4,289 research outputs found
Object-based 2D-to-3D video conversion for effective stereoscopic content generation in 3D-TV applications
Three-dimensional television (3D-TV) has gained increasing popularity in the broadcasting domain, as it enables enhanced viewing experiences in comparison to conventional two-dimensional (2D) TV. However, its application has been constrained due to the lack of essential contents, i.e., stereoscopic videos. To alleviate such content shortage, an economical and practical solution is to reuse the huge media resources that are available in monoscopic 2D and convert them to stereoscopic 3D. Although stereoscopic video can be generated from monoscopic sequences using depth measurements extracted from cues like focus blur, motion and size, the quality of the resulting video may be poor as such measurements are usually arbitrarily defined and appear inconsistent with the real scenes. To help solve this problem, a novel method for object-based stereoscopic video generation is proposed which features i) optical-flow based occlusion reasoning in determining depth ordinal, ii) object segmentation using improved region-growing from masks of determined depth layers, and iii) a hybrid depth estimation scheme using content-based matching (inside a small library of true stereo image pairs) and depth-ordinal based regularization. Comprehensive experiments have validated the effectiveness of our proposed 2D-to-3D conversion method in generating stereoscopic videos of consistent depth measurements for 3D-TV applications
A graphical model based solution to the facial feature point tracking problem
In this paper a facial feature point tracker that is motivated by applications
such as human-computer interfaces and facial expression analysis systems is
proposed. The proposed tracker is based on a graphical model framework. The
facial features are tracked through video streams by incorporating statistical relations in time as well as spatial relations between feature points. By exploiting the spatial relationships between feature points, the proposed method provides robustness in real-world conditions such as arbitrary head movements and occlusions. A Gabor feature-based occlusion detector is developed and used to handle occlusions. The performance of the proposed tracker has been evaluated
on real video data under various conditions including occluded facial gestures
and head movements. It is also compared to two popular methods, one based
on Kalman filtering exploiting temporal relations, and the other based on active
appearance models (AAM). Improvements provided by the proposed approach
are demonstrated through both visual displays and quantitative analysis
Tracking and Retexturing Cloth for RealTime Virtual Clothing Applications
Abstract. In this paper, we describe a dynamic texture overlay method from monocular images for real-time visualization of garments in a virtual mirror environment. Similar to looking into a mirror when trying on clothes, we create the same impression but for virtually textured garments. The mirror is replaced by a large display that shows the mirrored image of a camera capturing e.g. the upper body part of a person. By estimating the elastic deformations of the cloth from a single camera in the 2D image plane and recovering the illumination of the textured surface of a shirt in real time, an arbitrary virtual texture can be realistically augmented onto the moving garment such that the person seems to wear the virtual clothing. The result is a combination of the real video and the new augmented model yielding a realistic impression of the virtual piece of cloth
Markerless deformation capture of hoverfly wings using multiple calibrated cameras
This thesis introduces an algorithm for the automated deformation capture of hoverfly
wings from multiple camera image sequences. The algorithm is capable of extracting
dense surface measurements, without the aid of fiducial markers, over an arbitrary number
of wingbeats of hovering flight and requires limited manual initialisation. A novel motion
prediction method, called the ‘normalised stroke model’, makes use of the similarity of adjacent
wing strokes to predict wing keypoint locations, which are then iteratively refined in
a stereo image registration procedure. Outlier removal, wing fitting and further refinement
using independently reconstructed boundary points complete the algorithm. It was tested
on two hovering data sets, as well as a challenging flight manoeuvre. By comparing the
3-d positions of keypoints extracted from these surfaces with those resulting from manual
identification, the accuracy of the algorithm is shown to approach that of a fully manual
approach. In particular, half of the algorithm-extracted keypoints were within 0.17mm of
manually identified keypoints, approximately equal to the error of the manual identification
process. This algorithm is unique among purely image based flapping flight studies in the
level of automation it achieves, and its generality would make it applicable to wing tracking
of other insects
A Novel Euler's Elastica based Segmentation Approach for Noisy Images via using the Progressive Hedging Algorithm
Euler's Elastica based unsupervised segmentation models have strong
capability of completing the missing boundaries for existing objects in a clean
image, but they are not working well for noisy images. This paper aims to
establish a Euler's Elastica based approach that properly deals with random
noises to improve the segmentation performance for noisy images. We solve the
corresponding optimization problem via using the progressive hedging algorithm
(PHA) with a step length suggested by the alternating direction method of
multipliers (ADMM). Technically, all the simplified convex versions of the
subproblems derived from the major framework of PHA can be obtained by using
the curvature weighted approach and the convex relaxation method. Then an
alternating optimization strategy is applied with the merits of using some
powerful accelerating techniques including the fast Fourier transform (FFT) and
generalized soft threshold formulas. Extensive experiments have been conducted
on both synthetic and real images, which validated some significant gains of
the proposed segmentation models and demonstrated the advantages of the
developed algorithm
Detection of partial occlusions of assembled components to simplify the disassembly tasks
An automatic disassembly cell requires of a
computer vision system for recognition and localization of
the products and each of theirs components. The detection
of occlusions adds more information to the knowledge base
to identify components and products, and generate a
trustworthy and precise relational model (generic graph of
hierarchic relations among the different components that
make up the product). In this paper, a method to detect
partial occlusions in assembled components is presented.
This method is based on the fusion of regions and edges
information, and it offers a certain degree of simplification
for the recognition and modelled of the disassembly tasks,
of the set of components which compose the product. The
proposed approach to detect regions is a hybrid approach
between RGB and HSV spaces. The bi-dimensional histogram
V/S is employed for the selection of the appropriate
thresholds which serve as an aid to diminish the influence
of the highlights and shadows in images. The goal of this
paper is to present an approach for the detection of
occlusions in assembled components from a combination
of HSV and RGB spaces, a bi-dimensional histogram and
an edge detector.This work was funded by the following
Spanish MCYT project “DESAURO: Desensamblado Automático
Selectivo para Reciclado mediante Robots Cooperativos y Sistema
Multisensorial” (MCYT (DPI2002-02103)
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