31,295 research outputs found
Stereo correspondence from motion correspondence
Abstract This paper introduces a new framework for stereo c orrespondence r ecovery using one motion of a stereo rig. Both the stereo correspondence and the motion of the stereo rig are unknown. By combining the stereo g e ometry and the motion correspondence w e a r e able to infer the stereo c orrespondence f r om motion correspondence without having to systematically use the intensity-based stereo matching algorithms. The stereo c orrespondence recovery consists of two consecutive steps: the rst step uses metric data associated with the stereo rig while the second step uses feature c orrespondences only. Experiments involving real stereo p airs indicate the feasibility and robustness of the approach
Robust Visual Correspondence: Theory and Applications
Visual correspondence represents one of the most important tasks in computer vision. Given two sets of pixels (i.e. two images), it aims at finding corresponding pixel pairs belonging to the two sets (homologous pixels). As a matter of fact, visual correspondence is commonly employed in fields such as stereo correspondence, change detection, image registration, motion estimation, pattern matching, image vector quantization. The visual correspondence task can be extremely challenging in presence of disturbance factors which typically affect images. A common source of disturbances can be related to photometric distortions between the images under comparison. These can be ascribed to the camera sensors employed in the image acquisition process (due to dynamic variations of camera parameters such as auto-exposure and auto-gain, or to the use of different cameras), or can be induced by external factors such as changes of the amount of light emitted by the sources or viewing of non-lambertian surfaces at different angles. All of these factors tend to produce brightness changes in corresponding pixels of the two images that can not be neglected in real applications implying visual correspondence between images acquired from different spatial points (e.g. stereo vision) and/or different time instants (e.g. pattern matching, change detection). In addition to photometric distortions, differences between corresponding pixels can also be due to the noise introduced by camera sensors. Finally, the acquisition of images from different spatial points or different time instants can also induce occlusions. Evaluation assessments have also been proposed which compared visual correspondence approaches for tasks such as stereo correspondence (Chambon & Crouzil, 2003), image registration (Zitova & Flusser, 2003) and image motion (Giachetti, 2000)
Doctor of Philosophy
dissertation3D reconstruction from image pairs relies on finding corresponding points between images and using the corresponding points to estimate a dense disparity map. Today's correspondence-finding algorithms primarily use image features or pixel intensities common between image pairs. Some 3D computer vision applications, however, don't produce the desired results using correspondences derived from image features or pixel intensities. Two examples are the multimodal camera rig and the center region of a coaxial camera rig. Additionally, traditional stereo correspondence-finding techniques which use image features or pixel intensities sometimes produce inaccurate results. This thesis presents a novel image correspondence-finding technique that aligns pairs of image sequences using the optical flow fields. The optical flow fields provide information about the structure and motion of the scene which is not available in still images, but which can be used to align images taken from different camera positions. The method applies to applications where there is inherent motion between the camera rig and the scene and where the scene has enough visual texture to produce optical flow. We apply the technique to a traditional binocular stereo rig consisting of an RGB/IR camera pair and to a coaxial camera rig. We present results for synthetic flow fields and for real images sequences with accuracy metrics and reconstructed depth maps
Classic Mosaics and Visual Correspondence via Graph-Cut based Energy Optimization
Computer graphics and computer vision were traditionally two distinct research fields focusing on opposite topics. Lately, they have been increasingly borrowing ideas and tools from each other. In this thesis, we investigate two problems in computer vision and graphics that rely on the same tool, namely energy optimization with graph cuts.
In the area of computer graphics, we address the problem of generating artificial classic mosaics, still and animated. The main purpose of artificial mosaics is to help a user to create digital art. First we reformulate our previous static mosaic work in a more principled global optimization framework. Then, relying on our still mosaic algorithm, we develop a method for producing animated mosaics directly from real video sequences, which is the first such method, we believe. Our mosaic animation style is uniquely expressive. Our method estimates the motion of the pixels in the video, renders the frames with mosaic effect based on both the colour and motion information from the input video. This algorithm relies extensively on our novel motion segmentation approach, which is a computer vision problem.
To improve the quality of our animated mosaics, we need to improve the motion segmentation algorithm. Since motion and stereo problems have a similar setup, we start with the problem of finding visual correspondence for stereo, which has the advantage of having datasets with ground truth, useful for evaluation. Most previous methods for stereo correspondence do not provide any measure of reliability in their estimates. We aim to find the regions for which correspondence can be determined reliably. Our main idea is to find corresponding regions that have a sufficiently strong texture cue on the boundary, since texture is a reliable cue for matching. Unlike the previous work, we allow the disparity range within each such region to vary smoothly, instead of being constant. This produces blob-like semi-dense visual features for which we have a high confidence in their estimated ranges of disparities
LEVEL-BASED CORRESPONDENCE APPROACH TO COMPUTATIONAL STEREO
One fundamental problem in computational stereo reconstruction is correspondence.
Correspondence is the method of detecting the real world object reflections in two
camera views. This research focuses on correspondence, proposing an algorithm to
improve such detection for low quality cameras (webcams) while trying to achieve
real-time image processing.
Correspondence plays an important role in computational stereo reconstruction and it
has a vast spectrum of applicability. This method is useful in other areas such as
structure from motion reconstruction, object detection, tracking in robot vision and
virtual reality. Due to its importance, a correspondence method needs to be accurate
enough to meet the requirement of such fields but it should be less costly and easy to
use and configure, to be accessible by everyone.
By comparing current local correspondence method and discussing their weakness
and strength, this research tries to enhance an algorithm to improve previous works to
achieve fast detection, less costly and acceptable accuracy to meet the requirement of
reconstruction. In this research, the correspondence is divided into four stages. Two
stages of preprocessing which are noise reduction and edge detection have been
compared with respect to different methods available. In the next stage, the feature
detection process is introduced and discussed focusing on possible solutions to reduce
errors created by system or problem occurring in the scene such as occlusion. Lastly,
in the final stage it elaborates different methods of displaying reconstructed result.
Different sets of data are processed based on the steps involved in correspondence and
the results are discussed and compared in detail. The finding shows how this system
can achieve high speed and acceptable outcome despite of poor quality input. As a
conclusion, some possible improvements are proposed based on ultimate outcome
LEVEL-BASED CORRESPONDENCE APPROACH TO COMPUTATIONAL STEREO
One fundamental problem in computational stereo reconstruction is correspondence.
Correspondence is the method of detecting the real world object reflections in two
camera views. This research focuses on correspondence, proposing an algorithm to
improve such detection for low quality cameras (webcams) while trying to achieve
real-time image processing.
Correspondence plays an important role in computational stereo reconstruction and it
has a vast spectrum of applicability. This method is useful in other areas such as
structure from motion reconstruction, object detection, tracking in robot vision and
virtual reality. Due to its importance, a correspondence method needs to be accurate
enough to meet the requirement of such fields but it should be less costly and easy to
use and configure, to be accessible by everyone.
By comparing current local correspondence method and discussing their weakness
and strength, this research tries to enhance an algorithm to improve previous works to
achieve fast detection, less costly and acceptable accuracy to meet the requirement of
reconstruction. In this research, the correspondence is divided into four stages. Two
stages of preprocessing which are noise reduction and edge detection have been
compared with respect to different methods available. In the next stage, the feature
detection process is introduced and discussed focusing on possible solutions to reduce
errors created by system or problem occurring in the scene such as occlusion. Lastly,
in the final stage it elaborates different methods of displaying reconstructed result.
Different sets of data are processed based on the steps involved in correspondence and
the results are discussed and compared in detail. The finding shows how this system
can achieve high speed and acceptable outcome despite of poor quality input. As a
conclusion, some possible improvements are proposed based on ultimate outcome
Multiview photometric stereo
This paper addresses the problem of obtaining complete, detailed reconstructions of textureless shiny objects. We present an algorithm which uses silhouettes of the object, as well as images obtained under changing illumination conditions. In contrast with previous photometric stereo techniques, ours is not limited to a single viewpoint but produces accurate reconstructions in full 3D. A number of images of the object are obtained from multiple viewpoints, under varying lighting conditions. Starting from the silhouettes, the algorithm recovers camera motion and constructs the object's visual hull. This is then used to recover the illumination and initialize a multiview photometric stereo scheme to obtain a closed surface reconstruction. There are two main contributions in this paper: First, we describe a robust technique to estimate light directions and intensities and, second, we introduce a novel formulation of photometric stereo which combines multiple viewpoints and, hence, allows closed surface reconstructions. The algorithm has been implemented as a practical model acquisition system. Here, a quantitative evaluation of the algorithm on synthetic data is presented together with complete reconstructions of challenging real objects. Finally, we show experimentally how, even in the case of highly textured objects, this technique can greatly improve on correspondence-based multiview stereo results
- …