waashk/dblp huggingface dataset:
a fast recursive 3d model reconstruction algorithm for multimedia applications. a recursive two-step method to recover structure and motion from image sequences based on kalman filtering is described in this paper. the algorithm consists of two major steps. the first step is an extended kalman filter for the estimation of the object's pose. the second step is a set of extended kalman filters, one for each model point, for refining the positions of the model features in the 3d space. the initial guess is a planar model formed under the assumption of orthographic projection on the first image. these two steps alternate from frames to frames. the planar model converges to the final structure as the image sequence is scanned sequentially. the performance of the algorithm is demonstrated with both synthetic data and real world objects. comparisons with different approaches have been performed and show that our method is more efficient.
on estimation of secret message length in jsteg-like steganography. image steganalysis has attracted more attention recently. in this paper, a new method for detecting secret message and estimating the secret message length of bit-streams embedded using j-steg like steganography is proposed. first, based on the model of statistical distribution of quantized dct coefficients, the histogram of cover image is estimated from stego image. then the secret message is detected and the secret message length is estimated with the estimated cover histogram. interestingly, our study on steganalysis of j-steg like embedding offers a proof of zhang et al.'s method [a fast and effective steganalytic technique against jsteg-like algorithms]. the methodology described in this paper is a framework which can also be applied to many other stegaographic methods such as jpeg2000 file format embedding.
svm vs regularized least squares classification. support vector machines (svms) and regularized least squares (rls) are two recent promising techniques for classification. svms implement the structure risk minimization principle and use the kernel trick to extend it to the non-linear case. on the other hand, rls minimizes a regularized functional directly in a reproducing kernel hilbert space defined by a kernel. while both have a sound mathematical foundation, rls is strikingly simple. on the other hand, svms in general have a sparse representation of solutions. in addition, the performance of svms has been well documented but little can be said of rls. this paper applies these two techniques to a collection of data sets and presents results demonstrating virtual identical performance by the two methods.
steganalysis of data hiding in binary images. it is commonly believed that data hiding in binary image can't be detected due to little and noise like changes of data hiding. this paper proposes a steganalytic technique that can detect the existence of hidden message embedded in binary images. the technique is based on statistical analysis of the image statistics such as black-and-white border complexity, block noisiness and block coarseness. the probability of both false positive and false negative rates can also be estimated. the experimental results show that the proposed method can detect hiding fact very effectively.
learning optimal filter representation for texture classification. crucial to texture classification are texture features and classifiers that operate on the features. there are several approaches to computing texture features. of particular interest is multichannel filtering because of its simplicity. multichannel filtering works by decomposing the frequency domain of an image, resulting in a bank of filtered feature images. many techniques have been proposed to optimize multichannel filtering. however, the optimization is with respect to image representation, thus giving no guarantee for texture classification. this paper proposes a novel technique for learning optimal filters for texture classification. we use regularization techniques such as support vector machines (svms) to learn multichannel filters. since filter training in our approach is naturally tied to classifier training, the resulting filters are optimized for classification. experimental results validate the efficacy of our proposed technique.
reconstructing a dynamic surface from video sequences using graph cuts in 4d space-time. this paper is concerned with the problem of dynamically reconstructing the 3d surface of an object undergoing non-rigid motion. the problem is cast as reconstructing a continuous optimal 3d hyper-surface in 4d space-time from a set of calibrated video sequences. the imaging model of video cameras in 4d space-time is derived and a photo-inconsistency cost function is defined for a hyper-surface in the 4d space-time. we use a 4d node-cut algorithm to find a global minimum of the cost function and obtain the corresponding optimal hyper-surface. experimental results show that the proposed algorithm is effective in recovering continuously changing shapes and exhibits good noise resistance.
3-d object representation from multi-view range data applying deformable superquadrics. this paper presents a new framework for recovering superquadrics with global deformations from multi-view real range data. the framework aims at improving confidence and accuracy of recovered models by utilizing multi-view information, and consists of the initial superquadricmodel recovery, view registration, view integration, and final model recovery from integrated data. a quadrant analysis technique is proposed to aid the recovery of bending superquadrics. a modified range data registration method based on recovered superquadrics is also proposed to handle tapered superquadrics. experimental results indicate the proposed framework of multi-view representation significantly improved the accuracy and confidence of recoveredsuperquadrics compared with existing recovery strategies which rely on single-view range images.
subband noise estimation for adaptive wavelet shrinkage. in this article, we present an adaptive image denoising method based on subband noise modeling. for wavelet shrinkage, choosing the threshold depends on correctly estimating the noise variance. by modeling the inter-subband noise variance with a parameterized normalized exponential function, the problem becomes identifying the maximum noise variance. such a maximum exists in the highest decomposition level and can be estimated by locating the extreme of the first derivative of the subband variance function. the experiments demonstrate that our method outperforms peers, especially in the cases of large noise variance.
euclidean quality assessment for binary images. for binary images is proposed. in the algorithm euclidean distance between noise and signal points measures the noise energy and the euler number change of images describes the structural effects caused by noise. furthermore assessment on image quality is calculated in quantity in psnr form. generally image assessments could be classified into two categories: subjective and objective assessment. the latter is judged by the correlation coefficient with subjective quality measurement mean opinion score (mos). the experiments show that the results of the algorithm are highly correlative with subjective mos and the algorithm is more simple and computational saving than traditional objective assessments.
robust frontal face detection in complex environment. we have constructed a simple and fast system to detect frontal human faces in complex environment. there are two main contributions of our work: 1) we use a fast image segmentation method based on connected components labelling to select candidate face areas. 2) we propose a positive-negative attractor template to examine face areas. a valley detector is used to search the valley-like points of eyes and mouths. we test the system on images in complex environment and with confusing objects. the experiment shows a robust detection result with few false detected faces.
recognition of strong and weak connection models in continuous sign language. a new method to recognize continuous sign language based on hidden markov model (hmm) is proposed in this paper. according to the dependence of linguistic context, connections between elementary subwords are classified as strong connection and weak connection. the recognition of strong connection is accomplished with the aid of subword trees, which describe the connection of subwords in each sign language word; in weak connection, the main problem is how to extract the best matched subwords and find their end-points with little help of context information. the proposed method improves the summing process of viterbi decoding algorithm which is constrained in every individual model and compares the end score at each frame to find the ending frame of a subword. experimental results show an accuracy of 70% for continuous sign sentences that comprise no more than 4 subwords.
a new data selection principle for semi-supervised incremental learning. current semi-supervised incremental learning approaches select unlabeled examples with predicted high confidence for model re-training. we show that for many applications this data selection strategy is not correct. this is because the confidence score is primarily a metric to measure the classification correctness on a particular example, rather than one to measure the example's contribution to the training of an improved model, especially in the case that the information used in the confidence annotator is correlated with that generated by the classifier. to address this problem, we propose a performance-driven principle for unlabeled data selection in which only the unlabeled examples that help to improve classification accuracy are selected for semisupervised learning. encouraging results are presented for a variety of public benchmark datasets.
simulated static electric field (ssef) snake for deformable models. in this paper, a novel design of external force for snake is proposed. this kind of external force is actually a field, which we call the simulated static electric field (ssef). this field is created by simulated static electric charges, and the forces in this field are computed by the coulomb's law which is used in the analysis of static charge fields. the existing problems, such as the capture range, initialization, noise sensitivity and poor convergence to concavities, which appeared in the other designs of snake can be largely solved by this new scheme. also, cracked boundaries extracted from real images can be located. several comparisons with gradient vector flow (gvf) snake are presented in this paper to show the performance of our snake.
robust visual tracking via pixel classification and integration. we propose a novel framework for tracking non-rigid objects via pixel classification and integration (pci). given a new input frame, the tracker first performs object classification on each pixel and then finds the region that has the highest integral of scores. there are several key advantages of the proposed approach: it is computationally very efficient; it finds a global, instead of local (e.g., mean-shift), optimal solution within a search range; and it is inherently robust to different object scales with minimum extra computation.within this framework, a mixture of long-term and shortterm appearance model is further introduced to perform pci. as a result, the tracker is able to adapt to both slow and rapid appearance changes without drifting. challenging video sequences are presented to illustrate how the proposed tracker handles large motion, dramatic shape changes, scale variations, illumination variations and partial occlusions.
fragile watermarking scheme exploiting non-deterministic block-wise dependency. block-wise dependency is recognized as a key requirement in fragile watermarking schemes to thwart the vector quantization (vq) attack.it has also been observed that deterministic dependency is still susceptible to transplantation attack or even simple cover-up attack. in this work, a new fragile watermarking scheme exploiting non-deterministic dependency is proposed. to watermark the original image, each pixel is adjusted according to the consistency between a key-dependent binary watermark bit, the information from the pixel itself and the non-deterministic secret dependency information calculated froma neighborhood centered at that pixel. localization resolution is enhanced with a post-processing operation on the output difference map.
discovery of the tri-edge inequality with binary vector dissimilarity measures. in certain spaces using some distance measures, the sum of any two distances is always bigger than the third one. such a special property is called the tri-edge inequality (tei). in this paper, the tri-edge inequality characterizing several binary distance measures is mathematically proven and experimentally verified, and the implications of tei are discussed as well.
ear recognition using improved non-negative matrix factorization. an improved non-negative matrix factorization with sparseness constraints (inmfsc) is proposed by imposing an additional constraint on the objective function of nmfsc, which can control the sparseness of both the basis vectors and the coefficient matrix simultaneously. the update rules to solve the objective function with constraints are presented. research of ear recognition and its application is a new subject in the field of biometrics authentication. in practical application, ear is maybe partially occluded by hair etc. so the proposed inmfsc is applied on ear recognition with normal images and partially occluded images. experiment results show that, compared with the traditional nmfsc, the proposed method not only obtains higher recognition rate, but also improves the sparseness and the orthogonality of coefficient matrix.
an approach for constructing sparse kernel classifier. this paper presents a new approach for constructing sparse kernel classifier with large margin. firstly, we propose a kernel function pursuit strategy for selecting a small number of kernel functions which are used for expanding final classifier. and then an added constraint controls the sparseness of the final classifier and an approach is provided to solve the optimization problem with l2 loss function and complexity measure. the experiment results show that sparse kernel classifier can achieved higher efficiency for both training and testing without sacrificing prediction accuracy.
motion segmentation by multibody trifocal tensor using line correspondence. we present a geometric approach to multibody motion segmentation from line correspondences. given three perspective views containing multiple moving objects, we demonstrate that after applying a polynomial embedding to the line correspondences, they became related by the so-called multibody trilinear constrain and its associated multibody trifocal tensor. we show how to obtain the individual trifocal tensor from the second order derivatives of the multibody trilinear constraint. given trifocal tensors, we obtain the clustering of the motions and correspondences. experimental results on synthetic and real dynamic scenes are presented.
convex set-based estimation of image flows. we introduce a set-based approach for estimating image motion based on an optical flow constraint and a finite number of arbitrary differential constraints describing physically plausible vector fields. compared to related variational estimation approaches, our approach strictly satifies each separate constraint and becomes not more involved in the presence of higher-order differential operators. the approach is implemented using established sub-gradient projection schemes onto the set of feasible solutions. our approach is particularly suited if quantitative prior knowledge about structural flow properties is available, and for the regularized estimation of highly non-rigid image motion.
a global geometric approach for image clustering. we propose an appearance-based image clustering approach called ggci (global geometric clustering for image). for face images taken with varying pose, expression, eyes (wearing sunglasses or not) or object images under different viewing conditions, ggci uses easily measured local metric information to learn the underlying global geometry of images space, then apply the extended nearest neighbor approach to cluster images. different from the usual nearest neighbor approach, ggci considers the density around the nearest points within clusters. moreover, our approach clusters based on the geodesic distance measure instead of euclidean distance measure, which better reflects the intrinsic geometric structure of manifold embedded in high dimensional image space. experimental results suggest that the proposed ggci approach achieves lower error rates in image clustering when manifolds are embedded in image space.
fast and robust search method for short video clips from large video collection. in this paper a fast and robust method is proposed to search a large video collection for given short clips. compared with existing video searching methods which use visual features only, our scheme performs a two-phase hierarchical matching technique using visual and audio features successively. considering that video sampling rate (25 or 30 fps) is much lower than that of audio (8 to 48 khz), a coarse search is implemented with sub-sampled video frames first, and then potential matches will be verified and accurately located using fine audio features. both features are extracted directly from mpeg compressed video for computational efficiency. experiments have been conducted on over 10.5 hours of video to search for re-occurrences of 83 tv commercials and one news lead-out clip. all the 220 instances are correctly detected with no false alarm. our experiments also show that the proposed method is robust to variations of video bit rate, frame rate, frame size and color shifting.
perspective symmetry invariant and its applications. face is a perceptually symmetric object; however, it often appears not so in captured image due to the rotation in depth within the 3d space. in this paper, we explore the invariant of the symmetry point pair when the pose and symmetry radius are changed. under the general perspective camera model, we present an invariant, called perspective symmetry invariant (psi), which only depends on the focus length and the rotation angle in depth when the angle is relatively small. psi is discriminating for pose estimation; meanwhile, it can be used to synthesize face shape in novel views with different psi values. encouraging experiments on these two contributions of psi are presented.
restoringwarped document images using shape-from-shading and surface interpolation. with current high resolution handheld digital devices such as camera phones and pdas, image capturing of documents like books and posters has become a convenient and efficient way of collecting and disseminating information. nevertheless, a simple snapshot of such documents in an uncontrolled environment often results in distorted images. one particular example is when capturing documents with non-planar geometric shapes, such as thick bound book pages, rolled posters, etc. the resultant images often exhibit both perspective and geometric distortions. this paper proposes a method to remove these warping distortions through a shape recovery process based on shape-from- shading (sfs) followed by restoration using surface interpolation techniques. we evaluated the proposed method using various snapshot images captured by normal handheld digital cameras. the ocr results on the original images and the restored images are compared. the precision is shown to be increased up to 22.3%. the comparison with a 2-d interpolation approach also shows a clear improvement on the restored images near the warping area.
estimation of 3d shape of warped document surface for image restoration. while scanning pages from a thick, bound book, there are two sources of distortion in the document images: 1) shade along the book 'spine', and 2) warping of the book surface in the shade area. in this paper, we propose a fast method to estimate the 3d shape of a book surface. based on this shape, we remove the shade and correct the warping to restore the document images. the experiments show that the photometric and geometric distortions are mostly removed. the ocr tests on the original and restored document images are also presented.
real-time object recognition using relational dependency based on graphical model. this paper proposes a real-time object recognition using the relational dependency among the objects that is represented by the graphical model. when we recognize the objects, it is effective to use the relational dependency in which several different objects co-exist each other. the relational dependency has been modeled by the transition matrix in the graphical model. the transition matrix precisely represents the conditional probability of object's existence at time t, given the existence of others at time t-1. we use a very fast cascaded adaboost detector in order to detect all object candidates in the image. then, the existence probability of the object from a given object candidate is estimated by a logistic regression using the softmax function. the estimated existence probability is updated by the trained transition matrix to reflect the relational dependency of the objects. the object's existence is determined by the threshold level. experiment results validate that the proposed method is a very fast and effective way of recognizing the objects in terms of high recognition rate and low false alarm rate.
a robust and accurate segmentation of iris images using optimal partitioning. an effective and accurate identification of human individuals from their iris features is largely dependent on proper segmentation of the iris and the pupil features from camera images. most modern segmentation schemes exploit the circular geometry of the iris to fit a circle or an ellipse to an edge map of the iris. in this paper, we present a new method for automatically localizing and segmenting iris features by optimal partitioning using the relative distribution of gray-level intensities across an image. first, the eye images are unrolled after detecting the center of the pupil from the image local minima. for each radial sample, segments corresponding to regions that are statistically different are computed using dynamic programming applied to a poisson-based cost function. the results are a set of change points marking the edges of different features including those of the pupil and the iris. the radius of the pupil and the iris are then obtained by searching for the best fit of two lines connecting the detected edge points. the proposed method is superior to other methods in that artifacts such as excessive or weak illumination, blurring and occlusion by eyelids do not interfere with the segmentation process. moreover, our algorithm is also robust and accurate even in the presence of eyewear such as glasses. applying this method to 122 images revealed a 98% segmentation accuracy. the algorithm has been shown to be effective in images with large field of view containing other facial features
online multicamera tracking with a switching state-space model. the paper presents a novel method for online tracking of multiple objects with non-overlapping cameras. the method is based on a generative model defining probabilistic dependencies between observations, the underlying color properties of objects and their dynamics. it allows for a full bayesian inference of trajectories. we developed an on-line algorithm for efficient, approximate inference and we demonstrate it to be accurate in an office environment.