3D inference and modelling for video retrieval

A new scheme is proposed for extracting planar surfaces from 2D image sequences. We firstly perform feature correspondence over two neighboring frames, followed by the estimation of disparity and depth maps, provided a calibrated camera. We then apply iterative Random Sample Consensus (RANSAC) plane fitting to the generated 3D points to find a dominant plane in a maximum likelihood estimation style. Object points on or off this dominant plane are determined by measuring their Euclidean distance to the plane. Experimental work shows that the proposed scheme leads to better plane fitting results than the classical RANSAC method

Under vehicle perception for high level safety measures using a catadioptric camera system

In recent years, under vehicle surveillance and the classification of the vehicles become an indispensable task that must be achieved for security measures in certain areas such as shopping centers, government buildings, army camps etc. The main challenge to achieve this task is to monitor the under frames of the means of transportations. In this paper, we present a novel solution to achieve this aim. Our solution consists of three main parts: monitoring, detection and classification. In the first part we design a new catadioptric camera system in which the perspective camera points downwards to the catadioptric mirror mounted to the body of a mobile robot. Thanks to the catadioptric mirror the scenes against the camera optical axis direction can be viewed. In the second part we use speeded up robust features (SURF) in an object recognition algorithm. Fast appearance based mapping algorithm (FAB-MAP) is exploited for the classification of the means of transportations in the third part. Proposed technique is implemented in a laboratory environment

Image Based View Synthesis

This dissertation deals with the image-based approach to synthesize a virtual scene using sparse images or a video sequence without the use of 3D models. In our scenario, a real dynamic or static scene is captured by a set of un-calibrated images from different viewpoints. After automatically recovering the geometric transformations between these images, a series of photo-realistic virtual views can be rendered and a virtual environment covered by these several static cameras can be synthesized. This image-based approach has applications in object recognition, object transfer, video synthesis and video compression. In this dissertation, I have contributed to several sub-problems related to image based view synthesis. Before image-based view synthesis can be performed, images need to be segmented into individual objects. Assuming that a scene can approximately be described by multiple planar regions, I have developed a robust and novel approach to automatically extract a set of affine or projective transformations induced by these regions, correctly detect the occlusion pixels over multiple consecutive frames, and accurately segment the scene into several motion layers. First, a number of seed regions using correspondences in two frames are determined, and the seed regions are expanded and outliers are rejected employing the graph cuts method integrated with level set representation. Next, these initial regions are merged into several initial layers according to the motion similarity. Third, the occlusion order constraints on multiple frames are explored, which guarantee that the occlusion area increases with the temporal order in a short period and effectively maintains segmentation consistency over multiple consecutive frames. Then the correct layer segmentation is obtained by using a graph cuts algorithm, and the occlusions between the overlapping layers are explicitly determined. Several experimental results are demonstrated to show that our approach is effective and robust. Recovering the geometrical transformations among images of a scene is a prerequisite step for image-based view synthesis. I have developed a wide baseline matching algorithm to identify the correspondences between two un-calibrated images, and to further determine the geometric relationship between images, such as epipolar geometry or projective transformation. In our approach, a set of salient features, edge-corners, are detected to provide robust and consistent matching primitives. Then, based on the Singular Value Decomposition (SVD) of an affine matrix, we effectively quantize the search space into two independent subspaces for rotation angle and scaling factor, and then we use a two-stage affine matching algorithm to obtain robust matches between these two frames. The experimental results on a number of wide baseline images strongly demonstrate that our matching method outperforms the state-of-art algorithms even under the significant camera motion, illumination variation, occlusion, and self-similarity. Given the wide baseline matches among images I have developed a novel method for Dynamic view morphing. Dynamic view morphing deals with the scenes containing moving objects in presence of camera motion. The objects can be rigid or non-rigid, each of them can move in any orientation or direction. The proposed method can generate a series of continuous and physically accurate intermediate views from only two reference images without any knowledge about 3D. The procedure consists of three steps: segmentation, morphing and post-warping. Given a boundary connection constraint, the source and target scenes are segmented into several layers for morphing. Based on the decomposition of affine transformation between corresponding points, we uniquely determine a physically correct path for post-warping by the least distortion method. I have successfully generalized the dynamic scene synthesis problem from the simple scene with only rotation to the dynamic scene containing non-rigid objects. My method can handle dynamic rigid or non-rigid objects, including complicated objects such as humans. Finally, I have also developed a novel algorithm for tri-view morphing. This is an efficient image-based method to navigate a scene based on only three wide-baseline un-calibrated images without the explicit use of a 3D model. After automatically recovering corresponding points between each pair of images using our wide baseline matching method, an accurate trifocal plane is extracted from the trifocal tensor implied in these three images. Next, employing a trinocular-stereo algorithm and barycentric blending technique, we generate an arbitrary novel view to navigate the scene in a 2D space. Furthermore, after self-calibration of the cameras, a 3D model can also be correctly augmented into this virtual environment synthesized by the tri-view morphing algorithm. We have applied our view morphing framework to several interesting applications: 4D video synthesis, automatic target recognition, multi-view morphing

Estimation of Motion from a Sequence of Images Using Spherical Projective Geometry

Motion is an important cue for many applications. Here we propose a solution for estimating motion from a sequence of images using three algorithms, viz., Batch, Recursive and Bootstrap methods. The motion derived using spherical projection relates the image motion to the object motion. This equation is reformulated into a dynamical space state model, for which Kalman filter can be easily applied to yield the estimate of depth. We also propose a new approach for establishing correspondences using local planar invariants and hierarchical groupings. The proposed algorithm provides a simple yet robust method having lower time complexity and less ambiguity in matching than its predecessors

Survey for Emission-Line Galaxies: Universidad Complutense de Madrid List 3

A new low-dispersion objective-prism search for low-redshift (z<0.045) emission-line galaxies (ELG) has been carried out by the Universidad Complutense de Madrid with the Schmidt Telescope at the Calar-Alto Observatory. This is a continuation of the UCM Survey, which was performed by visual selection of candidates in photographic plates via the presence of the Halpha+[NII]6584 blend in emission. In this new list we have applied an automatic procedure, fully developed by us, for selecting and analyzing the ELG candidates on the digitized images obtained with the MAMA machine. The analyzed region of the sky covers 189 square degrees in nine fields near R.A.=14h & 17h, Dec=25 deg. The final sample contains 113 candidates. Special effort has been made to obtain a large amount of information directly from our uncalibrated plates by using several external calibrations. The parameters obtained for the ELG candidates allow for the study of the statistical properties for the sample.Comment: 13 pages, 18 PostScript figures, 6 JPEG figures, Table 2 corrected. Accepted for publication in Astrophysical Journal Supplements, also available at http://www.ucm.es/info/Astrof/opera/LIST3_ApJS99