Image Feature Information Extraction for Interest Point Detection: A Comprehensive Review

Interest point detection is one of the most fundamental and critical problems in computer vision and image processing. In this paper, we carry out a comprehensive review on image feature information (IFI) extraction techniques for interest point detection. To systematically introduce how the existing interest point detection methods extract IFI from an input image, we propose a taxonomy of the IFI extraction techniques for interest point detection. According to this taxonomy, we discuss different types of IFI extraction techniques for interest point detection. Furthermore, we identify the main unresolved issues related to the existing IFI extraction techniques for interest point detection and any interest point detection methods that have not been discussed before. The existing popular datasets and evaluation standards are provided and the performances for eighteen state-of-the-art approaches are evaluated and discussed. Moreover, future research directions on IFI extraction techniques for interest point detection are elaborated

Methods for multi-spectral image fusion: identifying stable and repeatable information across the visible and infrared spectra

Fusion of images captured from different viewpoints is a well-known challenge in computer vision with many established approaches and applications; however, if the observations are captured by sensors also separated by wavelength, this challenge is compounded significantly. This dissertation presents an investigation into the fusion of visible and thermal image information from two front-facing sensors mounted side-by-side. The primary focus of this work is the development of methods that enable us to map and overlay multi-spectral information; the goal is to establish a combined image in which each pixel contains both colour and thermal information. Pixel-level fusion of these distinct modalities is approached using computational stereo methods; the focus is on the viewpoint alignment and correspondence search/matching stages of processing. Frequency domain analysis is performed using a method called phase congruency. An extensive investigation of this method is carried out with two major objectives: to identify predictable relationships between the elements extracted from each modality, and to establish a stable representation of the common information captured by both sensors. Phase congruency is shown to be a stable edge detector and repeatable spatial similarity measure for multi-spectral information; this result forms the basis for the methods developed in the subsequent chapters of this work. The feasibility of automatic alignment with sparse feature-correspondence methods is investigated. It is found that conventional methods fail to match inter-spectrum correspondences, motivating the development of an edge orientation histogram (EOH) descriptor which incorporates elements of the phase congruency process. A cost function, which incorporates the outputs of the phase congruency process and the mutual information similarity measure, is developed for computational stereo correspondence matching. An evaluation of the proposed cost function shows it to be an effective similarity measure for multi-spectral information

Methods for iris classification and macro feature detection

This work deals with two distinct aspects of iris-based biometric systems: iris classification and macro-feature detection. Iris classification will benefit identification systems where the query image has to be compared against all identities in the database. By preclassifying the query image based on its texture, this comparison is executed only against those irises that are from the same class as the query image. In the proposed classification method, the normalized iris is tessellated into overlapping rectangular blocks and textural features are extracted from each block. A clustering scheme is used to generate multiple classes of irises based on the extracted features. A minimum distance classifier is then used to assign the query iris to a particular class. The use of multiple blocks with decision level fusion in the classification process is observed to enhance the accuracy of the method.;Most iris-based systems use the global and local texture information of the iris to perform matching. In order to exploit the anatomical structures within the iris during the matching stage, two methods to detect the macro-features of the iris in multi-spectral images are proposed. These macro-features typically correspond to anomalies in pigmentation and structure within the iris. The first method uses the edge-flow technique to localize these features. The second technique uses the SIFT (Scale Invariant Feature Transform) operator to detect discontinuities in the image. Preliminary results show that detection of these macro features is a difficult problem owing to the richness and variability in iris color and texture. Thus a large number of spurious features are detected by both the methods suggesting the need for designing more sophisticated algorithms. However the ability of the SIFT operator to match partial iris images is demonstrated thereby indicating the potential of this scheme to be used for macro-feature detection

A multi-rule-based relative radiometric normalization for multi-sensor satellite images

Relative radiometric normalization (RRN) is a widely used method for enhancing the radiometric consistency among multi-temporal satellite images. Diverse satellite images enhance the information for observing the Earth’s surface and bring additional uncertainties in the applications using multi-sensor images, such as change detection, multi-temporal analysis, image fusion, etc. To address this challenge, we developed a multi-rule-based RRN method for multi-sensor satellite images, which involves the identification of spectral- and spatial-invariant pseudo-invariant features (PIFs) and a Partial least-squares (PLS) regression-based RRN modeling using neighboring target pixels around PIFs. The proposed RRN method was validated on four datasets and demonstrated excellent effectiveness in identifying high-quality PIFs with spectral- and spatial-invariant properties, estimating precise regression models, and enhancing the radiometric consistency of reference-target image pair. Our method outperformed six RRN methods and effectively processed well-registered medium- and high-resolution images from the same sensor. This letter highlights the potential of our method for generating more comparable bi-temporal multi-sensor images