waashk/dblp huggingface dataset:
image segmentation using co-em strategy. inspired by the idea of multi-view, we proposed an image segmentation algorithm using co-em strategy in this paper. image data are modeled using gaussian mixture model (gmm), and two sets of features, i.e. two views, are employed using co-em strategy instead of conventional single view based em to estimate the parameters of gmm. compared with the single view based gmm-em methods, there are several advantages with the proposed segmentation method using co-em strategy. first, imperfectness of single view can be compensated by the other view in the co-em. second, employing two views, co-em strategy can offer more reliability to the segmentation results. third, the drawback of local optimality for single view based em can be overcome to some extent. fourth, the convergence rate is improved. the average time is far less than single view based methods. we test the proposed method on large number of images with no specified contents. the experimental results verify the above advantages, and outperform the single view based gmm-em segmentation methods.
efficiently solving the fractional trust region problem. normalized cuts has successfully been applied to a wide range of tasks in computer vision, it is indisputably one of the most popular segmentation algorithms in use today. a number of extensions to this approach have also been proposed, ones that can deal with multiple classes or that can incorporate a priori information in the form of grouping constraints. it was recently shown how a general linearly constrained normalized cut problem can be solved. this was done by proving that strong duality holds for the lagrangian relaxation of such problems. this provides a principled way to perform multi-class partitioning while enforcing any linear constraints exactly. the lagrangian relaxation requires the maximization of the algebraically smallest eigenvalue over a one-dimensional matrix sub-space. this is an unconstrained, piece-wise differentiable and concave problem. in this paper we show how to solve this optimization efficiently even for very large-scale problems. the method has been tested on real data with convincing results.
sequential l norm minimization for triangulation. it has been shown that various geometric vision problems such as triangulation and pose estimation can be solved optimally by minimizing l∞ error norm. this paper proposes a novel algorithm for sequential estimation. when a measurement is given at a time instance, applying the original batch bi-section algorithm is very much inefficient because the number of seocnd order constraints increases as time goes on and hence the computational cost increases accordingly. this paper shows that, the upper and lower bounds, which are two input parameters of the bi-section method, can be updated through the time sequence so that the gap between the two bounds is kept as small as possible. furthermore, we may use only a subset of all the given measurements for the l∞ estimation. this reduces the number of constraints drastically. finally, we do not have to reestimate the parameter when the reprojection error of the measurement is smaller than the estimation error. these three provide a very fast l∞ estimation through the sequence; our method is suitable for real-time or on-line sequential processing under l∞ optimality. this paper particularly focuses on the triangulation problem, but the algorithm is general enough to be applied to any l∞ problems.
transformesh : a topology-adaptive mesh-based approach to surface evolution. most of the algorithms dealing with image based 3-d reconstruction involve the evolution of a surface based on a minimization criterion. the mesh parametrization, while allowing for an accurate surface representation, suffers from the inherent problems of not being able to reliably deal with selfintersections and topology changes. as a consequence, an important number of methods choose implicit representations of surfaces, e.g. level set methods, that naturally handle topology changes and intersections. nevertheless, these methods rely on space discretizations, which introduce an unwanted precision-complexity trade-off. in this paper we explore a new mesh-based solution that robustly handles topology changes and removes self intersections, therefore overcoming the traditional limitations of this type of approaches. to demonstrate its efficiency, we present results on 3-d surface reconstruction from multiple images and compare them with state-of-the art results.
analyzing the influences of camera warm-up effects on image acquisition. this article presents an investigation of the impact of camera warmup on the image acquisition process and therefore on the accuracy of segmented image features. based on an experimental study we show that the camera image is shifted to an extent of some tenth of a pixel after camera start-up. the drift correlates with the temperature of the sensor board and stops when the camera reaches its thermal equilibrium. a further study of the observed image flow shows that it originates from a slight displacement of the image sensor due to thermal expansion of the mechanical components of the camera. this sensor displacement can be modeled using standard methods of projective geometry in addition with bi-exponential decay terms to model the temporal dependence. the parameters of the proposed model can be calibrated and then used to compensate warmup effects. further experimental studies show that our method is applicable to different types of cameras and that the warm-up behaviour is characteristic for a specific camera.
converting thermal infrared face images into normal gray-level images. in this paper, we address the problem of producing visible spectrum facial images as we normally see by using thermal infrared images. we apply canonical correlation analysis (cca) to extract the features, converting a many-to-many mapping between infrared and visible images into a one-to-one mapping approximately. then we learn the relationship between two feature spaces in which the visible features are inferred from the corresponding infrared features using locally-linear regression (llr) or, what is called, sophisticated lle, and a locally linear embedding (lle) method is used to recover a visible image from the inferred features, recovering some information lost in the infrared image. experiments demonstrate that our method maintains the global facial structure and infers many local facial details from the thermal infrared images.
face mosaicing for pose robust video-based recognition. this paper proposes a novel face mosaicing approach to modeling human facial appearance and geometry in a unified framework. the human head geometry is approximated with a 3d ellipsoid model. multi-view face images are back projected onto the surface of the ellipsoid, and the surface texture map is decomposed into an array of local patches, which are allowed to move locally in order to achieve better correspondences among multiple views. finally the corresponding patches are trained to model facial appearance. and a deviation model obtained from patch movements is used to model the face geometry. our approach is applied to pose robust face recognition. using the cmu pie database, we show experimentally that the proposed algorithm provides better performance than the baseline algorithms. we also extend our approach to video-based face recognition and test it on the face in action database.
simultaneous appearance modeling and segmentation for matching people under occlusion. we describe an approach to segmenting foreground regions corresponding to a group of people into individual humans. given background subtraction and ground plane homography, hierarchical parttemplate matching is employed to determine a reliable set of human detection hypotheses, and progressive greedy optimization is performed to estimate the best configuration of humans under a bayesian map framework. then, appearance models and segmentations are simultaneously estimated in an iterative sampling-expectation paradigm. each human appearance is represented by a nonparametric kernel density estimator in a joint spatial-color space and a recursive probability update scheme is employed for soft segmentation at each iteration. additionally, an automatic occlusion reasoning method is used to determine the layered occlusion status between humans. the approach is evaluated on a number of images and videos, and also applied to human appearance matching using a symmetric distance measure derived from the kullback-leiber divergence.
crystal vision-applications of point groups in computer vision. methods from the representation theory of finite groups are used to construct efficient processing methods for the special geometries related to the finite subgroups of the rotation group. we motivate the use of these subgroups in computer vision, summarize the necessary facts from the representation theory and develop the basics of fourier theory for these geometries. we illustrate its usage for data compression in applications where the processes are (on average) symmetrical with respect to these groups. we use the icosahedral group as an example since it is the largest finite subgroup of the 3d rotation group. other subgroups with fewer group elements can be studied in exactly the same way.
three-stage motion deblurring from a video. in this paper, a novel approach is proposed to remove the motion blur from a video, which is degraded and distorted by fast camera motion. our approach is based on the image statistics rather than the traditional motion estimation. the image statistics has been successfully applied for blind motion deblurring for a single image by fergus et al [3] and levin [10]. here a three-stage method is used to deal with the video. first, the "unblurred" frames in the video can be found based on the image statistics. then the blur functions can be obtained by comparing the blurred frames with the unblurred ones. finally a standard deconvolution algorithm is used to reconstruct the video. our experiments show that our algorithms are efficient.
interpolation between eigenspaces using rotation in multiple dimensions. we propose a method for interpolation between eigenspaces. techniques that represent observed patterns as multivariate normal distribution have actively been developed to make it robust over observation noises. in the recognition of images that vary based on continuous parameters such as camera angles, one cause that degrades performance is training images that are observed discretely while the parameters are varied continuously. the proposed method interpolates between eigenspaces by analogy from rotation of a hyper-ellipsoid in high dimensional space. experiments using face images captured in various illumination conditions demonstrate the validity and effectiveness of the proposed interpolation method.
sign recognition using constrained optimization. sign recognition has been one of the challenging problems in computer vision for years. for many sign languages, signs formed by two overlapping hands are a part of the vocabulary. in this work, an algorithm for recognizing such signs with overlapping hands is presented. two formulations are proposed for the problem. for both approaches, the input blob is converted to a graph representing the finger and palm structure which is essential for sign understanding. the first approach uses a graph subdivision as the basic framework, while the second one casts the problem to a label assignment problem and integer programming is applied for finding an optimal solution. experimental results are shown to illustrate the feasibility of our approaches.
automatic range image registration using mixed integer linear programming. a coarse registration method using mixed integer linear programming (milp) is described that finds global optimal registration parameter values that are independent of the values of invariant features. we formulate the range image registration problem using milp. our algorithm using milp formulation finds the best balanced optimal registration for robustly aligning two range images with the best balanced accuracy. it adjusts the error tolerance automatically in accordance with the accuracy of the given range image data. experimental results show that this method of coarse registration is highly effective.
an all-subtrees approach to unsupervised parsing. we investigate generalizations of the all-subtrees "dop" approach to unsupervised parsing. unsupervised dop models assign all possible binary trees to a set of sentences and next use (a large random subset of) all subtrees from these binary trees to compute the most probable parse trees. we will test both a relative frequency estimator for unsupervised dop and a maximum likelihood estimator which is known to be statistically consistent. we report state-of-the-art results on english (wsj), german (negra) and chinese (ctb) data. to the best of our knowledge this is the first paper which tests a maximum likelihood estimator for dop on the wall street journal, leading to the surprising result that an unsupervised parsing model beats a widely used supervised model (a treebank pcfg).
spoken dialogue interpretation with the dop model. we show how the dop model can be used for fast and robust processing of spoken input in a practical spoken dialogue system called ovis. ovis, openbaar vervoer informatie systeem ("public transport information system"), is a dutch spoken language information system which operates over ordinary telephone lines. the prototype system is the immediate goal of the nwo1 priority programme "language and speech technology". in this paper, we extend the original dop model to context-sensitive interpretation of spoken input. the system we describe uses the ovis corpus (10,000 trees enriched with compositional semantics) to compute from an input word-graph the best utterance together with its meaning. dialogue context is taken into account by dividing up the ovis corpus into context-dependent subcorpora. each system question triggers a subcorpus by which the user answer is analyzed and interpreted. our experiments indicate that the context-sensitive dop model obtains better accuracy than the original model, allowing for fast and robust processing of spoken input.
a bootstrapping approach to unsupervised detection of cue phrase variants. we investigate the unsupervised detection of semi-fixed cue phrases such as "this paper proposes a novel approach...1" from unseen text, on the basis of only a handful of seed cue phrases with the desired semantics. the problem, in contrast to bootstrapping approaches for question answering and information extraction, is that it is hard to find a constraining context for occurrences of semi-fixed cue phrases. our method uses components of the cue phrase itself, rather than external context, to bootstrap. it successfully excludes phrases which are different from the target semantics, but which look superficially similar. the method achieves 88% accuracy, outperforming standard bootstrapping approaches.
a probabilistic corpus-driven model for lexical-functional analysis. we develop a data-oriented parsing (dop) model based on the syntactic representations of lexical-functional grammar (lfg). we start by summarizing the original dop model for tree representations and then show how it can be extended with corresponding functional structures. the resulting lfg-dop model triggers a new, corpus-based notion of grammaticality, and its probability models exhibit interesting behavior with respect to specificity and the interpretation of ill-formed strings.
polynominal learnability and locality of formal grammars. we apply a complexity theoretic notion of feasible learnability called "polynomial learnability" to the evaluation of grammatical formalisms for linguistic description. we show that a novel, nontrivial constraint on the degree of "locality" of grammars allows not only context free languages but also a rich class of mildy context sensitive languages to be polynomially learnable. we discuss possible implications of this result to the theory of natural language acquisition.
lexical and syntactic rules in a tree adjoining grammar. taking examples from english and french idioms, this paper shows that not only constituent structures rules but also most syntactic rules (such as topicalization, wh-question, pronominalization ...) are subject to lexical constraints (on top of syntactic, and possibly semantic, ones). we show that such puzzling phenomena are naturally handled in a 'lexicalized' formalism such as tree adjoining grammar. the extended domain of locality of tags also allows one to 'jexicalize' syntactic rules while defining them at the level of constituent structures.
japanese dependency parsing using co-occurrence information and a combination of case elements. in this paper, we present a method that improves japanese dependency parsing by using large-scale statistical information. it takes into account two kinds of information not considered in previous statistical (machine learning based) parsing methods: information about dependency relations among the case elements of a verb, and information about co-occurrence relations between a verb and its case element. this information can be collected from the results of automatic dependency parsing of large-scale corpora. the results of an experiment in which our method was used to rerank the results obtained using an existing machine learning based parsing method showed that our method can improve the accuracy of the results obtained using the existing method.
construct algebra: analytical dialog management. in this paper we describe a systematic approach for creating a dialog management system based on a construct algebra, a collection of relations and operations on a task representation. these relations and operations are analytical components for building higher level abstractions called dialog motivators. the dialog manager, consisting of a collection of dialog motivators, is entirely built using the construct algebra.
scaling to very very large corpora for natural language disambiguation. the amount of readily available on-line text has reached hundreds of billions of words and continues to grow. yet for most core natural language tasks, algorithms continue to be optimized, tested and compared after training on corpora consisting of only one million words or less. in this paper, we evaluate the performance of different learning methods on a prototypical natural language disambiguation task, confusion set disambiguation, when trained on orders of magnitude more labeled data than has previously been used. we are fortunate that for this particular application, correctly labeled training data is free. since this will often not be the case, we examine methods for effectively exploiting very large corpora when labeled data comes at a cost.
bootstrapping. this paper refines the analysis of co-training, defines and evaluates a new co-training algorithm that has theoretical justification, gives a theoretical justification for the yarowsky algorithm, and shows that co-training and the yarowsky algorithm are based on different independence assumptions.
headline generation based on statistical translation. extractive summarization techniques cannot generate document summaries shorter than a single sentence, something that is often required. an ideal summarization system would understand each document and generate an appropriate summary directly from the results of that understanding. a more practical approach to this problem results in the use of an approximation: viewing summarization as a problem analogous to statistical machine translation. the issue then becomes one of generating a target document in a more concise language from a source document in a more verbose language. this paper presents results on experiments using this approach, in which statistical models of the term selection and term ordering are jointly applied to produce summaries in a style learned from a training corpus.
relating probabilistic grammars and automata. both probabilistic context-free grammars (pcfgs) and shift-reduce probabilistic pushdown automata (ppdas) have been used for language modeling and maximum likelihood parsing. we investigate the precise relationship between these two formalisms, showing that, while they define the same classes of probabilistic languages, they appear to impose different inductive biases.
paraphrasing with bilingual parallel corpora. previous work has used monolingual parallel corpora to extract and generate paraphrases. we show that this task can be done using bilingual parallel corpora, a much more commonly available resource. using alignment techniques from phrase-based statistical machine translation, we show how paraphrases in one language can be identified using a phrase in another language as a pivot. we define a paraphrase probability that allows paraphrases extracted from a bilingual parallel corpus to be ranked using translation probabilities, and show how it can be refined to take contextual information into account. we evaluate our paraphrase extraction and ranking methods using a set of manual word alignments, and contrast the quality with paraphrases extracted from automatic alignments.
an unsupervised morpheme-based hmm for hebrew morphological disambiguation. morphological disambiguation is the process of assigning one set of morphological features to each individual word in a text. when the word is ambiguous (there are several possible analyses for the word), a disambiguation procedure based on the word context must be applied. this paper deals with morphological disambiguation of the hebrew language, which combines morphemes into a word in both agglutinative and fusional ways. we present an un-supervised stochastic model - the only resource we use is a morphological analyzer-which deals with the data sparseness problem caused by the affixational morphology of the hebrew language.we present a text encoding method for languages with affixational morphology in which the knowledge of word formation rules (which are quite restricted in hebrew) helps in the disambiguation. we adapt hmm algorithms for learning and searching this text representation, in such a way that segmentation and tagging can be learned in parallel in one step. results on a large scale evaluation indicate that this learning improves disambiguation for complex tag sets. our method is applicable to other languages with affix morphology.
evaluation tool for rule-based anaphora resolution methods. in this paper we argue that comparative evaluation in anaphora resolution has to be performed using the same pre-processing tools and on the same set of data. the paper proposes an evaluation environment for comparing anaphora resolution algorithms which is illustrated by presenting the results of the comparative evaluation of three methods on the basis of several evaluation measures.
evaluation of semantic clusters. semantic clusters of a domain form an important feature that can be useful for performing syntactic and semantic disambiguation. several attempts have been made to extract the semantic clusters of a domain by probabilistic or taxonomic techniques. however, not much progress has been made in evaluating the obtained semantic clusters. this paper focuses on an evaluation mechanism that can be used to evaluate semantic clusters produced by a system against those provided by human experts.