Silhouette

Human identification at distance by analysis of gait patterns extracted from video has recently become very popular research in biometrics. This paper presents multi-projections based approach to extract gait patterns for human recognition. Binarized silhouette of a motion object is represented by 1-D signals which are the basic image features called the distance vectors. The distance vectors are differences between the bounding box and silhouette, and extracted using four projections to silhouette. Eigenspace transformation is applied to time-varying distance vectors and the statistical distance based supervised pattern classification is then performed in the lower-dimensional eigenspace for human identification. A fusion strategy developed is finally executed to produce final decision. Based on normalized correlation on the distance vectors, gait cycle estimation is also performed to extract the gait cycle. Experimental results on four databases demonstrate that the right person in top two matches 100% of the times for the cases where training and testing sets corresponds to the same walking styles, and in top three-four matches 100% of the times for training and testing sets corresponds to the different walking styles.

The problem of determining the camera motion from apparent contours or silhouettes of a priori unknown curved 3D surfaces is considered. In a sequence of images, it is shown how to use the generalized epipolar constraint on apparent contours. One such constraint is obtained for each epipolar tangency point in each image pair. An accurate algorithm for computing the motion is presented based on a maximum likelihood estimate. It is shown how to generate initial estimates on the camera motion using only the tracked contours. It is also shown that in theory the motion can be calculated from the deformation of a single contour. The algorithm has been tested on several real image sequences, for both Euclidean and projective reconstruction. The resulting motion estimate is compared to motion estimates calculated independently using standard feature-based methods. The motion estimate is also used to classify the silhouettes as curves or apparent contours. The statistical evaluation shows that the technique gives accurate and stable results

In this paper, we present an algorithm to probabilistically estimate object shapes in a 3D dynamic scene using their silhouette information derived from multiple geometrically calibrated video camcorders. The scene is represented by a 3D volume. Every object in the scene is associated with a distinctive label to represent its existence at every voxel location. The label links together automatically-learned view-specific appearance models of the respective object, so as to avoid the photometric calibration of the cameras. Generative ...

Abstract Population of old generation that live alone is growing in most countries. Surveillance systems help them stay home and reduce the burden on the healthcare system. Automatic visual surveillance systems have advantages over wearable devices. They extract features from video sequences and use them for event classification. But these features are dependent on the position of cameras relative to the person. Therefore they need multi-camera for more accuracy that increases cost and complexity. In this paper we propose ...

Authoring virtual terrains can be a challenging task. Proce-dural and stochastic methods for automated terrain genera-tion produce plausible results but lack intuitive control of the terrain features, while data driven methods offer more cre-ative control at the cost of a limited feature set, higher stor-age requirements and blending artefacts. Moreover, artists often prefer a workflow involving varied reference material such as photographs, concept art, elevation maps and satel-lite images, for the incorporation of which there is little sup-port from commercial content-creation tools. We present a sketch-based toolset for asset-guided creation and intuitive editing of virtual terrains, allowing the manipulation of both elevation maps and 3D meshes, and exploiting a layer-based interface. We employ a frequency-band subdivision of eleva-tion maps to allow using the appropriate editing tool for each level of detail. Using our system, we show that a user can start from various input ty...

Most current psychological theories of face recognition suggest that faces are stored as multiple 2D views. This research aims to explore the application of 3D face representations by means of a new paradigm. Participants were required to match frontal views of faces to silhouettes of the same faces. The formats of the face stimuli were modified in different experiments to make 3D representations accessible (Experiments 1 and 2) or inaccessible (Experiment 3). Multiple 2D view-based algorithms were not applicable due to the singularity of the frontal-view faces. The results disclosed the application and adaptability of 3D face representations. Participants can readily solve the tasks when the face images retain the information essential for the formation of a 3D face representations. However, the performance substantially declined when the 3D information in faces was eliminated (Experiment 3). Performance also varied between different face orientations and different participant groups.

We present a method for inhomogeneous 2D texture mapping guided by a feature mask, that preserves some regions of the image, such as foreground objects or other prominent parts. The method is able to arbitrarily warp a given image while preserving the shape of its features by constraining their deformation to be a similarity transformation. In particular, our method allows global or local changes to the aspect ratio of the texture without causing undesirable shearing to the features. The algorithmic core of our method is a particular formulation of the Laplacian editing technique, suited to accommodate similarity constraints on parts of the domain. The method is useful in digital imaging, texture design and any other applications involving image warping, where parts of the image have high familiarity and should retain their shape after modification.

We propose an adaptive form of frameless rendering with the potential to dramatically increase rendering speed over conventional interactive rendering approaches. Without the rigid sampling patterns of framed renderers, sampling and reconstruction can adapt with very fine granularity to spatio-temporal color change. A sampler uses closed-loop feedback to guide sampling toward edges or motion in the image. Temporally deep buffers store all the samples created over a short time interval for use in reconstruction and as sampler feedback. GPU-based reconstruction responds both to sampling density and space-time color gradients. Where the displayed scene is static, spatial color change dominates and older samples are given significant weight in reconstruction, resulting in sharper and eventually antialiased images. Where the scene is dynamic, more recent samples are emphasized, resulting in less sharp but more up-to-date images. We also use sample reprojection to improve reconstruction a...

This paper describes a novel extension of the photon mapping algorithm, capable of handling both volume multiple inelastic scattering and curved light paths simultaneously. The extension is based on the Full Radiative Transfer Equation (FRTE) and Fermat's law, and yields ...