11469103
Panoramic video segmentation using color mixture models
Consider a color camera mounted at a fixed point and a target object to be tracked. We propose a method to replace the common "blue screen" technique in performing segmentation of the object. The target object image in the video sequence is separated from the background through a segmentation process employing the color mixture model techniques. The background information is recovered through a similarity search with the panorama of the original background . The foreground segment can then be encoded by traditional compression while the scene background is represented as a panorama. Finally, the foreground object combines with the corresponding panoramic segment or any desired image on-the-fly to reconstruct the video frame. Our system can be used in low bandwidth applications as well as special demonstration in which the demonstrator can appear/disappear at any time he/she wishes.
3120289
Recursive Recovery of Position and Orientation from Stereo Image Sequences without Three-Dimensional Structures
Traditional vision-based 3-D motion estimation algorithms for robots require given or calculated 3-D models while the motion is being tracked. We propose a high-speed extended-Kalman-filter-based approach that recovers position and orientation from stereo image sequences without prior knowledge as well as the procedure for the reconstruction of 3-D structures. Empowered by the use of the trifocal tensor, the computation step of 3-D models can be eliminated. The algorithm is thus more flexible and can be applied to a wide range of domains. The twist motion model is also adopted to parameterize the 3-D motion such that the motion representation in the proposed algorithm is robust and minimal. As the number of parameters to be estimated is reduced, our algorithm is more efficient, stable and accurate compared to traditional approaches. The proposed method has been verified using a real image sequence with ground truth.
14489674
Automatic lyrics alignment for Cantonese popular music
From lyrics-display on electronic music players and Karaoke videos to surtitles for live Chinese opera performance, one feature is common to all these everyday functionalities temporal: synchronization of the written text and its corresponding musical phrase. Our goal is to automate the process of lyrics alignment, a procedure which, to date, is still handled manually in the Cantonese popular song (Cantopop) industry. In our system, a vocal signal enhancement algorithm is developed to extract vocal signals from a CD recording in order to detect the onsets of the syllables sung and to determine the corresponding pitches. The proposed system is specifically designed for Cantonese, in which the contour of the musical melody and the tonal contour of the lyrics must match perfectly. With this prerequisite, we use a dynamic time warping algorithm to align the lyrics. The robustness of this approach is supported by experiment results. The system was evaluated with 70 twenty-second music segments and most samples have their lyrics aligned correctly.
28364216
Dynamic Programming in the Recognition of Connected Handwritten Script
2219807
Computing Pose Sequences Directly from Videos
An innovative extended Kalman filter (EKF) algorithm for pose tracking has been proposed in this paper. It has the advantages of both structure and motion-based (SAM-based) and traditional model-based pose estimation algorithms. With no prior information about the scene, the pose sequence can be computed directly from images while the updating of the 3-D structure is not necessary. To achieve the goal, a constant velocity motion model is used as the dynamic system and the trifocal tensor point transfer function is applied to the measurement model of the filter. The resulting algorithm is stable, accurate and efficient. An empirical comparison with existing EKFs which deal with the same problem has been made and shows that our approach outperformed the others. The proposed method has been tested with various video sequences to demonstrate its performance in real situations.
466097
Merging artificial objects with marker-less video sequences based on the interacting multiple model method
Inserting synthetic objects into video sequences has gained much interest in recent years. Fast and robust vision-based algorithms are necessary to make such an application possible. Traditional pose tracking schemes using recursive structure from motion techniques adopt one Kalman filter and thus only favor a certain type of camera motion. We propose a robust simultaneous pose tracking and structure recovery algorithm using the interacting multiple model (IMM) to improve performance. In particular, a set of three extended Kalman filters (EKFs), each describing a frequently occurring camera motion in real situations (general, pure translation, pure rotation), is applied within the IMM framework to track the pose of a scene. Another set of EKFs,one filter for each model point, is used to refine the positions of the model features in the 3-D space. The filters for pose tracking and structure refinement are executed in an interleaved manner. The results are used for inserting virtual objects into the original video footage. The performance of the algorithm is demonstrated with both synthetic and real data. Comparisons with different approaches have been performed and show that our method is more efficient and accurate.
6918962
Head Model Acquisition from Silhouettes
This paper describes a practical system developed for generating 3D models of human heads from silhouettes alone. The input to the system is an image sequence acquired from circular motion. Both the camera motion and the 3D structure of the head are estimated using silhouettes which are tracked throughout the sequence. Special properties of the camera motion and their relationships with the intrinsic parameters of the camera are exploited to provide a simple parameterization of the fundamental matrix relating any pair of views in the sequence. Such a parameterization greatly reduces the dimension of the search space for the optimization problem. In contrast to previous methods, this work can cope with incomplete circular motion and more widely spaced images. Experiments on real image sequences are carried out, showing accurate recovery of 3D shapes.
37272834
Issues in Integrating Schemas for Reverse Engineering
Abstract After adopting a standard format such as GXL to exchange graphs of artifacts for reverse engineering tools, the next logical step is to define an appropriate schema for the information contained in the graphs. Various researchers have developed schemas, but in practice, it is still hard to choose an existing one. Typically, researchers end up needing to implement new schemas for the particulars of their tools or case studies. In the paper, we discuss a potential scenario for integrating schemas, with the aim of improving the interoperability among reverse engineering tools.
9524579
Epipolar geometry from profiles under circular motion
Addresses the problem of motion estimation from profiles (apparent contours) of an object rotating on a turntable in front of a single camera. A practical and accurate technique for solving this problem from profiles alone is developed. It is precise enough to reconstruct the shape of the object. No correspondences between points or lines are necessary. Symmetry of the surface of revolution swept out by the rotating object is exploited to obtain the image of the rotation axis and the homography relating epipolar lines in two views robustly and elegantly. These, together with geometric constraints for images of rotating objects, are used to obtain first the image of the horizon, which is the projection of the plane that contains the camera centers, and then the epipoles, thus fully determining the epipolar geometry of the image sequence. The estimation of this geometry by this sequential approach avoids many of the problems found in other algorithms. The search for the epipoles, by far the most critical step, is carried out as a simple 1D optimization. Parameter initialization is trivial and completely automatic at all stages. After the estimation of the epipolar geometry, the Euclidean motion is recovered using the fixed intrinsic parameters of the camera obtained either from a calibration grid or from self-calibration techniques. Finally, the spinning object is reconstructed from its profiles using the motion estimated in the previous stage. Results from real data are presented, demonstrating the efficiency and usefulness of the proposed methods.
12775677
Camera Pose Estimation and Reconstruction from Image Profiles under Circular Motion
This paper addresses the problem of motion estimation and reconstruction of 3D models from profiles of an object rotating on a turntable, obtained from a single camera. Its main contribution is the development of a practical and accurate technique for solving this problem from profiles alone, which is, for the first time, precise enough to allow the reconstruction of the object. No correspondence between points or lines are necessary, although the method proposed can be equally used when these features are available, without any further adaptation. Symmetry properties of the surface of revolution swept out by the rotating object are exploited to obtain the image of the rotation axis and the homography relating epipolar lines, in a robust and elegant way. These, together with geometric constraints for images of rotating objects, are then used to obtain first the image of the horizon, which is the projection of the plane that contains the camera centres, and then the epipoles, thus fully determining the epipolar geometry of the sequence of images. The estimation of the epipolar geometry by this sequential approach (image of rotation axis -- homography -- image of the horizon -- epipoles) avoids many of the problems usually found in other algorithms for motion recovery from profiles. In particular, the search for the epipoles, by far the most critical step, is carried out as a simple one-dimensional optimisation problem. The initialisation of the parameters is trivial and completely automatic for all stages of the algorithm. After the estimation of the epipolar geometry, the Euclidean motion is recovered using the fixed intrinsic parameters of the camera, obtained either from a calibration grid or from self-calibration techniques. Finally, the spinning object is reconstructed from its profiles, using the motion estimated in the previous stage. Results from real data are presented, demonstrating the efficiency and usefulness of the proposed methods.
31248498
A pruning method for the recursive least squared algorithm
The recursive least squared (RLS) algorithm is an effective online training method for neural networks. However, its conjunctions with weight decay and pruning have not been well studied. This paper elucidates how generalization ability can be improved by selecting an appropriate initial value of the error covariance matrix in the RLS algorithm. Moreover, how the pruning of neural networks can be benefited by using the final value of the error covariance matrix will also be investigated. Our study found that the RLS algorithm is implicitly a weight decay method, where the weight decay effect is controlled by the initial value of the error covariance matrix; and that the inverse of the error covariance matrix is approximately equal to the Hessian matrix of the network being trained. We propose that neural networks are first trained by the RLS algorithm and then some unimportant weights are removed based on the approximate Hessian matrix. Simulation results show that our approach is an effective training and pruning method for neural networks.