Clique descriptor of affine invariant regions for robust wide baseline image matching

Assuming that the image distortion between corresponding regions of a stereo pair of images with wide baseline can be approximated as an affine transformation if the regions are reasonably small, recent image matching algorithms have focused on affine invariant region (IR) detection and its description to increase the robustness in matching. However, the distinctiveness of an intensity-based region descriptor tends to deteriorate when an image includes homogeneous texture or repetitive pattern. To address this problem, we investigated the geometry of a local IR cluster (also called a clique) and propose a new clique-based image matching method. In the proposed method, the clique of an IR is estimated by Delaunay triangulation in a local affine frame and the Hausdorff distance is adopted for matching an inexact number of multiple descriptor vectors. We also introduce two adaptively weighted clique distances, where the neighbour distance in a clique is appropriately weighted according to characteristics of the local feature distribution. Experimental results show the clique-based matching method produces more tentative correspondences than variants of the SIFT-based method

Robust surface modelling of visual hull from multiple silhouettes

Reconstructing depth information from images is one of the actively researched themes in computer vision and its application involves most vision research areas from object recognition to realistic visualisation. Amongst other useful vision-based reconstruction techniques, this thesis extensively investigates the visual hull (VH) concept for volume approximation and its robust surface modelling when various views of an object are available. Assuming that multiple images are captured from a circular motion, projection matrices are generally parameterised in terms of a rotation angle from a reference position in order to facilitate the multi-camera calibration. However, this assumption is often violated in practice, i.e., a pure rotation in a planar motion with accurate rotation angle is hardly realisable. To address this problem, at first, this thesis proposes a calibration method associated with the approximate circular motion. With these modified projection matrices, a resulting VH is represented by a hierarchical tree structure of voxels from which surfaces are extracted by the Marching cubes (MC) algorithm. However, the surfaces may have unexpected artefacts caused by a coarser volume reconstruction, the topological ambiguity of the MC algorithm, and imperfect image processing or calibration result. To avoid this sensitivity, this thesis proposes a robust surface construction algorithm which initially classifies local convex regions from imperfect MC vertices and then aggregates local surfaces constructed by the 3D convex hull algorithm. Furthermore, this thesis also explores the use of wide baseline images to refine a coarse VH using an affine invariant region descriptor. This improves the quality of VH when a small number of initial views is given. In conclusion, the proposed methods achieve a 3D model with enhanced accuracy. Also, robust surface modelling is retained when silhouette images are degraded by practical noise

Evaluation of close-range stereo matching algorithms using stereoscopic measurements

The performance of binocular stereo reconstruction is highly dependent on the quality of the stereo matching result. In order to evaluate the performance of different stereo matchers, several quality metrics have been developed based on quantifying error statistics with respect to a set of independent measurements usually referred to as ground truth data. However, such data are frequently not available, particularly in practical applications or planetary data processing. To address this, we propose a ground truth independent evaluation protocol based on manual measurements. A stereo visualization tool has been specifically developed to evaluate the quality of the computed correspondences. We compare the quality of disparity maps calculated from three stereo matching algorithms, developed based on a variation of GOTCHA, which has been used in planetary robotic rover image reconstruction at UCL-MSSL (Otto and Chau, 1989). From our evaluation tests with the images pairs from Mars Exploration Rover (MER) Pancam and the field data collected in PRoViScout 2012, it has been found that all three processing pipelines used in our test (NASA-JPL, JR, UCL-MSSL) trade off matching accuracy and completeness differently. NASA-JPL's stereo pipeline produces the most accurate but less complete disparity map, while JR's pipeline performs best in terms of the reconstruction completeness

Local hull-based surface construction of volumetric data from silhouettes

The marching cubes (MC) is a general method which can construct a surface of an object from its volumetric data generated using a shape from silhouette method. Although MC is efficient and straightforward to implement, a MC surface may have discontinuity even though the volumetric data is continuous. This is because surface construction is more sensitive to image noise than the construction of volumetric data. To address this problem, we propose a surface construction algorithm which aggregates local surfaces constructed by the 3-D convex hull algorithm. Thus, the proposed method initially classifies local convexities from imperfect MC vertices based on sliced volumetric data. Experimental results show that continuous surfaces are obtained from imperfect silhouette images of both convex and nonconvex objects

Similarity invariant Delaunay graph matching

Delaunay tessellation describes a set of arbitrarily distributed points as unique triangular graphs which preserves most local point configuration called a clique regardless of noise addition and partial occlusion. In this paper, this structure is utilised in a matching method and proposed a clique-based Hausdorff Distance (HD) to address point pattern matching problems. Since the proposed distance exploits similarity invariant features extracted from a clique, it is invariant to rotation, translation and scaling. Furthermore, it inherits noise robustness from HD and has partial matching ability because matching performs on local entities. Experimental results show that the proposed method performs better than the existing variants of the general HD

Triangular mesh generation of octrees of non-convex 3D objects

A general surface-generating algorithm, the Marching Cube, produces triangular meshes from octants where the vertices of octants are clearly classified into either inside or outside the object. However, the algorithm is ambiguous for octrees corresponding to nonconvex objects generated using a Shape from Silhouette technique. This paper presents a methodology which involves Delaunay triangulation to generate surface meshes for such octrees. Since the general 3D Delaunay triangulation creates 3D convex hull which consists of tetrahedron meshes, we propose a method which applies the Delaunay algorithm locally in order to deal with non-convex objects. The proposed method first slices an octree and detects the clusters in each slice. All clusters between adjacent slices are linked based on a 3D probability density cube. The Delaunay algorithm is then applied to locally-linked clusters. Finally the accumulation of triangular meshes forms a final non-convex surface mesh