Multi-Scale Memetic Image Registration

Many technological applications of our time rely on images captured by multiple cameras. Such applications include the detection and recognition of objects in captured images, the tracking of objects and analysis of their motion, and the detection of changes in appearance. The alignment of images captured at different times and/or from different angles is a key processing step in these applications. One of the most challenging tasks is to develop fast algorithms to accurately align images perturbed by various types of transformations. The paper reports a new method used to register images in the case of geometric perturbations that include rotations, translations, and non-uniform scaling. The input images can be monochrome or colored, and they are preprocessed by a noise-insensitive edge detector to obtain binarized versions. Isotropic scaling transformations are used to compute multi-scale representations of the binarized inputs. The algorithm is of memetic type and exploits the fact that the computation carried out in reduced representations usually produces promising initial solutions very fast. The proposed method combines bio-inspired and evolutionary computation techniques with clustered search and implements a procedure specially tailored to address the premature convergence issue in various scaled representations. A long series of tests on perturbed images were performed, evidencing the efficiency of our memetic multi-scale approach. In addition, a comparative analysis has proved that the proposed algorithm outperforms some well-known registration procedures both in terms of accuracy and runtime.

Particle Swarm Optimisation for Edge Detection in Noisy Images

<p>Detection of continuous and connected edges is very important in many applications, such as detecting oil slicks in remote sensing and detecting cancers in medical images. The detection of such edges is a hard problem particularly in noisy images and most edge detection algorithms suffer from producing broken and thick edges in such images. The main goal of this thesis is to reduce broken edges by proposing an optimisation model and a solution method in order to detect edges in noisy images. This thesis suggests a newapproach in the framework of particle swarm optimisation (PSO) to overcome noise and reduce broken edges through exploring a large area and extracting the global structure of the edges. A fitness function is developed based on the possibility score of a curve being fitted on an edge and the curvature cost of the curve with two constraints. Unlike traditional algorithms, the new method can detect edges with greater continuity in noisy images. Furthermore, a new truncation method within PSO is proposed to truncate the real values of particle positions to integers in order to increase the diversity of the particles. This thesis also proposes a local thresholding technique for the PSObased edge detection algorithm to overcome the problem of detection of edges in noisy images with illuminated areas. The local thresholding technique is proposed based on themain idea of the Sauvola-Pietkinenmethod which is a way of binarisation of illuminated images. It is observed that the new local thresholding can improve the performance of the PSO-based edge detectors in the illuminated noisy images. Since the performance of using static topologies in various applications and in various versions of PSO is different , the performance of six different static topologies (fully connected, ring, star, tree-based, von Neumann and toroidal topologies)within threewell-known versions of PSO (Canonical PSO, Bare Bones PSO and Fully Informed PSO) are also investigated in the PSO-based edge detector. It is found that different topologies have different effects on the accuracy of the PSO-based edge detector. This thesis also proposes a novel dynamic topology called spatial random meaningful topology (SRMT) which is an adoptation version of a gradually increasing directed neighbourhood (GIDN). The new dynamic topology uses spatial meaningful information to compute the neighbourhood probability of each particle to be a neighbour of other particles. It uses this probability to randomly select the neighbours of each particle at each iteration of PSO. The results show that the performance of the proposed method is higher than that of other topologies in noisy images in terms of the localisation accuracy of edge detection.</p>

Particle Swarm Optimisation for Edge Detection in Noisy Images

<p>Detection of continuous and connected edges is very important in many applications, such as detecting oil slicks in remote sensing and detecting cancers in medical images. The detection of such edges is a hard problem particularly in noisy images and most edge detection algorithms suffer from producing broken and thick edges in such images. The main goal of this thesis is to reduce broken edges by proposing an optimisation model and a solution method in order to detect edges in noisy images. This thesis suggests a newapproach in the framework of particle swarm optimisation (PSO) to overcome noise and reduce broken edges through exploring a large area and extracting the global structure of the edges. A fitness function is developed based on the possibility score of a curve being fitted on an edge and the curvature cost of the curve with two constraints. Unlike traditional algorithms, the new method can detect edges with greater continuity in noisy images. Furthermore, a new truncation method within PSO is proposed to truncate the real values of particle positions to integers in order to increase the diversity of the particles. This thesis also proposes a local thresholding technique for the PSObased edge detection algorithm to overcome the problem of detection of edges in noisy images with illuminated areas. The local thresholding technique is proposed based on themain idea of the Sauvola-Pietkinenmethod which is a way of binarisation of illuminated images. It is observed that the new local thresholding can improve the performance of the PSO-based edge detectors in the illuminated noisy images. Since the performance of using static topologies in various applications and in various versions of PSO is different , the performance of six different static topologies (fully connected, ring, star, tree-based, von Neumann and toroidal topologies)within threewell-known versions of PSO (Canonical PSO, Bare Bones PSO and Fully Informed PSO) are also investigated in the PSO-based edge detector. It is found that different topologies have different effects on the accuracy of the PSO-based edge detector. This thesis also proposes a novel dynamic topology called spatial random meaningful topology (SRMT) which is an adoptation version of a gradually increasing directed neighbourhood (GIDN). The new dynamic topology uses spatial meaningful information to compute the neighbourhood probability of each particle to be a neighbour of other particles. It uses this probability to randomly select the neighbours of each particle at each iteration of PSO. The results show that the performance of the proposed method is higher than that of other topologies in noisy images in terms of the localisation accuracy of edge detection.</p>

The Application of Hough Transform and Canny Edge Detector Methods for the Visual Detection of Cumuliform Clouds

The increase in flying time of unmanned aerial vehicles (UAV) is a relevant and difficult task for UAV designers. It is especially important in such tasks as monitoring, mapping, or signal retranslation. While the majority of research is concentrated on increasing the battery capacity, it is also important to utilize natural renewable energy sources, such as solar energy, thermals, etc. This article proposed a method for the automatic recognition of cumuliform clouds. Practical application of this method allows diverting of an unmanned aerial vehicle towards the identified cumuliform cloud and improving its probability of flying into a thermal flow, thus increasing the flight time of the UAV, as is performed by glider and paraglider pilots. The proposed method is based on the application of Hough transform and Canny edge detector methods, which have not been used for such a task before. For testing the proposed method a dataset of different clouds was generated and marked by experts. The achieved average accuracy of 87% on the unbalanced dataset demonstrates the practical applicability of the proposed method for detecting thermals related to cumuliform clouds. The article also provides the concept of VilniusTech developed UAV, implementing the proposed method.