waashk/dblp huggingface dataset:
multi-scale classification of 3-d objects. we describe an approach to the classification of 3-d objects using a multi-scale representation. this approach starts with a smoothing algorithm for representing objects at different scales. smoothing is applied in curvature space directly, thus avoiding the usual shrinkage problems and allowing for efficient implementations. a 3-d similarity measure that integrates the representations of the objects at multiple scales is introduced. given a library of models, objects that are similar based on this multi-scale measure are grouped together into classes. the objects that are in the same class are combined into a single prototype object. finally, the prototypes are used for hierarchical recognition by first comparing the scene representation to the prototypes and then matching it only to the objects in the most likely class rather than to the entire library of models. beyond its application to object recognition, this approach provides an attractive implementation of the intuitive notions of scale and approximate similarity for 3-d shapes.
isotropic processing for gradient estimation. this paper concerns the influence of edge direction on the estimation of edge contrast and orientation. we show that the gradient estimated using radial filters is not affected by edge orientation. for non-radial filters the gradient can be affected by edge orientation. for instance, we find that the estimated edge orientation using a non-radial filter may be biased, even if the signal is noise-free. however, there are non-radial filters for which gradient is unaffected by edge orientation as in the case of radial filters. the properties of these functions are given in this paper. the results are illustrated by the study of the canny, deriche, and shen & castan detectors. we take into account discretization errors. these results give a clear indication of the effect of the rotation invariance property of an edge detector on its response, thus providing a more precise meaning for this property in edge detection.
using geometric corners to build a 2d mosaic from a set of image. the main problem for building a mosaic is the computation of the warping functions (homographies). in fact two cases are to be distinguished. the first is when the homography is mainly a translation (i.e. the rotation around the optical axis and the zooming factor are small). the second is the general case (when the rotation around the optical axis and zooming are arbitrary). some efficient methods have been developed to solve the first case. but the second case is more difficult, in particular, when the rotation around the optical axis is very large (90 degrees or more). often in this case human interaction is needed to provide a first approximation of the transformation that will bring us back to the first case. in this article we present a method to solve this problem without human interaction for any rotation around the optical axis and fairly large zooming factors.
self-maintaining camera calibration over time. the success of an intelligent robotic system depends on the performance of its vision-system which in turn depends to a great extend upon the quality of its calibration. during the execution of a task the vision-system is subject to external influences such as vibrations, thermal expansion etc. which affect and possibly render invalid the initial calibration. moreover, it is possible that the parameters of the vision-system like e.g. the zoom or the focus are altered intentionally in order to perform specific vision-tasks. this paper describes a technique for automatically maintaining calibration of stereovision systems over time without using again any particular calibration apparatus. it uses all available information, i.e. both spatial and temporal data. uncertainty is systematically manipulated and maintained. synthetical and real data are used to validate the proposed technique, and the results compare very favourably with those given by classical calibration methods.
nonlinear dynamical shape priors for level set segmentation. the introduction of statistical shape knowledge into level set based segmentation methods was shown to improve the segmentation of familiar structures in the presence of noise, clutter or partial occlusions. while most work has been focused on shape priors which are constant in time, it is clear that when tracking deformable shapes certain silhouettes may become more or less likely over time. in fact, the deformations of familiar objects such as the silhouettes of a walking person are often characterized by pronounced temporal correlations.in this paper, we propose a nonlinear dynamical shape prior for level set based image segmentation. specifically, we propose to approximate the temporal evolution of the eigenmodes of the level set function by means of a mixture of autoregressive models. we detail how such shape priors "with memory" can be integrated into a variational framework for level set segmentation. as an application, we experimentally validate that the nonlinear dynamical prior drastically improves the tracking of a person walking in different directions, despite large amounts of clutter and noise.
spectral matting. we present spectral matting: a new approach to natural image matting that automatically computes a basis set of fuzzy matting components from the smallest eigenvectors of a suitably defined laplacian matrix. thus, our approach extends spectral segmentation techniques, whose goal is to extract hard segments, to the extraction of soft matting components. these components may then be used as building blocks to easily construct semantically meaningful foreground mattes, either in an unsupervised fashion, or based on a small amount of user input.
bottom-up recognition and parsing of the human body. recognizing humans, estimating their pose and segmenting their body parts are key to high-level image understanding. because humans are highly articulated, the range of deformations they undergo makes this task extremely challenging. previous methods have focused largely on heuristics or pairwise part models in approaching this problem. we propose a bottom-up growing, similar to parsing, of increasingly more complete partial body masks guided by a set of parse rules. at each level of the growing process, we evaluate the partial body masks directly via shape matching with exemplars (and also image features), without regard to how the hypotheses are formed. the body is evaluated as a whole, not the sum of its parts, unlike previous approaches. multiple image segmentations are included at each of the levels of the growing/parsing, to augment existing hypotheses or to introduce ones. our method yields both a pose estimate as well as a segmentation of the human. we demonstrate competitive results on this challenging task with relatively few training examples on a dataset of baseball players with wide pose variation. our method is comparatively simple and could be easily extended to other objects. we also give a learning framework for parse ranking that allows us to keep fewer parses for similar performance.
efficient mrf deformation model for non-rigid image matching. we propose a novel mrf-based model for deformable image matching (also known as registration). the deformation is described by a field of discrete variables, representing displacements of (blocks of) pixels. discontinuities in the deformation are prohibited by imposing hard pairwise constraints in the model. exact maximum a posteriori inference is intractable and we apply a linear programming relaxation technique. we show that, when reformulated in the form of two coupled fields of x- and y-displacements, the problem leads to a simpler relaxation to which we apply the sequential tree-reweighted message passing (trw-s) algorithm [wainwright-03, kolmogorov-05]. this enables image registration with large displacements at a single scale. we employ fast message updates for a special type of interaction as was proposed [felzenszwalb and huttenlocher-04] for the max-product belief propagation (bp) and introduce a few independent speedups. in contrast to bp, the trw-s allows us to compute per-instance approximation ratios and thus to evaluate the quality of the optimization. the performance of our technique is demonstrated on both synthetic and real-world experiments.
on controlling light transport in poor visibility environments. poor visibility conditions due to murky water, bad weather, dust and smoke severely impede the performance of vision systems. passive methods have been used to restore scene contrast under moderate visibility by digital postprocessing. however, these methods are ineffective when the quality of acquired images is poor to begin with. in this work, we design active lighting and sensing systems for controlling light transport before image formation, and hence obtain higher quality data. first, we present a technique of polarized light striping based on combining polarization imaging and structured light striping. we show that this technique out-performs different existing illumination and sensing methodologies. second, we present a numerical approach for computing the optimal relative sensor-source position, which results in the best quality image. our analysis accounts for the limits imposed by sensor noise.
epitomic location recognition. this paper presents a novel method for location recognition, which exploits an epitomic representation to achieve both high efficiency and good generalization. a generative model based on epitomic image analysis captures the appearance and geometric structure of an environment while allowing for variations due to motion, occlusions, and non-lambertian effects. the ability to model translation and scale invariance together with the fusion of diverse visual features yields enhanced generalization with economical training. experiments on both existing and new labeled image databases result in recognition accuracy superior to state of the art with real-time computational performance.
accurate camera calibration from multi-view stereo and bundle adjustment. the advent of high-resolution digital cameras and sophisticated multi-view stereo algorithms offers the promise of unprecedented geometric fidelity in image-based modeling tasks, but it also puts unprecedented demands on camera calibration to fulfill these promises. this paper presents a novel approach to camera calibration where top-down information from rough camera parameter estimates and the output of a multi-view-stereo system on scaled-down input images is used to effectively guide the search for additional image correspondences and significantly improve camera calibration parameters using a standard bundle adjustment algorithm (lourakis and argyros 2008). the proposed method has been tested on six real datasets including objects without salient features for which image correspondences cannot be found in a purely bottom-up fashion, and objects with high curvature and thin structures that are lost in visual hull construction even with small errors in camera parameters. three different methods have been used to qualitatively assess the improvements of the camera parameters. the implementation of the proposed algorithm is publicly available at furukawa and ponce (2008b).
global stereo reconstruction under second order smoothness priors. second-order priors on the smoothness of 3d surfaces are a better model of typical scenes than first-order priors. however, stereo reconstruction using global inference algorithms, such as graph cuts, has not been able to incorporate second-order priors because the triple cliques needed to express them yield intractable (nonsubmodular) optimization problems. this paper shows that inference with triple cliques can be effectively performed. our optimization strategy is a development of recent extensions to \alpha--expansion, based on the “ qpbo” algorithm. the strategy is to repeatedly merge proposal depth maps using a novel extension of qpbo. proposal depth maps can come from any source, for example, frontoparallel planes as in \alpha-expansion, or indeed any existing stereo algorithm, with arbitrary parameter settings.
efficient sequential correspondence selection by cosegmentation. in many retrieval, object recognition, and wide-baseline stereo methods, correspondences of interest points (distinguished regions) are commonly established by matching compact descriptors such as sifts. we show that a subsequent cosegmentation process coupled with a quasi-optimal sequential decision process leads to a correspondence verification procedure that 1) has high precision (is highly discriminative), 2) has good recall, and 3) is fast. the sequential decision on the correctness of a correspondence is based on simple statistics of a modified dense stereo matching algorithm. the statistics are projected on a prominent discriminative direction by svm. wald's sequential probability ratio test is performed on the svm projection computed on progressively larger cosegmented regions. we show experimentally that the proposed sequential correspondence verification (scv) algorithm significantly outperforms the standard correspondence selection method based on sift distance ratios on challenging matching problems.
robust higher order potentials for enforcing label consistency. this paper proposes a novel framework for labelling problems which is able to combine multiple segmentations in a principled manner. our method is based on higher order conditional random fields and uses potentials defined on sets of pixels (image segments) generated using unsupervised segmentation algorithms. these potentials enforce label consistency in image regions and can be seen as a generalization of the commonly used pairwise contrast sensitive smoothness potentials. the higher order potential functions used in our framework take the form of the robust p n model and are more general than the p n potts model recently proposed by kohli et al. we prove that the optimal swap and expansion moves for energy functions composed of these potentials can be computed by solving a st-mincut problem. this enables the use of powerful graph cut based move making algorithms for performing inference in the framework. we test our method on the problem of multi-class object segmentation by augmenting the conventional crf used for object segmentation with higher order potentials defined on image regions. experiments on challenging data sets show that integration of higher order potentials quantitatively and qualitatively improves results leading to much better definition of object boundaries. we believe that this method can be used to yield similar improvements for many other labelling problems.
cost-sensitive face recognition. most traditional face recognition systems attempt to achieve a low recognition error rate, implicitly assuming that the losses of all misclassifications are the same. in this paper, we argue that this is far from a reasonable setting because, in almost all application scenarios of face recognition, different kinds of mistakes will lead to different losses. for example, it would be troublesome if a door locker based on a face recognition system misclassified a family member as a stranger such that she/he was not allowed to enter the house, but it would be a much more serious disaster if a stranger was misclassified as a family member and allowed to enter the house. we propose a framework which formulates the face recognition problem as a multiclass cost-sensitive learning task, and develop two theoretically sound methods for this task. experimental results demonstrate the effectiveness and efficiency of the proposed methods.
generating referring expressions involving relations. in this paper, we review dale's [1989] algorithm for determining the content of a referring expression. the algorithm, which only permits the use of one-place predicates, it revised and extended to deal with n-ary predicates. we investigate the problem of blocking "recursion" in complex noun phrases and propose a solution in the context of our algorithm.
disjunctions and inheritance in the context feature structure system. substantial efforts have been made in order to cope with disjunctions in constraint based grammar formalisms (e.g. [kasper, 1987; maxwell and kaplan, 1991; d&ouml;rre and eisele, 1990].). this paper describes the roles of disjunctions and inheritance in the use of feature structures and their formal semantics. with the notion of contexts we abstract from the graph structure of feature structures and properly define the search space of alternatives. the graph unification algorithm precomputes nogood combinations, and a specialized search procedure which we propose here uses them as a controlling factor in order to delay decisions as long as there is no logical necessity for deciding.
the incremental generation of passive sentences. this paper sketches some basic features of the synphonics account of the computational modelling of incremental language production with the example of the generation of passive sentences. the synphonics approach aims at linking psycholinguistic insights into the nature of the human natural language production process with well-established assumptions in theoretical and computational linguistics concerning the representation and processing of grammatical knowledge. we differentiate between two possible kinds of stimuli within the generation process that trigger the formation of passive sentences: a formulator-external stimulus and a formulator-internal one. the formulator-external stimulus is determined by the conceptual/contextual condition of agent backgrounding: an agentless semantic representation is verbalized by way of constructing an ergativized verbal complex in the morphological structure-building component, rather than by mapping the semantic representation directly onto a passive lemma. the formulator-internal stimulus is an effect of the constraints of rapid, incremental utterance production; in particular, it causes the formulator to integrate a thematically underspecified increment in a prominent structural enviornment. in this case, the formation of passives is a matter of an additional constraint on the lemma selection process: lemma selection is constrained by the structural representation of the utterance produced so far.
stochastic hpsg. in this paper we provide a probabilistic interpretation for typed feature structures very similar to those used by pollard and sag. we begin with a version of the interpretation which lacks a treatment of re-entrant feature structures, then provide an extended interpretation which allows them. we sketch algorithms allowing the numerical parameters of our probabilistic interpretations of hpsg to be estimated from corpora.
parsing idioms in lexicalized tags. we show how idioms can be parsed in lexicalized tags. we rely on extensive studies of frozen phrases pursued at l.a.d.l. that show that idioms are pervasive in natural language and obey, generally speaking, the same morphological and syntactical patterns as 'free' structures. by idiom we mean a structure in which some items are lexically frozen and have a semantics that is not compositional. we thus consider idioms of different syntactic categories: np, s, adverbials, compound prepositions... in both english and french.in lexicalized tags, the same grammar is used for idioms as for 'free' sentences. we assign them regular syntactic structures while representing them semantically as one non-compositional entry. syntactic transformations and insertion of modifiers may thus apply to them as to any 'free' structures. unlike previous approaches, their variability becomes the general case and their being totally frozen the exception. idioms are generally represented by extended elementary trees with 'heads' made out of several items (that need not be contiguous) with one of the items serving as an index. when an idiomatic tree is selected by this index, lexical items are attached to some nodes in the tree. idiomatic trees are selected by a single head node however the head value imposes lexical values on other nodes in the tree. this operation of attaching the head item of an idiom and its lexical parts is called lexical attachment. the resulting tree has the lexical items corresponding to the pieces of the idiom already attached to it.we generalize the parsing strategy defined for lexicalized tag to the case of 'heads' made out of several items. we propose to parse idioms in two steps which are merged in the two steps parsing strategy that is defined for 'free' sentences. the first step performed during the lexical pass selects trees corresponding to the literal and idiomatic interpretation. however it is not always the case that the idiomatic trees are selected as possible candidates. we require that all basic pieces building the minimal idiomatic expression must be present in the input string (with possibly some order constraints). this condition is a necessary condition for the idiomatic reading but of course it is not sufficient. the second step performs the syntax analysis as in the usual case. during the second step, idiomatic reading might be rejected. idioms are thus parsed as any 'free' sentences. except during the selection process, idioms do not require any special parsing mechanism. we are also able to account for cases of ambiguity between idiomatic and literal interpretations.factoring recursion from dependencies in tags allows discontinuous constituents to be parsed in an elegant way. we also show how regular 'transformations' are taken into account by the parser.
deterministic parsing and unbounded dependencies. this paper assesses two new approaches to deterministic parsing with respect to the analysis of unbounded dependencies (uds). uds in english are highly locally (and often globally) ambiguous. several researchers have argued that the difficulty of uds undermines the programme of deterministic parsing. however, their conclusion is based on critiques of various versions of the marcus parser which represents only one of many possible approaches to deterministic parsing. we examine the predictions made by a lr(1) deterministic parser and the lexicat deterministic parser concerning the analysis of uds. the lr(1) technique is powerful enough to resolve the local ambiguities we examine. however, the lexicat model provides a more psychologically plausible account of the parsing of uds, which also offers a unified account of the resolution of local and global ambiguities in these constructions.
a multi-purpose interface to an on-line dictionary. we argue that there are two qualitatively different modes of using a machine-readable dictionary in the context of research in computational linguistics: batch processing of the source with the purpose of collating information for subsequent use by a natural language application, and placing the dictionary on-line in an environment which supports fast interactive access to data selected on the basis of a number of linguistic constraints. while it is the former mode of dictionary use which is characteristic of most computational linguistics work to date, it is the latter which has the potential of making maximal use of the information typically found in a machine-readable dictionary. we describe the mounting of the machine-readable source of the longman dictionary of contemporary english on a single user workstation to make it available as a development tool for a number of research projects.
evaluating and combining approaches to selectional preference acquisition. previous work on the induction of selectional preferences has been mainly carried out for english and has concentrated almost exclusively on verbs and their direct objects. in this paper, we focus on class-based models of selectional preferences for german verbs and take into account not only direct objects, but also subjects and prepositional complements. we evaluate model performance against human judgments and show that there is no single method that overall performs best. we explore a variety of parametrizations for our models and demonstrate that model combination enhances agreement with human ratings.