waashk/dblp huggingface dataset:
detecting and segmenting un-occluded items by actively casting shadows. we present a simple and practical approach for segmenting unoccluded items in a scene by actively casting shadows. by 'items', we refer to objects (or part of objects) enclosed by depth edges. our approach utilizes the fact that under varying illumination, un-occluded items will cast shadows on occluded items or background, but will not be shadowed themselves. we employ an active illumination approach by taking multiple images under different illumination directions, with illumination source close to the camera. our approach ignores the texture edges in the scene and uses only the shadow and silhouette information to determine the occlusions. we show that such a segmentation does not require the estimation of a depth map or 3d information, which can be cumbersome, expensive and often fails due to the lack of texture and presence of specular objects in the scene. our approach can handle complex scenes with self-shadows and specularities. results on several real scenes along with the analysis of failure cases are presented.
qualitative and quantitative behaviour of geometrical pdes in image processing. we analyse a series of approaches to evolve images. it is motivated by combining gaussian blurring, the mean curvature motion (used for denoising and edge-preserving), and maximal blurring (used for inpainting). we investigate the generalised method using the combination of second order derivatives in terms of gauge coordinates. for the qualitative behaviour, we derive a solution of the pde series and mention its properties briefly. relations with general diffusion equations are discussed. quantitative results are obtained by a novel implementation whose stability and convergence is analysed. the practical results are visualised on a real-life image, showing the expected qualitative behaviour. when a constraint is added that penalises the distance of the results to the input image, one can vary the desired amount of blurring and denoising.
pose-invariant facial expression recognition using variable-intensity templates. in this paper, we propose a method for pose-invariant facial expression recognition from monocular video sequences. the advantage of our method is that, unlike existing methods, our method uses a simple model, called the variable-intensity template, for describing different facial expressions. this makes it possible to prepare a model for each person with very little time and effort. variable-intensity templates describe how the intensities of multiple points, defined in the vicinity of facial parts, vary with different facial expressions. by using this model in the framework of a particle filter, our method is capable of estimating facial poses and expressions simultaneously. experiments demonstrate the effectiveness of our method. a recognition rate of over 90% is achieved for all facial orientations, horizontal, vertical, and in-plane, in the range of ±40 degrees, ±20 degrees, and ±40 degrees from the frontal view, respectively.
efficient search in document image collections. this paper presents an efficient indexing and retrieval scheme for searching in document image databases. in many non-european languages, optical character recognizers are not very accurate. word spotting - word image matching - may instead be used to retrieve word images in response to a word image query. the approaches used for word spotting so far, dynamic time warping and/or nearest neighbor search, tend to be slow. here indexing is done using locality sensitive hashing (lsh) - a technique which computes multiple hashes - using word image features computed at word level. efficiency and scalability is achieved by content-sensitive hashing implemented through approximate nearest neighbor computation. we demonstrate that the technique achieves high precision and recall (in the 90% range), using a large image corpus consisting of seven kalidasa's (a well known indian poet of antiquity) books in the telugu language. the accuracy is comparable to using dynamic time warping and nearest neighbor search while the speed is orders of magnitude better - 20000 word images can be searched in milliseconds.
texture-independent feature-point matching (tifm) from motion coherence. this paper proposes a novel and efficient feature-point matching algorithm for finding point correspondences between two uncalibrated images. the striking feature of the proposed algorithm is that the algorithm is based on the motion coherence/smoothness constraint only, which states that neighboring features in an image tend to move coherently. in the algorithm, the correspondences of feature points in a neighborhood are collectively determined in a way such that the smoothness of the local motion field is maximized. the smoothness constraint does not rely on any image feature, and is self-contained in the motion field. it is robust to the camera motion, scene structure, illumination, etc. this makes the proposed algorithm texture-independent and robust. experimental results show that the proposed method outperforms existing methods for feature-point tracking in image sequences.
mapaco-training: a novel online learning algorithm of behavior models. the traditional co-training algorithm, which needs a great number of unlabeled examples in advance and then trains classifiers by iterative learning approach, is not suitable for online learning of classifiers. to overcome this barrier, we propose a novel semi-supervised learning algorithm, called mapaco-training, by combining the cotraining with the principle of maximum a posteriori adaptation. this mapaco-training algorithm is an online multi-class learning algorithm, and has been successfully applied to online learning of behaviors modeled by hidden markov model. the proposed algorithm is tested with the li's database as well as schuldt's dataset.
cardiac motion estimation from tagged mri using 3d-harp and nurbs volumetric model. concerning analysis of tagged cardiac mr images, harmonic phase (harp) is a promising technique with the largest potential for clinical use in terms of rapidity and automation without tags detection and tracking. however, it is usually applied to 2d images and only provides "apparent motion" information. in this paper, harp is integrated with a nonuniform rational b-spline (nurbs) volumetric model to densely reconstruct 3d motion of left ventricle (lv). the nurbs model represents anatomy of lv compactly, and displacement information that harp provides within short-axis and long-axis images drives the model to deform. after estimating the motion at each phase, we smooth the nurbs models temporally to achieve a 4d continuous time-varying representation of lv motion. experimental results on in vivo data show that the proposed strategy could estimate 3d motion of lv rapidly and effectively benefiting from both harp and nurbs model.
task scheduling in large camera networks. camera networks are increasingly being deployed for security. in most of these camera networks, video sequences are captured, transmitted and archived continuously from all cameras, creating enormous stress on available transmission bandwidth, storage space and computing facilities. we describe an intelligent control system for scheduling pan-tilt-zoom cameras to capture video only when task-specific requirements can be satisfied. these videos are collected in real time during predicted temporal "windows of opportunity". we present a scalable algorithm that constructs schedules in which multiple tasks can possibly be satisfied simultaneously by a given camera. we describe two scheduling algorithms: a greedy algorithm and another based on dynamic programming (dp). we analyze their approximation factors and present simulations that show that the dp method is advantageous for large camera networks in terms of task coverage. results from a prototype real time active camera system however reveal that the greedy algorithm performs faster than the dp algorithm, making it more suitable for a real time system. the prototype system, built using existing low-level vision algorithms, also illustrates the applicability of our algorithms.
a local probabilistic prior-based active contour model for brain mr image segmentation. this paper proposes a probabilistic prior-based active contour model for segmenting human brain mr images. our model is formulated with the maximum a posterior (map) principle and implemented under the level set framework. probabilistic atlas for the structure of interest, e.g., cortical gray matter or caudate nucleus, can be seamlessly integrate into the level set evolution procedure to provide crucial guidance in accurately capturing the target. unlike other region-based active contour models, our solution uses locally varying gaussians to account for intensity inhomogeneity and local variations existing in many mr images are better handled. experiments conducted on whole brain as well as caudate segmentation demonstrate the improvement made by our model.
an active multi-camera motion capture for face, fingers and whole body. this paper explores a novel endeavor of deploying only four active-tracking cameras and fundamental vision-based technologies for 3d motion capture of a full human body figure, which includes facial expression, motion of fingers of both hands and a whole body. the proposed methods suggest alternatives to extract motion parameters of the mentioned body parts from four single-view image sequences. the proposed ellipsoidal model- and flow-based facial expression motion capture solution tackles both 3d head pose and non-rigid facial motion effectively and we observe that a set of 22 self-defined feature points suffice the expression representation. the body figure and fingers motion capture is solved with a combination of articulated model and flow-based methods.
exploiting inter-frame correlation for fast video to reference image alignment. strong temporal correlation between adjacent frames of a video signal has been successfully exploited in standard video compression algorithms. in this work, we show that the temporal correlation in a video signal can also be used for fast video to reference image alignment. to this end, we first divide the input video sequence into groups of pictures (gops). then for each gop, only one frame is completely correlated with the reference image, while for the remaining frames, upper and lower bounds on the correlation coefficient (ρ) are calculated. these newly proposed bounds are significantly tighter than the existing cauchy-schwartz inequality based bounds on ρ. these bounds are used to eliminate majority of the search locations and thus resulting in significant speedup, without effecting the value or location of the global maxima. in our experiments, up to 80% search locations are found to be eliminated and the speedup is up to five times the fft based implementation and up to seven times the spatial domain techniques.
a family of quadratic snakes for road extraction. the geographic information system industry would benefit from flexible automated systems capable of extracting linear structures from satellite imagery. quadratic snakes allow global interactions between points along a contour, and are well suited to segmentation of linear structures such as roads. however, a single quadratic snake is unable to extract disconnected road networks and enclosed regions. we propose to use a family of cooperating snakes, which are able to split, merge, and disappear as necessary. we also propose a preprocessing method based on oriented filtering, thresholding, canny edge detection, and gradient vector flow (gvf) energy. we evaluate the performance of the method in terms of precision and recall in comparison to ground truth data. the family of cooperating snakes consistently outperforms a single snake in a variety of road extraction tasks, and our method for obtaining the gvf is more suitable for road extraction tasks than standard methods.
automated removal of partial occlusion blur. this paper presents a novel, automated method to remove partial occlusion from a single image. in particular, we are concerned with occlusions resulting from objects that fall on or near the lens during exposure. for each such foreground object, we segment the completely occluded region using a geometric flow. we then look outward from the region of complete occlusion at the segmentation boundary to estimate the width of the partially occluded region. once the area of complete occlusion and width of the partially occluded region are known, the contribution of the foreground object can be removed. we present experimental results which demonstrate the ability of this method to remove partial occlusion with minimal user interaction. the result is an image with improved visibility in partially occluded regions, which may convey important information or simply improve the image's aesthetics.
hierarchical learning of dominant constellations for object class recognition. the importance of spatial configuration information for object class recognition is widely recognized. single isolated local appearance codes are often ambiguous. on the other hand, object classes are often characterized by groups of local features appearing in a specific spatial structure. learning these structures can provide additional discriminant cues and boost recognition performance. however, the problem of learning such features automatically from raw images remains largely uninvestigated. in contrast to previous approaches which require accurate localization and segmentation of objects to learn spatial information, we propose learning by hierarchical voting to identify frequently occurring spatial relationships among local features directly from raw images. the method is resistant to common geometric perturbations in both the training and test data. we describe a novel representation developed to this end and present experimental results that validate its efficacy by demonstrating the improvement in class recognition results realized by including the additional learned information.
multistrategical approach in visual learning. in this paper, we propose a novel visual learning framework to develop flexible and accurate object recognition methods. currently, most of visual learning based recognition methods adopt the monostrategy learning framework using a single feature. however, the real-world objects are so complex that it is quite difficult for monostrategy method to correctly classify them. thus, utilizing a wide variety of features is required to precisely distinguish them. in order to utilize various features, we propose multistrategical visual learning by integrating multiple visual learners. in our method, multiple visual learners are collaboratively trained. specifically, a visual learner l intensively learns the examples misclassified by the other visual learners. instead, the other visual learners learn the examples misclassified by l. as a result, a powerful object recognition method can be developed by integrating various visual learners even if they have mediocre recognition performance.
attention monitoring for music contents based on analysis of signal-behavior structures. in this paper, we propose a method to estimate user attention to displayed content signals with temporal analysis of their exhibited behavior. detecting user attention and controlling contents are key issues in our "networked interaction therapy system" that effectively attracts the attention of memory-impaired people. in our proposed method, user behavior, including body motions (beat actions), is detected with auditory/vision-based methods. this design is based on our observations of the behavior of memory-impaired people under video watching conditions. user attention to the displayed content is then estimated based on body motions synchronized to auditorial signals. estimated attention levels can be used for content control to attract deeper attention of viewers to the display system. experimental results suggest that the proposed method effectively extracts user attention to musical signals.
eye-gaze detection from monocular camera image using parametric template matching. in the coming ubiquitous-computing society, an eyegaze interface will be one of the key technologies as an input device. most of the conventional eyegaze tracking algorithms require specific light sources, equipments, devices, etc. in a previous work, the authors developed a simple eye-gaze detection system using a monocular video camera. this paper proposes a fast eye-gaze detection algorithm using the parametric template matching. in our algorithm, the iris extraction by the parametric template matching is applied to the eye-gaze detection based on physiological eyeball model. the parametric template matching can carry out an accurate sub-pixel matching by interpolating a few template images of a user's eye captured in the calibration process for personal error. so, a fast calculation can be realized with keeping the detection accuracy. we construct an eye-gaze communication interface using the proposed algorithm, and verified the performance through key typing experiments using visual keyboard on display.
improved background mixture models for video surveillance applications. background subtraction is a method commonly used to segment objects of interest in image sequences. by comparing new frames to a background model, regions of interest can be found. to cope with highly dynamic and complex environments, a mixture of several models has been proposed. this paper proposes an update of the popular mixture of gaussian models technique. experimental analysis shows a lack of this technique to cope with quick illumination changes. a different matching mechanism is proposed to improve the general robustness and a comparison with related work is given. finally, experimental results are presented to show the gain of the updated technique, according to the standard scheme and the related techniques.
fragments based parametric tracking. the paper proposes a parametric approach for color based tracking. the method fragments a multimodal color object into multiple homogeneous, unimodal, fragments. the fragmentation process consists of multi level thresholding of the object color space followed by an assembling. each homogeneous region is then modelled using a single parametric distribution and the tracking is achieved by fusing the results of the multiple parametric distributions. the advantage of the method lies in tracking complex objects with partial occlusions and various deformations like non-rigid, orientation and scale changes. we evaluate the performance of the proposed approach on standard and challenging real world datasets.
less is more: coded computational photography. computational photography combines plentiful computing, digital sensors, modern optics, actuators, and smart lights to escape the limitations of traditional cameras, enables novel imaging applications and simplifies many computer vision tasks. however, a majority of current computational photography methods involve taking multiple sequential photos by changing scene parameters and fusing the photos to create a richer representation. the goal of coded computational photography is to modify the optics, illumination or sensors at the time of capture so that the scene properties are encoded in a single (or a few) photographs. we describe several applications of coding exposure, aperture, illumination and sensing and describe emerging techniques to recover scene parameters from coded photographs.
tracking and classifying of human motions with gaussian process annealed particle filter. this paper presents a framework for 3d articulated human body tracking and action classification. the method is based on nonlinear dimensionality reduction of high dimensional data space to low dimensional latent space. motion of human body is described by concatenation of low dimensional manifolds which characterize different motion types. we introduce a body pose tracker, which uses the learned mapping function from low dimensional latent space to high dimensional body pose space. the trajectories in the latent space provide low dimensional representations of body poses performed during motion. they are used to classify human actions. the approach was checked on humaneva dataset as well as on our own one. the results and the comparison to other methods are presented.
mirror localization for catadioptric imaging system by observing parallel light pairs. this paper describes a method of mirror localization to calibrate a catadioptric imaging system. while the calibration of a catadioptric system includes the estimation of various parameters, we focus on the localization of the mirror. the proposed method estimates the position of the mirror by observing pairs of parallel lights, which are projected from various directions. although some earlier methods for calibrating catadioptric systems assume that the system is single viewpoint, which is a strong restriction on the position and shape of the mirror, our method does not restrict the position and shape of the mirror. since the constraint used by the proposed method is that the relative angle of two parallel lights is constant with respect to the rigid transformation of the imaging system, we can omit both the translation and rotation between the camera and calibration objects from the parameters to be estimated. therefore, the estimation of the mirror position by the proposed method is independent of the extrinsic parameters of a camera. we compute the error between the model of the mirror and the measurements, and then estimate the position of the mirror by minimizing this error. we test our method using both simulation and real experiments, and evaluate the accuracy thereof.
automated billboard insertion in video. the paper proposes an approach to superimpose virtual contents for advertising in an existing image sequence with no or minimal user interaction. our approach automatically recognizes planar surfaces in the scene over which a billboard can be inserted for seamless display to the viewers. the planar surfaces are segmented in the image frame using a homography dependent scheme. in each of the segmented planar regions, a rectangle with the largest area is located to superimpose a billboard into the original image sequence. it can also provide a viewing index based on the occupancy of the virtual real estate for charging the advertiser.
gait identification based on multi-view observations using omnidirectional camera. we propose a method of gait identification based on multiview gait images using an omnidirectional camera. we first transform omnidirectional silhouette images into panoramic ones and obtain a spatio-temporal gait silhouette volume (gsv). next, we extract frequency-domain features by fourier analysis based on gait periods estimated by autocorrelation of the gsvs. because the omnidirectional camera makes it possible to observe a straight-walking person from various views, multiview features can be extracted from the gsvs composed of multi-view images. in an identification phase, distance between a probe and a gallery feature of the same view is calculated, and then these for all views are integrated for matching. experiments of gait identification including 15 subjects from 5 views demonstrate the effectiveness of the proposed method.