Journal ArticleDOI
Video stabilization based on a 3D perspective camera model
TLDR
This paper presents a novel approach to stabilize video sequences based on a 3D perspective camera model that uses approximate geometry representation and analyze the resulting warping errors to show that by appropriately constraining warping error, visually plausible results can be achieved even using planar structures.Abstract:
This paper presents a novel approach to stabilize video sequences based on a 3D perspective camera model. Compared to previous methods which are based on simplified models, our stabilization system can work in situations where significant depth variations exist in the scenes and the camera undergoes large translational movement. We formulate the stabilization problem as a quadratic cost function on smoothness and similarity constraints. This allows us to precisely control the smoothness by solving a sparse linear system of equations. By taking advantage of the sparseness, our optimization process is very efficient. Instead of recovering dense depths, we use approximate geometry representation and analyze the resulting warping errors. We show that by appropriately constraining warping error, visually plausible results can be achieved even using planar structures. A variety of experiments have been implemented, which demonstrates the robustness and efficiency of our approach.read more
Citations
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Journal ArticleDOI
Subspace video stabilization
TL;DR: This article focuses on the problem of transforming a set of input 2D motion trajectories so that they are both smooth and resemble visually plausible views of the imaged scene, and offers the first method that both achieves high-quality video stabilization and is practical enough for consumer applications.
Proceedings ArticleDOI
Video stabilization using robust feature trajectories
TL;DR: A method to directly stabilize a video without explicitly estimating camera motion, thus assuming neither motion models nor dominant motion is proposed and can deal with complicated videos containing near, large and multiple moving objects.
Proceedings ArticleDOI
Video stabilization with a depth camera
TL;DR: Though the depth image is noisy, incomplete and low resolution, it facilitates both camera motion estimation and frame warping, which make the video stabilization a much well posed problem.
Journal ArticleDOI
Spatially and Temporally Optimized Video Stabilization
TL;DR: A robust and efficient technique that achieves high-quality camera motion on videos where 3D reconstruction is difficult or long feature trajectories are not available and the Bézier representation enables strong smoothness of each feature trajectory and reduces the number of variables in the optimization problem.
Patent
Methods and Apparatus for Video Completion
TL;DR: In this paper, a video completion technique applies a subspace constraint technique that finds and tracks feature points in the video, which are used to form a model of the camera motion and to predict locations of background scene points in frames where the background is occluded.
References
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Journal ArticleDOI
Distinctive Image Features from Scale-Invariant Keypoints
TL;DR: This paper presents a method for extracting distinctive invariant features from images that can be used to perform reliable matching between different views of an object or scene and can robustly identify objects among clutter and occlusion while achieving near real-time performance.
Journal ArticleDOI
Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography
TL;DR: New results are derived on the minimum number of landmarks needed to obtain a solution, and algorithms are presented for computing these minimum-landmark solutions in closed form that provide the basis for an automatic system that can solve the Location Determination Problem under difficult viewing.
Book
Multiple view geometry in computer vision
Richard Hartley,Andrew Zisserman +1 more
TL;DR: In this article, the authors provide comprehensive background material and explain how to apply the methods and implement the algorithms directly in a unified framework, including geometric principles and how to represent objects algebraically so they can be computed and applied.
Distinctive Image Features from Scale-Invariant Keypoints
TL;DR: The Scale-Invariant Feature Transform (or SIFT) algorithm is a highly robust method to extract and consequently match distinctive invariant features from images that can then be used to reliably match objects in diering images.
Multiple View Geometry in Computer Vision.
TL;DR: This book is referred to read because it is an inspiring book to give you more chance to get experiences and also thoughts and it will show the best book collections and completed collections.