17735204
3D Face Reconstruction Under Imperfect Tracking Circumstances Using Shape Model Constraints
We propose a factorization structure from motion (SfM) framework which employs 3D active shape constraints for a 3D face model application. Two types of shape model, individual shape models and a generic model, are used to approximate non-linear manifold variation. When the 3D shape models are trained, they help the SfM algorithm to reconstruct the 3D face structure under noisy observation (tracking) circumstances. By minimizing two sets of errors, the reconstruction error generated by the linear transform of the shape models and projection error obtained by re-projecting the 3D shape to 2D positions, the 3D face shapes can be recovered optimally. Experimental results show that this algorithm accurately reconstructs the 3D shape of familiar and non-familiar faces from video sequences under circumstances of imperfect face tracking or noisy observations.
1979571
Face Video Competition
Person recognition using facial features, e.g., mug-shot images, has long been used in identity documents. However, due to the widespread use of web-cams and mobile devices embedded with a camera, it is now possible to realise facial video recognition, rather than resorting to just still images. In fact, facial video recognition offers many advantages over still image recognition; these include the potential of boosting the system accuracy and deterring spoof attacks. This paper presents the first known benchmarking effort of person identity verification using facial video data. The evaluation involves 18 systems submitted by seven academic institutes.
1612186
3D facial geometry recovery via group-wise optical flow
We describe an algorithm for automatically finding correspondences from face video sequences. This method is useful to many applications such as face tracking, face modeling and 3D face recovery. Given a sequence of images, the face feature points are tracked by a model-constraint optical flow algorithm. By employing a minimum description length (MDL) point-refinement framework, the drift-off error caused by the optical flow algorithm can be reduced and the correspondences can be matched robustly by optimizing the statistical model. As a result, the face is able to be tracked precisely. Furthermore, it offers a new method of building an appearance model automatically. The objective root mean square error (RMSE) is used to prove the efficiency of the algorithm. At the same time, the performance is evaluated subjectively by generating 3D face models based upon it.
15200693
A formal study of information retrieval heuristics
Empirical studies of information retrieval methods show that good retrieval performance is closely related to the use of various retrieval heuristics, such as TF-IDF weighting. One basic research question is thus what exactly are these "necessary" heuristics that seem to cause good retrieval performance. In this paper, we present a formal study of retrieval heuristics. We formally define a set of basic desirable constraints that any reasonable retrieval function should satisfy, and check these constraints on a variety of representative retrieval functions. We find that none of these retrieval functions satisfies all the constraints unconditionally. Empirical results show that when a constraint is not satisfied, it often indicates non-optimality of the method, and when a constraint is satisfied only for a certain range of parameter values, its performance tends to be poor when the parameter is out of the range. In general, we find that the empirical performance of a retrieval formula is tightly related to how well it satisfies these constraints. Thus the proposed constraints provide a good explanation of many empirical observations and make it possible to evaluate any existing or new retrieval formula analytically.
5471705
Probabilistic Models for Expert Finding
A common task in many applications is to find persons who are knowledgeable about a given topic (i.e., expert finding). In this paper, we propose and develop a general probabilistic framework for studying expert finding problem and derive two families of generative models (candidate generation models and topic generation models) from the framework. These models subsume most existing language models proposed for expert finding. We further propose several techniques to improve the estimation of the proposed models, including incorporating topic expansion, using a mixture model to model candidate mentions in the supporting documents, and defining an email count-based prior in the topic generation model. Our experiments show that the proposed estimation strategies are all effective to improve retrieval accuracy.
5098005
RotoTexture: Automated Tools for Texturing Raw Video
We propose a video editing system that allows a user to apply a time-coherent texture to a surface depicted in the raw video from a single uncalibrated camera, including the surface texture mapping of a texture image and the surface texture synthesis from a texture swatch. Our system avoids the construction of a 3D shape model and instead uses the recovered normal field to deform the texture so that it plausibly adheres to the undulations of the depicted surface. The texture mapping method uses the nonlinear least-squares optimization of a spring model to control the behavior of the texture image as it is deformed to match the evolving normal field through the video. The texture synthesis method uses a coarse optical flow to advect clusters of pixels corresponding to patches of similarly oriented surface points. These clusters are organized into a minimum advection tree to account for the dynamic visibility of clusters. We take a rather crude approach to normal recovering and optical flow estimation, yet the results are robust and plausible for nearly diffuse surfaces such as faces and t-shirts
44946141
The Design of an Observer-Based Neural Adaptive Controller for a Class of Nonlinear Systems with Input-Constrained
29388694
A fuzzy logic approach for detection of video shot boundaries
Video temporal segmentation is normally the first and important step for content-based video applications. Many features including the pixel difference, colour histogram, motion, and edge information etc. have been widely used and reported in the literature to detect shot cuts inside videos. Although existing research on shot cut detection is active and extensive, it still remains a challenge to achieve accurate detection of all types of shot boundaries with one single algorithm. In this paper, we propose a fuzzy logic approach to integrate hybrid features for detecting shot boundaries inside general videos. The fuzzy logic approach contains two processing modes, where one is dedicated to detection of abrupt shot cuts including those short dissolved shots, and the other for detection of gradual shot cuts. These two modes are unified by a mode-selector to decide which mode the scheme should work on in order to achieve the best possible detection performances. By using the publicly available test data set from Carleton University, extensive experiments were carried out and the test results illustrate that the proposed algorithm outperforms the representative existing algorithms in terms of the precision and recall rates.