waashk/dblp huggingface dataset:
co-segmentation of image pairs with quadratic global constraint in mrfs. this paper provides a novel method for co-segmentation, namely simultaneously segmenting multiple images with same foreground and distinct backgrounds. our contribution primarily lies in four-folds. first, image pairs are typically captured under different imaging conditions, which makes the color distribution of desired object shift greatly, hence it brings challenges to color-based co-segmentation. here we propose a robust regression method to minimize color variances between corresponding image regions. secondly, although having been intensively discussed, the exact meaning of the term "co-segmentation" is rather vague and importance of image background is previously neglected, this motivate us to provide a novel, clear and comprehensive definition for co-segmentation. thirdly, it is an involved issue that specific regions tend to be categorized as foreground, so we introduce "risk term" to differentiate colors, which has not been discussed before in the literatures to our best knowledge. lastly and most importantly, unlike conventional linear global terms in mrfs, we propose a sum-of-squared-difference (ssd) based global constraint and deduce its equivalent quadratic form which takes into account the pairwise relations in feature space. reasonable assumptions are made and global optimal could be efficiently obtained via alternating graph cuts.
microscopic surface shape estimation of a transparent plate using a complex image. this paper proposes a method to estimate the surface shape of a transparent plate using a reflection image on the plate. the reflection image on a transparent plate is a complex image that consists of a reflection on the surface and on the rear surface of the plate. a displacement between the two reflection images holds the range information to the object, which can be extracted from a single complex image. the displacement in the complex image depends not only on the object range but also on the normal vectors of the plate surfaces, plate thickness, relative refraction index, and the plate position. these parameters can be estimated using multiple planar targets with random texture at known distances. experimental results show that the proposed method can detect microscopic surface shape differences between two different commercially available transparent acrylic plates.
pose estimation from circle or parallel lines in a single image. the paper is focused on the problem of pose estimation from a single view in minimum conditions that can be obtained from images. under the assumption of known intrinsic parameters, we propose and prove that the pose of the camera can be recovered uniquely in three situations: (a) the image of one circle with discriminable center; (b) the image of one circle with preassigned world frame; (c) the image of any two pairs of parallel lines. compared with previous techniques, the proposed method does not need any 3d measurement of the circle or lines, thus the required conditions are easily satisfied in many scenarios. extensive experiments are carried out to validate the proposed method.
discriminant clustering embedding for face recognition with image sets. in this paper, a novel local discriminant embedding method, discriminant clustering embedding (dce), is proposed for face recognition with image sets. dce combines the effectiveness of submanifolds, which are extracted by clustering for each subject's image set, characterizing the inherent structure of face appearance manifold and the discriminant property of discriminant embedding. the low-dimensional embedding is learned via preserving the neighbor information within each submanifold, and separating the neighbor submanifolds belonging to different subjects from each other. compared with previous work, the proposed method could not only discover the most powerful discriminative information embedded in the local structure of face appearance manifolds more sufficiently but also preserve it more efficiently. extensive experiments on real world data demonstrate that dce is efficient and robust for face recognition with image sets.
learning-based super-resolution system using single facial image and multi-resolution wavelet synthesis. a learning-based super-resolution system consisting of training and synthesis processes is presented. in the proposed system, a multiresolution wavelet approach is applied to carry out the robust synthesis of both the global geometric structure and the local high-frequency detailed features of a facial image. in the training process, the input image is transformed into a series of images of increasingly lower resolution using the haar discrete wavelet transform (dwt). the images at each resolution level are divided into patches, which are then projected onto an eigenspace to derive the corresponding projection weight vectors. in the synthesis process, a low-resolution input image is divided into patches, which are then projected onto the same eigenspace as that used in the training process. modeling the resulting projection weight vectors as a markov network, the maximum a posteriori (map) estimation approach is then applied to identity the best-matching patches with which to reconstruct the image at a higher level of resolution. the experimental results demonstrate that the proposed reconstruction system yields better results than the bi-cubic spline interpolation method.
content-based matching of videos using local spatio-temporal fingerprints. fingerprinting is the process of mapping content or fragments of it, into unique, discriminative hashes called fingerprints. in this paper, we propose an automated video identification algorithm that employs fingerprinting for storing videos inside its database. when queried using a degraded short video segment, the objective of the system is to retrieve the original video to which it corresponds to, both accurately and in real-time. we present an algorithm that first, extracts key frames for temporal alignment of the query and its actual database video, and then computes spatio-temporal fingerprints locally within such frames, to indicate a content-match. all stages of the algorithm have been shown to be highly stable and reproducible even when strong distortions are applied to the query.
a new framework for grayscale and colour non-lambertian shape-from-shading. in this paper we show how arbitrary surface reflectance properties can be incorporated into a shape-from-shading scheme, by using a riemannian minimisation scheme to minimise the brightness error. we show that for face images an additional regularising constraint on the surface height function is all that is required to recover accurate face shape from single images, the only assumption being of a single light source of known direction. the method extends naturally to colour images, which add additional constraints to the problem. for our experimental evaluation we incorporate the torrance and sparrow surface reflectance model into our scheme and show how to solve for its parameters in conjunction with recovering a face shape estimate. we demonstrate that the method provides a realistic route to non-lambertian shape-from-shading for both grayscale and colour face images.
comparative studies on multispectral palm image fusion for biometrics. hand biometrics, including fingerprint, palmprint, hand geometry and hand vein pattern, have obtained extensive attention in recent years. physiologically, skin is a complex multi-layered tissue consisting of various types of components. optical research suggests that different components appear when the skin is illuminated with light sources of different wavelengths. this motivates us to extend the capability of camera by integrating information from multispectral palm images to a composite representation that conveys richer and denser pattern for recognition. besides, usability and security of the whole system might be boosted at the same time. in this paper, comparative study of several pixel level multispectral palm image fusion approaches is conducted and several well-established criteria are utilized as objective fusion quality evaluation measure. among others, curvelet transform is found to perform best in preserving discriminative patterns from multispectral palm images.
improved space carving method for merging and interpolating multiple range images using information of light sources of active stereo. to merge multiple range data obtained by range scanners, filling holes caused by unmeasured regions, the space carving method is a simple and effective method. however, this method often fails if the number of the input range images is small, because unseen voxels that are not carved out remains in the volume area. in this paper, we propose an improved algorithm of the space carving method that produces stable results. in the proposed method, a discriminant function defined on volume space is used to estimate whether each voxel is inside or outside the objects. also, in particular case that the range images are obtained by active stereo method, the information of the positions of the light sources can be used to improve the accuracy of the results.
image and video matting with membership propagation. two techniques are devised for a natural image matting method using semi-supervised object extraction. one is a guiding scheme for placement of user strokes specifying object or background regions and the other is a scheme of adjustment of object colors for conforming to composited background colors. we draw strokes at inhomogeneous color regions disclosed with an unsupervised cluster extraction method from which the semi-supervised algorithm is derived. objects are composited with a new background after their color adjustment using a color transfer method with eigencolor mapping. this image matting method is then extended to videos. strokes are drawn only in the first frame from which memberships are propagated to successive frames to extract objects in every frame. performance of the proposed method is examined with images and videos experimented with existing matting methods.
high capacity watermarking in nonedge texture under statistical distortion constraint. high-capacity image watermarking scheme aims at maximize bit rate of hiding information, neither eliciting perceptible image distortion nor facilitating special watermark attack. texture, in preattentive vision, delivers itself by concise high-order statistics, and holds high capacity for watermark. however, traditional distortion constraint, e.g. just-noticeable-distortion (jnd), cannot evaluate texture distortion in visual perception and thus imposes too strict constraint. inspired by recent work of image representation [9], which suggests texture extraction and mix probability principal component analysis for learning texture feature, we propose a distortion measure in the subspace spanned by texture principal components, and an adaptive distortion constraint depending on image local roughness. the proposed spread-spectrum watermarking scheme generates watermarked images with larger snr than jnd-based schemes at the same distortion level allowed, and its watermark has a power spectrum approximately directly proportional to the host image's and thereby more robust against wiener filtering attack.
localized content-based image retrieval using semi-supervised multiple instance learning. in this paper, we propose a semi-supervised multiple-instance learning (ssmil) algorithm, and apply it to localized content-based image retrieval (lcbir), where the goal is to rank all the images in the database, according to the object that users want to retrieve. ssmil treats lcbir as a semi-supervised problem and utilize the unlabeled pictures to help improve the retrieval performance. the comparison result of ssmil with several state-of-art algorithms is promising.
where's the weet-bix? this paper proposes a new retrieval problem and conducts the initial study. this problem aims at finding the location of an item in a supermarket by means of visual retrieval. it is modelled as object-based retrieval and approached using the local invariant features. two existing retrieval methods are investigated and their similarity measures are modified to better fit this new problem. more importantly, through the study this new retrieval problem proves itself to be a challenging task. an instant application of it is to help the customer find what they want without physically wandering around the shelves but a wide range of potential applications could be expected.
evolving measurement regions for depth from defocus. depth from defocus (dfd) is a 3d recovery method based on estimating the amount of defocus induced by finite lens apertures. given two images with different camera settings, the problem is to measure the resulting differences in defocus across the image, and to estimate a depth based on these blur differences. most methods assume that the scene depth map is locally smooth, and this leads to inaccurate depth estimates near discontinuities. in this paper, we propose a novel dfd method that avoids smoothing over discontinuities by iteratively modifying an elliptical image region over which defocus is estimated. our method can be used to complement any depth from defocus method based on spatial domain measurements. in particular, this method improves the dfd accuracy near discontinuities in depth or surface orientation.
face recognition by using elongated local binary patterns with average maximum distance gradient magnitude. in this paper, we propose a new face recognition approach based on local binary patterns (lbp). the proposed approach has the following novel contributions. (i) as compared with the conventional lbp, anisotropic structures of the facial images can be captured effectively by the proposed approach using elongated neighborhood distribution, which is called the elongated lbp (elbp). (ii) a new feature, called average maximum distance gradient magnitude (amdgm), is proposed. amdgm embeds the gray level difference information between the reference pixel and neighboring pixels in each elbp pattern. (iii) it is found that the elbp and amdgm features are well complement with each other. the proposed method is evaluated by performing facial expression recognition experiments on two databases: orl and feret. the proposed method is compared with two widely used face recognition approaches. furthermore, to test the robustness of the proposed method under the condition that the resolution level of the input images is low, we also conduct additional face recognition experiments on the two databases by reducing the resolution of the input facial images. the experimental results show that the proposed method gives the highest recognition accuracy in both normal environment and low image resolution conditions.
a convex programming approach to the trace quotient problem. the trace quotient problem arises in many applications in pattern classification and computer vision, e.g., manifold learning, low-dimension embedding, etc. the task is to solve a optimization problem involving maximizing the ratio of two traces, i.e., maxw tr(f(w))/tr(h(w)). this optimization problem itself is non-convex in general, hence it is hard to solve it directly. conventionally, the trace quotient objective function is replaced by a much simpler quotient trace formula, i.e., maxw tr (h(w)-1f(w)), which accommodates a much simpler solution. however, the result is no longer optimal for the original problem setting, and some desirable properties of the original problem are lost. in this paper we proposed a new formulation for solving the trace quotient problem directly. we reformulate the original non-convex problem such that it can be solved by efficiently solving a sequence of semidefinite feasibility problems. the solution is therefore globally optimal. besides global optimality, our algorithm naturally generates orthonormal projection matrix. moreover it relaxes the restriction of linear discriminant analysis that the projection matrix's rank can only be at most c - 1, where c is the number of classes. our approach is more flexible. experiments show the advantages of the proposed algorithm.
kernel discriminant analysis based on canonical differences for face recognition in image sets. a novel kernel discriminant transformation (kdt) algorithm based on the concept of canonical differences is presented for automatic face recognition applications. for each individual, the face recognition system compiles a multi-view facial image set comprising images with different facial expressions, poses and illumination conditions. since the multi-view facial images are non-linearly distributed, each image set is mapped into a highdimensional feature space using a nonlinear mapping function. the corresponding linear subspace, i.e. the kernel subspace, is then constructed via a process of kernel principal component analysis (kpca). the similarity of two kernel subspaces is assessed by evaluating the canonical difference between them based on the angle between their respective canonical vectors. utilizing the kernel fisher discriminant (kfd), a kdt algorithm is derived to establish the correlation between kernel subspaces based on the ratio of the canonical differences of the between-classes to those of the within-classes. the experimental results demonstrate that the proposed classification system outperforms existing subspace comparison schemes and has a promising potential for use in automatic face recognition applications.
visual odometry for non-overlapping views using second-order cone programming. we present a solution for motion estimation for a set of cameras which are firmly mounted on a head unit and do not have overlapping views in each image. this problem relates to ego-motion estimation of multiple cameras, or visual odometry. we reduce motion estimation to solving a triangulation problem, which finds a point in space from multiple views. the optimal solution of the triangulation problem in linfinity norm is found using socp (second-order cone programming) consequently, with the help of the optimal solution for the triangulation, we can solve visual odometry by using socp as well.
stereo vision enabling precise border localization within a scanline optimization framework. a novel algorithm for obtaining accurate dense disparity measurements and precise border localization from stereo pairs is proposed. the algorithm embodies a very effective variable support approach based on segmentation within a scanline optimization framework. the use of a variable support allows for precisely retrieving depth discontinuities while smooth surfaces are well recovered thanks to the minimization of a global function along multiple scanlines. border localization is further enhanced by symmetrically enforcing the geometry of the scene along depth discontinuities. experimental results show a significant accuracy improvement with respect to comparable stereo matching approaches.
a fast and noise-tolerant method for positioning centers of spiraling and circulating vector fields. identification of centers of circulating and spiraling vector fields are important in many applications. tropical cyclone tracking, rotating object identification, analysis of motion video and movement of fluids are but some examples. in this paper, we introduce a fast and noise tolerant method for finding centers of circulating and spiraling vector field pattern. the method can be implemented using integer operations only. it is 1.4 to 4.5 times faster than traditional methods, and the speedup can be further boosted up to 96.6 by the incorporation of search algorithms. we show the soundness of the algorithm using experiments on synthetic vector fields and demonstrate its practicality using application examples in the field of multimedia and weather forecasting.
discriminating 3d faces by statistics of depth differences. in this paper, we propose an efficient 3d face recognition method based on statistics of range image differences. each pixel value of range image represents normalized depth value of corresponding point on facial surface, and so depth differences between two range images' pixels of the same position on face can straightforwardly describe the differences between two faces' structures. here, we propose to use histogram proportion of depth differences to discriminate intra and inter personal differences for 3d face recognition. depth differences are computed from a neighbor district instead of direct subtraction to avoid the impact of non-precise registration. furthermore, three schemes are proposed to combine the local rigid region(nose) and holistic face to overcome expression variation for robust recognition. promising experimental results are achieved on the 3d dataset of frgc2.0, which is the most challenging 3d database so far.
color-stripe structured light robust to surface color and discontinuity. multiple color stripes have been employed for structured light-based rapid range imaging to increase the number of uniquely identifiable stripes. the use of multiple color stripes poses two problems: (1) object surface color may disturb the stripe color and (2) the number of adjacent stripes required for identifying a stripe may not be maintained near surface discontinuities such as occluding boundaries. in this paper, we present methods to alleviate those problems. log-gradient filters are employed to reduce the influence of object colors, and color stripes in two and three directions are used to increase the chance of identifying correct stripes near surface discontinuities. experimental results demonstrate the effectiveness of our methods.
viewpoint insensitive action recognition using envelop shape. action recognition is a popular and important research topic in computer vision. however, it is challenging when facing viewpoint variance. so far, most researches in action recognition remain rooted in view-dependent representations. some view invariance approaches have been proposed, but most of them suffer from some weaknesses, such as lack of abundant information for recognition, dependency on robust meaningful feature detection or point correspondence. to perform viewpoint and subject independent action recognition, we propose a representation named "envelop shape" which is viewpoint insensitive. "envelop shape" is easy to acquire from silhouettes using two orthogonal cameras. it makes full use of two cameras' silhouettes to dispel influence caused by human body's vertical rotation, which is often the primary viewpoint variance. with the help of "envelop shape", we obtained inspiring results on action recognition independent of subject and viewpoint. results indicate that "envelop shape" representation contains enough discriminating features for action recognition.
shape recovery from turntable image sequence. this paper makes use of both feature points and silhouettes to deliver fast 3d shape recovery from a turntable image sequence. the algorithm exploits object silhouettes in two views to establish a 3d rim curve, which is defined with respect to the two frontier points arising from two views. the images of this 3d rim curve in the two views are matched using cross correlation technique with silhouette constraint incorporated. a 3d planar rim curve is then reconstructed using point-based reconstruction method. a set of rims enclosing the object can be obtained from an image sequence captured under circular motion. the proposed method solves the problem of reconstruction of concave object surface, which is usually left unresolved in general silhouette-based reconstruction methods. in addition, the property of the organized reconstructed rim curves allows fast surface extraction. experimental results with real data are presented.