The Experts below are selected from a list of 13623 Experts worldwide ranked by ideXlab platform
Thorsten Thormahlen - One of the best experts on this subject based on the ideXlab platform.
-
Registration of Sub-Sequence and Multi-Camera Reconstructions for Camera Motion Estimation
2010Co-Authors: Thorsten Thormahlen, Nils Hasler, Michael Wand, Hans-peter SeidelAbstract:JVRB, 7(2010), no. 2. - This paper presents different application scenarios for which the registration of sub-sequence reconstructions or multi-Camera reconstructions is essential for successful Camera Motion estimation and 3D reconstruction from video. The registration is achieved by merging unconnected feature point tracks between the reconstructions. One application is drift removal for sequential Camera Motion estimation of long sequences. The state-of-the-art in drift removal is to apply a RANSAC approach to find unconnected feature point tracks. In this paper an alternative spectral algorithm for pairwise matching of unconnected feature point tracks is used. It is then shown that the algorithms can be combined and applied to novel scenarios where independent Camera Motion estimations must be registered into a common global coordinate system. In the first scenario multiple moving Cameras, which capture the same scene simultaneously, are registered. A second new scenario occurs in situations where the tracking of feature points during sequential Camera Motion estimation fails completely, e.g., due to large occluding objects in the foreground, and the unconnected tracks of the independent reconstructions must be merged. In the third scenario image sequences of the same scene, which are captured under different illuminations, are registered. Several experiments with challenging real video sequences demonstrate that the presented techniques work in practice.
-
Camera Motion Style Transfer
2010 Conference on Visual Media Production, 2010Co-Authors: Christian Kurz, Thorsten Thormahlen, Tobias Ritschel, Elmar Eisemann, Hans-peter SeidelAbstract:When depicting both virtual and physical worlds, the viewer's impression of presence in these worlds is strongly linked to Camera Motion. Plausible and artist-controlled Camera movement can substantially increase scene immersion. While physical Camera Motion exhibits subtle details of position, rotation, and acceleration, these details are often missing for virtual Camera Motion. In this work, we analyze Camera movement using signal theory. Our system allows us to stylize a smooth user-defined virtual base Camera Motion by enriching it with plausible details. A key component of our system is a database of videos filmed by physical Cameras. These videos are analyzed with a Camera-Motion estimation algorithm (structure-from-Motion) and labeled manually with a specific style. By considering spectral properties of location, orientation and acceleration, our solution learns Camera Motion details. Consequently, an arbitrary virtual base Motion, defined in any conventional animation package, can be automatically modified according to a user-selected style. As shown in our experiments, the resulting shots are still fully artist-controlled, but appear richer and more physically plausible.
-
Merging of Feature Tracks for Camera Motion Estimation from Video
IET 5th European Conference on Visual Media Production (CVMP 2008), 2008Co-Authors: Thorsten Thormahlen, Nils Hasler, Michael Wand, Hans-peter SeidelAbstract:In this paper different application scenarios are presented for which the merging of unconnected feature point tracks is essential for successful Camera Motion estimation and 3D reconstruction from video. The first application is drift removal for sequential Camera Motion estimation of long sequences. The state-of-the-art in drift removal is to apply a RANSAC approach to find unconnected feature point tracks. In this paper an alternative spectral algorithm for pairwise matching of unconnected feature point tracks is used. It is then shown that the algorithms can be combined and applied to novel scenarios where independent Camera Motion estimations must be registered into a common global coordinate system. In the first scenario multiple moving Cameras, which capture the same scene simultaneously, are registered. A second new scenario occurs in situations where the tracking of feature points during sequential Camera Motion estimation fails completely, e.g., due to large occluding objects in the foreground, and the unconnected tracks of the independent reconstructions must be merged. Three experiments with challenging real video sequences demonstrate that the presented techniques work in practice. (8 pages)
-
keyframe selection for Camera Motion and structure estimation from multiple views
European Conference on Computer Vision, 2004Co-Authors: Thorsten Thormahlen, Hellward Broszio, Axel WeissenfeldAbstract:Estimation of Camera Motion and structure of rigid objects in the 3D world from multiple Camera images by bundle adjustment is often performed by iterative minimization methods due to their low computational effort. These methods need a robust initialization in order to converge to the global minimum. In this paper a new criterion for keyframe selection is presented. While state of the art criteria just avoid degenerated Camera Motion configurations, the proposed criterion selects the keyframe pairing with the lowest expected estimation error of initial Camera Motion and object structure. The presented results show, that the convergence probability of bundle adjustment is significantly improved with the new criterion compared to the state of the art approaches.
-
ECCV (1) - Keyframe Selection for Camera Motion and Structure Estimation from Multiple Views
Lecture Notes in Computer Science, 2004Co-Authors: Thorsten Thormahlen, Hellward Broszio, Axel WeissenfeldAbstract:Estimation of Camera Motion and structure of rigid objects in the 3D world from multiple Camera images by bundle adjustment is often performed by iterative minimization methods due to their low computational effort. These methods need a robust initialization in order to converge to the global minimum. In this paper a new criterion for keyframe selection is presented. While state of the art criteria just avoid degenerated Camera Motion configurations, the proposed criterion selects the keyframe pairing with the lowest expected estimation error of initial Camera Motion and object structure. The presented results show, that the convergence probability of bundle adjustment is significantly improved with the new criterion compared to the state of the art approaches.
Axel Weissenfeld - One of the best experts on this subject based on the ideXlab platform.
-
keyframe selection for Camera Motion and structure estimation from multiple views
European Conference on Computer Vision, 2004Co-Authors: Thorsten Thormahlen, Hellward Broszio, Axel WeissenfeldAbstract:Estimation of Camera Motion and structure of rigid objects in the 3D world from multiple Camera images by bundle adjustment is often performed by iterative minimization methods due to their low computational effort. These methods need a robust initialization in order to converge to the global minimum. In this paper a new criterion for keyframe selection is presented. While state of the art criteria just avoid degenerated Camera Motion configurations, the proposed criterion selects the keyframe pairing with the lowest expected estimation error of initial Camera Motion and object structure. The presented results show, that the convergence probability of bundle adjustment is significantly improved with the new criterion compared to the state of the art approaches.
-
ECCV (1) - Keyframe Selection for Camera Motion and Structure Estimation from Multiple Views
Lecture Notes in Computer Science, 2004Co-Authors: Thorsten Thormahlen, Hellward Broszio, Axel WeissenfeldAbstract:Estimation of Camera Motion and structure of rigid objects in the 3D world from multiple Camera images by bundle adjustment is often performed by iterative minimization methods due to their low computational effort. These methods need a robust initialization in order to converge to the global minimum. In this paper a new criterion for keyframe selection is presented. While state of the art criteria just avoid degenerated Camera Motion configurations, the proposed criterion selects the keyframe pairing with the lowest expected estimation error of initial Camera Motion and object structure. The presented results show, that the convergence probability of bundle adjustment is significantly improved with the new criterion compared to the state of the art approaches.
Mohamed-chaker Larabi - One of the best experts on this subject based on the ideXlab platform.
-
Camera Motion influence on dynamic saliency central bias
2009Co-Authors: Etienne Baudrier, Vincent Rosselli, Mohamed-chaker LarabiAbstract:Saliency models have been extensively studied for static images and the focus is now on moving images. There is a central bias in both cases that is emphasized in the dynamic case. One aspect in this latter is the Camera Motion that influences the scene interpretation. The movie director exploits this Motion to make the observer focus on the targeted object which is often in the center of the scene. This aspect is not taken into account in current saliency dynamic models. In this paper, we study the Camera Motion influence on the gaze distribution in order to include it in a new saliency model. Observers' gazes are recorded with an eye tracker, Camera Motions (e.g. tracking, zoom...) are calculated thanks to a polynomial projection of the Motion field and the Motion influence is statistically tested on the recorded gazes.
-
ICASSP - Camera Motion influence on dynamic saliency central bias
2009 IEEE International Conference on Acoustics Speech and Signal Processing, 2009Co-Authors: Etienne Baudrier, Vincent Rosselli, Mohamed-chaker LarabiAbstract:Saliency models have been extensively studied for static images and the focus is now on moving images. There is a central bias in both cases that is emphasized in the dynamic case. One aspect in this latter is the Camera Motion that influences the scene interpretation. The movie director exploits this Motion to make the observer focus on the targeted object which is often in the center of the scene. This aspect is not taken into account in current saliency dynamic models. In this paper, we study the Camera Motion influence on the gaze distribution in order to include it in a new saliency model. Observers' gazes are recorded with an eye tracker, Camera Motions (e.g. tracking, zoom…) are calculated thanks to a polynomial projection of the Motion field and the Motion influence is statistically tested on the recorded gazes.
Hyung-myung Kim - One of the best experts on this subject based on the ideXlab platform.
-
Threshold-Based Camera Motion Characterization of MPEG Video
ETRI Journal, 2004Co-Authors: Jaegon Kim, Hyun Sung Chang, Jinwoong Kim, Hyung-myung KimAbstract:We propose an efficient scheme for Camera Motion characterization in MPEG-compressed video. The proposed scheme detects six types of basic Camera Motions through threshold-based qualitative interpretation, in which fixed thresholds are applied to Motion model parameters estimated from MPEG Motion vectors (MVs). The efficiency and robustness of the scheme are validated by the experiment with real compressed video sequences.
-
efficient Camera Motion characterization for mpeg video indexing
International Conference on Multimedia and Expo, 2000Co-Authors: Jaegon Kim, Hyun Sung Chang, Jinwoong Kim, Hyung-myung KimAbstract:A novel approach to Camera Motion analysis is proposed to index videos compressed in MPEG-1 or MPEG-2. Specifically, it fits the Motion vectors in the MPEG stream into the two-dimensional affine model to detect basic Camera operations automatically. The proposed approach involves (1) the construction of Motion vector fields (MVFs) by normalizing the types of Motion vectors and filtering out noise; and (2) the qualitative interpretation of Camera Motions from the estimated model parameters in two levels (frame and temporal segment). Fine segmentation can also be obtained for a video, based on the homogeneity of Camera Motion in each unit. The advantages of our method lie in its computational efficiency and robustness to noisy environments such as false Motion vectors and object Motion. The proposed approach is validated by an experiment with real compressed video sequences.
-
Efficient linear three-dimensional Camera Motion estimation method with applications to video coding
Optical Engineering, 1998Co-Authors: Eung Tae Kim, Hyung-myung KimAbstract:In this paper, we describe a method for estimating and compensating 3D Camera Motion in image sequences for applications to video coding systems. To improve the coding efficiency, a two-stage Motion compensation (MC) method is used, consisting of global MC for predicting Camera Motion and local MC for predicting object Motion. For global MC, a new linear Motion parameter model is presented to describe the Camera Motion parameters: zoom, pan, tilt, swing, and focal length. To estimate Camera Motion parameters based on the proposed model, the mixed least squares-total least squares (LS-TLS) method is used. The proposed estimation procedure consists of feature correspondence establishment, finding the parameters by fitting the correspondence data to the proposed model equation, and outliers rejection to reduce feature matching error. Unlike the existing linear techniques, the proposed method accurately estimates even the large rotation angles and the focal length. Experimental results show that the proposed method outperforms the conventional methods under identical conditions, especially for large rotation images.
Fumiaki Sugaya - One of the best experts on this subject based on the ideXlab platform.
-
ICME - Camera Motion Detection using Video Mosaicing
2006 IEEE International Conference on Multimedia and Expo, 2006Co-Authors: Masaki Naito, Matsumoto Kazunori, Keiichiro Hoashi, Fumiaki SugayaAbstract:In this paper, Camera Motion detection methods using a background image generated by video mosaicing based on the correlation between feature points on a frame pair are described. In this method, a telop (video caption) removal method, iterative foreground and background image separation method and appropriate frame pair selection from consecutive frames are introduced to generate background images accurately. Parameters indicating the location of each frame on the background image are retrieved and used to detect the Camera Motion. Except for the simple threshold-based method, a method using Hidden Markov models (HMMs) is introduced to detect variable length Camera Motion based on the maximum likelihood criterion. The effectiveness of the proposed method is evaluated by using a TRECVID 2005 low-level feature extraction task [1].