from the synthesizedpseudo-sketches. experiments on a data set containing606 people clearly demonstrate the efficacy of thealgorithm.
photometric stereo under perspective projection. photometric stereo is a fundamental approach in computer vision. at its core lies a set of image irradiance equations each taken with a different illumination. the vast majority of studies in this field have assumed orthography as the projection model. this paper re-examines the basic set of equations of photometric stereo, under an assumption of perspective projection. we show that the resulting system is linear (as is the case under the orthographic model; nevertheless, the unknowns are different in the perspective case). we then suggest a simple reconstruction algorithm based on the perspective formulae, and compare it to its orthographic counterpart on synthetic as well as real images. this algorithm obtained lower error rates than the orthographic one in all of the error measures. these findings strengthen the hypothesis that a more realistic set of assumptions, the perspective one, improves reconstruction significantly.
a new perspective [on] shape-from-shading. shape-from-shading (sfs) is a fundamental problem incomputer vision. the vast majority of research in this fieldhave assumed orthography as its projection model. thispaper re-examines the basis of sfs, the image irradianceequation, under an assumption of perspective projection.the paper also shows that the perspective image irradianceequation depends merely on the natural logarithm of thedepth function (and not on the depth function itself), and assuch it is invariant to scale changes of the depth function.we then suggest a simple reconstruction algorithm basedon the perspective formula, and compare it to existing orthographicsfs algorithms. this simple algorithm obtainedlower error rates than legacy sfs algorithms, and equatedwith and sometimes surpassed state-of-the-art algorithms.these findings lend support to the assumption that transitionto a more realistic set of assumptions improves reconstructionsignificantly.
non-orthogonal binary subspace and its applications in computer vision. this paper presents a novel approach that represents an image or a set of images using a non-orthogonal binary subspace (nbs) spanned by box-like base vectors. these base vectors possess the property that the inner product operation with them can be computed very efficiently. we investigate the optimized orthogonal matching pursuit method for finding the best nbs base vectors. it is demonstrated in this paper how the nbs based expansion can be applied to speed up several common computer vision algorithms, including normalized cross correlation (ncc), sum of squared difference (ssd) matching, appearance subspace projection and subspace-based object recognition. promising experimental results on facial and natural images are demonstrated in this paper.
comparison of graph cuts with belief propagation for stereo, using identical mrf parameters. recent stereo algorithms have achieved impressive resultsby modelling the disparity image as a markov randomfield (mrf). an important component of an mrf-basedapproach is the inference algorithm used to find the mostlikely setting of each node in the mrf. algorithms havebeen proposed which use graph cuts or belief propagationfor inference. these stereo algorithms differ in both theinference algorithm used and the formulation of the mrf.it is unknown whether to attribute the responsibility for differencesin performance to the mrf or the inference algorithm.we address this through controlled experiments bycomparing the belief propagation algorithm and the graphcuts algorithm on the same mrf's, which have been createdfor calculating stereo disparities. we find that the labellingsproduced by the two algorithms are comparable.the solutions produced by graph cuts have a lower energythan those produced with belief propagation, but this doesnot necessarily lead to increased performance relative tothe ground-truth.
local symmetries of shapes in arbitrary dimension. motivated by a need to define an object-centered reference system determined by the most salient characteristics of the shape, many methods have been proposed, all of which directly or indirectly involve an axis about which the shape is locally symmetric. recently, a function v, called "the edge strength function", has been successfully used to determine efficiently the axes of local symmetries of 2-d shapes. the level curves of v are interpreted as succesively smoother versions of the initial shape boundary. the local minima of the absolute gradient \left\| {\nabla v} \right\| along the level curves of v are shown to be a robust criterion for determining the shape skeleton. more generally, at an extremal point of \left\| {\nabla v} \right\| along a level curve, the level curve is locally symmetric with respect to the gradient vector \nabla v. that is, at such a point, the level curve is approximately a conic section whose one of the principal axes coincides with the gradient vector. thus, the locus ofthe extremal points of \left\| {\nabla v} \right\| along the level curves determines the axes of local symmetries of the shape. in this paper, we extend this method to shapes of arbitrary dimension.
modelling shapes with uncertainties: higher order polynomials, variable bandwidth kernels and non parametric density estimation. in this paper, we introduce a new technique for shape modelling in the space of implicit polynomials. registration consists of recovering an optimal one-to-one transformation of a higher order polynomial along with uncertainties measures that are determined according to the covariance matrix of the correspondences at the zero isosurface. in the modelling phase, these measures are used to weight the importance of the training samples phase according to a variable bandwidth non-parametric density estimation process. the selection of the most appropriate kernels to represent the training set is done through the maximum likelihood criterion. excellent results for patterns of digits, related with the registration and the modelling aspects of our approach demonstrate the potentials of our method.
curve and surface smoothing without shrinkage. for a number of computational purposes, including visualization of scientific data and registration of multimodal medical data, smooth curves must be approximated by polygonal curves, and surfaces by polyhedral surfaces. an inherent problem of these approximation algorithms is that the resulting curves and surfaces appear faceted. boundary-following and iso-surface construction algorithms are typical examples. to reduce the apparent faceting, smoothing methods are used. in this paper, we introduce a new method for smoothing piecewise linear shapes of arbitrary dimension and topology. this new method is in fact a linear low-pass filter that removes high-curvature variations, and does not produce shrinkage. its computational complexity is linear in the number of edges or faces of the shape, and the required storage is linear in the number of vertices.
estimating the tensor of curvature of a surface from a polyhedral approximation. estimating principal curvatures and principal directions of a surface from a polyhedral approximation with a large number of small faces, such as those produced by iso-surface construction algorithms, has become a basic step in many computer vision algorithms, particularly in those targeted at medical applications. we describe a method to estimate the tensor of curvature of a surface at the vertices of a polyhedral approximation. principal curvatures and principal directions are obtained by computing in closed form the eigenvalues and eigenvectors of certain 3/spl times/3 symmetric matrices defined by integral formulas, and closely related to the matrix representation of the tensor of curvature. the resulting algorithm is linear, both in time and in space, as a function of the number of vertices and faces of the polyhedral surface.
surface reconstruction from feature based stereo. this paper describes an approach to recovering surface models of complex scenes from the quasi-sparse data returned by a feature based stereo system. the method can be used to merge stereo results obtained from different viewpoints into a single coherent surface mesh. the technique proceeds by exploiting the free space theorem which provides a principled mechanism for reasoning about the structure of the scene based on quasi-sparse correspondences in multiple image. effective methods for overcoming the difficulties posed by missing features and outliers are discussed. results obtained by applying this approach to actual images are presented.
image segmentation by reaction-diffusion bubbles. figure-ground segmentation is a fundamental problem in computer vision. the main difficulty is the integration of low-level, pixel-based local image features to obtain global object-based descriptions. active contours in the form of snakes, balloons, and level-set modeling techniques have been proposed that satisfactorily address this question for certain applications. however, these methods require manual initialization, do not always perform well near sharp protrusions or indentations, or often cross gaps. we propose an approach inspired by these methods and a shock-based representation of shape in terms of parts, protrusions, and bends. since initially it is not clear where the objects or their parts are, parts are hypothesized in the form of fourth order shocks randomly initialized in homogeneous areas of images. these shocks then form evolving contours, or bubbles, which grow, shrink, merge, split and disappear to capture the objects in the image. in the homogeneous areas of the image bubbles deform by a reaction-diffusion process. in the inhomogeneous areas, indicated by differential properties computed from low-level processes such as edge-detection, texture, optical-flow and stereo, etc., bubbles do not deform. as such, the randomly initialized bubbles integrate low-level information and, in the process, segment the figures from the ground.
symmetry maps of free-form curve segments via wave propagation. this paper presents an approach for computing the symmetries (skeletons) of an edge map consisting of a collection of curve segments. this approach is a combination of analytic computations in the style of computational geometry and discrete propagations on a grid in the style of the numerical solutions of pde's. specifically, waves from each of the initial curve segments are initialized and propagated as a discrete wavefront along discrete directions. in addition, to avoid error built up due to the discrete nature of propagation, shockwaves are detected and explicitly propagated along a secondary dynamic grid. the propagation of shockwaves, integrated with the propagation of the wavefront along discrete directions, leads to an exact simulation of propagation by the eikonal equation. the resulting symmetries are simply the collection of shockwaves formed in this process which can be manipulated locally, exactly, and efficiently under local changes in an edge map (gap completion, removal of spurious elements, etc). the ability to express grouping operations in the language of symmetry maps makes it an appropriate intermediate representation between low-level edge maps and high level object hypotheses.
design of multi-parameter steerable functions using cascade basis reduction. a new cascade basis reduction method of computing the optimal least-squares set of basis functions to steer a given function is presented. the method combines the lie group-theoretic and the singular value decomposition approaches such that their respective strengthscomplement each other. since the lie group-theoretic approach is used, the set of basis and steering functions computed can be expressed in analytic form. because the singular value decomposition method is used, this set of basis and steering functions is optimal in the least-squares sense. most importantly, the computational complexity in designing basis functions for transformation groups with large numbers of parameters is significantly reduced. the efficiency of the cascade basis reduction method is demonstrated by designing a set of basis functions to steer a gabor function under the four-parameter linear transformation group.
segmentating cortical gray matter for functional mri visualization. we describe a system that is being used to segment gray matter and create connected cortical representations from mri. the method exploits knowledge of the anatomy of the cortex and incorporates structural constraints into the segmentation. first, the white matterand csf regions in the mr volume are segmented using some novel techniques of posterior anisotropic diffusion. then, the user selects the cortical white matter component of interest, and its structure is verified by checking for cavities and handles. after this, a connected representation of the gray matter is created by a constrained growing-out from the white matter boundary. because the connectivity is computed, the segmentation can be used as input to several methods of visualizing the spatial pattern of cortical activity within gray matter. in our case, the connected representation of gray matter is used to create a representation of theflattened cortex. then, fmri measurements are over-laid on the flattened representation, yielding a representation of the volumetric data within a single image.
animat vision: active vision in artificial animals. we propose and demonstrate a new paradigm for active vision research that draws upon recent advances in the fields of artificial life and computer graphics. a software alternative to the prevailing hardware vision mindset, animat vision prescribes artificial animals, or animats, situated in physics-based virtual worlds as autonomous virtual robots possessing active perception systems. to be operative in its world, an animat must autonomously control its eyes and muscle-actuated body, applying computer vision algorithms to continuously analyze the retinal image streams acquired by its eyes in order to locomote purposefully through its world. we describe an initial animat vision implementation within lifelike artificial fishes inhabiting a physics-based, virtual marine world. emulating the appearance, motion, and behavior of real fishes in their natural habitats, these animats are capable of spatially nonuniform retinal imaging, foveation, retinal image stabilization, color object recognition, and perceptually-guided navigation. these capabilities allow them to pursue moving targets such as fellow artificial fishes. animat vision offers a fertile approach to the development, implementation, and evaluation of computational theories that profess sensorimotor competence for animal or robotic situated agents.
shape classifer based on generalized probabilistic descent method with hidden markov descriptor. the goal of this paper is to present a weighted likelihood discriminant for minimum error shape classification. different from traditional maximum likelihood (ml) methods, in which classification is based on probabilities from independent individual class models as is the case for general hidden markov model (hmm) methods, proposed method utilizes information from all classes to minimize classification error. the proposed approach uses a hmm for shape curvature as its 2-d shape descriptor. in this contribution we introduce a weighted likelihood discriminant function and present a minimum error classification strategy based on generalized probabilistic descent (gpd) method. we believe our sound theory based implementation reduces classification error by combining hmm with gpd theory. we show comparative results obtained with our approach and classic ml classification alongwith fourier descriptor and zernike moments based classification for fighter planes and vehicle shapes.
multi-view geometry of 1d radial cameras and its application to omnidirectional camera calibration. we study the multi-view geometry of 1d radial cameras. a broad a class of both central and non-central cameras, such as fish-eye and catadioptric cameras, can be reduced to 1d radial cameras under the assumption of known center of radial distortion. for cameras in general configuration, we introduce a quadrifocal tensor that can be computed linearly from 15 or more features seen in four views. from this tensor a metric reconstruction of the 1d cameras as well as the observed features can be obtained. in a second phase this reconstruction can then be used as a calibration object to estimate a non-parametric non-central model for the cameras. we study some degenerate cases, including pure rotation. in the case of a purely rotating camera we obtain a trifocal tensor that can be estimated linearly from 7 points in three views. this allows us to obtain a metric reconstruction of the plane at infinity. next, we use the plane at infinity as a calibration device to non-parametrically estimate the radial distortion. we demonstrate the results of our approach on real and synthetic images.
shape from symmetry. we describe a technique for reconstructing probable occluded surfaces from 3-d range images. the technique exploits the fact that many objects possess shape symmetries that can be recognized even from partial 3-d views. our approach identifies probable symmetries and uses them to extend the partial 3-d shape model into the occluded space. to accommodate objects consisting of multiple parts, we describe a technique for segmenting objects into parts characterized by different symmetries. results are provided for a real-world database of 3-d range images of common objects, acquired through an active stereo rig.
recovering 3d motion of multiple objects using adaptive hough transform. presents a method to determine the 3d motion of multiple objects from two perspective views. in our method, segmentation is determined based on a 3d rigidity constraint. we divide the input image into overlapping patches, and for each sample of the translation parameter space, we compute the rotation parameters of patches using a least-squares fit. every patch votes for a sample in the translation and rotation parameter space. for a patch containing multiple motions, we use an m-estimator to compute rotation parameters of a dominant motion. we use the adaptive hough transform to refine the relevant parameter space in a "coarse-to-fine" fashion. applications of the proposed method to both synthetic and real images are demonstrated with promising results.
inference of non-overlapping camera network topology by measuring statistical dependence. we present an approach for inferring the topology of a camera network by measuring statistical dependence between observations in different cameras. two cameras are considered connected if objects seen departing in one camera are seen arriving in the other. this is captured by the degree of statistical dependence between the cameras. the nature of dependence is characterized by the distribution of observation transformations between cameras, such as departure to arrival transition times, and color appearance. we show how to measure statistical dependence when the correspondence between observations in different cameras is unknown. this is accomplished by non-parametric estimates of statistical dependence and bayesian integration of the unknown correspondence. our approach generalizes previous work which assumed restricted parametric transition distributions and only implicitly dealt with unknown correspondence. results are shown on simulated and real data. we also describe a technique for learning the absolute locations of the cameras with global positioning system (gps) side information.
computation of coherent optical flow by using multiple constraints. the optical flow constitutes one of the most widely adopted representations to define and characterize the evolution of image features over time. in order to compute the velocity field, it is necessary to define a set of constraints on the temporal change of image features. we consider the implications in using multiple constraints arising from multiple data points. the first step is the analysis of differential constraints and how they can be applied, locally, to compute the image velocity. this analysis allows to relate each constraint to a particular gray level pattern. this approach is extended to multiple image points, allowing also the characterization of the temporal behaviour of the image features and to detect erroneous measurements due to occlusions, depth discontinuities or shadows. several experiments are presented from real image sequences.
machine learning and multiscale methods in the identification of bivalve larvae. this paper describes a novel application of support vector machines and multiscale texture and color invariants to a problem in biological oceanography: the identification of 6 species of bivalve larvae. our data consists of polarized color images of scallop and other bivalve larvae (between 2 and 17 days old) collected from the ocean by a shipboard optical imaging system of our design. larvae of scallops, clams, and oysters are small (100 microns) with few distinguishing features when observed under standard light microscopy. however, the use of polarized light with a full wave retardation plate produces a vivid color, bi-refringence pattern. the patterns display very subtle differences between species, often not discernable to human observers. we show that a soft-margin support vector machine with gaussian rbf kernel is a good discriminator on a feature set extracted from gabor wavelet transforms and color distribution angles of each image. by constraining the gabor center frequencies to be low, the resulting system can attain classification accuracy in excess of 90% for vertically oriented images, and in excess of 80% for randomly oriented images.
entropy-of-likelihood feature selection for image correspondence. feature points for image correspondence are often selectedaccording to subjective criteria (e.g. edge density,nostrils). in this paper, we present a general, non-subjectivecriterion for selecting informative feature points, based onthe correspondence model itself. we describe the approachwithin the framework of the bayesian markov random field(mrf) model, where the degree of feature point informationis encoded by the entropy of the likelihood term. we proposethat feature selection according to minimum entropy-of-likelihood (eol) is less likely to lead to correspondence ambiguity, thus improving the optimization process in termsof speed and quality of solution. experimental resultsdemonstrate the criterion's ability to select optimal featurespoints in a wide variety of image contexts (e.g. objects,faces). comparison with the automatic kanade-lucas-tomasifeature selection criterion shows correspondence tobe significantly faster with feature points selected accordingto minimum eol in difficult correspondence problems.