Maximal disk based histogram for shape retrieval

2002-2003 > Academic research: refereed > Refereed conference paperVersion of RecordPublishe

From 3D Point Clouds to Pose-Normalised Depth Maps

We consider the problem of generating either pairwise-aligned or pose-normalised depth maps from noisy 3D point clouds in a relatively unrestricted poses. Our system is deployed in a 3D face alignment application and consists of the following four stages: (i) data filtering, (ii) nose tip identification and sub-vertex localisation, (iii) computation of the (relative) face orientation, (iv) generation of either a pose aligned or a pose normalised depth map. We generate an implicit radial basis function (RBF) model of the facial surface and this is employed within all four stages of the process. For example, in stage (ii), construction of novel invariant features is based on sampling this RBF over a set of concentric spheres to give a spherically-sampled RBF (SSR) shape histogram. In stage (iii), a second novel descriptor, called an isoradius contour curvature signal, is defined, which allows rotational alignment to be determined using a simple process of 1D correlation. We test our system on both the University of York (UoY) 3D face dataset and the Face Recognition Grand Challenge (FRGC) 3D data. For the more challenging UoY data, our SSR descriptors significantly outperform three variants of spin images, successfully identifying nose vertices at a rate of 99.6%. Nose localisation performance on the higher quality FRGC data, which has only small pose variations, is 99.9%. Our best system successfully normalises the pose of 3D faces at rates of 99.1% (UoY data) and 99.6% (FRGC data)

Region-based indexing in an image database

Image retrieval systems based on the image-query-by-example paradigm locate their answer set using a similarity measure of the query image with all images stored in the database. Although this approach generally works for quick re-location of `identical' or partly occluded images, it does not support the more interesting query type aimed at finding images with a particular image fragment. In this paper we introduce a regionbased indexing scheme to support retrieval of images on the basis of both global and local image features

Towards optical intensity interferometry for high angular resolution stellar astrophysics

Most neighboring stars are still detected as point sources and are beyond the angular resolution reach of current observatories. Methods to improve our understanding of stars at high angular resolution are investigated. Air Cherenkov telescopes (ACTs), primarily used for Gamma-ray astronomy, enable us to increase our understanding of the circumstellar environment of a particular system. When used as optical intensity interferometers, future ACT arrays will allow us to detect stars as extended objects and image their surfaces at high angular resolution. Optical stellar intensity interferometry (SII) with ACT arrays, composed of nearly 100 telescopes, will provide means to measure fundamental stellar parameters and also open the possibility of model-independent imaging. A data analysis algorithm is developed and permits the reconstruction of high angular resolution images from simulated SII data. The capabilities and limitations of future ACT arrays used for high angular resolution imaging are investigated via Monte-Carlo simulations. Simple stellar objects as well as stellar surfaces with localized hot or cool regions can be accurately imaged. Finally, experimental efforts to measure intensity correlations are expounded. The functionality of analog and digital correlators is demonstrated. Intensity correlations have been measured for a simulated star emitting pseudo-thermal light, resulting in angular diameter measurements. The StarBase observatory, consisting of a pair of 3 m telescopes separated by 23 m, is described.Comment: PhD dissertatio

Coronal loop detection from solar images and extraction of salient contour groups from cluttered images.

This dissertation addresses two different problems: 1) coronal loop detection from solar images: and 2) salient contour group extraction from cluttered images. In the first part, we propose two different solutions to the coronal loop detection problem. The first solution is a block-based coronal loop mining method that detects coronal loops from solar images by dividing the solar image into fixed sized blocks, labeling the blocks as Loop or Non-Loop , extracting features from the labeled blocks, and finally training classifiers to generate learning models that can classify new image blocks. The block-based approach achieves 64% accuracy in IO-fold cross validation experiments. To improve the accuracy and scalability, we propose a contour-based coronal loop detection method that extracts contours from cluttered regions, then labels the contours as Loop and Non-Loop , and extracts geometric features from the labeled contours. The contour-based approach achieves 85% accuracy in IO-fold cross validation experiments, which is a 20% increase compared to the block-based approach. In the second part, we propose a method to extract semi-elliptical open curves from cluttered regions. Our method consists of the following steps: obtaining individual smooth contours along with their saliency measures; then starting from the most salient contour, searching for possible grouping options for each contour; and continuing the grouping until an optimum solution is reached. Our work involved the design and development of a complete system for coronal loop mining in solar images, which required the formulation of new Gestalt perceptual rules and a systematic methodology to select and combine them in a fully automated judicious manner using machine learning techniques that eliminate the need to manually set various weight and threshold values to define an effective cost function. After finding salient contour groups, we close the gaps within the contours in each group and perform B-spline fitting to obtain smooth curves. Our methods were successfully applied on cluttered solar images from TRACE and STEREO/SECCHI to discern coronal loops. Aerial road images were also used to demonstrate the applicability of our grouping techniques to other contour-types in other real applications