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

Image similarity in medical images

Recent experiments have indicated a strong influence of the substrate grain orientation on the self-ordering in anodic porous alumina. Anodic porous alumina with straight pore channels grown in a stable, self-ordered manner is formed on (001) oriented Al grain, while disordered porous pattern is formed on (101) oriented Al grain with tilted pore channels growing in an unstable manner. In this work, numerical simulation of the pore growth process is carried out to understand this phenomenon. The rate-determining step of the oxide growth is assumed to be the Cabrera-Mott barrier at the oxide/electrolyte (o/e) interface, while the substrate is assumed to determine the ratio β between the ionization and oxidation reactions at the metal/oxide (m/o) interface. By numerically solving the electric field inside a growing porous alumina during anodization, the migration rates of the ions and hence the evolution of the o/e and m/o interfaces are computed. The simulated results show that pore growth is more stable when β is higher. A higher β corresponds to more Al ionized and migrating away from the m/o interface rather than being oxidized, and hence a higher retained O:Al ratio in the oxide. Experimentally measured oxygen content in the self-ordered porous alumina on (001) Al is indeed found to be about 3% higher than that in the disordered alumina on (101) Al, in agreement with the theoretical prediction. The results, therefore, suggest that ionization on (001) Al substrate is relatively easier than on (101) Al, and this leads to the more stable growth of the pore channels on (001) Al

A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications

Particle swarm optimization (PSO) is a heuristic global optimization method, proposed originally by Kennedy and Eberhart in 1995. It is now one of the most commonly used optimization techniques. This survey presented a comprehensive investigation of PSO. On one hand, we provided advances with PSO, including its modifications (including quantum-behaved PSO, bare-bones PSO, chaotic PSO, and fuzzy PSO), population topology (as fully connected, von Neumann, ring, star, random, etc.), hybridization (with genetic algorithm, simulated annealing, Tabu search, artificial immune system, ant colony algorithm, artificial bee colony, differential evolution, harmonic search, and biogeography-based optimization), extensions (to multiobjective, constrained, discrete, and binary optimization), theoretical analysis (parameter selection and tuning, and convergence analysis), and parallel implementation (in multicore, multiprocessor, GPU, and cloud computing forms). On the other hand, we offered a survey on applications of PSO to the following eight fields: electrical and electronic engineering, automation control systems, communication theory, operations research, mechanical engineering, fuel and energy, medicine, chemistry, and biology. It is hoped that this survey would be beneficial for the researchers studying PSO algorithms

Recent Advances in Spatial Data Analysis

This article views spatial analysis as a research paradigm that provides a unique set of specialised techniques and models for a wide range of research questions in which the prime variables of interest vary significantly over space. The heart of spatial analysis is concerned with the analysis and modeling of spatial data. Spatial point patterns and area referenced data represent the most appropriate perspectives for applications in the social sciences. The researcher analysing and modeling spatial data tends to be confronted with a series of problems such as the data quality problem, the ecological fallacy problem, the modifiable areal unit problem, boundary and frame effects, and the spatial dependence problem. The problem of spatial dependence is at the core of modern spatial analysis and requires the use of specialised techniques and models in the data analysis. The discussion focuses on exploratory techniques and model-driven [confirmatory] modes of analysing spatial point patterns and area data. In closing, prospects are given towards a new style of data-driven spatial analysis characterized by computational intelligence techniques such as evolutionary computation and neural network modeling to meet the challenges of huge quantities of spatial data characteristic in remote sensing, geodemographics and marketing. (author's abstract)Series: Discussion Papers of the Institute for Economic Geography and GIScienc