Fast concurrent object classification and localization

Object localization and classification are important problems incomputer vision. However, in many applications, exhaustive searchover all class labels and image locations is computationallyprohibitive. While several methods have been proposed to makeeither classification or localization more efficient, few havedealt with both tasks simultaneously. This paper proposes anefficient method for concurrent object localization andclassification based on a data-dependent multi-classbranch-and-bound formalism. Existing bag-of-featuresclassification schemes, which can be expressed as weightedcombinations of feature counts can be readily adapted to ourmethod. We present experimental results that demonstrate the meritof our algorithm in terms of classification accuracy, localizationaccuracy, and speed, compared to baseline approaches includingexhaustive search, the ISM method, and single-class branch andbound

Multi-resolution dental image registration based on genetic algorithm

The Automated Dental Identification System (ADIS) is a Post Mortem Dental Identification System. This thesis presents dental image registration, required for the preprocessing steps of the image comparison component of ADIS. We proposed a multi resolution dental image registration based on genetic algorithms. The main objective of this research is to develop techniques for registration of extracted subject regions of interest with corresponding reference regions of interest.;We investigated and implemented registration using two multi resolution techniques namely image sub sampling and wavelet decomposition. Multi resolution techniques help in the reduction of search data since initial registration is carried at lower levels and results are updated as the levels of resolutions increase. We adopted edges as image features that needed to be aligned. Affine transformations were selected to transform the subject dental region of interest to achieve better alignment with the reference region of interest. These transformations are known to capture complex image distortions. The similarity between subject and reference image has been computed using Oriented Hausdorff Similarity measure that is robust to severe noise and image degradations. A genetic algorithm was adopted to search for the best transformation parameters that give maximum similarity score.;Testing results show that the developed registration algorithm yielded reasonable results in accuracy for dental test cases that contained slight misalignments. The relative percentage errors between the known and estimated transformation parameters were less than 20% with a termination criterion of a ten minute time limit. Further research is needed for dental cases that contain high degree of misalignment, noise and distortions

A Comparison of Search Strategies for Geometric Branch and Bound Algorithms

Over the last decade, a number of methods for geometric matching based on a branch-and-bound approach have been proposed. Such algorithms work by recursively subdividing transformation space and bounding the quality of match over each subdivision. No direct comparison of the major implementation strategies has been made so far, so it has been unclear what the relative performance of the different approaches is. This paper examines experimentally the relative performance of different implementation choices in the implementation of branch-and-bound algorithms for geometric matching: alternatives for the computation of upper bounds across a collection of features, and alternatives the order in which search nodes are expanded. Two major approaches to computing the bounds have been proposed: the matchlist based approach, and approaches based on point location data structures. A second issue that is addressed in the paper is the question of search strategy; branch-and-bound algorithms traditionally use a "best-first" search strategy, but a "depth-first" strategy is a plausible alternative. These alternative implementations are compared on an easily reproducible and commonly used class of test problems, a statistical model of feature distributions and matching within the COIL-20 image database. The experimental results show that matchlist based approaches outperform point location based approaches on common tasks. The paper also shows that a depth-first approach to matching results in a 50-200 fold reduction in memory usage with only a small increase in running time. Since matchlist-based approaches are significantly easier to implement and can easily cope with a much wider variety of feature types and error bounds that point location based approaches, ..