waashk/dblp huggingface dataset:
epipolar geometry and linear subspace methods: a new approach to weak calibration. this paper addresses the problem of estimating the epipolar geometry from point correspondences between two images taken by uncalibrated perspective cameras. it is shown that jepson&lsquo;s and heeger&lsquo;s linear subspace technique for infinitesimal motion estimation can be generalized to the finite motion case by choosing an appropriate basis for projective space. this yields a linear method for weak calibration. the proposed algorithm has been implemented and tested on both real and synthetic images, and it is compared to other linear and non-linear approaches to weak calibration.
object detection and localization by dynamic template warping. a simple method is presented for detecting, localizing and recognizing instances of classes of objects, while accommodating a wide variation in an object's pose. the method utilizes a small two-dimensional template that is warped into an image, and converts localization to a one-dimensional sub-problem, with the search for a match between image and template executed by dynamic programming. for roughly cylindrical objects (like heads), the method recovers three of the six degrees of freedom of motion (2 translation, 1 rotation), and accommodates two more degrees of freedom in the search process (1 rotation, 1 translation). experiments demonstrate that the method provides an efficient search strategy that outperforms normalized correlation. this is demonstrated in the example domain of face detection and localization, and can extended to more general detection tasks. an additional technique recovers rough object pose from the match results, and is used in a two stage recognition experiment in conjunction with maximization of mutual information.
automated model acquisition from range images with view planning. we present an incremental system that builds accurate cad models of objects from multiple range images. using a hybrid of surface mesh and volumetric representations, the system creates a "water-tight" 3d model at each step of the modeling process, allowing reasonable models to be built from a small number of views. we also present a method that can be used to plan the next view and reduce the number of scans needed to recover the object. results are presented for the creation of 3d models of a computer game controller, a hip joint prosthesis, and a mechanical strut.
vision for a smart kiosk. we describe a novel computer vision application: vision-based human sensing for a smart kiosk interface. a smart kiosk is a free-standing information dispensing computer appliance capable of engaging in public interactions with multiple people. vision sensing is a critical component of the kiosk interface, where it is used to determine the context for the interaction. we present a taxonomy of vision problems for a kiosk interface and describe a prototype kiosk which uses color stereo tracking and graphical output to interact with several users.
constrained phase congruency: simultaneous detection of interest points and of their scales. a novel feature detector -- the constrained phase congruency transform (cpct) is introduced. it simultaneously detects interest points as well as their scale in various orientations. the cpct is especially important in registration applications: the local transformation between interest points can be determined based on their orientational scales. the cpct detects the features in mach bands and in sinusoidal waves. this cannot be done simply by looking for local maxima in intensity gradient nor by looking for local energy maxima. i conjecture that constraining the general phase congruency is sufficient for feature detection. the correct detection of features' location and of their scale is demonstrated. the robustness of the cpct is achieved by constraining the local phase. only four easy-to-detect phases are used in the computations. they correspond to symmetry and anti-symmetry in their neighborhood. the scale at any location and orientation is determined by the scale of the channel that conforms to the constraints and maximizes energy.
fast binary image processing using binary decision diagrams. many classical image processing tasks can be realized as evaluations of a boolean function over subsets of an image. for instance, the simplicity test used in 3d thinning requires examining the 26 neighbors of each voxel and computing a single boolean function of these inputs. in this article, we show how binary decision diagrams can be used to produce automatically very efficient and compact code for such functions. the total number of operations performed by a generated function is at most one test and one branching for each input value (e.g., in the case of 3d thinning, 26 tests and branchings). at each stage, the function is guaranteed to examine only the pertinent input data, i.e., the values which affect the result. as an example, we consider the 3d simplicity test in digital topology, and thinning processes. we produce functions much faster than our previously optimized implementations and than any other implementation we know of. in the case of 3d simplicity test, on average, at each voxel only 8.7 neighboring voxel values are examined.
modeling clutter and context for target detection in infrared images. in order to reduce false alarms and to improve the target detection performance of an automatic target detection and recognition system operating in a cluttered environment, it is important to develop the models not only for man-made targets but also of natural background clutters. because of the high complexity of natural clutters, this clutter model can only be reliably built through learning from real examples. if available, contextual information that characterizes each training example can be used to further improve the learned clutter model. in this paper, we present such a clutter model aided target detection system. emphases are placed on two topics: (1) learning the background clutter model from sensory data through a self-organizing process, (2) reinforcing the learned clutter model using contextual information.
inferring body pose without tracking body parts. abstract a novel approach for estimating articulated body posture and motion from monocular video sequences is proposed. human pose is defined as the instantaneous two dimensional configuration (i.e., the projection onto the image plane) of a single articulated body in terms of the position of a predetermined set of joints. first, statistical segmentation of the human bodies from the background is performed and low-level visual features are found given the segmented body shape. the goal is to be able to map these, generally low level, visual features to body configurations. the system estimates different mappings, each one with a specific cluster in the visual feature space. given a set of body motion sequences for training, unsupervised clustering is obtained via the expectation maximation algorithm. then, for each of the clusters, a function is estimated to build the mapping between low-level features to 3d pose. currently this mapping is modeled by a neural network. given new visual features, a mapping from each cluster is performed to yield a set of possible poses. from this set, the system selects the most likely pose given the learned probability distribution and the visual feature similarity between hypothesis and input. performance of the proposed approach is characterized using a new set of known body postures, showing promising results.
3d trajectory recovery for tracking multiple objects and trajectory guided recognition of actions. abstract a mechanism is proposed that integrates low-level (image processing), mid-level (recursive 3d trajectory estimation), and high-level (action recognition) processes. it is assumed that the system observes multiple moving objects via a single, uncalibrated video camera. a novel extended kalman filter formulation is used in estimating the relative 3d motion trajectories up to a scale factor. the recursive estimation process provides a prediction and error measure that is exploited in higher-level stages of action recognition. conversely, higher-level mechanisms provide feedback that allows the system to reliably segment and maintain the tracking of moving objects before, during, and after occlusion. the 3d trajectory, occlusion, and segmentation information are utlized in extracting stabilized views of the moving object. trajectory-guided recognition (tgr) is proposed as a new and efficient method for adaptive classification of action. the tgr approach is demonstrated using ``motion history images'''' that are then recognized via a mixture of gaussian classifier. the system was tested in recognizing various dynamic human outdoor activities; e.g., running, walking, roller blading, and cycling. experiments with synthetic data sets are used to evaluate stability of the trajectory estimator with respect to noise.
a general filter for measurements with any probability distribution. the kalman filter is a very efficient optimal filter, however it has the precondition that the noises of the process and of the measurement are gaussian. the authors introduce 'the general distribution filter' which is an optimal filter that can be used even where the distributions are not gaussian. an efficient practical implementation of the filter is possible where the distributions are discrete and compact or can be approximated as such.
affine structure and photometry. motion of an observer relative to objects in a scene provides information about the structure of the scene. changing patterns of shading due to motion relative to the light source provide information about surface structure, albedos, and light sources. one can stratify this photometric information into affine, unitary, and metric structure, much like the stratification of structure from motion. for lambertian surfaces, if either motion or photometry give us more than affine structure, the two cues can be combined to yield full metric information. edge constraints plus unitary photometry also give us full metric photometry. affine structure alone contains much of the quantitative structure information, allowing us to judge such things as the ordinal relationships between the albedos.
optimal servoing for active foveated vision. foveated vision and two-mode tracking, as inspired by the human oculomotor system, are often used in active vision system. the purpose of this paper is to provide answers to the following basic questions which arise from implementations. first, is it beneficial to have foveated vision and what is the optimal size of the foveal window? second, is there a need for two control mechanisms (smooth pursuit and saccade) for improved performance and how can one efficiently switch between them? in order to do so, a setup is proposed in which these strategies can be evaluated in a systematic manner. it is shown that the fovea appears as a compromise between the tightness of the tracking specifications and computational constraints. introducing a model for the later and postulating some a priori knowledge of the target behavior, it is possible to compute the size of the fovea in an optimal way. as a by-product, "smooth-pursuit" can be defined in a natural way, and the use of a two-mode tracking scheme is justified. the second mode, i.e. "saccadic control", aims at re-centering the target on the fovea so that the smooth pursuit controller can continue to operate. it is shown that a control strategy can indeed be defined so that this objective can be met under appropriate operating conditions.
velocity and disparity cues for robust real-time binocular tracking. we have designed and implemented a real-time binocular tracking system which uses two independent cues commonly found in the primary functions of biological visual systems to robustly track moving targets in complex environments, without a-priori knowledge of the target shape or texture: a fast optical flow segmentation algorithm quickly locates independently moving objects for target acquisition and provides a reliable velocity estimate for smooth tracking. in parallel, target position is generated from the output of a zero-disparity filter where a phase-based disparity estimation technique allows dynamic control of the camera vergence to adapt the horopter geometry to the target location. the system takes advantage of the optical properties of our custom-designed foveated wide-angle lenses, which exhibit a wide field of view along with a high resolution fovea. methods to cope with the distortions introduced by the space-variant resolution, and a robust real-time implementation on a high performance active vision head are presented.
robust recovery of camera rotation from three frames. computing camera rotation from image sequences can be used for image stabilization, and when the camera rotation is known the computation of translation and scene structure are much simplified as well. a robust approach for recovering camera rotation is presented, which does not assume any specific scene structure (e.g. no planar surface is required), and which avoids prior computation of the epipole. given two images taken from two different viewing positions, the rotation matrix between the images can be computed from any three homgraphy matrices. the homographies are computed using the trilinear tensor which describes the relations between the projections of a 3d point into three images. the entire computation is linear for small angles, and is therefore fast and stable. iterating the linear computation can then be used to recover larger rotations as well.
neural network-based face detection. we present a neural network-based upright frontal face detection system. a retinally connected neural network examines small windows of an image and decides whether each window contains a face. the system arbitrates between multiple networks to improve performance over a single network. we present a straightforward procedure for aligning positive face examples for training. to collect negative examples, we use a bootstrap algorithm, which adds false detections into the training set as training progresses. this eliminates the difficult task of manually selecting nonface training examples, which must be chosen to span the entire space of nonface images. simple heuristics, such as using the fact that faces rarely overlap in images, can further improve the accuracy. comparisons with several other state-of-the-art face detection systems are presented, showing that our system has comparable performance in terms of detection and false-positive rates.
analyzing articulated motion using expectation-maximization. we present a novel application of the expectation-maximization algorithm to the global analysis of articulated motion. the approach utilizes a kinematic model to constrain the motion estimates, producing a segmentation of the flow field into parts with different articulated motions. experiments with synthetic and real images are described.
cylindrical rectification to minimize epipolar distortion. we propose anew rectification method for aligning epipolar lines of a pair of stereo images taken under any camera geometry. it effectively remaps both images onto the surface of a cylinder instead of a plane, which is used in common rectification methods. for a large set of camera motions, remapping to a plane has the drawback of creating rectified images that are potentially infinitely large and presents a loss of pixel information along epipolar lines. in contrast, cylindrical rectification guarantees that the rectified images are bounded for all possible camera motions and minimizes the loss of pixel information along epipolar line. the processes (e.g., stereo matching, etc.) subsequently applied to the rectified images are thus more accurate and general since they can accommodate any camera geometry.
efficient guaranteed search for gray-level patterns. we address the problem of locating a gray-level pattern in a gray-level image. the pattern can have been transformed by an affine transformation, and may have undergone some additional changes. we define a difference function based on comparing each pixel of the pattern with a window in the image, and search efficiently for transformations that minimize the difference function. the search is guaranteed: it will always find the transformation minimizing the difference function, and not get fooled by a local minimum; it is also efficient, in that it does not need to examine every transformation in order to achieve this guarantee. this technique can be applied to object location, motion tracking, optical flow, or block-based motion compensation in video image sequence compression (e.g., mpeg).
similarity queries in image database. query-by-content image database will be based on similarity, rater than on matching, where similarity is a measure that is defined and meaningful for every pair of images in the image space. since it is the human user that, in the end, has to be satisfied with the results of the query, it is natural to base the similarity measure that we will use on the characteristics of human similarity assessment. in the first part of this paper, we review some of these characteristics and define a similarity measure based on them. another problem that similarity-based databases will have to face is how to combine different queries into a single complex query. we present a solution based on three operators that are the analogous of the and, or, and not operators one uses in traditional databases. these operators are powerful enough to express queries of unlimited complexity, yet have a very intuitive behavior, making easy for the user to specify a query tailored to a particular need.
vector-valued active contours. a framework for object segmentation in vector-valued images is presented in this paper. the first scheme proposed is based on geometric active contours moving towards the objects to be detected in the vector-valued image. objects boundaries are obtained as geodesics or minimal weighted distance curves in a riemannian space. the metric in this space is given by a definition of edges in vector-valued images. the curve flow corresponding to the proposed active contours holds formal existence, uniqueness, stability, and correctness results. the techniques is applicable for example to color and texture images. the scheme automatically handles changes in the deforming curve topology. we conclude the paper presenting an extension of the color active contours which leads to a possible image flow for vector-valued image segmentation. the algorithm is based on moving each one of the image level-sets according to the proposed color active contours. this extension also shows the relation of the color geodesic active contours with a number of partial-differential-equations based image processing algorithms as anisotropic diffusion and shock filters.
on occluding contour artifacts in stereo vision. we study occluding contour artifacts in area-based stereo matching: they are false responses of the matching operator to the occlusion boundary and cause the objects extend beyond their true boundaries in disparity maps. most of the matching methods suffer from these artifacts; the effect is so strong that it cannot be ignored. we show what gives rise to the artifacts and design a matching criterion that accommodates the presence of occlusions as opposed to methods that identify and remove the artifacts. this approach leads to the problem of measurement contamination studied in statistics. we show that such a problem is hard given finite computational resources, unless more independent measurements directly related to occluding contours is available. what can be achieved is a substantial reduction of the artifacts, especially for large matching templates. reduced artifacts allow for easier hierarchical matching and for easy fusion of reconstructions from different viewpoints into a coherent whole.
complexity analysis of rbf networks for pattern recognition. the problem of non-parametric probability density function (pdf) estimation using radial basis function (rbf) neural networks is addressed here. we investigate two criteria, based on a modified kullback-leibler distance, that lead to an appropriate choice of the network architecture complexity. in the first criterion the modification consists in the addition of a term that penalizes complex architectures (mpl criterion). the second strategy involves the regularization of the network through the imposition of lower bounds on the standard deviation derived from conditions of existence of rejection tests (lbsd criterion). experimental results indicate that the mpl criterion outperforms the lbsd method.
quantitative measures of change based on feature organization: eigenvalues and eigenvectors. one important task of site monitoring is change detection from aerial images. change, in general, can be of various types. in this paper we address the problem of detecting change due to construction activity. specifically, we would like to know about new construction at a previously undeveloped site and possibly monitor its progress. model based approaches are not suited for this kind of change as it usually happens in unmodelled areas. since it is difficult to infer construction activity by predicting and verifying specific local features, we rely on more global statistical indicators. the thesis of this paper is that the change induced by human activity can be inferred from changes in the organization among the visual features. not only will the attributes of the individual image features change but also the relationships among these features will evolve. with the progress of construction we expect to see increased structure among the image features. we exploit this emerging structure, or organization, to infer change. in this paper, we propose four measures to quantify the global statistical properties of the individual features and the relationships among them. we base these measures on the theory of graph spectra. we provide extensive analysis of the robustness of these measures under various imaging conditions. we also demonstrate the ability of these organization based measures to discriminate between no development, onset of construction, and full development.
illumination distribution from shadows. the image irradiance of a three-dimensional object is known to be the function of three components: the distribution of light sources, the shape, and reflectance of a real object surface. in the past, recovering the shape and reflectance of an object surface from the recorded image brightness has been intensively investigated. on the other hand, there has been little progress in recovering illumination from the knowledge of the shape and reflectance of a real object. in this paper, we propose a new method for estimating the illumination distribution of a real scene from image brightness observed on a real object surface in that scene. more specifically, we recover the illumination distribution of the scene from a radiance distribution inside shadows cast by an object of known shape onto another object surface of known shape and reflectance. by using the occlusion information of incoming light, we are able to reliably estimate the illumination distribution of a real scene, even in a complex illumination environment.
name-it: association of face and name in video. this paper proposes a novel approach to extract meaningful content information from video by collaborative integration of image understanding and natural language processing. as an actual example, we developed a system that associates faces and names in videos, called name-it, which is given news videos as a knowledge source, then automatically extracts face and name association as content information. the system can infer the name of a given unknown face image, or guess faces which are likely to have the name given to the system. this paper explains the method with several successful matching results which reveal effectiveness in integrating heterogeneous techniques as well as the importance of real content information extraction from video, especially face-name association.