waashk/dblp huggingface dataset:
topologically adaptable snakes. the paper presents a typologically adaptable snakes model for image segmentation and object representation. the model is embedded in the framework of domain subdivision using simplicial decomposition. this framework extends the geometric and topological adaptability of snakes while retaining all of the features of traditional snakes, such as user interaction, and overcoming many of the limitations of traditional snakes. by superposing a simplicial grid over the image domain and using this grid to iteratively reparameterize the deforming snakes model, the model is able to flow into complex shapes, even shapes with significant protrusions or branches, and to dynamically change topology as necessitated by the data. snakes can be created and can split into multiple parts or seamlessly merge into other snakes. the model can also be easily converted to and from the traditional parametric snakes model representation. we apply a 2d model to various synthetic and real images in order to segment objects with complicated shapes and topologies.
multiple-cue illumination estimation in textured scenes. in this paper, we present a method that integrates cuesfrom shading, shadow and specular reflections for estimatingdirectional illumination in a textured scene. textureposes a problem for lighting estimation, since texture edgescan be mistaken for changes in illumination condition, andunknown variations in albedo make reflectance model fittinginpractical. unlike previous works which all assumeknown or uniform reflectance, our method can deal with theeffects of textures by capitalizing on physical consistenciesthat exist among the lighting cues. since scene textures donot exhibit such coherence, we use this property to minimizethe influence of texture on illumination direction estimation.for the recovered light source directions, a technique forestimating their intensities in the presence of texture is alsoproposed.
a unifying framework for structure and motion recovery from image sequences. the paper proposes a statistical framework that enables 3d structure and motion to be computed optimally from an image sequence, on the assumption that feature measurement errors are independent and gaussian distributed. the analysis and results demonstrate that computing both camera/scene motion and 3d structure is essential to computing either with any accuracy. having computed optimal estimates of structure and motion over a small number of initial images, a recursive version of the algorithm (previously reported) recomputes sub optimal estimates given new image data. the algorithm is designed explicitly for real time implementation, and the complexity is proportional to the number of tracked features. 3d projective, affine and euclidean models of structure and motion recovery have been implemented, incorporating both point and line features into the computation. the framework can handle any feature type and camera model that may be encapsulated as a projection equation from scene to image.
rigidity checking of 3d point correspondences under perspective projection. an algorithm is described which rapidly verifies the potential rigidity of three dimensional point correspondences from a pair of two dimensional views under perspective projection. the output of the algorithm is a simple yes or no answer to the question ``could these corresponding points from two views be the projection of a rigid configuration?'''' potential applications include 3d object recognition from a single previous view and correspondence matching for stereo or motion over widely separated views. our analysis begins with the observation that it is often the case that two views cannot provide an accurate structure-from-motion estimate because of ambiguity and ill- conditioning. however, it is argued that an accurate yes/no answer to the rigidity question is possible and experimental results support this assertion with as few as six pairs of corresponding points over a wide range of scene structures and viewing geometries. rigidity checking verifies point correspondences by using 3d recovery equations as a matching condition. the proposed algorithm improves upon other methods that fall under this approach because it works with as few as six corresponding points under full perspective projection, handles correspondences from widely separated views, makes full use of the disparity of the correspondences, and is integrated with a linear algorithm for 3d recovery due to kontsevich. the rigidity decision is based on the residual error of an integrated pair of linear and nonlinear structure-from-motion estimators. results are given for experiments with synthetic and real image data. a complete implementation of this algorithm is being made publicly available.
a generative/discriminative learning algorithm for image classification. we have developed a two-phase generative/discriminative learning procedure for the recognition of classes of objects and concepts in outdoor scenes. our method uses both multiple types of object features and context within the image. the generative phase normalizes the description length of images, which can have an arbitrary number of extracted features of each type. in the discriminative phase, a classifier learns which images, as represented by this fixed-length description, contain the target object. we have tested the approach by comparing it to several other approaches in the literature and by experimenting with several different data sets and combinations of features. our results, using color, texture, and structure features, show a significant improvement over previously published results in image retrieval. using salient region features, we are competitive with recent results in object recognition.
local features for object class recognition. in this paper we compare the performance of local detectors and descriptors in the context of object class recognition. recently, many detectors / descriptors have been evaluated in the context of matching as well as invariance to viewpoint changes [20]. however, it is unclear if these results can be generalized to categorization problems, which require different properties of features. we evaluate 5state-of-the-art scale invariant region detectors and 5 descriptors. local features are computed for 20 object classes and clustered using hierarchical agglomerative clustering. we measure the quality of appearance clusters and location distributions using entropy as well as precision. we also measure how the clusters generalize from training set to novel test data. our results indicate that extended sift descriptors [22] computed on hessian-laplace [20] regions perform best. second score is obtained by salient regions [11]. the results also show that these two detectors provide complementary features. the new detectors/descriptorssignificantly improve the performance of a state-of-the art recognition approach [16] in pedestrian detection task.
globally optimal solutions for energy minimization in stereo vision using reweighted belief propagation. a wide range of low level vision problems have been formulated in terms of finding the most probable assignment of a markov random field (or equivalently the lowest energy configuration). perhaps the most successful example is stereo vision. for the stereo problem, it has been shown that finding the global optimum is np hard but good results have been obtained using a number of approximate optimization algorithms. in this paper we show that for standard benchmark stereo pairs, the global optimum can be found in about 30 minutes using a variant of the belief propagation (bp) algorithm. we extend previous theoretical results on reweighted belief propagation to account for possible ties in the beliefs and using these results we obtain easily checkable conditions that guarantee that the bp disparities are the global optima. we verify experimentally that these conditions are typically met for the standard benchmark stereo pairs and discuss the implications of our results for further progress in stereo.
a multigrid approach for hierarchical motion estimation. this paper focuses on the estimation of the apparent motion field between two consecutive frames in an image sequence. the approach developed here is a trade-off between methods based on global parameterized flow models and local dense optic flow estimators.the method relies on an adaptive multigrid minimization approach. in addition to accelerated convergence toward good estimates, it allows to mix different parameterizations of the estimate relative to adaptive partitions of the image.the performances of the resulting algorithms are demonstrated in the difficult context of a non-convex energy. experimental results on real world meteosat sequences are presented.
using specularities for recognition. recognition systems have generally treated specular highlightsas noise. we show how to use these highlights asa positive source of information that improves recognitionof shiny objects. this also enables us to recognize verychallenging shiny transparent objects, such as wine glasses.specifically, we show how to find highlights that are consistentwith an hypothesized pose of an object of known 3dshape. we do this using only a qualitative description ofhighlight formation that is consistent with most models ofspecular reflection, so no specific knowledge of an object'sreflectance properties is needed. we first present a methodthat finds highlights produced by a dominant compact lightsource, whose position is roughly known. we then show howto estimate the lighting automatically for objects whose reflectionis part specular and part lambertian. we demonstratethis method for two classes of objects. first, we showthat specular information alone can suffice to identify objectswith no lambertian reflectance, such as transparentwine glasses. second, we use our complete system to recognizeshiny objects, such as pottery.
a general framework for object detection. this paper presents a general trainable framework for object detection in static images of cluttered scenes. the detection technique we develop is based on a wavelet representation of an object class derived from a statistical analysis of the class instances. by learning an object class in terms of a subset of an overcomplete dictionary of wavelet basis functions, we derive a compact representation of an object class which is used as an input to a support vector machine classifier. this representation overcomes both the problem of in-class variability and provides a low false detection rate in unconstrained environments.we demonstr ate the capabilities of the technique in two domains whose inherent information content differs significantly. the first system is face detection and the second is the domain of people which, in contrast to faces, vary greatly in color, texture, and patterns. unlike previous approaches, this system learns from examples and does not rely on any a priori (hand-crafted) models or motion-based segmentation. the paper also presents a motion-based extension to enhance the performance of the detection algorithm over video sequences. the results presented here suggest that this architecture may well be quite general.
detection of multiple, partially occluded humans in a single image by bayesian combination of edgelet part detectors. this paper proposes a method for human detection in crowded scene from static images. an individual human is modeled as an assembly of natural body parts. we introduce edgelet features, which are a new type of silhouette oriented features. part detectors, based on these features, are learned by a boosting method. responses of part detectors are combined to form a joint likelihood model that includes cases of multiple, possibly inter-occluded humans. the human detection problem is formulated as maximum a posteriori (map) estimation. we show results on a commonly used previous dataset as well as new data-sets that could not be processed by earlier methods.
a pattern classification approach to dynamical object detection. current systems for object detection in video sequences rely on explicit dynamical models like kalman filters or hidden markov models. there is significant overhead needed in the development of such systems as well as the a priori assumption that the object dynamics can be described with such a dynamical model. this paper describes a new pattern classification technique for object detection in video sequences that uses a rich, over complete dictionary of wavelet features to describe an object class. unlike previous work where a small subset of features was selected from the dictionary, this system does no feature selection and learns the model in the full 1,326 dimensional feature space. comparisons using different sized sets of several types of features are given. we extend this representation into the time domain without assuming any explicit model of dynamics. this data driven approach produces a model of the physical structure and short-time dynamical characteristics of people from a training set of examples; no assumptions are made about the motion of people, just that short sequences characterize their dynamics sufficiently for the purposes of detection. one of the main benefits of this approach is that transient false positives are reduced. this technique compares favorably with the static detection approach and could be applied to other object classes. we also present a real-time version of one of our static people detection systems.
joint haar-like features for face detection. in this paper, we propose a new distinctive feature, called joint haar-like feature, for detecting faces in images. this is based on co-occurrence of multiple haar-like features. feature co-occurrence, which captures the structural similarities within the face class, makes it possible to construct an effective classifier. the joint haar-like feature can be calculated very fast and has robustness against addition of noise and change in illumination. a face detector is learned by stagewise selection of the joint haar-like features using adaboost. a small number of distinctive features achieve both computational efficiency and accuracy. experimental results with 5,676 face images and 30,000 nonface images show that our detector yields higher classification performance than viola and jones¿ detector, which uses a single feature for each weak classifier. given the same number of features, our method reduces the error by 37%. our detector is 2.6 times as fast as viola and jones¿ detector to achieve the same performance.
a pde-based level-set approach for detection and tracking of moving objects. this papers pr esents a framework for detecting and tracking moving objects in a sequence of images. using a statistical approach, where the inter-frame difference is modeled by a mixture of two laplacian or gaussian distributions, and an energy minimization based approach, we reformulate the motion detection and tracking problem as a front propagation problem. the euler-lagrange equation of the designed energy functional is first derived and the flow minimizing the energy is then obtained. following the work by caselles et al [cks95],and malladi et al [msv95, msv93], the contours to be detected and tracked are modeled as geodesic active contours evolving toward the minimum of the designed energy, under the influence of internal and external image dependent forces. using the level set formulation scheme of osher and sethian [os88] complex curves can be detected and tracked and topological changes for the evolving curves are naturally managed. to reduce the computational cost required by a direct implementation of the formulation scheme of osher and sethian [os88], a new approach exploiting aspects from the classical narrow band [as95] and fast marching [set96] methods is proposed and favorably compared to them. in order to further reduce the cpu time, a multi-scale approach has also been considered. very promising experimental results are provided using real video sequences.
task-oriented generation of visual sensing strategies. in vision-guided robotic operations, vision is used for extracting necessary information for achieving the task. since visual sensing is usually performed with limited resources, visual sensing strategies should be planned so that only necessary information is obtained efficiently. this paper describes a method of systematically generating visual sensing strategies based on knowledge of the task to be performed. the generation of the appropriate visual sensing strategy entails knowing what information to extract, where to get it, and how to get it. this is facilitated by the knowledge of the task, which describes what objects are involved in the operation, and how they are assembled. our method has been implemented using a laser range finder as the sensor. experimental results show the feasibility of the method, and point out the importance of task-oriented evaluation of visual sensing strategies.
polarization-based transparent surface modeling from two views. in this paper, we propose a novel method to recover thesurface shape of transparent objects. the degree of polarizationof the light reflected from the object surface dependson the reflection angle which, in turn, depends on the object'ssurface normal; thus, by measuring the degree of polarization,we are able to calculate the surface normal of theobject. however, degree of polarization and surface normaldoes not correspond one-to-one, making us to analyze twopolarization images taken from two different view in orderto solve the ambiguity. a parabolic curve will be a strongclue to correspond a point in one image to a point in theother image, where both points represent the same point onobject surface. by comparing the degree of polarization atsuch corresponding points, the true surface normal can bedetermined.
geodesic active regions for supervised texture segmentation. this paper presents a novel variational method for supervised texture segmentation. the textured feature space is generated by filtering the given textured images using isotropic and anisotropic filters, and analyzing their responses as multi-component conditional probability density functions.the texture segmentation is obtained by unifying region and boundary-based information as an improved geodesic active contour model. the defined objective function is minimized using a gradient-descent method where a level set approach is used to implement the obtained pde.according to this pde, the curve propagation towards the final solution is guided by boundary and region-based segmentation forces, and is constrained by a regularity force. the level set implementation is performed using a fast front propagation algorithm where topological changes are naturally handled. the performance of our method is demonstrated on a variety of synthetic and real textured frames.
polarization-based inverse rendering from a single view. this paper presents a method to estimate geometrical,photometrical, and environmental information of a single-viewedobject in one integrated framework under fixed viewingposition and fixed illumination direction. these threetypes of information are important to render a photorealisticimage of a real object. photometrical information representsthe texture and the surface roughness of an object,while geometrical and environmental information representthe 3d shape of an object and the illumination distribution,respectively. the proposed method estimates the 3d shapeby computing the surface normal from polarization data,calculates the texture of the object from the diffuse only reflectioncomponent, determines the illumination directionsfrom the position of the brightest intensity in the specularreflection component, and finally computes the surfaceroughness of the object by using the estimated illuminationdistribution.
gradient vector flow fast geodesic active contours. abstract--in this paper, we propose an edge-driven bidirectional geometric flow for boundary extraction. to this end, we combine the geodesic active contour flow [check end of sentence] and the gradient vector flow external force for snakes [check end of sentence]. the resulting motion equation is considered within a level set formulation [check end of sentence], can deal with topological changes and important shape deformations. an efficient numerical schema is used for the flow implementation that exhibits robust behavior and has fast convergence rate [check end of sentence], [check end of sentence]. promising results on real and synthetic images demonstrate the potentials of the flow.
principal manifolds and bayesian subspaces for visual recognition. we investigate the use of linear and nonlinear principal manifolds for learning low-dimensional representations for visual recognition. three techniques: principal component analysis (pca), independent component analysis (ica) and nonlinear pca (nlpca) are examined and tested in a visual recognition experiment using a large gallery of facial images from the "feret" database. we compare the recognition performance of a nearest-neighbor matching rule with each principal manifold representation to that of a maximum a posteriori (map) matching rule using a bayesian similarity measure derived from probabilistic subspaces and demonstrate the superiority of the latter.
shape and appearance repair for incomplete point surfaces. this paper presents a new surface content completion framework that can restore both shape and appearance from scanned, incomplete point set inputs. first, the geometric holes can be robustly identified from noisy and defective data sets without the need of any normal or orientation information, using the method of active deformable models. the geometry and texture information of the holes can then be determined either automatically from the models¿ context, or semi-automatically with minimal users¿ intervention. the central idea for this repair process is to establish a quantitative similarity measurement among local surface patches based on their local parameterizations and curvature computation. the geometry and texture information of each hole can be completed by warping the candidate region and gluing it to the hole. the displacement for the alignment process is computed by solving a poisson equation in 2d. our experiments show that the unified framework, founded upon the techniques of deformable models, local parameterization, and pde modeling, can provide a robust and elegant solution for content completion of defective, complex point surfaces.
probabilistic visual learning for object detection. we present an unsupervised technique for visual learning which is based on density estimation in high-dimensional spaces using an eigenspace decomposition. two types of density estimates are derived for modeling the training data: a multivariate gaussian (for a unimodal distributions) and a multivariate mixture-of-gaussians model (for multimodal distributions). these probability densities are then used to formulate a maximum-likelihood estimation framework for visual search and target detection for automatic object recognition. this learning technique is tested in experiments with modeling and subsequent detection of human faces and non-rigid objects such as hands.