3D model reconstruction with noise filtering using boundary edges.

Lau Tak Fu.Thesis submitted in: October 2003.Thesis (M.Phil.)--Chinese University of Hong Kong, 2004.Includes bibliographical references (leaves 93-98).Abstracts in English and Chinese.Chapter 1 - --- Introduction --- p.9Chapter 1.1 --- Scope of the work --- p.9Chapter 1.2 --- Main contribution --- p.11Chapter 1.3 --- Outline of the thesis --- p.12Chapter 2 - --- Background --- p.14Chapter 2.1 --- Three dimensional models from images --- p.14Chapter 2.2 --- Un-calibrated 3D reconstruction --- p.14Chapter 2.3 --- Self calibrated 3D reconstruction --- p.16Chapter 2.4 --- Initial model formation using image based --- p.18Chapter 2.5 --- Volumes from Silhouettes --- p.19Chapter 3 - --- Initial model reconstruct the problem with mismatch noise --- p.22Chapter 3.1 --- Perspective Camera Model --- p.24Chapter 3.2 --- "Intrinsic parameters, Extrinsic parameters and camera motion" --- p.25Chapter 3.2.1 --- Intrinsic parameters --- p.25Chapter 3.2.2 --- Extrinsic parameter and camera motion --- p.27Chapter 3.3 --- Lowe's method --- p.29Chapter 3.4 --- Interleave bundle adjustment for structure and motion recovery from multiple images --- p.32Chapter 3.5 --- Feature points mismatch analysis --- p.38Chapter 4 - --- Feature selection by using look forward silhouette clipping --- p.43Chapter 4.1 --- Introduction to silhouette clipping --- p.43Chapter 4.2 --- Silhouette clipping for 3D model --- p.45Chapter 4.3 --- Implementation --- p.52Chapter 4.3.1 --- Silhouette extraction program --- p.52Chapter 4.3.2 --- Feature filter for alternative bundle adjustment algorithm --- p.59Chapter 5 - --- Experimental data --- p.61Chapter 5.1 --- Simulation --- p.61Chapter 5.1.1 --- Input of simulation --- p.61Chapter 5.1.2 --- Output of the simulation --- p.66Chapter 5.1.2.1 --- Radius distribution --- p.66Chapter 5.1.2.2 --- 3D model output --- p.74Chapter 5.1.2.3 --- VRML plotting --- p.80Chapter 5.2 --- Real Image testing --- p.82Chapter 5.2.1 --- Toy house on a turntable test --- p.82Chapter 5.2.2 --- Other tests on turntable --- p.86Chapter 6 - --- Conclusion and discussion --- p.8

Visual perception of solid shape from occluding contours

The relative motion of object and observer induces a motion field in the observer's visual image that is smooth everywhere except along the object's occluding contours. Thus, occluding contours and smooth motion fields can be viewed as complementary and as separate sources of information about an object's shape. I studied how the human visual system perceives solid shape from the occluding contours of rotating objects and from the smooth motion field induced by moving planar surface patches.I propose a three-stage model for the perception of solid shape from the occluding contours of a rotating object. First, the object's motion is determined. I argue that this is only possible using points of correspondence and only when the object's axis of rotation is frontoparallel. In the second stage, the motion field along the contour is used to compute relative depth and surface curvature along the rim, the contour's pre-image. Third, local shape descriptors are propagated inside the figure to yield a global percept of solid shape. To determine which shape descriptors are computed by human subjects, I used a novel task in which subjects have to discriminate between flat ellipses and solid ellipsoids with varying thickness. I found that discriminability is proportional to the inverse of radial curvature but is not proportional to Gaussian or mean curvature. Certain slants of the axis of rotation decrease discriminability. Subjects who could discriminate ellipsoids and ellipses perceived the ellipsoids' angular velocity more veridically than did subjects who could not discriminate the two.Any smooth motion field can locally be described by divergence, curl, and deformation. If the motion field is induced by a rotating plane, the amount of deformation is proportional to the plane's slant and its angular velocity. Similarly, for translating planes, deformation is proportional to slant and image motion. Slant judgments of human observers were to a first-order approximation proportional to deformation per se, that is, observers do not take object motion into account. Recent psychophysical evidence suggests that human subjects need motion discontinuities for this. Thus, contours might be necessary to correctly perceive slant from smooth motion fields

Flow Imaging Using MRI: Quantification and Analysis

A complex and challenging problem in flow study is to obtain quantitative flow information in opaque systems, for example, blood flow in biological systems and flow channels in chemical reactors. In this regard, MRI is superior to the conventional optical flow imaging or ultrasonic Doppler imaging. However, for high speed flows, complex flow behaviors and turbulences make it difficult to image and analyze the flows. In MR flow imaging, MR tagging technique has demonstrated its ability to simultaneously visualize motion in a sequence of images. Moreover, a quantification method, namely HARmonic Phase (HARP) analysis, can extract a dense velocity field from tagged MR image sequence with minimal manual intervention. In this work, we developed and validated two new MRI methods for quantification of very rapid flows. First, HARP was integrated with a fast MRI imaging method called SEA (Single Echo Acquisition) to image and analyze high velocity flows. Second, an improved HARP method was developed to deal with tag fading and data noise in the raw MRI data. Specifically, a regularization method that incorporates the law of flow dynamics in the HARP analysis was developed. Finally, the methods were validated using results from the computational fluid dynamics (CFD) and the conventional optimal flow imaging based on particle image velocimetry (PIV). The results demonstrated the improvement from the quantification using solely the conventional HARP method