Matching Interest Points Using Projective Invariant Concentric Circles

We present a new method to perform reliable matching between different images. This method exploits a projective invariant property between concentric circles and the corresponding projected ellipses to find complete region correspondences centered on interest points. The method matches interest points allowing for a full perspective transformation and exploiting all the available luminance information in the regions. Experiments have been conducted on many different data sets to compare our approach to SIFT local descriptors. The results show the new method offers increased robustness to partial visibility, object rotation in depth, and viewpoint angle change.Singapore-MIT Alliance (SMA

Fingerprint Matching using A Hybrid Shape and Orientation Descriptor

From the privacy perspective most concerns arise from the storage and misuse of biometric data (Cimato et al., 2009). ... is provided with a in-depth discussion of the state-of-the-art in iris biometric cryptosystems, which completes this work

Feature-Based Correspondences to Infer the Location of Anatomical Landmarks

A methodology has been developed for automatically determining inter-image correspondences between cliques of features extracted from a reference and a query image. Cliques consist of up to threefeatures and correspondences between them are determined via a hierarchy of similarity metrics based on the inherent properties of the features and geometric relationships between those features. As opposed to approaches that determine correspondences solely by voxel intensity, features that also include shape description are used. Specifically, medial-based features areemployed because they are sparse compared to the number of image voxels and can be automatically extracted from the image.The correspondence framework has been extended to automatically estimate the location of anatomical landmarks in the query image by adding landmarks to the cliques. Anatomical landmark locationsare then inferred from the reference image by maximizing landmark correspondences. The ability to infer landmark locations has provided a means to validate the correspondence framework in thepresence of structural variation between images. Moreover, automated landmark estimation imparts the user with anatomical information and can hypothetically be used to initialize andconstrain the search space of segmentation and registration methods.Methods developed in this dissertation were applied to simulated MRI brain images, synthetic images, and images constructed from several variations of a parametric model. Results indicate that the methods are invariant to global translation and rotation and can operate in the presence of structure variation between images.The automated landmark placement method was shown to be accurate as compared to ground-truth that was established both parametrically and manually. It is envisioned that these automated methods could prove useful for alleviating time-consuming and tedious tasks in applications that currently require manual input, and eliminate intra-user subjectivity

Computerized Analysis of Magnetic Resonance Images to Study Cerebral Anatomy in Developing Neonates

The study of cerebral anatomy in developing neonates is of great importance for the understanding of brain development during the early period of life. This dissertation therefore focuses on three challenges in the modelling of cerebral anatomy in neonates during brain development. The methods that have been developed all use Magnetic Resonance Images (MRI) as source data. To facilitate study of vascular development in the neonatal period, a set of image analysis algorithms are developed to automatically extract and model cerebral vessel trees. The whole process consists of cerebral vessel tracking from automatically placed seed points, vessel tree generation, and vasculature registration and matching. These algorithms have been tested on clinical Time-of- Flight (TOF) MR angiographic datasets. To facilitate study of the neonatal cortex a complete cerebral cortex segmentation and reconstruction pipeline has been developed. Segmentation of the neonatal cortex is not effectively done by existing algorithms designed for the adult brain because the contrast between grey and white matter is reversed. This causes pixels containing tissue mixtures to be incorrectly labelled by conventional methods. The neonatal cortical segmentation method that has been developed is based on a novel expectation-maximization (EM) method with explicit correction for mislabelled partial volume voxels. Based on the resulting cortical segmentation, an implicit surface evolution technique is adopted for the reconstruction of the cortex in neonates. The performance of the method is investigated by performing a detailed landmark study. To facilitate study of cortical development, a cortical surface registration algorithm for aligning the cortical surface is developed. The method first inflates extracted cortical surfaces and then performs a non-rigid surface registration using free-form deformations (FFDs) to remove residual alignment. Validation experiments using data labelled by an expert observer demonstrate that the method can capture local changes and follow the growth of specific sulcus

Feature Extraction Methods for Character Recognition

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