waashk/dblp huggingface dataset:
high dynamic range scene realization using two complementary images. many existing tone reproduction schemes are based on the use of a single high dynamic range (hdr) image and are therefore unable to accurately recover the local details and colors of the scene due to the limited information available. accordingly, the current study develops a novel tone reproduction system which utilizes two images with different exposures to capture both the local details and color information of the low- and high-luminance regions of a scene. by computing the local region of each pixel, whose radius is determined via an iterative morphological erosion process, the proposed system implements a pixel-wise local tone mapping module which compresses the luminance range and enhances the local contrast in the low-exposure image. and a local color mapping module is applied to capture the precise color information from the high-exposure image. subsequently, a fusion process is then performed to fuse the local tone mapping and color mapping results to generate highly realistic reproductions of hdr scenes.
human pose estimation from volume data and topological graph database. this paper proposes a novel volume-based motion capture method using a bottom-up analysis of volume data and an example topology database of the human body. by using a two-step graph matching algorithm with many example topological graphs corresponding to postures that a human body can take, the proposed method does not require any initial parameters or iterative convergence processes, and it can solve the changing topology problem of the human body. first, three-dimensional curved lines (skeleton) are extracted from the captured volume data using the thinning process. the skeleton is then converted into an attributed graph. by using a graph matching algorithm with a large amount of example data, we can identify the body parts from each curved line in the skeleton. the proposed method is evaluated using several video sequences of a single person and multiple people, and we can confirm the validity of our approach.
non-parametric background and shadow modeling for object detection. we propose a fast algorithm to estimate background models using parzen density estimation in non-stationary scenes. each pixel has a probability density which approximates pixel values observed in a video sequence. it is important to estimate a probability density function fast and accurately. in our approach, the probability density function is partially updated within the range of the window function based on the observed pixel value. the model adapts quickly to changes in the scene and foreground objects can be robustly detected. in addition, applying our approach to cast-shadow modeling, we can detect moving cast shadows. several experiments show the effectiveness of our approach.
on-line ensemble svm for robust object tracking. in this paper, we present a novel visual object tracking algorithm based on ensemble of linear svm classifiers. there are two main contributions in this paper. first of all, we propose a simple yet effective way for on-line updating linear svm classifier, where useful "key frames" of target are automatically selected as support vectors. secondly, we propose an on-line ensemble svm tracker, which can effectively handle target appearance variation. the proposed algorithm makes better usage of history information, which leads to better discrimination of target and the surrounding background. the proposed algorithm is tested on many video clips including some public available ones. experimental results show the robustness of our proposed algorithm, especially under large appearance change during tracking.
road sign detection using eigen color. this paper presents a novel color-based method to detect road signs directly from videos. a road sign usually has specific colors and high contrast to its background. traditional color-based approaches need to train different color detectors for detecting road signs if their colors are different. this paper presents a novel color model derived from karhunen-loeve (kl) transform to detect road sign color pixels from the background. the proposed color transform model is invariant to different perspective effects and occlusions. furthermore, only one color model is needed to detect various road signs. after transformation into the proposed color space, a rbf (radial basis function) network is trained for finding all possible road sign candidates. then, a verification process is applied to these candidates according to their edge maps. due to the filtering effect and discriminative ability of the proposed color model, different road signs can be very efficiently detected from videos. experiment results have proved that the proposed method is robust, accurate, and powerful in road sign detection.
logical dp matching for detecting similar subsequence. a logical dynamic programming (dp) matching algorithm is proposed for extracting similar subpatterns from two sequential patterns. in the proposed algorithm, local similarity between two patterns is measured by a logical function, called support. the dp matching with the support can extract all similar subpatterns simultaneously while compensating nonlinear fluctuation. the performance of the proposed algorithm was evaluated qualitatively and quantitatively via an experiment of extracting motion primitives, i.e., common subpatterns in gesture patterns of different classes.
multi-view gymnastic activity recognition with fused hmm. more and more researchers focus their studies on multiview activity recognition, because a fixed view could not provide enough information for recognition. in this paper, we use multi-view features to recognize six kinds of gymnastic activities. firstly, shape-based features are extracted from two orthogonal cameras in the form of r transform. then a multi-view approach based on fused hmm is proposed to combine different features for similar gymnastic activity recognition. compared with other activity models, our method achieves better performance even in the case of frame loss.
adaboost learning for human detection based on histograms of oriented gradients. we developed a novel learning-based human detection system, which can detect people having different sizes and orientations, under a wide variety of backgrounds or even with crowds. to overcome the affects of geometric and rotational variations, the system automatically assigns the dominant orientations of each block-based feature encoding by using the rectangular- and circular-type histograms of orientated gradients (hog), which are insensitive to various lightings and noises at the outdoor environment. moreover, this work demonstrated that gaussian weight and tri-linear interpolation for hog feature construction can increase detection performance. particularly, a powerful feature selection algorithm, adaboost, is performed to automatically select a small set of discriminative hog features with orientation information in order to achieve robust detection results. the overall computational time is further reduced significantly without any performance loss by using the cascade-ofrejecter structure, whose hyperplanes and weights of each stage are estimated by using the adaboost approach.
object detection combining recognition and segmentation. we develop an object detection method combining top-down recognition with bottom-up image segmentation. there are two main steps in this method: a hypothesis generation step and a verification step. in the top-down hypothesis generation step, we design an improved shape context feature, which is more robust to object deformation and background clutter. the improved shape context is used to generate a set of hypotheses of object locations and figure-ground masks, which have high recall and low precision rate. in the verification step, we first compute a set of feasible segmentations that are consistent with top-down object hypotheses, then we propose a false positive pruning (fpp) procedure to prune out false positives. we exploit the fact that false positive regions typically do not align with any feasible image segmentation. experiments show that this simple framework is capable of achieving both high recall and high precision with only a few positive training examples and that this method can be generalized to many object classes.
discriminative mean shift tracking with auxiliary particles. we present a new approach towards efficient and robust tracking by incorporating the efficiency of the mean shift algorithm with the robustness of the particle filtering. the mean shift tracking algorithm is robust and effective when the representation of a target is sufficiently discriminative, the target does not jump beyond the bandwidth, and no serious distractions exist. in case of sudden motion, the particle filtering outperforms the mean shift algorithm at the expense of using a large particle set. in our approach, the mean shift algorithm is used as long as it provides reasonable performance. auxiliary particles are introduced to conquer the distraction and sudden motion problems when such threats are detected. moreover, discriminative features are selected according to the separation of the foreground and background distributions. we demonstrate the performance of our approach by comparing it with other trackers on challenging image sequences.
efficient normalized cross correlation based on adaptive multilevel successive elimination. in this paper we propose an efficient normalized cross correlation (ncc) algorithm for pattern matching based on adaptive multilevel successive elimination. this successive elimination scheme is applied in conjunction with an upper bound for the cross correlation derived from cauchy-schwarz inequality. to apply the successive elimination, we partition the summation of cross correlation into different levels with the partition order determined by the gradient energies of the partitioned regions in the template. thus, this adaptive multi-level successive elimination scheme can be employed to early reject most candidates to reduce the computational cost. experimental results show the proposed algorithm is very efficient for pattern matching under different lighting conditions.
efficient texture representation using multi-scale regions. this paper introduces an efficient way of representing textures using connected regions which are formed by coherent multi-scale over-segmentations. we show that the recently introduced covariance-based similarity measure, initially applied on rectangular windows, can be used with our newly devised, irregular structure-coherent patches; increasing the discriminative power and consistency of the texture representation. furthermore, by treating texture in multiple scales, we allow for an implicit encoding of the spatial and statistical texture properties which are persistent across scale. the meaningfulness and efficiency of the covariance based texture representation is verified utilizing a simple binary segmentation method based on min-cut. our experiments show that the proposed method, despite the low dimensional representation in use, is able to effectively discriminate textures and that its performance compares favorably with the state of the art.
tracking iris contour with a 3d eye-model for gaze estimation. this paper describes a sophisticated method to track iris contour and to estimate eye gaze for blinking eyes with a monocular camera. a 3d eye-model that consists of eyeballs, iris contours and eyelids is designed that describes the geometrical properties and the movements of eyes. both the iris contours and the eyelid contours are tracked by using this eye-model and a particle filter. this algorithm is able to detect "pure" iris contours because it can distinguish iris contours from eyelids contours. the eye gaze is described by the movement parameters of the 3d eye model, which are estimated by the particle filter during tracking. other distinctive features of this algorithm are: 1) it does not require any special light sources (e.g. an infrared illuminator) and 2) it can operate at video rate. through extensive experiments on real video sequences we confirmed the robustness and the effectiveness of our method.
feature management for efficient camera tracking. in dynamic scenes with occluding objects many features need to be tracked for a robust real-time camera pose estimation. an open problem is that tracking too many features has a negative effect on the real-time capability of a tracking approach. this paper proposes a method for the feature management which performs a statistical analysis of the ability to track a feature and then uses only those features which are very likely to be tracked from a current camera position. thereby a large set of features in different scales is created, where every feature holds a probability distribution of camera positions from which the feature can be tracked successfully. as only the feature points with the highest probability are used in the tracking step, the method can handle a large amount of features in different scale without losing the ability of real time performance. both the statistical analysis and the reconstruction of the features' 3d coordinates are performed online during the tracking and no preprocessing step is needed.
multi-posture human detection in video frames by motion contour matching. in the paper, we proposed a method for moving human detection in video frames by motion contour matching. firstly, temporal and spatial difference of frames is calculated and contour pixels are extracted by global thresholding as the basic features. then, skeleton templates with multiple representative postures are built on these features to represent multiposture human contours. in the detection procedure, a dynamic programming algorithm is adopted to find best global match between the built templates and with extracted contour features. finally a thresholding method is used to classify a matching result into moving human or negatives. and in the matching process scale problem and interpersonal contour difference are considered. experiments on real video data prove the effectiveness of the proposed method.
measurement of reflection properties in ancient japanese drawing ukiyo-e. ukiyo-e is one famous traditional woodblock type japanese drawing. some pattern printed by special print techniques can only be seen from some special direction. this phenomenon relate to the reflection properties on the surface of ukiyo-e. in this paper, we propose a method to measure these reflection properties of ukiyo-e. fitstly, the normal on the surface and the direction of the fiber in japanese paper are computed from photos which are taken by a measuring machine named ogm. then, fit the reflection model to the measured data and the reflection properties of ukiyo-e can be obtained. based on these parameters, the the appearance of ukiyo-e can be rendered on real-time.
camera calibration using principal-axes aligned conics. the projective geometric properties of two principal-axes aligned (paa) conics in a model plane are investigated in this paper by utilized the generalized eigenvalue decomposition (ged). we demonstrate that one constraint on the image of the absolute conic (iac) can be obtained from a single image of two paa conics even if their parameters are unknown. and if the eccentricity of one of the two conics is given, two constraints on the iac can be obtained. an important merit of the algorithm using paa is that it can be employed to avoid the ambiguities when estimating extrinsic parameters in the calibration algorithms using concentric circles. we evaluate the characteristics and robustness of the proposed algorithm in experiments with synthetic and real data.
color constancy via convex kernel optimization. this paper introduces a novel convex kernel based method for color constancy computation with explicit illuminant parameter estimation. a simple linear render model is adopted and the illuminants in a new scene that contains some of the color surfaces seen in the training image are sequentially estimated in a global optimization framework. the proposed method is fully data-driven and initialization invariant. nonlinear color constancy can also be approximately solved in this kernel optimization framework with piecewise linear assumption. extensive experiments on real-scene images validate the practical performance of our method.
kernel-bayesian framework for object tracking. this paper proposes a general kernel-bayesian framework for object tracking. in this framework, the kernel based method--mean shift algorithm is embedded into the bayesian framework seamlessly to provide a heuristic prior information to the state transition model, aiming at effectively alleviating the heavy computational load and avoiding sample degeneracy suffered by the conventional bayesian trackers. moreover, the tracked object is characterized by a spatial-constraint mog (mixture of gaussians) based appearance model, which is shown more discriminative than the traditional mog based appearance model. meantime, a novel selective updating technique for the appearance model is developed to accommodate the changes in both appearance and illumination. experimental results demonstrate that, compared with bayesian and kernel based tracking frameworks, the proposed algorithm is more efficient and effective.
optimal learning high-order markov random fields priors of colour image. in this paper, we present an optimised learning algorithm for learning the parametric prior models for high-order markov random fields (mrf) of colour images. compared to the priors used by conventional low-order mrfs, the learned priors have richer expressive power and can capture the statistics of natural scenes. our proposed optimal learning algorithm is achieved by simplifying the estimation of partition function without compromising the accuracy of the learned model. the parameters in mrf colour image priors are learned alternatively and iteratively in an em-like fashion by maximising their likelihood. we demonstrate the capability of the proposed learning algorithm of high-order mrf colour image priors with the application of colour image denoising. experimental results show the superior performance of our algorithm compared to the state-of-the-art of colour image priors in [1], although we use a much smaller training image set.
near-optimal mosaic selection for rotating and zooming video cameras. applying graph-theoretic concepts to solve computer vision problems makes it not only trivial to analyze the complexity of the problem at hand, but also existing algorithms from the graph-theory literature can be used to find a solution. we consider the challenging tasks of frame selection for use in mosaicing, and feature selection from computer vision, andmachine learning, respectively, and demonstrate that we can map these problems into the existing graph theory problem of finding the maximum independent set. for frame selection, we represent the temporal and spatial connectivity of the images in a video sequence by a graph, and demonstrate that the optimal subset of images to be used in mosaicing can be determined by finding the maximum independent set of the graph. this process of determining the maximum independent set, not only reduces the overhead of using all the images, which may not be significantly contributing in building the mosaic, but also implicitly solves the "camera loop-back" problem. for feature selection, we conclude that we can apply a similar mapping to the maximum independent set problem to obtain a solution. finally, to demonstrate the efficacy of our frame selection method, we build a system for mosaicing, which uses our method of frame selection.
efficient registration of aerial image sequences without camera priors. we present an efficient approach for finding homographies between sequences of aerial images. we propose a two-step approach: a) initially solving for image-plane rotation and scale parameters without using correspondence (under affine assumption), and b) using these parameters to constrain the full homography search, and c) extending the results to full perspective projection. no flight meta-data, camera priors, or any other user defined information is used for the task. based on the perspective parameters estimated, the aerial images are stitched with the best matching image based on a probabilistic model, to compose a high resolution aerial image mosaic. while retaining the improved asymptotic worst-case complexity of [6], we demonstrate significant performance improvements in practice.
three dimensional position measurement for maxillofacial surgery by stereo x-ray images. this paper describes a method whereby a three dimensional position inside a human body can be measured using a simple x-ray stereo image pair. because the geometry of x-ray imaging is similar to that of ordinary photography, a standard stereo vision technique can be used. however, one problem is that the x-ray source position is unknown and should be computed from the x-ray image. in addition, a reference coordinate on which the measurement is based needs to be determined. the proposed method solves these two problems using a cubic wire frame called the reference object. although three dimensional positioning for a human body is possible by computer tomography (ct), it requires expensive equipment. in contrast, the proposed method only requires ordinary x-ray photography equipment, which is inexpensive and widely available even in developing countries.
the kernel orthogonal mutual subspace method and its application to 3d object recognition. this paper proposes the kernel orthogonal mutual subspace method (komsm) for 3d object recognition. komsm is a kernel-based method for classifying sets of patterns such as video frames or multiview images. it classifies objects based on the canonical angles between the nonlinear subspaces, which are generated from the image patterns of each object class by kernel pca. this methodology has been introduced in the kernel mutual subspace method (kmsm). however, komsm is different from kmsm in that nonlinear class subspaces are orthogonalized based on the framework proposed by fukunaga and koontz before calculating the canonical angles. this orthogonalization provides a powerful feature extraction method for improving the performance of kmsm. the validity of komsm is demonstrated through experiments using face images and images from a public database.
video mosaicing based on structure from motion for distortion-free document digitization. this paper presents a novel video mosaicing method capable of generating a geometric distortion-free mosaic image using a hand-held camera. for a document composed of curved pages, mosaic images of virtually flattened pages are generated. the process of our method is composed of two stages : real-time stage and off-line stage. in the realtime stage, image features are automatically tracked on the input images, and the viewpoint of each image as well as the 3-d position of each image feature are estimated by a structure-from-motion technique. in the offline stage, the estimated viewpoint and 3-d position of each feature are refined and utilized to generate a geometric distortion-free mosaic image. we demonstrate our prototype system on curved documents to show the feasibility of our approach.