waashk/dblp huggingface dataset:
recognition using region correspondences. recognition systems attempt to recover information about the identity of the observed objects and their location in the environment. a fundamental problem in recognition is the following. given a correspondence between some portions of an object model and some portions of an image, determine whether the image contains an instance of the object, and, in case it does, determine the transformation that relates the model to the image. the current approaches to this problem are divided into methods that use ``global'''' properties of the object (e.g., centroid and moments of inertia) and methods that use ``local'''' properties of the object (e.g., corners and line segments). global properties are sensitive to occlusion and, specifically, to self occlusion. local properties are difficult to locate reliably, and their matching involves intensive computation. a novel method for recognition that uses region information is presented. in our approach the model is divided into volumes, and the image is divided into regions. given a match between subsets of volumes and regions (without any explicit correspondence between different pieces of the regions) the alignment transformation is computed. the method applies to planar objects under similarity, affine, and projective transformations and to projections of 3-d objects undergoing affine and projective transformations. the new approach combines many of the advantages of the previous two approaches, while avoiding some of their pitfalls. like the global methods, our approach makes use of region information that reflects the true shape of the object. but like local methods, our approach can handle occlusion.
recognition of human body motion using phase space constraints. a new method for representing and recognizing human body movements is presented. the basic idea is to identify sets of constraints that are diagnostic of a movement: expressed using body-centered coordinates such as joint angles and in force only during a particular movement. assuming the availability of cartesian tracking data, we develop techniques for a representation of movements defined by space curves in subspaces of a "phase space." the phase space has axes of joint angles and torso location and attitude, and the axes of the subspaces are subsets of the axes of the phase space. using this representation we develop a system for learning new movements from ground truth data by searching for constraints. we then use the learned representation for recognizing movements in unsegmented data. we train and test the system on nine fundamental steps from classical ballet performed by two dancers; the system accurately recognizes the movements in the unsegmented stream of motion.
projective alignment with regions. we have recently proposed an approach to recognition that uses regions to determine the pose of objects while allowing for partial occlusion of the regions. regions introduce an attractive alternative to existing global and local approaches, since, unlike global features, they can handle occlusion and segmentation errors, and unlike local features they are not as sensitive to sensor errors, and they are easier to match. the region-based approach also uses image information directly, without the construction of intermediate representations, such as algebraic descriptions, which may be difficult to reliably compute. in this paper we further analyze properties of the method for planar objects undergoing projective transformations. in particular, we prove that three visible regions are sufficient to determine the transformation uniquely, and that for a large class of objects two regions are insufficient for this purpose. however, we show that when several regions are available the pose of the object can generally be recovered even when some or all regions are significantly occluded. our analysis is based on investigating the flow patterns of points under projective transformations in the presence of fixed points.
a caratheodory-fejer approach to robust multiframe tracking. a requirement common to most dynamic vision applicationsis the ability to track objects in a sequence of frames. thisproblem has been extensively studied in the past few years,leading to several techniques, such as unscented particlefilter based trackers, that exploit a combination of the (assumed)target dynamics, empirically learned noise distributionsand past position observations. while successful inmany scenarios, these trackers remain fragile to occlusionand model uncertainty in the target dynamics. as we showin this paper, these difficulties can be addressed by modelingthe dynamics of the target as an unknown operator thatsatisfies certain interpolation conditions. results from interpolationtheory can then be used to find this operator bysolving a convex optimization problem. as illustrated withseveral examples, combining this operator with kalman andupf techniques leads to both robustness improvement andcomputational complexity reduction.
when is it possible to identify 3d objects from single images using class constraints? one approach to recognizing objects seen from arbitrary viewpoint is by extracting invariant properties of the objects from single images. such properties are found in images of 3d objects only when the objects are constrained to belong to certain classes (e.g., bi;aterally symmetric objects). existing studies that follow this approach propose how to compute invariant representations for a handful of classes of objects. a fundmental question regarding the invariance approach is whether it can be applied to a wide range of classes. to answer this question it is essential to study the set of classes for which invariance exists. this paper introduces a new method for determining the existence of invariance for classes of objects together with the set of images from which these invariance can be computed. we developed algebraic tests that, given a class of objects undergoing affine projection, determine whether the objects in the class can be identified from single images. in addition, these tests allow us to determine the set of views of the objects which are degenerate. we apply these tests to several classes of objects and determine which of them is identifiable and which of their views are degenerate.
good continuations in digital image level lines. we propose a probabilistic algorithm able to detect the curves that are unexpectedy smooth in a set of digital curves. the only parameter is a false alarme rate, influencing the detection only by its logarithm. we experiment the good continuation criterion on image level lines. one of the conclusion is that, accordingly to gestalt theory, onecan detect egdes in a way that is widely independent of contrast. we also use the same kind of method to detect corners and junctions.
head-eye calibration. we deal with the calibration problem of an active head-eye system, which consists of a pair of cameras mounted on a head with 13 degrees of freedom. the aim of the calibration is to establish relative positions of different 3d systems: between camera and neck, eye and neck, etc., so that we can keep track of the camera position in a fixed (calibration) reference system as a function of the visual parameters of the head-eye system. we formulate the problem and propose both closed-form and nonlinear optimization approaches to solve it. experiments were carried out and comparison of results with other algorithms were made on both simulated and real data.
visual homing: surfing on the epipoles. we introduce a novel method for visual homing. using this method a robot can be sent to desired positions and orientations in 3-d space specified by single images taken from these positions. our method is based on recovering the epipolar geometry relating the current image taken by the robot and the target image. using the epipolar geometry, most of the parameters which specify the differences in position and orientation of the camera between the two images are recovered. however, since not all of the parameters can be recovered from two images, we have developed specific methods to bypass these missing parameters and resolve the ambiguities that exist. we present two homing algorithms for two standard projection models, weak and full perspective. our method determines the path of the robot on-line, the starting position of the robot is relatively not constrained, and a 3-d model of the environment is not required. the method is almost entirely memoryless, in the sense that at every step the path to the target position is determined independently of the previous path taken by the robot. because of this property the robot may be able, while moving toward the target, to perform auxiliary tasks or to avoid obstacles, without this impairing its ability to eventually reach the target position. we have performed simulations and real experiments which demonstrate the robustness of the method and that the algorithms always converge to the target pose.
a cylindrical surface model to rectify the bound document image. this article proposes a novel approach on how torectify the photo image of the bound document. thesurface of the document is modeled by a cylindricalsurface. by the geometry of camera image formation, theequations using the cue of directrixes to map the points onthe surface in the 3-d scene to the points on the imageplane are achieved. baselines of the horizontal text lineare extracted as projections of directrixes to estimate thebending extent of the surface, and then the images arerectified. the proposed method needs no auxiliary device.experimental results are presented to demonstrate thefeasibility and the application of the method.
3d object reconstruction from a single 2d line drawing without hidden lines. the human vision system can interpret a single 2d line drawing as a 3d object without much difficulty even if the hidden lines of the object are invisible. several reconstruction approaches have tried to emulate this ability, but they cannot recover the complete object if the hidden lines of the object are not shown. this paper proposes a novel approach for reconstructing complete 3d objects from line drawings without hidden lines. first, we develop some constraints and properties for the inference of the topology of the invisible edges and vertices of an object. then we present a reconstruction method based on perceptual symmetry and planarity of the object. we give a number of examples to demonstrate the ability of our approach.
iterative multi-step explicit camera calibration. perspective camera calibration has been in the last decades a research subject for a large group of researchers and as a result several camera calibration methodologies can be foundin the literature. however only a small number of those methods base their approaches on the use of monoplane calibration points to realize an explicit 3d camera calibration. to avoid the singularity obtained with the calibration equations when monoplane calibration points are used, this method computes the calibration parameters in a multi-step procedure and requires a first-guess solution for the intrinsic parameters. these parameters are updated and their accuracy increased through an iterative procedure. a stability analysis as a function of the pose of the camera is presented. camera pose view strategies for accurate camera orientation computation can be extracted from the pose view stability analysis.
real-time motion analysis with linear-programming. a method to compute motion models in real time from point-to-line correspondences using linear programming is presented. point-to-line correspondences are the most reliable motion measurements given the aperture effect, and it is shown how they can approximate other motion measurements as well.using an l1 error measure for image alignment based on point-to-line correspondences and minimizing this measure using linear programming, achieves results which are more robust than the commonly used l2 metric. while estimators based on l1 are not theoretically robust, experiments show that the proposed method is robust enough to allow accurate motion recovery in hundreds of consecutive frames. the entire computation is performed in real-time on a pc with no special hardware.
a model-based approach for automated feature extraction in fundus images. a new approach to automatically extract the main features in color fundus images are proposed in this paper. optic disk is localized by the principal component analysis (pca) and its shape is detected by a modified active shape model (asm). exudates are extracted by the combined region growing and edge detection. a fundus coordinate system is further set up based on the fovea localization to provide a better description of the features in fundus images. the success rates achieved are 99%, 94%, and 100% for disk localization, disk boundary detection, and fovea localization respectively. the sensitivity and specificity for exudate detection are 100% and 71%. the success of the proposed algorithms can be attributed to the utilization of the model-based methods.
active visual navigation using non-metric structure. demonstrates a method of using nonmetric visual information derived from an uncalibrated active vision system to navigate an autonomous vehicle through free-space regions detected in a cluttered environment. the structure of 3-space is recovered modulo an affine transformation using an uncalibrated active stereo head carried by the vehicle. the plane at infinity, necessary for recovering affine structure from projective structure, is found in a novel manner by making controlled rotations of the head. the structure is composed of 3d points obtained by detecting and matching image corners through the stereo image sequence. considerable care has been taken to ensure that the processing is reliable, robust and automatic. driveable regions are determined from the projection of the affine structure onto a plane parallel to the ground determined using projective constructs. two methods of negotiating the regions are explored. the first introduces metric information to allow control of a euclidean vehicle. the second uses visual servoing of the active head to navigate in the affinely described free-space regions.
large deformation diffeomorphic metric mapping of fiber orientations. this paper proposes a method to match diffusion tensor magnetic resonance images (dt-mri) through the large deformation diffeomorphic metric mapping of vector fields, focusing on the fiber orientations, considered as unit vector fields on the image volume. we study a suitable action of diffeomorphisms on such vector fields, and provide an extension of the large deformation diffeomorphic metric mapping framework to this type of dataset, resulting in optimizing for geodesics on the space of diffeomorphisms connecting two images. two different distance function of vector fields are considered. existence of the minimizers under smoothness assumptions on the compared vector fields is proved, and coarse to fine hierarchical strategies are detailed, to reduce both ambiguities and computation load. this is illustrated by numerical experiments on dt-mri heart and brain images.
learning geometric hashing functions for model-based object recognition. a major problem associated with geometric hashing and methods which have emerged from it is the non-uniform distribution of invariants over the hash space. this problem can affect the performance of the method significantly. finding a "good" geometric hash function which redistributes the invariants uniformly over the hash space is not easy. in this paper, a new approach is proposed for alleviating the above problem. it is based on the use of an "elastic hash table" which is implemented as a self-organizing feature map neural network (sofm-nn). in contrast to existing approaches which try to redistribute the invariants over the hash bins, we proceed oppositely, spreading the hash bins over the invariants. during training, the sofm-nn resembles an elastic net which deforms over the hash space. the objective of the deformation process is to spread more hash bins in hash space areas which are heavily occupied and less hash bins in lower density areas. the advantage of the proposed approach is that it is a process that adapts to the invariants through learning. hence, it makes absolutely no assumptions about the statistical characteristics of the invariants and the geometric hash function is actually computed through learning. furthermore, the well known "topology preserving" property of the sofm-nn guarantees that the computed geometric hash function should be well behaved. finally, the proposed approach is inherently parallelizable.
class-specific material categorisation. although a considerable amount of work has been published on material classification, relatively little of it studies situations with considerable variation within each class. many experiments use the exact same sample, or different patches from the same image, for training and test sets. thus, such studies are vulnerable to effectively recognising one particular sample of a material as opposed to the material category. in contrast, this paper places firm emphasis on the capability to generalise to previously unseen instances of materials. we adopt an appearance-based strategy, and conduct experiments on a new database which contains several samples of each of eleven material categories, imaged under a variety of pose, illumination and scale conditions. together, these sources of intra-class variation provide a stern challenge indeed for recognition. somewhat surprisingly, the difference in performance between various state-of-the-art texture descriptors proves rather small in this task. on the other hand, we clearly demonstrate that very significant gains can be achieved via different svm-based classification techniques. selecting appropriate kernel parameters proves crucial. this motivates a novel recognition scheme based on a decision tree. each node contains an svm to split one class from all others with a kernel parameter optimal for that particular node. hence, each decision is made using a different, optimal, class-specific metric. experiments show the superiority of this approach over several state-of-the-art classifiers.
using algebraic functions of views for indexing-based object recognition. current indexing-based approaches build the hash table using either a large number of reference views or 3d models. in this paper, we propose building the hash table using algebraic functions of views. during preprocessing, we consider groups of model points and we represent all the views (i.e., images) that they can produce in a hash table. these views are computed using algebraic functions of a small number of reference views which contain the group. fundamental to this procedure is a methodology based on singular value decomposition and interval arithmetic for estimating the ranges of values that the parameters of algebraic functions can assume. during recognition, scene groups are used to retrieve from the hash table the model groups that might have produced them. using algebraic functions of views for indexing-based recognition offers a number of advantages. first of all, the hash table can be built easier, without requiring 3d models or a large number of reference views. second, recognition does not rely on the similarities between new and reference views. third, verification becomes simpler. finally, the approach is more general and extendible.
recovering object surfaces from viewed changes in surface texture patterns. explores the reconstruction of object surfaces from viewed changes in surface texture patterns. our approach differs from those in the past in that instead of simply producing local estimates of the surface orientation, our algorithm recovers complete surfaces. past approaches only found the surface orientation locally and, therefore, did not take advantage of the surface integrability constraint. our algorithm does not assume that the surface texture pattern is isotropic, and it does not assume that the viewed surface is at some point fronto-parallel. furthermore, our algorithm has mechanisms for handling texture boundaries and, consequently, does not produce erratic results in the regions abutting these boundaries. results on real images are presented demonstrating the potential of our approach.
densities and maximum likelihood estimation of matching constraints. in this paper we present a theory for obtaining densities that are important for computer vision. as a result of the theory we compute the exact and novel density of the slope of a line fitted to image points. this density makes it possible to obtain confidence intervals for the slope or to make hypothesis testing about if two intersecting lines form a corner or not. the theory also lets us derive a novel technique for maximum likelihood estimation, that can be used for computing the fundamental matrix, conics, or any other constraint that can be expressed by polynomials of degree 2. we present exact and novel densities for the fundamental matrix and conic constraints, that are needed for the estimation. experiments show how the results can be used in practice to compute maximum likelihood estimates of the fundamental matrix. the acm portal is published by the association for computing machinery. copyright © 2010 acm, inc. terms of usage privacy policy code of ethics contact us useful downloads: adobe acrobat quicktime windows media player real player
stereo in the presence of specular reflection. the problem of accurate depth estimation using stereo in the presence of specular reflection is addressed. specular reflection, a fundamental and ubiquitous reflection mechanism, is viewpoint dependent and can cause large intensity differences at corresponding points, resulting in significant depth errors. we analyze the physics of specular reflection and the geometry of stereopsis which led us to a relationship between stereo vergence, surface roughness, and the likelihood of a correct match. given a lower bound on surface roughness, an optimal binocular stereo configuration can be determined which maximizes precision in depth estimation despite specular reflection. however, surface roughness is difficult to estimate in unstructured environments. therefore, trinocular configurations, independent of surface roughness, are determined such that at each scene point visible to all sensors, at least one stereo pair can compute produce depth. we have developed a simple algorithm to reconstruct depth from the multiple stereo pairs.
multi-scale gesture recognition from time-varying contours. a novel method is introduced to recognize and estimate the scale of time-varying human gestures. it exploits the changes in contours along spatio-temporal directions. each contour is first parameterized as a 2d function of radius vs. cumulative contour length, and a 3d surface is composed from a sequence of such functions. in a two-phase recognition process, dynamic timewarping is employed to rule out significantly different gesture models, and then mutual information (mi) is applied for matching the remaining models. the system has been tested on 8 gestures performed by 5 subjects with varied time scales. the two-phase process is compared against exhaustively testing three similarity measures based upon mi, correlation, and nonparametric kernel density estimation. experimental results demonstrate that the exhaustive application of mi is the most robust with a recognition rate of 90.6%, however, the two-phase approach is much more computationally efficient with a comparable recognition rate of 90.0%.