waashk/dblp huggingface dataset:
information fusion for multi-camera and multi-body structure and motion. information fusion algorithms have been successful in many vision tasks such as stereo, motion estimation, registration and robot localization. stereo and motion image analysis are intimately connected and can provide complementary information to obtain robust estimates of scene structure and motion. we present an information fusion based approach for multi-camera and multi-body structure and motion that combines bottom-up and top-down knowledge on scene structure and motion. the only assumption we make is that all scene motion consists of rigid motion. we present experimental results on synthetic and nonsynthetic data sets, demonstrating excellent performance compared to binocular based state-of-the-art approaches for structure and motion.
a basin morphology approach to colour image segmentation by region merging. the problem of colour image segmentation is investigated in the context of mathematical morphology. morphological operators are extended to colour images by means of a lexicographical ordering in a polar colour space, which are then employed in the preprocessing stage. the actual segmentation is based on the use of the watershed transformation, followed by region merging, with the procedure being formalized as a basin morphology, where regions are "eroded" in order to form greater catchment basins. the result is a fully automated processing chain, with multiple levels of parametrisation and flexibility, the application of which is illustrated by means of the berkeley segmentation dataset.
embedding a region merging prior in level set vector-valued image segmentation. in the scope of level set image segmentation, the number of regions is fixed beforehand. this number occurs as a constant in the objective functional and its optimization. in this study, we propose a region merging prior which optimizes the objective functional implicitly with respect to the number of regions. a statistical interpretation of the functional and learning over a set of relevant images and segmentation examples allow setting the weight of this prior to obtain the correct number of regions. this method is investigated and validated with color images and motion maps.
continuously tracking objects across multiple widely separated cameras. in this paper, we present a new solution to the problem of multi-camera tracking with non-overlapping fields of view. the identities of moving objects are maintained when they are traveling from one camera to another. appearance information and spatio-temporal information are explored and combined in a maximum a posteriori (map) framework. in computing appearance probability, a two-layered histogram representation is proposed to incorporate spatial information of objects. diffusion distance is employed to histogram matching to compensate for illumination changes and camera distortions. in deriving spatio-temporal probability, transition time distribution between each pair of entry zone and exit zone is modeled as a mixture of gaussian distributions. experimental results demonstrate the effectiveness of the proposed method.
spatiotemporal oriented energy features for visual tracking. this paper presents a novel feature set for visual tracking that is derived from "oriented energies". more specifically, energy measures are used to capture a target's multiscale orientation structure across both space and time, yielding a rich description of its spatiotemporal characteristics. to illustrate utility with respect to a particular tracking mechanism, we show how to instantiate oriented energy features efficiently within the mean shift estimator. empirical evaluations of the resulting algorithm illustrate that it excels in certain important situations, such as tracking in clutter with multiple similarly colored objects and environments with changing illumination. many trackers fail when presented with these types of challenging video sequences.
a cascade of feed-forward classifiers for fast pedestrian detection. we develop a method that can detect humans in a single image based on a new cascaded structure. in our approach, both the rectangle features and 1-d edge-orientation features are employed in the feature pool for weak-learner selection, which can be computed via the integral-image and the integral-histogram techniques, respectively. to make the weak learner more discriminative, real adaboost is used for feature selection and learning the stage classifiers from the training images. instead of the standard boosted cascade, a novel cascaded structure that exploits both the stage-wise classification information and the interstage cross-reference information is proposed. experimental results show that our approach can detect people with both efficiency and accuracy.
synchronized ego-motion recovery of two face-to-face cameras. a movie captured by a wearable camera affixed to an actor's body gives audiences the sense of "immerse in the movie". the raw movie captured by wearable camera needs stabilization with jitters due to ego-motion. however, conventional approaches often fail in accurate ego-motion estimation when there are moving objects in the image and no sufficient feature pairs provided by background region. to address this problem, we proposed a new approach that utilizes an additional synchronized video captured by the camera attached on the foreground object (another actor). formally we configure above sensor system as two face-to-face moving cameras. then we derived the relations between four views including two consecutive views from each camera. the proposed solution has two steps. firstly we calibrate the extrinsic relationship of two cameras with an ax=xb formulation, and secondly estimate the motion using calibration matrix. experiments verify that this approach can recover from failures of conventional approach and provide acceptable stabilization results for real data.
multiperspective distortion correction using collineations. we present a new framework for correcting multiperspective distortions using collineations. a collineation describes the transformation between the images of a camera due to changes in sampling and image plane selection. we show that image distortions in many previous models of cameras can be effectively reduced via proper collineations. to correct distortions in a specific multiperspective camera, we develop an interactive system that allows users to select feature rays from the camera and position them at the desirable pixels. our system then computes the optimal collineation to match the projections of these rays with the corresponding pixels. experiments demonstrate that our system robustly corrects complex distortions without acquiring the scene geometry, and the resulting images appear nearly undistorted.
multi-camera people tracking by collaborative particle filters and principal axis-based integration. this paper presents a novel approach to tracking people in multiple cameras. a target is tracked not only in each camera but also in the ground plane by individual particle filters. these particle filters collaborate in two different ways. first, the particle filters in each camera pass messages to those in the ground plane where the multicamera information is integrated by intersecting the targets' principal axes. this largely relaxes the dependence on precise foot positions when mapping targets from images to the ground plane using homographies. secondly, the fusion results in the ground plane are then incorporated by each camera as boosted proposal functions. a mixture proposal function is composed for each tracker in a camera by combining an independent transition kernel and the boosted proposal function. experiments show that our approach achieves more reliable results using less computational resources than conventional methods.
machine vision in early days: japan's pioneering contributions. the history of machine vision started in the mid-1960s by the efforts of japanese industry researchers. a variety of prominent vision-based systems was made possible by creating and evolving real-time image processing techniques, and was applied to factory automation, office automation, and even to social automation during the 1970-2000 period. in this article, these historical attempts are briefly explained to promote understanding of the pioneering efforts that opened the door and formed the bases of today's computer vision research.
how marginal likelihood inference unifies entropy, correlation and snr-based stopping in nonlinear diffusion scale-spaces. iterative smoothing algorithms are frequently applied in image restoration tasks. the result depends crucially on the optimal stopping (scale selection) criteria. an attempt is made towards the unification of the two frequently applied model selection ideas: (i) the earliest time when the 'entropy of the signal' reaches its steady state, suggested by j. sporring and j. weickert (1999), and (ii) the time of the minimal 'correlation' between the diffusion outcome and the noise estimate, investigated by p. mrázek and m. navara (2003). it is shown that both ideas are particular cases of the marginal likelihood inference. better entropy measures are discovered and their connection to the generalized signal-to-noise ratio is emphasized.
pedestrian detection using global-local motion patterns. we propose a novel learning strategy called global-local motion pattern classification (glmpc) to localize pedestrian-like motion patterns in videos. instead of modeling such patterns as a single class that alone can lead to high intra-class variability, three meaningful partitions are considered - left, right and frontal motion. an adaboost classifier based on the most discriminative eigenflow weak classifiers is learnt for each of these subsets separately. furthermore, a linear three-class svm classifier is trained to estimate the global motion direction. to detect pedestrians in a given image sequence, the candidate optical flow sub-windows are tested by estimating the global motion direction followed by feeding to the matched adaboost classifier. the comparison with two baseline algorithms including the degenerate case of a single motion class shows an improvement of 37% in false positive rate.
flea, do you remember me? the ability to detect and recognize individuals is essential for an autonomous robot interacting with humans even if computational resources are usually rather limited. in general a small user group can be assumed for interaction. the robot has to distinguish between multiple users and further on between known and unknown persons. for solving this problem we propose an approach which integrates detection, recognition and tracking by formulating all tasks as binary classification problems. because of its efficiency it is well suited for robots or other systems with limited resources but nevertheless demonstrates robustness and comparable results to state-of-the-art approaches. we use a common over-complete representation which is shared by the different modules. by means of the integral data structure an efficient feature computation is performed enabling the usage of this system for real-time applications such as for our autonomous robot flea.
optimal algorithms in multiview geometry. this is a survey paper summarizing recent research aimed at finding guaranteed optimal algorithms for solving problems in multiview geometry. many of the traditional problems in multiview geometry now have optimal solutions in terms of minimizing residual imageplane error. success has been achieved in minimizing l2 (least-squares) or l∞ (smallest maximum error) norm. the main methods involve second order cone programming, or quasi-convex optimization, and branch-andbound. the paper gives an overview of the subject while avoiding as far as possible the mathematical details, which can be found in the original papers.
coarse-to-fine statistical shape model by bayesian inference. in this paper, we take a predefined geometry shape as a constraint for accurate shape alignment. a shape model is divided in two parts: fixed shape and active shape. the fixed shape is a user-predefined simple shape with only a few landmarks which can be easily and accurately located by machine or human. the active one is composed of many landmarks with complex shape contour. when searching an active shape, pose parameter is calculated by the fixed shape. bayesian inference is introduced to make the whole shape more robust to local noise generated by the active shape, which leads to a compensation factor and a smooth factor for a coarse-to-fine shape search. this method provides a simple and stable means for online and offline shape analysis. experiments on cheek and face contour demonstrate the effectiveness of our proposed approach.
finding camera overlap in large surveillance networks. recent research on video surveillance across multiple cameras has typically focused on camera networks of the order of 10 cameras. in this paper we argue that existing systems do not scale to a network of hundreds, or thousands, of cameras. we describe the design and deployment of an algorithm called exclusion that is specifically aimed at finding correspondence between regions in cameras for large camera networks. the information recovered by exclusion can be used as the basis for other surveillance tasks such as tracking people through the network, or as an aid to human inspection. we have run this algorithm on a campus network of over 100 cameras, and report on its performance and accuracy over this network.
multiview pedestrian detection based on vector boosting. in this paper, a multiview pedestrian detection method based on vector boosting algorithm is presented. the extended histograms of oriented gradients (ehog) features are formed via dominant orientations in which gradient orientations are quantified into several angle scales that divide gradient orientation space into a number of dominant orientations. blocks of combined rectangles with their dominant orientations constitute the feature pool. the vector boosting algorithm is used to learn a tree-structure detector for multiview pedestrian detection based on ehog features. further a detector pyramid framework over several pedestrian scales is proposed for better performance. experimental results are reported to show its high performance.
a noise-insensitive object tracking algorithm. in this paper, we brought out a noise-insensitive pixel-wise object tracking algorithm whose kernel is a new reliable data grouping algorithm that introduces the reliability evaluation into the existing k-means clustering (called as rk-means clustering). the rk-means clustering concentrates on two problems of the existing k-mean clustering algorithm: 1) the unreliable clustering result when the noise data exists; 2) the bad/wrong clustering result caused by the incorrectly assumed number of clusters. the first problem is solved by evaluating the reliability of classifying an unknown data vector according to the triangular relationship among it and its two nearest cluster centers. noise data will be ignored by being assigned low reliability. the second problem is solved by introducing a new group merging method that can delete pairs of "too near" data groups by checking their variance and average reliability, and then combining them together. we developed a video-rate object tracking system (called as rk-means tracker) with the proposed algorithm. the extensive experiments of tracking various objects in cluttered environments confirmed its effectiveness and advantages.
calibrating pan-tilt cameras with telephoto lenses. pan-tilt cameras are widely used in surveillance networks. these cameras are often equipped with telephoto lenses to capture objects at a distance. such a camera makes full-metric calibration more difficult since the projection with a telephoto lens is close to orthographic. this paper discusses the problems caused by pan-tilt cameras with long focal length and presents a method to improve the calibration accuracy. experiments show that our method reduces the re-projection errors by an order of magnitude compared to popular homography-based approaches.
camera calibration from silhouettes under incomplete circular motion with a constant interval angle. in this paper, we propose an algorithm for camera calibration from silhouettes under circular motion with an unknown constant interval angle. unlike previous silhouette-based methods based on surface of revolution, the proposed algorithm can be applied to sparse and incomplete image sequences. under the assumption of circular motion with a constant interval angle, epipoles of successive image pairs remain constant and can be determined from silhouettes. a pair of epipoles formed by a certain interval angle can provide a constraint on the angle and focal length. with more pairs of epipoles recovered, the focal length can be determined from the one that most satisfies the constraints and determine the interval angle concurrently. the rest of camera parameters can be recovered from image invariants. finally, the estimated parameters are optimized by minimizing the epipolar tangency constraints. experimental results on both synthetic and real images are shown to demonstrate its performance.
hand posture estimation in complex backgrounds by considering mis-match of model. this paper proposes a novel method of estimating 3-d hand posture from images observed in complex backgrounds. conventional methods often cause mistakes by mis-matches of local image features. our method considers possibility of the mis-match between each posture model appearance and the other model appearances in a baysian stochastic estimation form by introducing a novel likelihood concept "mistakenly matching likelihood (mml)". the correct posture model is discriminated from mis-matches by mml-based posture candidate evaluation. the method is applied to hand tracking problem in complex backgrounds and its effectiveness is shown.
learning generative models for monocular body pose estimation. we consider the problem of monocular 3d body pose tracking from video sequences. this task is inherently ambiguous. we propose to learn a generative model of the relationship of body pose and image appearance using a sparse kernel regressor. within a particle filtering framework, the potentially multimodal posterior probability distributions can then be inferred. the 2d bounding box location of the person in the image is estimated along with its body pose. body poses are modelled on a low-dimensional manifold, obtained by lle dimensionality reduction. in addition to the appearance model, we learn a prior model of likely body poses and a nonlinear dynamical model, making both pose and bounding box estimation more robust. the approach is evaluated on a number of challenging video sequences, showing the ability of the approach to deal with low-resolution images and noise.
a theoretical approach to construct highly discriminative features with application in adaboost. adaboost is a practical method of real-time face detection, but abides by a crucial problem of overfitting for the big number of features used in a trained classifier due to the weak discriminative abilities of these features. this paper proposes a theoretical approach to construct highly discriminative features, which is named composed features, from haar-like features. both of the composed and haar-like features are employed to train a multi-view face detector. the primary experiments show promising results in reducing the number of features used in a classifier, which leads to the increase of the generalization ability of the classifier.
3d intrusion detection system with uncalibrated multiple cameras. in this paper, we propose a practical intrusion detection system using uncalibrated multiple cameras. our algorithm combines the contour based multi-planar visual hull method and a projective reconstruction method. to set up the detection system, no advance knowledge or calibration is necessary. a user can specify points in the scene directly with a simple colored marker, and the system automatically generates a restricted area as the convex hull of all specified points. to detect an intrusion, the system computes intersections of an object and each sensitive plane, which is the boundary of the restricted area, by projecting an object silhouette from each image to the sensitive plane using 2d homography. when an object exceeds one sensitive plane, the projected silhouettes from all cameras must have some common regions. therefore, the system can detect intrusion by any object with an arbitrary shape without reconstruction of the 3d shape of the object.
optical flow-driven motion model with automatic variance adjustment for adaptive tracking. we propose a statistical motion model for sequential bayesian tracking, called the optical flow-driven motion model, and show an adaptive particle filter algorithm with the motion model. it predicts the current state with the help of optical flows, i.e., it explores the state space with information based on the current and previous images of an image sequence. in addition, we introduce an automatic method for adjusting the variance of the motion model, which parameter is manually determined in most particle filters. in experiments with synthetic and real image sequences, we compare the proposed motion model with a random walk model, which is a widely used model for tracking, and show the proposed model outperform the random walk model in terms of accuracy even though their execution times are almost the same.
gesture recognition under small sample size. this paper addresses gesture recognition under small sample size, where direct use of traditional classifiers is difficult due to high dimensionality of input space.we propose a pairwise feature extraction method of video volumes for classification. the method of canonical correlation analysis is combined with the discriminant functions and scale-invariant-feature-transform (sift) for the discriminative spatiotemporal features for robust gesture recognition. the proposed method is practically favorable as it works well with a small amount of training samples, involves few parameters, and is computationally efficient. in the experiments using 900 videos of 9 hand gesture classes, the proposed method notably outperformed the classifiers such as support vector machine/relevance vector machine, achieving 85% accuracy.
robust foreground extraction technique using gaussian family model and multiple thresholds. we propose a robust method to extract silhouettes of foreground objects from color video sequences. to cope with various changes in the background, the background is modeled as generalized gaussian family of distributions and updated by the selective running average and static pixel observation. all pixels in the input video image are classified into four initial regions using background subtraction with multiple thresholds, after which shadow regions are eliminated using color components. the final foreground silhouette is extracted by refining the initial region using morphological processes. we have verified that the proposed algorithm works very well in various background and foreground situations through experiments.