waashk/dblp huggingface dataset:
indexing visual representations through the complexity map. in differential geometry curves are characterized as mappings from an interval to the plane. in topology curves are characterized as a hausdorff space with certain countability properties. neither of these definitions captures the role that curves play in vision, however, in which curves can denote simple objects (such as a straight line), or complicated objects (such as a jumble of string). the difference between these situations is in part a measure of their complexity, and in part a measure of their dimensionality. note that the map defining such curves is unknown, as is the proper way to represent them. we propose a formal complexity theory of curves appropriate for computational vision in general, and for problems like separating straight lines from jumbles in particular. the theory is applied to the problem of perceptual grouping.
optimal polyline tracking for artery motion compensation in coronary angiography. we propose a novel solution to the problem of motion compensation of coronary angiographs. as the heart is beating, it is difficult for the physician to observe closely a particular point (e.g. stenosis) on the artery tree. we propose to rigidly compensate the sequence so that the area around the point of interest appears stable. this is a difficult problem because the arteries deform in a nonrigid manner and only their 2d x-ray projection is observed. also, the lack of features around the selected point makes the matching subject to the aperture problem. the algorithm automatically extracts a section of the artery of interest, models it as a polyline, andtracks it. the problem is formulated as an energy minimization problem which is solved using a shortest path in a graph algorithm. the motion compensated sequence can be obtained by translating every pixel so that the point of interest remains stable. we have applied this algorithm to many examples in two sets of angiography data and have obtained excellent results.
shape representation via harmonic embedding. we present a novel representation of shape for closedplanar contours explicitly designed to possess a linearstructure. this greatly simplifies linear operations suchas averaging, principal component analysis or differentiation in the space of shapes. the representation reliesupon embedding the contour on a subset of the space ofharmonic functions of which the original contour is thezero level set.
controlling model complexity in flow estimation. this paper describes a novel application of statisticallearning theory (slt) to control model complexity in flowestimation. slt provides analytical generalization boundssuitable for practical model selection from small and noisydata sets of image measurements (normal flow). the methodaddresses the aperture problem by using the penalized risk(ridge regression). we demonstrate an application of thismethod on both synthetic and real image sequences and useit for motion interpolation and extrapolation. our experimentalresults show that our approach compares favorablyagainst alternative model selection methods such as theakaike's final prediction error, schwartz's criterion, generalizedcross-validation, and shibata's model selector.
learning-based object detection in cardiac mr images. an automated method for left ventricle detection in mr cardiac images is presented. ventricle detection is the first step in a fully automated segmentation system used to compute volumetric information about the heart.our method is based on learning the gray level appearance of the ventricle by maximizing the discrimination between positive and negative examples in a training set. the main differences from previously reported methods are feature definition and solution to the optimization problem involved in the learning process.our method was trained on a set of 1,350 mr cardiac images from which 101,250 positive examples and 123,096 negative examples were generated. the detection results on a test set of 887 different images demonstrate an excellent performance: 98% detection rate, a false alarm rate of 0:05% of the number of windows analyzed (10 false alarms per image) and a detection time of 2 seconds per 256 × 256 image on a sun ultra 10 for an 8-scale search. the false alarms are eventually eliminated by a position/scale consistency check along all the images that represent the same anatomical slice.
learning to identify and track faces in image sequences. we address the problem of robust face identification in the presence of pose, lighting, and expression variation. previous approaches to the problem have assumed similar models of variation for each individual, estimated from pooled training data. we describe a method of updating a first order global estimate of identity by learning the class-specific correlation between the estimate and the residual variation during a sequence. this is integrated with an optimal tracking scheme, in which identity variation is decoupled from pose, lighting and expression variation. the method results in robust tracking and a more stable estimate of facial identity under changing conditions.
recognizing action at a distance. our goal is to recognize human actions at a distance,at resolutions where a whole person may be, say, 30 pixelstall. we introduce a novel motion descriptor based onoptical flow measurements in a spatio-temporal volume foreach stabilized human figure, and an associated similaritymeasure to be used in a nearest-neighbor framework. makinguse of noisy optical flow measurements is the key challenge,which is addressed by treating optical flow not asprecise pixel displacements, but rather as a spatial patternof noisy measurements which are carefully smoothed andaggregated to form our spatio-temporal motion descriptor.to classify the action being performed by a human figurein a query sequence, we retrieve nearest neighbor(s) from adatabase of stored, annotated video sequences. we can alsouse these retrieved exemplars to transfer 2d/3d skeletonsonto the figures in the query sequence, as well as two formsof data-based action synthesis "do as i do" and "do as isay". results are demonstrated on ballet, tennis as well asfootball datasets.
texture synthesis by non-parametric sampling. a non-parametric method for texture synthesis is proposed. the texture synthesis process grows a new image outward from an initial seed, one pixel at a time. a markov random field model is assumed, and the conditional distribution of a pixel given all its neighbors synthesized so far is estimated by querying the sample image and finding all similar neighborhoods. the degree of randomness is controlled by a single perceptually intuitive parameter. the method aims at preserving as much local structure as possible and produces good results for a wide variety of synthetic and real-world textures.
variational-based method to extract parametric shapes from images. in this paper, we propose a variational method to segment image objects, which have a given parametric shape based on a level-set formulation of the mumford-shah functional, and the shape parameters. we define an energy functional composed by two complementary terms. the first one detects object boundaries using a chan-vese-like method. the second term constrains the contour to find a shape compatible with the parametric shape. the segmentation of the object of interest is given by the minimum of our energy functional. this minimum is computed with the calculus of variation and the gradient descent method that provide a system of evolution equations solved with the well-known level set method. we focus in this paper on the parametric category of image linear objects. applications of the proposed model are presented on synthetic and real images.
nonmetric lens distortion calibration: closed-form solutions, robust estimation and model selection. this paper addresses the problem of calibrating camera lens distortion, which can be significant in medium to wide angle lenses. while almost all existing nonmetric distortion calibration methods need user involvement in one form or another, we present an automatic approach based on the robust the-least-median-of-squares (lmeds) estimator. our approach is thus less sensitive to erroneous input data such as image curves that are mistakenly considered as projections of 3d linear segments. our approach uniquely uses fast, closed-form solutions to the distortion coefficients, which serve as an initial point for a non-linear optimization algorithm to straighten imaged lines. moreover we propose a method for distortion model selection based on geometrical inference. successful experiments to evaluate the performance of this approach on synthetic and real data are reported.
an automatic drowning detection surveillance system for challenging outdoor pool environments. automatically understanding events happening at a site is the ultimate goal of visual surveillance system. this paper investigates the challenges faced by automated surveillance systems operating in hostile conditions and demonstrates the developed algorithms via a system that detects water crises within highly dynamic aquatic environments. an efficient segmentation algorithm based on robust block-based background modeling and thresholding-with-hysteresis methodology enables swimmers to be reliably detected amid reflections, ripples, splashes and rapid lighting changes. partial occlusions are resolved using a markov random field framework that enhances the tracking capability of the system. visual indicators of water crises are identified based on professional knowledge of water crises detection, based on which a set of swimmer descriptors has been defined. through seamlessly fusing the extracted swimmer descriptors based on a novel functional link network, the system achieves promising results for water crises detection. the developed algorithms have been incorporated into a live system with robust performance for different hostile environments faced by an outdoor swimming pool.
feature hierarchies for object classification. the paper describes a method for automatically extracting informative feature hierarchies for object classification, and shows the advantage of the features constructed hierarchically over previous methods. the extraction process proceeds in a top-down manner: informative top-level fragments are extracted first, and by a repeated application of the same feature extraction process the classification fragments are broken down successively into their own optimal components. the hierarchical decomposition terminates with atomic features that cannot be usefully decomposed into simpler features. the entire hierarchy, the different features and sub-features, and their optimal parameters, are learned during a training phase using training examples. experimental comparisons show that these feature hierarchies are significantly more informative and better for classification compared with similar non-hierarchical features as well as previous methods for using feature hierarchies.
facial expression recognition using a dynamic model and motion energy. previous efforts at facial expression recognition have been based on the facial action coding system (facs), a representation developed in order to allow human psychologists to code expression from static facial "mugshots." we develop new more accurate representations for facial expression by building a video database of facial expressions and then probabilistically characterizing the facial muscle activation associated with each expression using a detailed physical model of the skin and muscles. this produces a muscle based representation of facial motion, which is then used to recognize facial expressions in two different ways. the first method uses the physics based model directly, by recognizing expressions through comparison of estimated muscle activations. the second method uses the physics based model to generate spatio temporal motion energy templates of the whole face for each different expression. these simple, biologically plausible motion energy "templates" are then used for recognition. both methods show substantially greater accuracy at expression recognition than has been previously achieved.
identifying individuals in video by combining "generative" and discriminative head models. the objective of this work is automatic detection and identification of individuals in unconstrained consumer video, given a minimal number of labelled faces as training data. whilst much work has been done on (mainly frontal) face detection and recognition, current methods are notsufficiently robust to deal with the wide variations in pose and appearance found in such video. these include variations in scale, illumination, expression, partial occlusion, motion blur, etc. we describe two areas of innovation: the first is to capture the 3-d appearance of the entire head, rather than just the face region, so that visual features such as the hairline can be exploited. the second is to combine discriminative and ¿generative¿ approaches for detection and recognition. images rendered using the head model are used to train a discriminative tree-structured classifier giving efficient detection and pose estimates over a very wide pose range with three degrees of freedom. subsequent verification of the identity is obtained using the head model in a ¿generative¿ framework. we demonstrate excellent performance in detecting and identifying three characters and their poses in a tv situation comedy.
transfer of fixation for an active stereo platform via affine structure recovery. this paper describes an algorithm for stereo tracking using 3d affine transfer of a body-centred fixation point. transfer is based on corners detected in the image and matched over time and in stereo. the paper presents a method of basing the transfer on all the available data, providing immunity to noise and poor conditioning. the paper also shows an implementation at video rates on a four axis active camera platform. graceful degradation in the presence of insufficient data and fixed latency tracking in parallel with the structure calculation provide robust performance. recovered trajectories are shown in an approximately euclidean frame while structure transfer is demonstrated by the evolution of the target's convex hull.
fast recognition of multi-view faces with feature selection. we propose a discriminative feature selection method utilizing support vector machines for the challenging task of multi-view face recognition. according to the statistical relationship between the two tasks, feature selection and multi-class classification, we integrate the two tasks into a single consistent framework and effectively realize the goal of discriminative feature selection. the classification process can be made faster without degrading the generalization performance through this discriminative feature selection method. on the umist multi-view face database, our experiments show that this discriminative feature selection method can speed up the multi-view face recognition process without degrading the correct rate and outperform the traditional kernel subspace methods.
probabilistic contour extraction using hierarchical shape representation. in this paper, we address the issue of extracting contour of the object with a specific shape. a hierarchical graphical model is proposed to represent shape variations. a complex shape is decomposed into several components which are described as principal component analysis (pca) based models in various levels. the hierarchical representation allows for chain-like conditional dependency within a single level and bidirectional communication between different levels. additionally, a sequential monte-carlo (smc) based inference algorithm that can explore the graphical structure is proposed to estimate the contour. the experiments performed on real-world hand and face images show that the proposed method is effective in combating occlusion and cluttered background. moreover, it is possible to isolate the localization error to an individual component of a shape attributed to the hierarchical representation.
on the geometry and algebra of the point and line correspondences between n images. we explore the geometric and algebraic relations that exist between correspondences of points and lines in an arbitrary number of images. we propose to use the formalism of the grassmann-cayley algebra as the simplest way to make both geometric and algebraic statements in a very synthetic and effective way (i.e. allowing actual computation if needed). we have a fairly complete picture of the situation in the case of points; there are only three types of algebraic relations which are satisfied by the coordinates of the images of a 3-d point: bilinear relations arising when we consider pairs of images among the n and which are the well-known epipolar constraints, trilinear relations arising when we consider triples of images among the n, and quadrilinear relations arising when we consider four-tuples of images among the n. in the case of lines, we show how the traditional perspective projection equation can be suitably generalized and that in the case of three images there exist two independent trilinear relations between the coordinates of the images of a 3-d line.
on exploiting occlusions in multiple-view geometry. occlusions are common place in man-made and natural environments; they often result in photometric features where a line terminates at an occluding boundary, resembling a "t". we show that the 2-d motion of such t-junctions in multiple views carries non-trivial information on the 3-d structure of the scene and its motion relative to the camera. we show how the constraint among multiple views of t-junctions can be used to reliably detect them and differentiate them from ordinary point features. finally, we propose an integrated algorithm to recursively and causally estimate structure and motion in the presence of t-junctions along with other point-features.
on the epipolar geometry of the crossed-slits projection. the crossed-slits (x-slits) camera is defined by two non-intersectingslits, which replace the pinhole in the commonperspective camera. each point in space is projected to theimage plane by a ray which passes through the point and thetwo slits. the x-slits projection model includes the pushb-roomcamera as a special case. in addition, it describesa certain class of panoramic images, which are generatedfrom sequences obtained by translating pinhole cameras.in this paper we develop the epipolar geometry of the x-slitsprojection model. we show an object which is similarto the fundamental matrix; our matrix, however, describesa quadratic relation between corresponding image points(using the veronese mapping). similarly the equivalent ofepipolar lines are conics in the image plane. unlike the pin-holecase, epipolar surfaces do not usually exist in the sensethat matching epipolar lines lie on a single surface; we analyzethe cases when epipolar surfaces exist, and characterizetheir properties. finally, we demonstrate the matchingof points in pairs of x-slits panoramic images.
realtime ibr with omnidirectional crossed-slits projection. the crossed-slits (x-slits) projection can be used to generate new views of a scene from a sequence of perspective images. compared with other image-based rendering (ibr) techniques, x-slits image generation is simple and requires a relatively small number of input images, which makes it suitable for realtime ibr. in this paper we extend this model to omnidirectional cameras and a circular slit. we show how it can be used for realtime image-based rendering of omnidirectional images, and how to optimize it for speed and quality. we analyze the inherent geometric distortions of the circular x-slits projection, and describe a normalization mechanism to reduce distortions, creating a realistic virtual environment. essentially the same mechanism is used to augment the x-slits images with artificial objects, when using standard graphics tools which assume perspective projection.
3d-2d projective registration of free-form curves and surfaces. some medical interventions require knowing the correspondence between an mri/ct pre-operative image and the actual position of the patient. examples occur in neurosurgery, radiotherapy, interventional radiology, but also in video surgery (laparoscopy). we present in this article three new techniques for performing this task without artificial markers. we find the 3d-2d projective transformation (composition of a rigid displacement and a perspective projection) which maps a 3d object onto a 2d image of this object. depending on the object model (curve or surface), and on the 2d image acquisition system (x-ray, video), the techniques are different but the framework is common. it does not depend on the initial relative positions of the objects and deals with the occlusions and the outliers. results are presented on real medical data to demonstrate the validity of our approach.
bayesian structural content abstraction for region-level image authentication. we present a hierarchical representation of image structure and use it for image content authentication. firstly, we model the image with the markov pixon random field. within the bayesian framework, the optimal label map and regional pixon map can be obtained, based on which wedefine an undirected graph, namely bayesian structural content abstraction (basca). this representation captures the spatial topology information of homogeneous regions as well as their finest scale and interactions. then, an efficient optimization scheme has been proposed to iteratively minimize the distance (or learning error) to all content-identical image samples generated by an acceptable operation setdefined by the user. in addition, we use the regional pixon map to remove spurious vertices and thus to establish a basca hierarchy naturally. the basca itself and its features can act as the signature of the protected image. our experimental results show that the proposed approach has much less false positive and comparable false negative probability compared with the existing methods
sectored snakes: evaluating learned-energy segmentations. we describe how to teach deformable models to maximize image segmentation correctness based on userspecified criteria, and we present a method for evaluating which criteria work best. we present sectored snakes, a formulation that demonstrably improves upon regular snakes.a traditional deformable model ("snake" in 2d) fails to find an object's boundary when the strongest nearby image edges are not the ones sought. but models can be trained to respond to other image features instead, by learning their probability distributions. the implementor must then decide on which of many image qualities to teach the model. to this end, we show how to evaluate the efficacy of any resulting deformable model, given a sampling of ground truth, a model of the range of shapes tried during optimization, and a measure of shape closeness.in the domain of abdominal ct images, we demonstrate such evaluation on a simple "sectoring" of a snake, in which intensity and perpendicular gradient are observed over equal-length segments. this speci.c set of qualities shows a measured improvement over an objective function that is uniform around the shape, and it follows naturally from examination of the latter's failures due to images variations around the organ boundary.