2993896
An Efficient Iterative Pose Estimation Algorithm
We propose a novel model-based algorithm which finds the 3D pose of an object from an image by breaking down the estimation process into two linear processing stages, namely the depth recovery and the pose calculation. The depth recovery stage determines the new positions of the model point set in 3D space whereas the pose calculation step is a least-square estimation of the transformation parameters between the point set formed from the previous stage and the model set. The estimates are iteratively refined until converged. The advantage of using our algorithm is that the computational cost is much reduced. We test our algorithm by applying it to both synthetic as well as real time head tracking problem with satisfactory results.
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.
13225873
Model reconstruction and pose acquisition using extended Lowe's method
Finding the pose and structure of an unknown object from an image sequence has many applications in graphics, virtual reality, and multimedia processing. In this paper, we address this problem by using a two-stage iterative method. Starting from an initial guess of the structure, the first stage estimates the pose of the object. The second stage uses the estimated pose information to refine the structure. This process is repeated until the difference between the observed data and data re-projected from the estimated model is minimized. This method is a variation of the classical bundle adjustment method, but is faster in execution and is simpler to implement. We used the Kanade-Lucas-Tomasi feature tracker for obtaining the image features. Synthetic and real data have been tested with good results.
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.
44975216
Workshop on graphical documentation for programmers
Programmers often use graphical forms of documentation that rely on software visualization techniques to make complicated information easier to understand. However, it is an open question exactly which types of graphical documentation are most suitable for which types of program understanding tasks (and in which specific usage contexts). This workshop will focus on this issue by exploring the suitability of three classes of graphical documentation (static, interactive, and editable) for selected program understanding tasks that are representative of common software maintenance and evolution scenarios. This workshop is a sequel to the workshop "Documentation for Software Engineers: What is Needed to Aid System Understanding?" which took place at SIGDOC 2001.
15199591
Structure and motion from silhouettes
This paper addresses the problem of recovering structure and motion from silhouettes. Silhouettes are projections of contour generators which are viewpoint dependent, and hence do not readily provide point correspondences for exploitation in motion estimation. Previous works have exploited correspondences induced by epipolar tangencies, and a successful solution has been developed in the special case of circular motion (turnable sequences). However, the main drawbacks are (1) new views cannot be added easily at a later time, and (2) part of the structure will always remain invisible under circular motion. In this paper we overcome the above problems by incorporating arbitrary general views and estimating the camera poses using silhouettes alone. We present a complete and practical system which produces high quality 3D models from 2D uncalibrated silhouettes. The 3D models thus obtained can be refined incrementally by adding new arbitrary views and estimating their poses. Experimental results on various objects are presented, demonstrating the quality of the reconstructions.
18572162
Structure and motion estimation from apparent contours under circular motion
In this paper we address the problem of recovering structure and motion from the apparent contours of a smooth surface. Fixed image features under circular 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 allows a trivial initialization of the motion parameters, which all bear physical meanings. It also greatly reduces the dimension of the search space for the optimization problem, which can now be solved using only 2 epipolar tangents. In contrast to previous methods, the motion estimation algorithm introduced here can cope with incomplete circular motion and more widely spaced images. Existing techniques for model reconstruction from apparent contours are then reviewed and compared. Experiment on real data has been carried out and the 3D model reconstructed from the estimated motion is presented.
17308003
Complete Axiomatizations for Reasoning about Knowledge and Branching Time
Sound and complete axiomatizations are provided for a number of different logics involving modalities for the knowledge of multiple agents and operators for branching time, extending previous work of Halpern, van der Meyden and Vardi [to appear, SIAM Journal on Computing] for logics of knowledge and linear time. The paper considers the system constraints of synchrony, perfect recall and unique initial states, which give rise to interaction axioms. The language is based on the temporal logic CTL*, interpreted with respect to a version of the bundle semantics.
7622452
Tracking based motion segmentation under relaxed statistical assumptions
We present a novel and efficient motion segmentation and tracking algorithm that follows the shift and align paradigm. We introduce two statistical tests to evaluate the similarity of aligned image pixels or patches and we use them to determine the spatial extend of each segment. The one statistical test is fast and accurate when the noise is moderate and the other employs a sophisticated noise model involving the Mahalanobis distance to handle correlated noise. Direct computation of the Mahalanobis distance is prohibitively expensive so we apply the Sherman-Morrison-Woodbury identity and amortization to reduce the cost by several orders of magnitude. We tested both versions of the algorithm on a variety of image sequences (indoor and outdoor, real and synthetic, constant and varying lighting, stationary and moving camera, one of them with known ground truth) with very good results.