waashk/dblp huggingface dataset:
recovering non-rigid 3d shape from image streams. this paper addresses the problem of recovering 3d non-rigid shape models from image sequences. for example, given a video recording of a talking person, we would like to estimate a 3d model of the lips and the full head and its internal modes of variation. many solutions that recover 3d shape from 2d image sequences have been proposed; these so-called structure-from-motion techniques usually assume that the 3d object is rigid. for example, tomasi and kanade''s factorization technique is based on a rigid shape matrix, which produces a tracking matrix of rank 3 under orthographic projection. we propose a novel technique based on a non-rigid model, where the 3d shape in each frame is a linear combination of a set of basis shapes. under this model, the tracking matrix is of higher rank, and can be factored in a three step process to yield to pose, configuration and shape. we demonstrate this simple but effective algorithm on video sequences of speaking people. we were able to recover 3d non-rigid facial models with high accuracy.
indexing to 3d model aspects using 2d contour features. we present a shape-based method of indexing to model aspects from a single intensity image. objects are assumed to be rigid. a model aspect is represented by a 2 1/2d edgemap and the parts of the object silhouette. part decomposition is derived from a codon representation of the object silhouette. invariant features extracted from each part are then used to index into a hash table to generate model-aspect hypotheses. knowledge about parts is incorporated in voting schemes to order hypotheses for efficient verification of candidate models. verification of model-aspect hypotheses is carried out by an alignment algorithm that is robust to partial occlusion. results of tests using 658 model aspects from 100 objects demonstrate that accurate recognition can be achieved with very few verification attempts.
orientation space filtering for multiple orientation line segmentation. the goal of this paper is to present an appropriate method for the segmentation of lines at intersections (x-junctions) and branches (t-junctions), which can be regarded as local regions where lines occur at multiple orientations. a novel representation called ¿orientation space¿ is proposed, which is derived by adding the orientation axis to the abscissa and the ordinate of the image. the orientation space representation is constructed by treating the orientation parameter, to which gabor filters can be tuned, as a continuous variable. the problem of segmenting lines at multiple orientations is dealt with by thresholding 3d imagesin the orientation space and then detecting the connected components therein. in this way, x-junctions and t-junctions can be separated effectively. curve grouping can also be accomplished. the segmentation of mathematically modeled x-, t-, and l-junctions is demonstrated and analyzed. the sensitivity limits of the method are also discussed. experimental results using both synthesized and real images show the method to be effective for junction segmentation and curve grouping.
competitive mixture of deformable models for pattern classification. following the success of applying deformable models to feature extraction, a natural next step is to apply such models to pattern classification. recently, we have cast a deformable model under a bayesian framework for classification, giving promising results. however, deformable model methods are computationally expensive due to the required iterative optimization process. the problem is even more severe when there are a large number of models (e.g., for character recognition), because each of them has to deform and match with the input data before a final classification can be derived. in this paper, we propose to combine the deformable models into a mixture, in which the individual models compete with each other to survive the matching process during classification. models that do not compete well are eliminated early, thus allowing substantial savings in computation. this process of competition- elimination has been applied to handwritten digit recognition in which significant speedup can be achieved without sacrificing recognition accuracy.
local blur estimation and super-resolution. until now, all super-resolution algorithms have presumed that the images were taken under the same illumination conditions. this paper introduces a new approach to super-resolution---based on edge models and a local blur estimate---which circumvents these difficulties. the paper presents the theory and the experimental results using the new approach.
a divide-and-conquer strategy in shape from shading problem. a divide-and-conquer strategy in shape from shading problem under fully perspective condition is proposed for the information recovery of book surfaces. the whole recovery process is composed of three sequential steps : preprocessing, apparent shape recovery, and ortho-image generation. pure shade images are extracted at preprocessing step by introducing phenomenological model of interreflection and by removing pigment parts from observed images. using existing invariances, equations of slope and that of ortho-image being explicit are derived from equation of shading and that of observation being implicit. recurrence relation is derived from definition of mean slope in discrete image. theoretically, it become possible to recover unique shape without iteration using derived equations in case of lambertian cylinder. however, a feed-back shape recovery process is implemented as practical algorithm in order to overcome self-shadows. results of simulations and real experiments show the properness and acceptability of the proposed strategy and implemented algorithms.
global minimum for active contour models: a minimal path approach. a new boundary detection approach for shape modeling is presented. it detects the global minimum of an active contour model&lsquo;s energy between two end points. initialization is made easier and the curve is not trapped at a local minimum by spurious edges. we modify the &ldquo;snake&rdquo; energy by including the internal regularization term in the external potential term. our method is based on finding a path of minimal length in a riemannian metric. we then make use of a new efficient numerical method to find this shortest path.it is shown that the proposed energy, though based only on a potential integrated along the curve, imposes a regularization effect like snakes. we explore the relation between the maximum curvature along the resulting contour and the potential generated from the image.the method is capable to close contours, given only one point on the objects&lsquo; boundary by using a topology-based saddle search routine.we show examples of our method applied to real aerial and medical images.
a space-sweep approach to true multi-image matching. the problem of determining feature correspondences across multiple views is considered. the term "true multi-image" matching is introduced to describe techniques that make full and efficient use of the geometric relationships between multiple images and the scene. a true multi-image technique must generalize to any number of images, be of linear algorithmic complexity in the number of images, and use all the images in an equal manner. a new space-sweep approach to true multi-image matching is presented that simultaneously determines 2d feature correspondences and the 3d positions of feature points in the scene. the method is based on the premise that areas of space where several viewing rays intersect are the likely locations of observed 3d scene features. it is shown that the intersections of viewing rays with a plane sweeping through space can be determined very efficiently, and a statistical model is developed to tell how likely it is that a given number of viewing rays will pass through an area of the plane by chance. the method is illustrated on a seven-image matching example from the aerial image domain. 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
multi-image focus of attention for rapid site model construction. a multi-image focus of attention mechanism has been developed that can quickly distinguish raised objects like buildings from structured background clutter typical to many aerial image scenarios. the underlying approach is the space-sweep stereo method, in which features from multiple images are backprojected onto a virtual, horizontal plane that is methodically swept through the scene. backprojected gradient orientations from multiple images are highly correlated when they come from scene locations containing structural edges that are roughly horizontal, like building roofs and terrain; otherwise, they tend to be uniformly distributed. these observations are used to define a structural salience measure that can determine whether a given volume of space contains a statistically signijicant number of structural edges, without first pe$otrning precise reconstruction of those edges. the utility of structural salience for computing focus of attention regions is illustrated on sample data from ft.hood, texas.
face detection with information-based maximum discrimination. antonio j. colmenarez and thomas s. huang in this paper we present a visual learning technique that maximizes the discrimination between positive and negative examples in a training set. we demonstrate our technique in the context of face detection with complex background without color or motion information, which has proven to be a challenging problem. we use a family of discrete markov processes to model the face and background patterns and estimate the probability models using the data statistics. then, we convert the learning process into an optimization, selecting the markov process that optimizes the information-based discrimination between the two classes. the detection process is carried out by computing the likelihood ratio using the probability model obtained from the learning procedure. we show that because of the discrete nature of these models, the detection process is, by almost two orders of magnitude, less computationally expensive than neural network approaches. however, no improvement in terms of correct-answer/false-alarm tradeoff is achieved.
robust analysis of feature spaces: color image segmentation. a general technique for the recovery of significant image features is presented. the technique is based on the mean shift algorithm, a simple nonparametric procedure for estimating density gradients. drawbacks of the current methods (including robust clustering) are avoided. feature space of any nature can be processed, and as an example, color image segmentation is discussed. the segmentation is completely autonomous, only its class is chosen by the user. thus, the same program can produce a high quality edge image, or provide, by extracting all the significant colors, a preprocessor for content-based query systems. a 512/spl times/512 color image is analyzed in less than 10 seconds on a standard workstation. gray level images are handled as color images having only the lightness coordinate.
depth from scattering. light power is affected when it crosses the atmosphere; there is a simple, albeit non-linear, relationship between the radiance of an image at any given wavelength and the distance between object and viewer. this phenomenon is called atmospheric scattering and has been extensively studied by physicists and meterologists. we present the first analysis of this phenomenon from an image understanding perspective: we investigate a group of techniques for extraction of depth cues solely from the analysis of atmospheric scattering effects in images. depth from scattering techniques are discussed for indoor and outdoor environments, and experimental tests with real images are presented. we have found that depth cues in outdoor scenes can be recovered with surprising accuracy and can be used as an additional information source for autonomous vehicles.
scale-space vector fields for feature analysis. this paper describes a vectorial representation that can be used to assess the symmetry of objects in 2d images. the method exploits a magneto-static analogy. commencing from the gradient-field extracted from filtered grey-scale images we construct a vector-potential. our magneto-static analogy is that tangential gradient vectors represent the elements of a current distribution on the image plane. by embedding the image plane in an augmented 3-dimensional space, we compute the vector potential by performing volume integration over the current distribution. the associated magnetic field is computed by taking the curl of the vector-potential. the auxiliary spatial dimension provides a natural scale-space sampling of the generating current-distribution; as the height above the image plane is increased, so the volume over which averaging is effected also increases. we extract edge and symmetry lines through a topographic analysis of the vector-field at various heights above the image plane. symmetry axes are lines of where the curl of the vector-potential vanishes; at edges the divergence of the vector-potential vanishes.
multi-modal tracking of faces for video communications. this paper describes a system which uses multiple visual processes to detect and track faces for video compression and transmission. the system is based on an architecture in which a supervisor selects and activates visual processes in cyclic manner. control of visual processes is made possible by a confidence factor which accompanies each observation. fusion of results into a unified estimation for tracking is made possible by estimating a covariance matrix with each observation. visual processes for face tracking are described using blink detection, normalized color histogram matching, and cross correlation (ssd and ncc). ensembles of visual processes are organized into processing states so as to provide robust tracking. transition between states is determined by events detected by processes. the result of face detection is fed into recursive estimator (kalman filter). the output from the estimator drives a pd controller for a pan/tilt/zoom camera. the resulting system provides robust and precise tracking which operates continuously at approximately 20 images per second on a 150 megahertz computer work-station.
character extraction of license plates from video. in this paper, we present a new approach to extract characters on a license plate of a moving vehicle given a sequence of perspective distortion corrected license plate images. we model the extraction of characters as a markov random field (mrf). with the mrf modeling, the extraction of characters is formulated as the problem of maximizing the a posteriori probability based on given prior and observations. a genetic algorithm with local greedy mutation operator is employed to optimize the objective function. experiments and comparison study were conducted. it is shown that our approach provides better performance than other single frame methods.
hand segmentation using learning-based prediction and verification for hand sign recognition. this paper presents a prediction-and-verification segmentation scheme wing attention images from multiple fixations. a major advantage of this scheme is that it can handle a large number of different deformable objects presented in complex backgrounds. the scheme is also relatively efficient since the segmentation is guided by the past knowledge through a prediction-and-verification scheme. the system has been tested to segment hands in the sequences of intensity images, where each sequence represents a hand sign. the experimental result showed a 95% correct segmentation rate with a 3% false rejection rate.
reflectance and texture of real-world surfaces authors. in this work, we investigate the visual appearance of real-world surfaces and the dependence of appearance on imaging conditions. we present a brdf (bidirectional reflectance distribution function) database with reflectance measurements for over 60 different samples, each observed with over 200 different combinations of viewing and source directions. we fit the brdf measurements to two recent models to obtain a brdf parameter database. these brdf parameters can be directly used for both image analysis and image synthesis. finally, we present a btf (bidirectional texture function) database with image textures from over 60 different samples, each observed with over 200 different combinations of viewing and source directions. each of these unique databases has important implications for a variety of vision algorithms and each is made publicly available.
active intrinsic calibration using vanishing points. we propose a new method for the estimation of the intrinsic parameters of an active camera. during a fixed axis camera rotation every point is moving on a conic section. if the point used is a vanishing point the conic section is invariant to possible translations of the observer. given the rotation axis and the inter-frame correspondence of a set of parallel lines we are able to compute the intrinsic parameters without knowledge of the rotation angles. we propagate the error covariances and we remove the bias in the computation of the conic. we experimentally study the sensitivity of calibration to the amount of rotation and we compare our performance to the performance of a recent active calibration technique.
integrated person tracking using stereo, color, and pattern detection. we present an approach to real-time person tracking in crowded and/or unknown environments using integration of multiple visual modalities. we combine stereo, color, and face detection modules into a single robust system, and show an initial application in an interactive, face-responsive display. dense, real-time stereo processing is used to isolate users from other objects and people in the background. skin-hue classification identifies and tracks likely body parts within the silhouette of a user. face pattern detection discriminates and localizes the face within the identified body parts. faces and bodies of users are tracked over several temporal scales: short-term (user stays within the field of view), medium-term (user exits/reenters within minutes), and long term (user returns after hours or days). short-term tracking is performed using simple region position and size correspondences, while medium and long-term tracking are based on statistics of user appearance. we discuss the failure modes of each individual module, describe our integration method, and report results with the complete system in trials with thousands of users.
active face tracking and pose estimation in an interactive room. we demonstrate real-time face tracking and pose estimation in an unconstrained office environment with an active foveated camera. using vision routines previously implemented for an interactive environment, we determine the spatial location of a user's head and guide an active camera to obtain foveated images of the face. faces are analyzed using a set of eigenspaces indexed over both pose and world location. closed loop feedback from the estimated facial location is used to guide the camera when a face is present in the foveated view. our system can detect the head pose of an unconstrained user in real-time as he or she moves about an open room.
focus: searching for multi-colored objects in a diverse image database. we describe a new multi-phase, color-based image retrieval system, focus (fast object color-based query system), with an online user interface which is capable of identifying multi-colored query objects in an image in the presence of significant, interfering backgrounds. the query object may occur in arbitrary sizes, orientations and locations in the database images. the color features used to describe an image have been developed based on the need for speed in matching and ease of computation on complex images while maintaining the scale and rotation invariance properties. the first phase matches the color content of an image computed as the peaks in the color histogram of the image, with the query object colors using an efficient indexing mechanism. the second phase matches the spatial relationships between color regions in the image with the query using a spatial proximity graph (spg) structure designed for the purpose. the method is fast and has low storage overhead. test results with multi-colored query objects from artificial and natural domains show that focus is quite effective in handling interfering backgrounds and large variations in scale. the experimental results on a database of diverse images highlights the capabilities of the system.
the representation and recognition of human movement using temporal templates. a new view-based approach to the representation and recognition of action is presented. the basis of the representation is a temporal template -- a static vector-image where the vector value at each point is a function of the motion properties at the corresponding spatial location in an image sequence. using 18 aerobics exercises as a test domain, we explore the representational power of a simple, two component version of the templates: the first value is a binary value indicating the presence of motion, and the second value is a function of the recency of motion in a sequence. we then develop a recognition method which matches these temporal templates against stored instances of views of known actions. the method automatically performs temporal segmentation, is invariant to linear changes in speed, and runs in real-time on a standard platform. we recently incorporated this technique into the kidsroom: an interactive, narrative play-space for children.
the integration of optical flow and deformable models with applications to human face shape and motion estimation. we present a formal methodology for the integration of optical flow and deformable models. the optical flow constraint equation provides a non-holonomic constraint on the motion of the deformable model. in this augmented system, forces computed from edges and optical flow are used simultaneously. when this dynamic system is solved, a model-based least-squares solution for the optical flow is obtained and improved estimation results are achieved. the use of a 3-d model reduces or eliminates problems associated with optical flow computation. this approach instantiates a general methodology for treating visual cues as constraints on deformable models. we apply this framework to human face shape and motion estimation. our 3-d deformable face model uses a small number of parameters to describe a rich variety of face shapes and facial expressions. we present experiments in extracting the shape and motion of a face from image sequences.
from projective to euclidean reconstruction. to make a euclidean reconstruction of the world seen through a stereo rig, we can either use a calibration grid, and the results will rely on the precision of the grid and the extracted points of interest, or use self-calibration. past work on self-calibration is focussed on the use of only one camera, and gives sometimes very unstable results. in this paper, we use a stereo rig which is supposed to be weakly calibrated using a method such as the one described in deriche et al. (1994). then, by matching two sets of points of the same scene reconstructed from different points of view, we try to find both the homography that maps the projective reconstruction to the euclidean space and the displacement from the first set of points to the second set of points. we present results of the euclidean reconstruction of a whole object from uncalibrated cameras using the method proposed here.