Dynamic 3D Urban Scene Modeling Using Multiple Pushbroom Mosaics

In this paper, a unified, segmentation-based approach is proposed to deal with both stereo reconstruction and moving objects detection problems using multiple stereo mosaics. Each set of parallel-perspective (pushbroom) stereo mosaics is generated from a video sequence captured by a single video camera. First a colorsegmentation approach is used to extract the so-called natural matching primitives from a reference view of a pair of stereo mosaics to facilitate both 3D reconstruction of textureless urban scenes and man-made moving targets (e.g. vehicles). Multiple pairs of stereo mosaics are used to improve the accuracy and robustness in 3D recovery and occlusion handling. Moving targets are detected by inspecting their 3D anomalies, either violating the epipolar geometry of the pushbroom stereo or exhibiting abnormal 3D structure. Experimental results on both simulated and real video sequences are provided to show the effectiveness of our approach. 1

Robust Techniques for Feature-based Image Mosaicing

Since the last few decades, image mosaicing in real time applications has been a challenging field for image processing experts. It has wide applications in the field of video conferencing, 3D image reconstruction, satellite imaging and several medical as well as computer vision fields. It can also be used for mosaic-based localization, motion detection & tracking, augmented reality, resolution enhancement, generating large FOV etc. In this research work, feature based image mosaicing technique using image fusion have been proposed. The image mosaicing algorithms can be categorized into two broad horizons. The first is the direct method and the second one is based on image features. The direct methods need an ambient initialization whereas, Feature based methods does not require initialization during registration. The feature-based techniques are primarily followed by the four steps: feature detection, feature matching, transformation model estimation, image resampling and transformation. SIFT and SURF are such algorithms which are based on the feature detection for the accomplishment of image mosaicing, but both the algorithms has their own limitations as well as advantages according to the applications concerned. The proposed method employs this two feature based image mosaicing techniques to generate an output image that works out the limitations of the both in terms of image quality The developed robust algorithm takes care of the combined effect of rotation, illumination, noise variation and other minor variation. Initially, the input images are stitched together using the popular stitching algorithms i.e. Scale Invariant Feature Transform (SIFT) and Speeded-Up Robust Features (SURF). To extract the best features from the stitching results, the blending process is done by means of Discrete Wavelet Transform (DWT) using the maximum selection rule for both approximate as well as detail-components

Mosaicking video with parallax.

Cheung Man-Tai.Thesis (M.Phil.)--Chinese University of Hong Kong, 2001.Includes bibliographical references (leaves 81-84).Abstracts in English and Chinese.List of Figures --- p.viList of Tables --- p.viiiChapter Chapter 1. --- Introduction --- p.1Chapter 1.1. --- Background --- p.1Chapter 1.1.1. --- Parallax --- p.2Chapter 1.2. --- Literature Review --- p.3Chapter 1.3. --- Research Objective --- p.6Chapter 1.4. --- Organization of Thesis --- p.6Chapter Chapter 2. --- The 3-Image Algorithm --- p.1Chapter 2.1. --- Projective Reconstruction --- p.10Chapter 2.2. --- Epipolar Geometry and Fundamental Matrix --- p.11Chapter 2.3. --- Determine the Projective Mapping --- p.12Chapter 2.3.1. --- Conditions for Initial Matches --- p.13Chapter 2.3.2. --- Obtaining the Feature Correspondence --- p.17Chapter 2.4. --- Registering Pixel Element --- p.21Chapter 2.4.1. --- Single Homography Approach --- p.22Chapter 2.4.2. --- Multiple Homography Approach --- p.23Chapter 2.4.3. --- Triangular Patches Clustering --- p.24Chapter 2.4.3.1. --- Delaunay Triangulation --- p.25Chapter 2.5. --- Mosaic Construction --- p.29Chapter Chapter 3. --- The n-Image Algorithm --- p.31Chapter Chapter 4. --- The Uneven-Sampling-Rate n-Image Algorithm --- p.34Chapter 4.1. --- Varying the Reference-Target Images Separation --- p.35Chapter 4.2. --- Varying the Target-Intermediate Images Separation --- p.38Chapter Chapter 5. --- Experiments --- p.43Chapter 5.1. --- Experimental Setup --- p.43Chapter 5.1.1. --- Measuring the Performance --- p.43Chapter 5.2. --- Experiments on the 3-Image Algorithm --- p.44Chapter 5.2.1. --- Planar Scene --- p.44Chapter 5.2.2. --- Comparison between a Global Parametric Transformation and the 3-Image Algorithm --- p.46Chapter 5.2.3. --- Generic Scene --- p.49Chapter 5.2.4. --- The Triangular Patches Clustering against the Multiple Homography Approach --- p.52Chapter 5.3. --- Experiments on the n-Image Algorithm --- p.56Chapter 5.3.1. --- Initial Experiment on the n-Image Algorithm --- p.56Chapter 5.3.2. --- Another Experiment on the n-Image Algorithm --- p.58Chapter 5.3.3. --- the n-Image Algorithm over a Longer Image Stream --- p.61Chapter 5.4. --- Experiments on the Uneven-Sampling-Rate n-Image Algorithm --- p.65Chapter 5.4.1. --- Varying Reference-Target Images Separation --- p.65Chapter 5.4.2. --- Varying Target-Intermediate Images Separation --- p.69Chapter 5.4.3. --- Comparing the Uneven-Sampling-Rate n-Image Algorithm and Global Transformation Method --- p.73Chapter Chapter 6. --- Conclusion and Discussion --- p.76Bibliography --- p.8

Computer Vision and Image Understanding xxx

Abstract 12 A compact visual representation, called the 3D layered, adaptive-resolution, and multi-13 perspective panorama (LAMP), is proposed for representing large-scale 3D scenes with large 14 variations of depths and obvious occlusions. Two kinds of 3D LAMP representations are 15 proposed: the relief-like LAMP and the image-based LAMP. Both types of LAMPs con-16 cisely represent almost all the information from a long image sequence. Methods to con-17 struct LAMP representations from video sequences with dominant translation are 18 provided. The relief-like LAMP is basically a single extended multi-perspective panoramic 19 view image. Each pixel has a pair of texture and depth values, but each pixel may also have 20 multiple pairs of texture-depth values to represent occlusion in layers, in addition to adap-21 tive resolution changing with depth. The image-based LAMP, on the other hand, consists of 22 a set of multi-perspective layers, each of which has a pair of 2D texture and depth maps, 23 but with adaptive time-sampling scales depending on depths of scene points. Several exam-24 ples of 3D LAMP construction for real image sequences are given. The 3D LAMP is a con-25 cise and powerful representation for image-based rendering. 2

Universal Mosaicing using Pipe Projection

Video mosaicing is commonly used to increase the visual field of view by pasting together many video frames. Existing mosaicing methods are effective only in very limited cases where the image motion is almost a uniform translation or the camera performs a pure pan. Forward camera motion or camera zoom are very problematic for traditional mosaicing. A mosaicing methodology..