waashk/dblp huggingface dataset:
fast 3-d interpretation from monocular image sequences on large motion fields. this paper proposes a fast method for dense 3-d interpretation to directly estimate a dense map of relative depth and motion from a monocular sequence of images on large motion fields. the nagel-enkelmann technique is employed in the variational formulation of the problem. diffusion-reaction equations are derived from the formulation so as to approximate the dense map on large motion fields and realize an anisotropic diffusion to preserve the discontinuities of the map. by combining the ideas of implicit schemes and multigrid methods, we present a new implicit multigrid block gauss-seidel relaxation scheme, which dramatically reduces the computation time for solving the largescale linear system of diffusion-reaction equations. using our method, we perform fast 3-d interpretation of image sequences with large motion fields. the efficiency and effectiveness of our method are experimentally verified with synthetic and real image sequences.
a regularized approach to feature selection for face detection. in this paper we present a trainable method for selecting features from an overcomplete dictionary of measurements. the starting point is a thresholded version of the landweber algorithm for providing a sparse solution to a linear system of equations. we consider the problem of face detection and adopt rectangular features as an initial representation for allowing straightforward comparisons with existing techniques. for computational efficiency and memory requirements, instead of implementing the full optimization scheme on tenths of thousands of features, we propose to first solve a number of smaller size optimization problems obtained by randomly sub-sampling the feature vector, and then recombining the selected features. the obtained set is still highly redundant, so we further apply feature selection. the final feature selection system is an efficient two-stages architecture. experimental results of an optimized version of the method on face images and image sequences indicate that this method is a serious competitor of other feature selection schemes recently popularized in computer vision for dealing with problems of real time object detection.
adaptively determining degrees of implicit polynomial curves and surfaces. fitting an implicit polynomial (ip) to a data set usually suffers from the difficulty of determining a moderate polynomial degree. an over-low degree leads to inaccuracy than one expects, whereas an overhigh degree leads to global instability. we propose a method based on automatically determining the moderate degree in an incremental fitting process through using qr decomposition. this incremental process is computationally efficient, since by reusing the calculation result from the previous step, the burden of calculation is dramatically reduced at the next step. simultaneously, the fitting instabilities can be easily checked out by judging the eigenvalues of an upper triangular matrix from qr decomposition, since its diagonal elements are equal to the eigenvalues. based on this beneficial property and combining it with tasdizen's ridge regression method, a new technique is also proposed for improving fitting stability.
iris tracking and regeneration for improving nonverbal interface. in this study, we discuss the quality of teleconference with respect to especially to the "eye-contact". recently, video conference system can be used easily even in the mobile phone environment with camera, and many people use it in daily life. since human is likely to look at the face of his partner on monitor not at camera, he will usually fail to send his own eye-contacted facial images to him, and vice versa. we pay attention to the disagreement of the eye-contact in teleconference caused by the separation between the input camera and output monitor devices. then we propose the eye-contact camera system for generating eye-contacted motion images to the receiver. in this system, iris contour is extracted after the face region extraction, the vertical and horizontal directions of the glance are calculated based on the relation among positions of the monitor, camera and receiver, and finally the iris center coordinates are shifted in the image so that the partner looks just looking at him, and vice versa. we implemented the system on note pc with web-camera for evaluating the usability.
multiple view geometry for non-rigid motions viewed from translational cameras. this paper introduces multiple view geometry under projective projections from four-dimensional space to two-dimensional space which can represent multiple view geometry under the projection of space with time. we show the multifocal tensors defined under space-time projective projections can be derived from non-rigid object motions viewed from multiple cameras with arbitrary translational motions, and they are practical for generating images of non-rigid object motions viewed from cameras with arbitrary translational motions. the method is tested in real image sequences.
shape representation and classification using boundary radius function. in this paper, a new method for the problem of shape representation and classification is proposed. in this method, we define a radius function on the contour of the shape which captures for each point of the boundary, attributes of its related internal part of the shape. we call these attributes as "depth" of the point. depths of boundary points generate a descriptor sequence which represents the shape. matching of sequences is performed using dynamic programming method and a distance measure is acquired. at last, different classes of shapes are classified using a hierarchical clustering method and the distance measure. the proposed method can analyze features of each part of the shape locally which this leads to the ability of part analysis and insensitivity to local deformations such as articulation, occlusion and missing parts. we show high efficiency of the proposed method by evaluating it for shape matching and classification of standard shape datasets.
efficient graph cuts for multiclass interactive image segmentation. interactive image segmentation has attracted much attention in the vision and graphics community recently. a typical application for interactive image segmentation is foreground/background segmentation based on user specified brush labellings. the problem can be formulated within the binary markov random field (mrf) framework which can be solved efficiently via graph cut [1]. however, no attempt has yet been made to handle segmentation of multiple regions using graph cuts. in this paper, we propose a multiclass interactive image segmentation algorithm based on the potts mrf model. following [2], this can be converted to a multiway cut problem first proposed in [2] and solved by expansion-move algorithms for approximate inference [2]. a faster algorithm is proposed in this paper for efficient solution of the multiway cut problem based on partial optimal labeling. to achieve this, we combine the one-vs-all classifier fusion framework with the expansion-move algorithm for label inference over large images. we justify our approach with both theoretical analysis and experimental validation.
motion observability analysis of the simplified color correlogram for visual tracking. compared with the color histogram, where the position information of each pixel is ignored, a simplified color correlogram (scc) representation encodes the spatial information explicitly and enables an estimation algorithm to recover the object orientation. this paper analyzes the capability of the scc (in a kernel based framework) in detecting and estimating object motion and presents a principled way to obtain motion observable sccs as object representations to achieve more reliable tracking. extensive experimental results demonstrate the reliability of the tracking procedure using the proposed algorithm.
simultaneous plane extraction and 2d homography estimation using local feature transformations. in this paper, we use local feature transformations estimated in the matching process as initial seeds for 2d homography estimation. the number of testing hypotheses is equal to the number of matches, naturally enabling a full search over the hypothesis space. using this property, we develop an iterative algorithm that clusters the matches under the common 2d homography into one group, i.e., features on a common plane. our clustering algorithm is less affected by the proportion of inliers and as few as two features on the common plane can be clustered together; thus, the algorithm robustly detects multiple dominant scene planes. the knowledge of the dominant planes is used for robust fundamental matrix computation in the presence of quasi-degenerate data.
identifying foreground from multiple images. in this paper, we present a novel foreground extraction method that automatically identifies image regions corresponding to a common space region seen from multiple cameras. we assume that background regions present some color coherence in each image and we exploit the spatial consistency constraint that several image projections of the same space region must satisfy. integrating both color and spatial consistency constraints allows to fully automatically segment foreground and background regions in multiple images. in contrast to standard background subtraction approaches, the proposed approach does not require any a priori knowledge on the background nor user interactions. we demonstrate the effectiveness of the method for multiple camera setups with experimental results on standard real data sets.
a novel multi-stage classifier for face recognition. a novel face recognition scheme based on multi-stages classifier, which includes methods of support vector machine (svm), eigenface, and random sample consensus (ransac), is proposed in this paper. the whole decision process is conducted cascade coarse-to-fine stages. the first stage adopts one-against-one-svm (oao-svm) method to choose two possible classes best similar to the testing image. in the second stage, "eigenface" method was employed to select one prototype image with the minimum distance to the testing image in each of the two classes chosen. finally, the real class is determined by comparing the geometric similarity, as done by "ransac" method, between these prototype images and the testing images. this multi-stage face recognition system has been tested on olivetti research laboratory (orl) face databases, and its experimental results give evidence that the proposed approach outperforms the other approaches either based on the single classifier or multi-parallel classifier, it can even obtain a nearly 100 percent recognition accuracy.
conic fitting using the geometric distance. we consider the problem of fitting a conic to a set of 2d points. it is commonly agreed that minimizing geometrical error, i.e. the sum of squared distances between the points and the conic, is better than using an algebraic error measure. however, most existing methods rely on algebraic error measures. this is usually motivated by the fact that pointto-conic distances are difficult to compute and the belief that non-linear optimization of conics is computationally very expensive. in this paper, we describe a parameterization for the conic fitting problem that allows to circumvent the difficulty of computing point-to-conic distances, and we show how to perform the non-linear optimization process efficiently.
statistical framework for shot segmentation and classification in sports video. in this paper, a novel statistical framework is proposed for shot segmentation and classification. the proposed framework segments and classifies shots simultaneously using same difference features based on statistical inference. the task of shot segmentation and classification is taken as finding the most possible shot sequence given feature sequences, and it can be formulated by a conditional probability which can be divided into a shot sequence probability and a feature sequence probability. shot sequence probability is derived from relations between adjacent shots by bi-gram, and feature sequence probability is dependent on inherent character of shot modeled by hmm. thus, the proposed framework segments shot considering the character of intra-shot to classify shot, while classifies shot considering character of inter-shot to segment shot, which obtain more accurate results. experimental results on soccer and badminton videos are promising, and demonstrate the effectiveness of the proposed framework.
temporal priors for novel video synthesis. in this paper we propose a method to construct a virtual sequence for a camera moving through a static environment given an input sequence from a different camera trajectory. existing image-based rendering techniques can generate photorealistic images given a set of input views, though the output images almost unavoidably contain small regions where the colour has been incorrectly chosen. in a single image these artifacts are often hard to spot, but become more obvious when viewing a real image with its virtual stereo pair, and even more so when when a sequence of novel views is generated, since the artifacts are rarely temporally consistent. to address this problem of consistency, we propose a new spatiotemporal approach to novel video synthesis. the pixels in the output video sequence are modelled as nodes of a 3-d graph. we define an mrf on the graph which encodes photoconsistency of pixels as well as texture priors in both space and time. unlike methods based on scene geometry which yield highly connected graphs, our approach results in a graph whose degree is independent of scene structure. the mrf energy is therefore tractable and we solve it for the whole sequence using a stateof-the-art message passing optimisation algorithm. we demonstrate the effectiveness of our approach in reducing temporal artifacts.
face mis-alignment analysis by multiple-instance subspace. in this paper, we systematically study the effect of poorly registered faces on the training and inferring stages of traditional face recognition algorithms. we then propose a novel multiple-instance based subspace learning scheme for face recognition. in this approach, we iteratively update the subspace training instances according to diverse densities, using class-balanced supervised clustering. we test our multiple instance subspace learning algorithm with fisherface for the application of face recognition. experimental results show that the proposed learning algorithm can improve the robustness of current methods with poorly aligned training and testing data.
backward segmentation and region fitting for geometrical visibility range estimation. we present a new application of computer vision: continuous measurement of the geometrical visibility range on inter-urban roads, solely based on a monocular image acquisition system. to tackle this problem, we propose first a road segmentation scheme based on a parzen-windowing of a color feature space with an original update that allows us to cope with heterogeneously paved-roads, shadows and reflections, observed under various and changing lighting conditions. second, we address the under-constrained problem of retrieving the depth information along the road based on the flat word assumption. this is performed by a new region-fitting iterative least squares algorithm, derived from half-quadratic theory, able to cope with vanishing-point estimation, and allowing us to estimate the geometrical visibility range.
image correspondence from motion subspace constraint and epipolar constraint. in this paper, we propose a novel method for inferring image correspondences on the pair of synchronized image sequences. in the proposed method, after tracking the feature points in each image sequence over several frames, we solve the image corresponding problem from two types of geometrical constraints: (1) the motion subspace obtained from the tracked feature points of a target sequence, and (2) the epipolar constraints between the two cameras. dissimilarly to the conventional correspondence estimation based on image matching using pixel values, the proposed approach enables us to obtain the correspondences even though the feature points, that can be seen from one camera view, but can not be seen (occluded or outside of the view) from the other camera. the validity of our method is demonstrated through the experiments using synthetic and real images.
analyzing facial expression by fusing manifolds. feature representation and classification are two major issues in facial expression analysis. in the past, most methods used either holistic or local representation for analysis. in essence, local information mainly focuses on the subtle variations of expressions and holistic representation stresses on global diversities. to take the advantages of both, a hybrid representation is suggested in this paper and manifold learning is applied to characterize global and local information discriminatively. unlike some methods using unsupervised manifold learning approaches, embedded manifolds of the hybrid representation are learned by adopting a supervised manifold learning technique. to integrate these manifolds effectively, a fusion classifier is introduced, which can help to employ suitable combination weights of facial components to identify an expression. comprehensive comparisons on facial expression recognition are included to demonstrate the effectiveness of our algorithm.
content-based image retrieval by indexing random subwindows with randomized trees. we propose a new method for content-based image retrieval which exploits the similarity measure and indexing structure of totally randomized tree ensembles induced from a set of subwindows randomly extracted from a sample of images. we also present the possibility of updating the model as new images come in, and the capability of comparing new images using a model previously constructed from a different set of images. the approach is quantitatively evaluated on various types of images with state-of-the-art results despite its conceptual simplicity and computational efficiency.
palmprint recognition under unconstrained scenes. this paper presents a novel real-time palmprint recognition system for cooperative user applications. this system is the first one achieving noncontact capturing and recognizing palmprint images under unconstrained scenes. its novelties can be described in two aspects. the first is a novel design of image capturing device. the hardware can reduce influences of background objects and segment out hand regions efficiently. the second is a process of automatic hand detection and fast palmprint alignment, which aims to obtain normalized palmprint images for subsequent feature extraction. the palmprint recognition algorithm used in the system is based on accurate ordinal palmprint representation. by integrating power of the novel imaging device, the palmprint preprocessing approach and the palmprint recognition engine, the proposed system provides a friendly user interface and achieves a good performance under unconstrained scenes simultaneously.
stereo matching using population-based mcmc. in this paper, we propose a new stereo matching method using the population-based markov chain monte carlo (pop-mcmc). pop-mcmc belongs to the sampling-based methods. since previous mcmc methods produce only one sample at a time, only local moves are available. however, since pop-mcmc uses multiple chains and produces multiple samples at a time, it enables global moves by exchanging information between samples, and in turn leads to faster mixing rate. in the view of optimization, it means that we can reach a state with the lower energy. the experimental results on real stereo images demonstrate that the performance of proposed algorithm is superior to those of previous algorithms.
accelerating pattern matching or how much can you slide? this paper describes a method that accelerates pattern matching. the distance between a pattern and a window is usually close to the distance of the pattern to the adjacement windows due to image smoothness. we show how to exploit this fact to reduce the running time of pattern matching by adaptively sliding the window often by more than one pixel. the decision how much we can slide is based on a novel rank we define for each feature in the pattern. implemented on a pentium 4 3ghz processor, detection of a pattern with 7569 pixels in a 640 × 480 pixel image requires only 3.4ms.
learning gabor magnitude features for palmprint recognition. palmprint recognition, as a new branch of biometric technology, has attracted much attention in recent years. various palmprint representations have been proposed for recognition. gabor feature has been recognized as one of the most effective representations for palmprint recognition, where gabor phase and orientation feature representations are extensively studied. in this paper, we explore a novel gabor magnitude feature-based method for palmprint recognition. the novelties are as follows: first, we propose an illumination normalization method for palmprint images to decrease the influence of illumination variations caused by different sensors and lighting conditions. second, we propose to use gabor magnitude features for palmprint representation. third, we utilize adaboost learning to extract most effective features and apply local discriminant analysis (lda) to reduce the dimension further for palmprint recognition. experimental results on three large palmprint databases demonstrate the effectiveness of proposed method. compared with state-of-the-art gabor-based methods, our method achieves higher accuracy.
total absolute gaussian curvature for stereo prior. in spite of the great progress in stereo matching algorithms, the prior models they use, i.e., the assumptions about the probability to see each possible surface, have not changed much in three decades. here, we introduce a novel prior model motivated by psychophysical experiments. it is based on minimizing the total sum of the absolute value of the gaussian curvature over the disparity surface. intuitively, it is similar to rolling and bending a flexible paper to fit to the stereo surface, whereas the conventional prior is more akin to spanning a soap film. through controlled experiments, we show that the new prior outperforms the conventional models, when compared in the equal setting.
a bayesian network for foreground segmentation in region level. this paper presents a probabilistic approach for automatically segmenting foreground objects from a video sequence. in order to save computation time and be robust to noise effect, a region detection algorithm incorporating edge information is first proposed to identify the regions of interest. next, we consider the motion of the foreground objects, and hence utilize the temporal coherence property on the regions detected. thus, foreground segmentation problem is formulated as follows. given two consecutive image frames and the segmentation result obtained priorly, we simultaneously estimate the motion vector field and the foreground segmentation mask in a mutually supporting manner. to represent the conditional joint probability density function in a compact form, a bayesian network is adopted, which is derived to model the interdependency of these two elements. experimental results for several video sequences are provided to demonstrate the effectiveness of our proposed approach.