With the increasing awareness of personal and national safety, security and surveillance systems are commonly encountered in our everyday lives. Computer vision technologies such as modeling and recognition are the key components of current surveillance systems. Over the last few years, many studies have been conducted for facial recognition using data acquired by imaging systems such as video and laser scanning. With the advent of low-cost digital cameras, photogrammetry has emerged as a cost-effective and straightforward technique to accurately acquire three-dimensional facial measurements. For identification and verification purposes, generated facial models should undergo surface registration and matching procedures. In addition to facial recognition, the surface matching can be used for identifying resulting discrepancies from different facial expressions. The presented research introduces a novel approach for using low-cost digital cameras and surface matching to generate and recognize corresponding facial models. Preliminary experimental results showed that the proposed algorithms could successfully match and detect discrepancies between two facial models. The performance, advantages, and limitations of this preliminary study will be discussed, along with recommendations for future research.
Close-range mapping applications such as cultural heritage restoration, virtual reality modeling for the entertainment
industry, and anatomical feature recognition for medical activities require 3D data that is usually acquired by high
resolution close-range laser scanners. Since these datasets are typically captured from different viewpoints and/or at
different times, accurate registration is a crucial procedure for 3D modeling of mapped objects. Several registration
techniques are available that work directly with the raw laser points or with extracted features from the point cloud.
Some examples include the commonly known Iterative Closest Point (ICP) algorithm and a recently proposed technique
based on matching spin-images. This research focuses on developing a surface matching algorithm that is based on the
Modified Iterated Hough Transform (MIHT) and ICP to register 3D data. The proposed algorithm works directly with
the raw 3D laser points and does not assume point-to-point correspondence between two laser scans. The algorithm can
simultaneously establish correspondence between two surfaces and estimates the transformation parameters relating
them. Experiment with two partially overlapping laser scans of a small object is performed with the proposed algorithm
and shows successful registration. A high quality of fit between the two scans is achieved and improvement is found
when compared to the results obtained using the spin-image technique. The results demonstrate the feasibility of the
proposed algorithm for registering 3D laser scanning data in close-range mapping applications to help with the
generation of complete 3D models.
In-vivo quantitative assessments of joint conditions and health status can help to increase understanding of the pathology of osteoarthritis, a degenerative joint disease that affects a large population each year. Magnetic resonance imaging (MRI) provides a non-invasive and accurate means to assess and monitor joint properties, and has become widely used for diagnosis and biomechanics studies. Quantitative analyses and comparisons of MR datasets require accurate alignment of anatomical structures, thus image registration becomes a necessary procedure for these applications. This research focuses on developing a registration technique for MR knee joint surfaces to allow quantitative study of joint injuries and health status. It introduces a novel idea of translating techniques originally developed for geographic data in the field of photogrammetry and remote sensing to register 3D MR data. The proposed algorithm works with surfaces that are represented by randomly distributed points with no requirement of known correspondences. The algorithm performs matching locally by identifying corresponding surface elements, and solves for the transformation parameters relating the surfaces by minimizing normal distances between them. This technique was used in three applications to: 1) register temporal MR data to verify the feasibility of the algorithm to help monitor diseases, 2) quantify patellar movement with respect to the femur based on the transformation parameters, and 3) quantify changes in contact area locations between the patellar and femoral cartilage at different knee flexion angles. The results indicate accurate registration and the proposed algorithm can be applied for in-vivo study of joint injuries with MRI.