A Two-Phase Genetic Algorithm for Image Registration

Image Registration (IR) is the process of aligning two (or more) images of the same scene taken at different times, different viewpoints and/or by different sensors. It is an important, crucial step in various image analysis tasks where multiple data sources are integrated/fused, in order to extract high-level information. Registration methods usually assume a relevant transformation model for a given problem domain. The goal is to search for the "optimal" instance of the transformation model assumed with respect to a similarity measure in question. In this paper we present a novel genetic algorithm (GA)-based approach for IR. Since GA performs effective search in various optimization problems, it could prove useful also for IR. Indeed, various GAs have been proposed for IR. However, most of them assume certain constraints, which simplify the transformation model, restrict the search space or make additional preprocessing requirements. In contrast, we present a generalized GA-based solution for an almost fully affine transformation model, which achieves competitive results without such limitations using a two-phase method and a multi-objective optimization (MOO) approach. We present good results for multiple dataset and demonstrate the robustness of our method in the presence of noisy data

INTEGRATION OF ACTIVE SENSORS FOR GEOMETRIC ANALYSIS OF THE CHAPEL OF THE HOLY SHROUD

Abstract. The digital acquisition of Cultural Heritage is a complex process, highly depending on the nature of the object as well as the purpose of its detection. Even if there are different survey techniques and sensors that allow the generation of realistic 3D models, defined by a good metric quality and a detail consistent with the geometric characteristics of the object, an interesting goal could be to develop a unified treatment of the methodologies. The Chapel of the Holy Shroud, with its intricate articulation, becomes the benchmark to test an integrated protocol between a terrestrial laser scanner (TLS) and a wearable mobile laser system (WMLS) based on a SLAM approach. In order to quantify the accuracy and precision of the latter solution, several forms of comparison are proposed. For the case study the ZEB-REVO, produced and marketed by GeoSLAM, is tested. Computations of cloud-to-cloud (C2C) absolute distances, comparisons of slices and extractions of planar features are performed, using stationary laser scanner (Faro FocusS S350) as a reference. Finally, the obtained results are reported, allowing us to assert that the quality of the WMLS measurements is compatible with the data provided by the manufacturer, thus making the instrumentation suitable for certain specific applications.</p

Transformation Model With Constraints for High Accuracy of 2D-3D Building Registration in Aerial Imagery

This paper proposes a novel rigorous transformation model for 2D-3D registration to address the difficult problem of obtaining a sufficient number of well-distributed ground control points (GCPs) in urban areas with tall buildings. The proposed model applies two types of geometric constraints, co-planarity and perpendicularity, to the conventional photogrammetric collinearity model. Both types of geometric information are directly obtained from geometric building structures, with which the geometric constraints are automatically created and combined into the conventional transformation model. A test field located in downtown Denver, Colorado, is used to evaluate the accuracy and reliability of the proposed method. The comparison analysis of the accuracy achieved by the proposed method and the conventional method is conducted. Experimental results demonstrated that: (1) the theoretical accuracy of the solved registration parameters can reach 0.47 pixels, whereas the other methods reach only 1.23 and 1.09 pixels; (2) the RMS values of 2D-3D registration achieved by the proposed model are only two pixels along the x and y directions, much smaller than the RMS values of the conventional model, which are approximately 10 pixels along the x and y directions. These results demonstrate that the proposed method is able to significantly improve the accuracy of 2D-3D registration with much fewer GCPs in urban areas with tall buildings

Developing a heritage BIM shared library for two case studies in Jordan’s heritage: The House of Art in Amman and the Qaqish House in the World Heritage City of As-Salt

International research is moving towards the development of BIM (Building Information Modelling) libraries applied to the built heritage where one of the main issues to be addressed is the modelling of complex or unique shapes that represent the specific construction components of every single asset. This perspective addresses the generation of parametric families of representative architectural geometry in the context of the management and representation of a building of heritage value. Jordan’s architectural heritage has gone through a long period of evolution and development: the result is a mixture of influences and traditions, making a great stride to conserve its buildings and Historical Heritage but has never adopted advanced digital technologies such as Building Information Modelling. In this framework, the present research aims to bridge the gap in Jordan by applying digital technologies to support heritage conservation plans by creating a 3D library of BIM objects related to typical elements of Jordanian and Arabic architecture, specifically in two cases of study. Co-production and collaboration with diverse stakeholders were central to the development of the methodology and design of the research.As a result, the first open-access HBIM shared library of historical features of Jordanian built heritage will be consolidated; this is crucial because it will set a precedent for the further documentation and conservation of the heritage of traditional cities in Jordan, MENA countries and internationally by promoting social cohesion, economic and technological development, tourism and the awareness of Jordan’s cultural heritage

Evaluation of the results of orthodontic treatment by non-rigid image registration and deformation-based morphometry

The goal of this research was to find out, whether the non-rigid registration of dental casts can be used in the evaluation of orthodontic treatment and to develop a program, which would at least partially automatize the evaluation process of images. The aim was also to experiment the evaluation of three-dimensional models of the casts. This research was delimited to cover only the evaluation of malocclusions within the dental arch. The relationships between the dental arches were not considered. This thesis was done in the University of Vaasa at the Department of Electrical Engineering and Energy Technology as a part of the HammasSkanneri research project, whose aim is to automatize the digitization and archiving of dental casts. This research used two-dimensional images of dental casts which were taken of orthodontically treated patients before and after orthodontic treatment. Non-rigid registration was performed by using a registration tool of Fiji software. The evaluation of the accuracy of the registration was performed by measuring distances between manually inserted landmarks, and by comparing the linear and angular parameters of the registered images and the original target images. The displacements of the teeth were approximated with the help of deformation-based morphometry. The accuracy of registration is within reasonable error limits, if the image is taken straight from above of the cast and the registration is performed with the help of landmarks inserted by a human. Estimation of the changes showed that the movement of teeth can be coarsely measured by using deformation-based morphometry based on change estimates that resemble the Jacobian estimates. A set of programs, which partially automatize the evaluation of the accuracy and the changes, were developed. Three-dimensional imaging of the casts was unsuccessful, and thus the development of 3D evaluation system was left as a future research topic.fi=Opinnäytetyö kokotekstinä PDF-muodossa.|en=Thesis fulltext in PDF format.|sv=Lärdomsprov tillgängligt som fulltext i PDF-format

A Survey on Evolutionary Computation for Computer Vision and Image Analysis: Past, Present, and Future Trends

Computer vision (CV) is a big and important field in artificial intelligence covering a wide range of applications. Image analysis is a major task in CV aiming to extract, analyse and understand the visual content of images. However, imagerelated tasks are very challenging due to many factors, e.g., high variations across images, high dimensionality, domain expertise requirement, and image distortions. Evolutionary computation (EC) approaches have been widely used for image analysis with significant achievement. However, there is no comprehensive survey of existing EC approaches to image analysis. To fill this gap, this paper provides a comprehensive survey covering all essential EC approaches to important image analysis tasks including edge detection, image segmentation, image feature analysis, image classification, object detection, and others. This survey aims to provide a better understanding of evolutionary computer vision (ECV) by discussing the contributions of different approaches and exploring how and why EC is used for CV and image analysis. The applications, challenges, issues, and trends associated to this research field are also discussed and summarised to provide further guidelines and opportunities for future research

Digital forensics trends and future

Nowadays, rapid evolution of computers and mobile phones has caused these devices to be used in criminal activities. Providing appropriate and sufficient security measures is a difficult job due to complexity of devices which makes investigating crimes involving these devices even harder. Digital forensic is the procedure of investigating computer crimes in the cyber world. Many researches have been done in this area to help forensic investigation to resolve existing challenges. This paper attempts to look into trends of applications of digital forensics and security at hand in various aspects and provide some estimations about future research trends in this area

Analysing and Enhancing the Coarse Registration Pipeline

The current and continual development of sensors and imaging systems capable of acquiring three-dimensional data provides a novel form in which the world can be expressed and examined. The acquisition process, however, is often limited by imaging systems only being able to view a portion of a scene or object from a single pose at a given time. A full representation can still be produced by shifting the system and registering subsequent acquisitions together. While many solutions to the registration problem have been proposed, there is no quintessential approach appropriate for all situations. This dissertation aims to coarsely register range images or point-clouds of a priori unknown pose by matching their overlapping regions. Using spherical harmonics to correlate normals in a coarse registration pipeline has been shown previously to be an effective means for registering partially overlapping point-clouds. The advantage of normals is their translation invariance, which permits the rotation and translation to be decoupled and determined separately. Examining each step of this pipeline in depth allows its registration capability to be quantified and identifies aspects which can be enhanced to further improve registration performance. The pipeline consists of three primary steps: identifying the rotation using spherical harmonics, identifying the translation in the Fourier domain, and automatically verifying if alignment is correct. Having achieved coarse registration, a fine registration algorithm can be used to refine and complete the alignment. Major contributions to knowledge are provided by this dissertation at each step of the pipeline. Point-clouds with known ground-truth are used to examine the pipeline's capability, allowing its limitations to be determined; an analysis which has not been performed previously. This examination allowed modifications to individual components to be introduced and measured, establishing their provided benefit. The rotation step received the greatest attention as it is the primary weakness of the pipeline, especially as the nature of the overlap between point-clouds is unknown. Examining three schemes for binning normals found that equiangular binning, when appropriately normalised, only had a marginal decrease in accuracy with respect to the icosahedron and the introduced Fibonacci schemes. Overall, equiangular binning was the most appropriate due to its natural affinity for fast spherical-harmonic conversion. Weighting normals was found to provide the greatest benefit to registration performance. The introduction of a straightforward method of combining two different weighting schemes using the orthogonality of complex values increased correct alignments by approximately 80% with respect to the next best scheme; additionally, point-cloud pairs with overlap as low as 5% were able to be brought into correct alignment. Transform transitivity, one of two introduced verification strategies, correctly classified almost 100% of point-cloud pair registrations when there are sufficient correct alignments. The enhancements made to the coarse registration pipeline throughout this dissertation provide significant improvements to its performance. The result is a pipeline with state-of-the-art capabilities that allow it to register point-cloud with minimal overlap and correct for alignments that are classified as misaligned. Even with its exceptional performance, it is unlikely that this pipeline has yet reached its pinnacle, as the introduced enhancements have the potential for further development