waashk/dblp huggingface dataset:
privacy preserving: hiding a face in a face. this paper proposes a detailed framework of privacy preserving techniques in real-time video surveillance systems. in the proposed system, the protected video data can be released in such a way that the identity of any individual contained in video cannot be recognized while the surveillance data remains practically useful, and if the original privacy information is demanded, it can be recoverable with a secrete key. the proposed system attempts to hide a face (real face, privacy information) in a face (new generated face for anonymity). to deal with the huge payload problem of privacy information hiding, an active appearance model (aam) based privacy information extraction and recovering is proposed in our system. a quantized index modulation based data hiding scheme is used to hide the privacy information. experimental results have shown that the proposed system can embed the privacy information into video without affecting its visual quality and keep its practical usefulness, at the same time, allows the privacy information to be revealed in a secure and reliable way.
initial pose estimation for 3d model tracking using learned objective functions. tracking 3d models in image sequences essentially requires determining their initial position and orientation. our previous work [14] identifies the objective function as a crucial component for fitting 2d models to images. we state preferable properties of these functions and we propose to learn such a function from annotated example images. this paper extends this approach by making it appropriate to also fit 3d models to images. the correctly fitted model represents the initial pose for model tracking. however, this extension induces nontrivial challenges such as out-of-plane rotations and self occlusion, which cause large variation to the model's surface visible in the image. we solve this issue by connecting the input features of the objective function directly to the model. furthermore, sequentially executing objective functions specifically learned for different displacements from the correct positions yields highly accurate objective values.
shape reconstruction from cast shadows using coplanarities and metric constraints. to date, various techniques of shape reconstruction using cast shadows have been proposed. the techniques have the advantage that they can be applied to various scenes including outdoor scenes without using special devices. previously proposed techniques usually require calibration of camera parameters and light source positions, and such calibration processes make the application ranges limited. if a shape can be reconstructed even when these values are unknown, the technique can be used to wider range of applications. in this paper, we propose a method to realize such a technique by constructing simultaneous equations from coplanarities and metric constraints, which are observed by cast shadows of straight edges and visible planes in the scenes, and solving them. we conducted experiments using simulated and real images to verify the technique.
on the critical point of gradient vector flow snake. in this paper, the so-called critical point problem of gradient vector flow (gvf) snake is studied in two respects: influencing factors and detection of the critical points. one influencing factor that particular attention should be paid to is the iteration number in the diffusion process, too large amount of diffusion would flood the object boundaries while too small amount would preserve excessive noise. here, the optimal iteration number is chosen by minimizing the correlation between the signal and noise in the filtered vector field. on the other hand, we single out all the critical points by quantizing the gvf vector field. after the critical points are singled out, the initial contour can be located properly to avoid the nuisance arising from critical points. several experiments are also presented to demonstrate the effectiveness of the proposed strategies.
super resolution of images of 3d scenecs. we address the problem of super resolved generation of novel views of a 3d scene with the reference images obtained from cameras in general positions; a problem which has not been tackled before in the context of super resolution and is also of importance to the field of image based rendering. we formulate the problem as one of estimation of the color at each pixel in the high resolution novel view without explicit and accurate depth recovery. we employ a reconstruction based approach using mrf-map formalism and solve using graph cut optimization. we also give an effective method to handle occlusion. we present compelling results on real images.
feature subset selection for multi-class svm based image classification. multi-class image classification can benefit much from feature subset selection. this paper extends an error bound of binary svms to a feature subset selection criterion for the multi-class svms. by minimizing this criterion, the scale factors assigned to each feature in a kernel function are optimized to identify the important features. this minimization problem can be efficiently solved by gradient-based search techniques, even if hundreds of features are involved. also, considering that image classification is often a small sample problem, the regularization issue is investigated for this criterion, showing its robustness in this situation. experimental study on multiple benchmark image data sets demonstrates the effectiveness of the proposed approach.
generative estimation of 3d human pose using shape contexts matching. we present a method for 3d pose estimation of human motion in generative framework. for the generalization of application scenario, the observation information we utilized comes from monocular silhouettes. we distill prior information of human motion by performing conventional pca on single motion capture data sequence. in doing so, the aims for both reducing dimensionality and extracting the prior knowledge of human motion are achieved simultaneously. we adopt the shape contexts descriptor to construct the matching function, by which the validity and the robustness of the matching between image features and synthesized model features can be ensured. to explore the solution space efficiently, we design the annealed genetic algorithm (aga) and hierarchical annealed genetic algorithm (haga) that searches the optimal solutions effectively by utilizing the characteristics of state space. results of pose estimation on different motion sequences demonstrate that the novel generative method can achieves viewpoint invariant 3d pose estimation.
view planning for cityscape archiving and visualization. this work explores full registration of scenes in a large area purely based images for city indexing and visualization. ground-based images including route panoramas, scene tunnels, panoramic views, and spherical views are acquired in the area and are associated with geospatial information. in this paper, we plan distributed locations and paths in the urban area based on the visibility, image properties, image coverage, and scene importance for image acquisition. the criterion is to use a small number of images to cover as large scenes as possible. lidar data are used in this view evaluation and real data are acquired accordingly. the extended images realize a compact and complete visual data archiving, which will enhance the perception of spatial relations of scenes.
markov random field modeled level sets method for object tracking with moving cameras. object tracking using active contours has attracted increasing interest in recent years due to acquisition of effective shape descriptions. in this paper, an object tracking method based on level sets using moving cameras is proposed. we develop an automatic contour initialization method based on optical flow detection. a markov random field (mrf)-like model measuring the correlations between neighboring pixels is added to improve the general region-based level sets speed model. the experimental results on several real video sequences show that our method successfully tracks objects despite object scale changes, motion blur, background disturbance, and gets smoother and more accurate results than the current region-based method.
constrained optimization for human pose estimation from depth sequences. a new 2-step method is presented for human upper-body pose estimation from depth sequences, in which coarse human part labeling takes place first, followed by more precise joint position estimation as the second phase. in the first step, a number of constraints are extracted from notable image features such as the head and torso. the problem of pose estimation is cast as that of label assignment with these constraints. major parts of the human upper body are labeled by this process. the second step estimates joint positions optimally based on kinematic constraints using dense correspondences between depth profile and human model parts. the proposed framework is shown to overcome some issues of existing approaches for human pose tracking using similar types of data streams. performance comparison with motion capture data is presented to demonstrate the accuracy of our approach.
shape from contour for the digitization of curved documents. we are aiming at extending the basic digital camera functionalities to the ability to simulate the flattening of a document, by virtually acting like a flatbed scanner. typically, the document is the warped page of an opened book. the problem is stated as a computer vision problem, whose resolution involves, in particular, a 3d reconstruction technique, namely shape from contour. assuming that a photograph is taken by a camera in arbitrary position or orientation, and that the model of the document surface is a generalized cylinder, we show how the corrections of its geometric distortions, including perspective distortion, can be achieved from a single view of the document. the performances of the proposed technique are assessed and illustrated through experiments on real images.
evaluating multi-class multiple-instance learning for image categorization. automatic image categorization is a challenging computer vision problem, to which multiple-instance learning (mil) has emerged as a promising approach. typical current mil schemes rely on binary one-versus-all classification, even for inherently multi-class problems. there are a few drawbacks with binary mil when applied to a multi-class classification problem. this paper describes multi-class multiple-instance learning (mcmil). to image categorization that bypasses the necessity of constructing a series of binary classifiers. we analyze mcmil in depth to show why it is advantageous over binary mil when strong target concept overlaps exist among the classes. we systematically valuate mcmil using two challenging image databases, and compare it with state-of-the-art binary mil approaches. the mcmil achieves competitive classification accuracy, robustness to labeling noise, and effectiveness in capturing the target concepts using smaller amount of training data. we show that the learned target concepts from mcmil conform to human interpretation of the images.
action recognition for surveillance applications using optic flow and svm. low quality images taken by surveillance cameras pose a great challenge to human action recognition algorithms. this is because they are usually noisy, of low resolution and of low frame rate. in this paper we propose an action recognition algorithm to overcome the above challenges. we use optic flow to construct motion descriptors and apply a svm to classify them. having powerful discriminative features, we significantly reduce the size of the feature set required. this algorithm can be applied to videos with low frame rate without scarifying efficiency or accuracy, and is robust to scale and view point changes. to evaluate our method, we used a database consisting of walking, running, jogging, hand clapping, hand waving and boxing actions. this grayscale database has images of low resolution and poor quality. this image database resembles images taken by surveillance cameras. the proposed method outperforms competing algorithms evaluated on the same database.
depth from stationary blur with adaptive filtering. this work achieves an efficient acquisition of scenes and their depths along long streets. a camera is mounted on a vehicle moving along a path and a sampling line properly set in the camera frame scans the 1d scene continuously to form a 2d route panorama. this paper extends a method to estimate depth from the camera path by analyzing the stationary blur in the route panorama. the temporal stationary blur is a perspective effect in parallel projection yielded from the sampling slit with a physical width. the degree of blur is related to the scene depth from the camera path. this paper analyzes the behavior of the stationary blur with respect to camera parameters and uses adaptive filtering to improve the depth estimation. it avoids feature matching or tracking for complex street scenes and facilitates real time sensing. the method also stores much less data than a structure from motion approach does so that it can extend the sensing area significantly.
an occupancy-depth generative model of multi-view images. this paper presents an occupancy based generative model of stereo and multi-view stereo images. in this model, the space is divided into empty and occupied regions. the depth of a pixel is naturally determined from the occupancy as the depth of the first occupied point in its viewing ray. the color of a pixel corresponds to the color of this 3d point. this model has two theoretical advantages. first, unlike other occupancy based models, it explicitly models the deterministic relationship between occupancy and depth and, thus, it correctly handles occlusions. second, unlike depth based approaches, determining depth from the occupancy automatically ensures the coherence of the resulting depth maps. experimental results computing the map of the model using message passing techniques are presented to show the applicability of the model.
highest accuracy fundamental matrix computation. we compare algorithms for fundamental matrix computation, which we classify into "a posteriori correction", "internal access", and "external access". doing experimental comparison, we show that the 7-parameter levenberg-marquardt (lm) search and the extended fns (efns) exhibit the best performance and that additional bundle adjustment does not increase the accuracy to any noticeable degree.
determining relative geometry of cameras from normal flows. determining the relative geometry of cameras is important in active binocular head or multi-camera system. most of the existing works rely upon the establishment of either motion correspondences or binocular correspondences. this paper presents a first solution method that requires no recovery of full optical flow in either camera, nor overlap in the cameras' visual fields and in turn the presence of binocular correspondences. the method is based upon observations that are directly available in the respective image stream - the monocular normal flow. experimental results on synthetic data and real image data are shown to illustrate the potential of the method.
detecting, tracking and recognizing license plates. this paper introduces a novel real-time framework which enables detection, tracking and recognition of license plates from video sequences. an efficient algorithm based on analysis of maximally stable extremal region (mser) detection results allows localization of international license plates in single images without the need of any learning scheme. after a one-time detection of a plate it is robustly tracked through the sequence by applying a modified version of the mser tracking framework which provides accurate localization results and additionally segmentations of the individual characters. therefore, tracking and character segmentation is handled simultaneously. finally, support vector machines are used to recognize the characters on the plate. an experimental evaluation shows the high accuracy and efficiency of the detection and tracking algorithm. furthermore, promising results on a challenging data set are presented and the significant improvement of the recognition rate due to the robust tracking scheme is proved.
dense 3d reconstruction of specular and transparent objects using stereo cameras and phase-shift method. in this paper, we first describe our approach to measuring the surface shape of specular objects and then we extend the method to measuring the surface shape of transparent objects by using stereo cameras and a display. we show that two viewpoints can uniquely determine the surface shape and surface normal by investigating the light path for each surface point. we can determine the light origin for each surface point by showing two-dimensional phase shifts on the display. we obtained dense and accurate results for both planar surfaces and curved surfaces.
fast optimal three view triangulation. we consider the problem of l2-optimal triangulation from three separate views. triangulation is an important part of numerous computer vision systems. under gaussian noise, minimizing the l2 norm of the reprojection error gives a statistically optimal estimate. this has been solved for two views. however, for three or more views, it is not clear how this should be done. a previously proposed, but computationally impractical, method draws on gröbner basis techniques to solve for the complete set of stationary points of the cost function. we show how this method can be modified to become significantly more stable and hence given a fast implementation in standard ieee double precision. we evaluate the precision and speed of the new method on both synthetic and real data. the algorithm has been implemented in a freely available software package which can be downloaded from the internet.
recognition of digital images of the human face at ultra low resolution via illumination spaces. recent work has established that digital images of a human face, collected under various illumination conditions, contain discriminatory information that can be used in classification. in this paper we demonstrate that sufficient discriminatory information persists at ultralow resolution to enable a computer to recognize specific human faces in settings beyond human capabilities. for instance, we utilized the haar wavelet to modify a collection of images to emulate pictures from a 25- pixel camera. from these modified images, a low-resolution illumination space was constructed for each individual in the cmu-pie database. each illumination space was then interpreted as a point on a grassmann manifold. classification that exploited the geometry on this manifold yielded error-free classification rates for this data set. this suggests the general utility of a low-resolution illumination camera for set-based image recognition problems.
an adaptive nonparametric discriminant analysis method and its application to face recognition. linear discriminant analysis (lda) is frequently used for dimension reduction and has been successfully utilized in many applications, especially face recognition. in classical lda, however, the definition of the between-class scatter matrix can cause large overlaps between neighboring classes, because lda assumes that all classes obey a gaussian distribution with the same covariance. we therefore, propose an adaptive nonparametric discriminant analysis (anda) algorithm that maximizes the distance between neighboring samples belonging to different classes, thus improving the discriminating power of the samples near the classification borders. to evaluate its performance thoroughly, we have compared our anda algorithm with traditional pca+lda, orthogonal lda (olda) and nonparametric discriminant analysis (nda) on the feret and orl face databases. experimental results show that the proposed algorithm outperforms the others.
sports classification using cross-ratio histograms. the paper proposes a novel approach for classification of sports images based on the geometric information encoded in the image of a sport's field. the proposed approach uses invariant nature of a crossratio under projective transformation to develop a robust classifier. for a given image, cross-ratios are computed for the points obtained from the intersection of lines detected using hough transform. these cross-ratios are represented by a histogram which forms a feature vector for the image. an svm classifier trained on aprior model histograms of crossratios for sports fields is used to decide the most likely sport's field in the image. experimental validation shows robust classification using the proposed approach for images of tennis, football, badminton, basketball taken from dissimilar view points.
multiplexed illumination for measuring brdf using an ellipsoidal mirror and a projector. measuring a bidirectional reflectance distribution function (brdf) requires long time because a target object must be illuminated from all incident angles and the reflected light must be measured from all reflected angles. a high-speed method is presented to measure brdfs using an ellipsoidal mirror and a projector. the method can change incident angles without a mechanical drive. moreover, it is shown that the dynamic range of the measured brdf can be significantly increased by multiplexed illumination based on the hadamard matrix.
image segmentation using iterated graph cuts based on multi-scale smoothing. we present a novel approach to image segmentation using iterated graph cuts based on multi-scale smoothing. we compute the prior probability obtained by the likelihood from a color histogram and a distance transform using the segmentation results from graph cuts in the previous process, and set the probability as the t-link of the graph for the next process. the proposed method can segment the regions of an object with a stepwise process from global to local segmentation by iterating the graph-cuts process with gaussian smoothing using different values for the standard deviation. we demonstrate that we can obtain 4.7% better segmentation than that with the conventional approach.
person-similarity weighted feature for expression recognition. in this paper, a new method to extract person-independent expression feature based on hosvd (higher-order singular value decomposition) is proposed for facial expression recognition. with the assumption that similar persons have similar facial expression appearance and shape, person-similarity weighted expression feature is used to estimate the expression feature of the test person. as a result, the estimated expression feature can reduce the influence of individual caused by insufficient training data and becomes less person-dependent, and can be more robust to new persons. the proposed method has been tested on cohn-kanade facial expression database and japanese female facial expression (jaffe) database. person-independent experimental results show the efficiency of the proposed method.