waashk/dblp huggingface dataset:
finding periodicity in space and time. an algorithm for simultaneous detection, segmentation, and characterization of spatiotemporal periodicity is presented. the use of periodicity templates is proposed to localize and characterize temporal activities. the templates not only indicate the presence and location of a periodic event, but also give an accurate quantitative periodicity measure. hence, they can be used as a new means of periodicity representation. the proposed algorithm can also be considered as a "periodicity filter" a low-level model of periodicity perception. the algorithm is computationally simple, and shown to be more robust than optical flow based techniques in the presence of noise. a variety of real-world examples are used to demonstrate the performance of the algorithm.
an expectation maximization approach to the synergy between image segmentation and object categorization. in this work we deal with the problem of modelling and exploiting the interaction between the processes of image segmentation and object categorization. we propose a novel framework to address this problem that is based on the combination of the expectation maximization (em) algorithm and generative models for object categories. using a concise formulation of the interaction between these two processes, segmentation is interpreted as the e step, assigning observations to models, whereas object detection/analysis is modelled as the m-step, fitting models to observations. we present in detail the segmentation and detection processes comprising the e and m steps and demonstrate results on the joint detection and segmentation of the object categories of faces and cars.
region segmentation via deformable model-guided split and merge. abstract an improved method for deformable shape-based image segmentation is described. image regions are merged together and/or split apart, based on their agreement with an a priori distribution on the global deformation parameters for a shape template. the quality of a candidate region merging is evaluated by a cost measure that includes: homogeneity of image properties within the combined region, degree of overlap with a deformed shape model, and a deformation likelihood term. perceptually-motivated criteria are used to determine where/how to split regions, based on the local shape properties of the region group''s bounding contour. a globally consistent interpretation is determined in part by the minimum description length principle. experiments show that the model-based splitting strategy yields a significant improvement in segmention over a method that uses merging alone.
3d pose estimation by fitting image gradients directly to polyhedral models. addresses the problem of pose estimation and tracking of vehicles in image sequences from traffic scenes recorded by a stationary camera. in a new algorithm, the vehicle pose is estimated by directly fitting image gradients to polyhedral vehicle models without an edge segment extraction process. the new approach is significantly more robust than approaches that rely on feature extraction because the new approach exploits more information from the image data. we can track vehicles that are partially occluded by textured objects, e.g. foliage, where classical approaches based on edge segment extraction fail. results from various experiments with real-world traffic scenes are presented.
meshfree particle method. many of the computer vision algorithms have been posed invarious forms of differential equations, derived from minimization of specific energy functionals, and the finite element representation and computation have become the de facto numerical strategies for solving these problems. however, for cases where domain mappings between numerical iterations or image frames involve large geometrical shape changes, such as deformable models for object segmentation and non rigid motion tracking, these strategies may exhibit considerable loss of accuracy when themesh elements become extremely skewed or compressed. we present a new computational paradigm, the meshfree particle method, where the object representation and the numerical calculation are purely based on the nodal points and do not require the meshing of the analysis domain. this meshfree strategy can naturally handle large deformation and domain discontinuity issues and achieve desired numerical accuracy through adaptive node and polynomial shape function refinement. we discuss in detail the element-free galerkin method, including the shape function construction using the moving least square approximation and the galerkin weak form formulation, and we demonstrate its applications to deformable model based segmentation and mechanically motivated left ventricular motion analysis.
what metrics can be approximated by geo-cuts, or global optimization of length/area and flux. in [3] we showed that graph cuts can find hyper-surfaces of globally minimal length (or area) under any riemannian metric. here we show that graph cuts on directed regular grids can approximate a significantly more general class of continuous non-symmetric metrics. using submodularity condition [1, 11], we obtain a tight characterization of graph-representable metrics. such "submodular" metrics have an elegant geometric interpretation via hyper-surface functionals combining length/area and flux. practically speaking, we extend "geo-cuts" algorithm [3] to a wider class of geometrically motivated hypersurface functionals and show how to globally optimize any combination of length/area and flux of a given vector field. the concept of flux was recently introduced into computer vision by [13] but it was mainly studied within variational framework so far. we are first to show that flux can be integrated into graph cuts as well. combining geometric concepts of flux and length/area within the global optimization framework of graph cuts allows principled discrete segmentation models and advances the state of the art for the graph cuts methods in vision. in particular, we address the "shrinking" problem of graph cuts, improve segmentation of long thin objects, and introduce useful shape constraints.
computing visual correspondence with occlusions via graph cuts. several new algorithms for visual correspondence based on graph cuts have recently been developed. while these methods give very strong results in practice, they do not handle occlusions properly. specifically, they treat the two input images asymmetrically, and they do not ensure that a pixel corresponds to at most one pixel in the other image. in this paper, we present two new methods which properly address occlusions, while preserving the advantages of graph cut algorithms. we give experimental results for stereo as well as motion, which demonstrate that our methods perform well both at detecting occlusions and computing disparities.
a new framework for approximate labeling via graph cuts. a new framework is presented that uses tools from duality theory of linear programming to derive graph-cut based combinatorial algorithms for approximating np-hard classification problems. the derived algorithms include ¿-expansion graph cut techniques merely as a special case, have guaranteed optimality properties even in cases where á-expansion techniques fail to do so and can provide very tight per-instance suboptimality bounds in all occasions.
gabor wavelets for 3-d object recognition. this paper presents a model-based object recognition approach that uses a hierarchical gabor wavelet representation. the key idea is to use magnitude, phase and frequency measures of gabor wavelet representation in an innovative flexible matching approach that can provide robust recognition. a gabor grid a topology-preserving map, efficiently encodes both signal energy and structural information of an object in a sparse multi-resolution representation. the gabor grid subsamples the gabor wavelet decomposition of an object model and is deformed to allow the indexed object model match with the image data. flexible matching between the model and the image minimizes a cost function based on local similarity and geometric distortion of the gabor grid. grid erosion and repairing is performed whenever a collapsed grid, due to object occlusion, is detected. the results on infrared imagery are presented. where objects undergo rotation, translation, scale, occlusion and aspect variations under changing environmental conditions.
multi-view aam fitting and camera calibration. in this paper we study the relationship between multiview active appearance model (aam) fitting and camera calibration. in the first part of the paper we propose an algorithm to calibrate the relative orientation of a set of n > 1 cameras by fitting an aam to sets of n images. in essence, we use the human face as a (non-rigid) calibration grid. our algorithm calibrates a set of 2 × 3 weak-perspective camera projection matrices, projections of the world coordinate system origin into the images, depths of the world coordinate system origin, and focal lengths. we demonstrate that the performance of this algorithm is comparable to a standard algorithm using a calibration grid. in the second part of the paper we show how calibrating the cameras improves the performance of multi-view aam fitting.
a multilevel banded graph cuts method for fast image segmentation. in the short time since publication of boykov and jolly¿s seminal paper [3], graph cuts have become well established as a leading method in 2d and 3d semi-automated image segmentation. although this approach is computationally feasible for many tasks, the memory overhead and supralinear time complexity of leading algorithms results in an excessive computational burden for high-resolution data. in this paper, we introduce a multilevel banded heuristic for computation of graph cuts that is motivated by the well-known narrow band algorithm in level set computation. we perform a number of numerical experiments to show that this heuristic drastically reduces both the running time and the memory consumption of graph cuts while producing nearly the same segmentation result as the conventional graph cuts. additionally, we are able to characterize the type of segmentation target for which our multilevel banded heuristic will yield different results from the conventional graph cuts. the proposed method has been applied to both 2d and 3d images with promising results.
integrated edge and junction detection with the boundary tensor. the boundaries of image regions necessarily consist of edges (in particular, step and roof edges), corners, and junctions. currently, different algorithms are used to detect each boundary type separately, but the integration of the results into a single boundary representation is difficult. therefore, a method for the simultaneous detection of all boundary types is needed. we propose to combine responses of suitable polar separable filters into what we will call the boundary tensor. the trace of this tensor is a measure of boundary strength, while the small eigenvalue and its difference to the large one represent corner / junction and edge strengths respectively. we prove that the edge strength measure behaves like a rotationally invariant quadrature filter. a number of examples demonstrate the properties of the new method and illustrate its application to image segmentation.
is levenberg-marquardt the most efficient optimization algorithm for implementing bundle adjustment?. in order to obtain optimal 3d structure and viewing parameter estimates, bundle adjustment is often used as the last step of feature-based structure and motion estimation algorithms. bundle adjustment involves the formulation of a large scale, yet sparse minimization problem, which is traditionally solved using a sparse variant of the levenberg- marquardt optimization algorithm that avoids storing and operating on zero entries. this paper argues that considerable computational benefits can be gained by substituting the sparse levenberg-marquardt algorithm in the implementation of bundle adjustment with a sparse variant of powell¿s dog leg non-linear least squares technique. detailed comparative experimental results provide strong evidence supporting this claim.
shapelets correlated with surface normals produce surfaces. this paper addresses the problem of deducing the surface shape of an object given just the surface normals. many shape measurement algorithms such as shape from shading and shape from texture only return the surface normals of an object, often with an ambiguity of ¿ in the surface tilt. the surface shape has to be inferred from these normals, typically via some integration process. however, reconstruction through the integration of surface gradients is sensitive to noise and the choice of integration paths across the surface. in addition, existing techniques cannot accommodate ambiguities in tilt. this paper presents a new approach to the reconstruction of surfaces from surface normals using basis functions, referred to here as shapelets. the surface gradients of the shapelets are correlated with the gradients of the surface and the correlations summed to form the reconstruction. this results in a simple reconstruction process that is very robust to noise. where there is an ambiguity of ¿ in the surface tilt, reconstructions of reduced quality are still possible up to a positive/negative shape ambiguity. intriguingly, some form of reconstruction is also possible using just slant information.
appearance modeling under geometric context. we propose a unified framework based on a general definition of geometric transform (get) for modeling appearance. get represents the appearance by applying designed functionals over certain geometric sets. we show that image warping, radon transform, trace transform, etc. are special cases of our definition. moreover, three different types of gets are designed to handle deformation, articulation and occlusion and applied to fingerprinting the appearance inside a contour. they include the contour-driven get, the feature curve based get and selecting functionals to model the appearance inside the convex hull of the contour. a multi-resolution representation that combines both shape and appearance information is also proposed. we apply our approach to image synthesis and object recognition. the proposed approach produces promising results when applied to fingerprinting the appearance of human and body parts despite the challenges due to articulated motion and deformations.
combining gradient and albedo data for rotation invariant classification of 3d surface texture. we present a new texture classification scheme whichis invariant to surface-rotation. many textureclassification approaches have been presented in the pastthat are image-rotation invariant, however, imagerotation is not necessarily the same as surface rotation.we have therefore developed a classifier that usesinvariants that are derived from surface properties ratherthan image properties. previously we developed ascheme that used surface gradient (normal) fieldsestimated using photometric stereo. in this paper weaugment these data with albedo information and an alsoemploy an additional feature set: the radial spectrum.we used 30 real textures to test the new classifier. aclassification accuracy of 91% was achieved whenalbedo and gradient 1d polar and radial features werecombined. the best performance was also achieved byusing 2d albedo and gradient spectra. the classificationaccuracy is 99%.
bayesian autocalibration for surveillance. in the context of visual surveillance of human activity, knowledge about a camera¿s internal and external parameters is useful, as it allows for the establishment of a connection between image and world measurements. unfortunately, calibration information is rarely available and difficult to obtain after a surveillance system has been installed. in this paper a method for camera autocalibration based on information gathered by tracking people is developed. it brings two main contributions: first, we show how a foot-to-head plane homology can be used to obtain the calibration parameters and then we show an approach how to efficiently estimate initial parameter estimates from measurements; second, we present a bayesian solution to the calibration problem that can elegantly handle measurement uncertainties, outliers, as well as prior information. it is shown how the full posterior distribution of calibration parameters given the measurements can be estimated, which allows making statements about the accuracy of both the calibration parameters and the measurements involving them.
independent 3d motion detection using residual parallax normal flow fields. this paper considers a specific problem of visual perception of motion, namely the problem of visual detection of independent 3d motion. most of the existing techniques for solving this problem rely on restrictive assumptions about the environment, the observer's motion, or both. moreover, they are based on the computation of a dense optical flow field, which amounts to solving the ill-posed correspondence problem. in this work, independent motion detection is formulated as a problem of robust parameter estimation applied to the visual input acquired by a rigidly moving observer. the proposed method automatically selects a planar surface in the scene and the residual planar parallax normal flow field with respect to the motion of this surface is computed at two successive time instants. the two resulting normal flow field are then combined in a linear model. the parameters of this model are related to the parameters of self-motion (ego-motion) and their robust estimation leads to a segmentation of the scene based on 3d motion. the method avoids a complete solution to the correspondence problem by selectively matching subsets of image points and by employing normal flow fields. experimental results demonstratethe effectiveness of the proposed method in detecting independent motion in scenes with large depth variations and unrestrited observer motion.
a task driven 3d object recognition system using bayesian networks. in this paper we propose a general framework to build a task oriented 3d object recognition system for cad based vision (cbv). features from 3d space curves representing the object's rims provide sufficient information to allow identification and pose estimation of industrial cad models. however, features relying on differential surface properties tend to be very vulnerable with respect to noise. to model the statistical behavior of the data we introduce bayesian netswhich model the relationship between objects and observable features. furthermore, task oriented selection of the optimal action to reduce the uncertainty of recognition results is incorporated into the bayesian nets. this enables the integration of intelligent recognition strategies depending on the already acquired evidence into a robust, and effcient, 3d cad based recognition system.
object recognition from local scale-invariant features. an object recognition system has been developed that uses a new class of local image features. the features are invariant to image scaling, translation, and rotation, and partially invariant to illumination changes and affine or 3d projection.these features share similar properties with neurons in inferior temporal cortex that are used for object recognition in primate vision. features are efficiently detected through a staged filtering approach that identifies stable points in scale space. image keys are created that allow for local geometric deformations by representing blurred image gradients in multiple orientation planes and at multiple scales.the keys are used as input to a nearest-neighbor indexing method that identifies candidate object matches. final verification of each match is achieved by finding a low-residual least-squares solution for the unknown model parameters. experimental results show that robust object recognition can be achieved in cluttered partially-occluded images with a computation time of under 2 seconds.
texture segmentation and shape in the same image. uniformly textured surfaces in 3d scenes provide important cues for image understanding. texture can be used for both segmentation and for 3d shape inference. unfortunately, virtually all current algorithms are based on assumptions that make it impossible to do texture segmentation and shape-from-texture in the same image. texture segmentation algorithms rely on an absence of 3d effects that tend to distort the texture. shape-from-texture algorithms depend on these effects, relying instead on the texture being already segmented. to really understand texture in images, texture segmentation and shape-from-texture must be viewed as a combined problem to be solved simultaneously. we present a solution to this problem with a region-growing algorithm that explicitly accounts for perspective distortions of otherwise uniform texture. we use the image spectrogram to compute local surface normals, which are in turn used to "frontalize" the texture. these frontalized texture patches are then subjected to a region-growing algorithm based on similarity in the local frequency domain and a minimum description length criteria. we show results of our algorithm on real texture images taken in the lab and outdoors.
vision based hand modeling and tracking for virtual teleconferencing and telecollaboration. the authors present a hand model that simultaneously satisfies both the synthesis and analysis requirements of model based compression. the model can be fitted to any person's hand and can be done using a single camera. once the model is fitted to a real human hand, it is then used in several tracking scenarios in order to verify its effectiveness. with successful tracking achieved, the model is ready to be incorporated into a virtual environment or model based compression scheme such as sign language communication over telephone lines or virtual teleconferences over computer networks at very low bit rates and at very high image quality.
reflectance function estimation and shape recovery from image sequence of a rotating object. we describe a technique for surface recovery of a rotating object illuminated under a collinear light source (where the light source lies on or near the optical axis). we show that the surface reflectance function can be directly estimated from the image sequence without any assumption on the reflectance property of the object surface. from the image sequence, the 3d locations of some singular surface points are calculated and their brightness values are extracted for the estimation of the reflectance function. we also show that the surface can be recovered by using shading information in two images of the rotating object. iteratively using the first-order taylor series approximation and the estimated reflectance function, the depth and orientation of the surface can be recovered simultaneously. the experimental results on real image sequences of both matte and specular surfaces demonstrate that the technique is feasible and robust.
discriminative random fields: a discriminative framework for contextual interaction in classification. in this work we present discriminative random fields(drfs), a discriminative framework for the classification ofimage regions by incorporating neighborhood interactionsin the labels as well as the observed data. the discriminativerandom fields offer several advantages over the conventionalmarkov random field (mrf) framework. first,the drfs allow to relax the strong assumption of conditionalindependence of the observed data generally used inthe mrf framework for tractability. this assumption is toorestrictive for a large number of applications in vision. second,the drfs derive their classification power by exploitingthe probabilistic discriminative models instead of thegenerative models used in the mrf framework. finally, allthe parameters in the drf model are estimated simultaneouslyfrom the training data unlike the mrf frameworkwhere likelihood parameters are usually learned separatelyfrom the field parameters. we illustrate the advantages ofthe drfs over the mrf framework in an application ofman-made structure detection in natural images taken fromthe corel database.