Particle Swarm Optimisation in Practice: Multiple Applications in a Digital Microscope System

We demonstrate that particle swarm optimisation (PSO) can be used to solve a variety of problems arising during operation of a digital inspection microscope. This is a use case for the feasibility of heuristics in a real-world product. We show solutions to four measurement problems, all based on PSO. This allows for a compact software implementation solving different problems. We have found that PSO can solve a variety of problems with small software footprints and good results in a real-world embedded system. Notably, in the microscope application, this eliminates the need to return the device to the factory for calibration

Do-It-Yourself Single Camera 3D Pointer Input Device

We present a new algorithm for single camera 3D reconstruction, or 3D input for human-computer interfaces, based on precise tracking of an elongated object, such as a pen, having a pattern of colored bands. To configure the system, the user provides no more than one labelled image of a handmade pointer, measurements of its colored bands, and the camera's pinhole projection matrix. Other systems are of much higher cost and complexity, requiring combinations of multiple cameras, stereocameras, and pointers with sensors and lights. Instead of relying on information from multiple devices, we examine our single view more closely, integrating geometric and appearance constraints to robustly track the pointer in the presence of occlusion and distractor objects. By probing objects of known geometry with the pointer, we demonstrate acceptable accuracy of 3D localization.Comment: 8 pages, 6 figures, 2018 15th Conference on Computer and Robot Visio

Computational Intelligence for Modeling, Control, Optimization, Forecasting and Diagnostics in Photovoltaic Applications

This book is a Special Issue Reprint edited by Prof. Massimo Vitelli and Dr. Luigi Costanzo. It contains original research articles covering, but not limited to, the following topics: maximum power point tracking techniques; forecasting techniques; sizing and optimization of PV components and systems; PV modeling; reconfiguration algorithms; fault diagnosis; mismatching detection; decision processes for grid operators

Model-based human upper body tracking using interest points in real-time video

Vision-based human motion analysis has received huge attention from researchers because of the number of applications, such as automated surveillance, video indexing, human machine interaction, traffic monitoring, and vehicle navigation. However, it contains several open problems. To date, despite very promising proposed approaches, no explicit solution has been found to solve these open problems efficiently. In this regard, this thesis presents a model-based human upper body pose estimation and tracking system using interest points (IPs) in real-time video. In the first stage, we propose a novel IP-based background-subtraction algorithm to segment the foreground IPs of each frame from the background ones. Afterwards, the foreground IPs of any two consecutive frames are matched to each other using a dynamic hybrid localspatial IP matching algorithm, proposed in this research. The IP matching algorithm starts by using the local feature descriptors of the IPs to find an initial set of possible matches. Then two filtering steps are applied to the results to increase the precision by deleting the mismatched pairs. To improve the recall, a spatial matching process is applied to the remaining unmatched points. Finally, a two-stage hierarchical-global model-based pose estimation and tracking algorithm based on Particle Swarm Optimiation (PSO) is proposed to track the human upper body through consecutive frames. Given the pose and the foreground IPs in the previous frame and the matched points in the current frame, the proposed PSO-based pose estimation and tracking algorithm estimates the current pose hierarchically by minimizing the discrepancy between the hypothesized pose and the real matched observed points in the first stage. Then a global PSO is applied to the pose estimated by the first stage to do a consistency check and pose refinement

Face Recognition using the LCS algorithm

Today, the topic of human identification based on physical characteristics is a necessity in various fields. As a biometric system, a facial recognition system is fundamentally a pattern recognition system that identifies a person based on specific physiological or behavioral feature vectors. The feature vector is typically stored in a database upon extraction. The main objective of this research is to study and assess the effect of selecting the proper image attributes using the Cuckoo search algorithm. Thus, the selection of an optimal subset, given the large size of the feature vector dimensions to expedite the facial recognition algorithm is essential and substantial. Initially, by using the existing database, image characteristics are extracted and selected as a binary optimal subset of facial features using the Cuckoo algorithm. This subset of optimal features are evaluated by classifying nearest neighbor and neural networks. By calculating the accuracy of this classification, it is clear that the proposed method is of higher accuracy compared to previous methods in facial recognition based on the selection of significant features by the proposed algorithm

Automatic semantic segmentation and classification of remote sensing data for agriculture

Automatic semantic segmentation has expected increasing interest for researchers in recent years on multispectral remote sensing (RS) system. The agriculture supports 58 % of the population, in which 51 % of geographical area is under cultivation. Furthermore, the RS in agriculture can be used for identification, area estimation and monitoring, crop detection, soil mapping, crop yield modelling and production modelling etc. The RS images are high resolution images which can be used for agricultural and land cover classifications. Due to its high dimensional feature space, the conventional feature extraction techniques represent a progress of issues when handling huge size information e.g., computational cost, processing capacity and storage load. In order to overcome the existing drawback, we propose an automatic semantic segmentation without losing the significant data. In this paper, we use SOMs for segmentation purpose. Moreover, we proposed the particle swarm optimization technique (PSO) algorithm for finding cluster boundaries directly from the SOMs. On the other hand, we propose the deep residual network to achieve faster training process. Deep Residual Networks have been proved to be a very successful model on RS image classification. The main aim of this work is to achieve the overall accuracy greater than 85 % (OA > 85 %). So, we use a convolutional neural network (CNN), which outperforms better classification of certain crop types and yielding the target accuracies more than 85 % for all major crops. Furthermore, the proposed methods achieve good segmentation and classification accuracy than existing methods. The simulation results are further presented to show the performance of the proposed method applied to synthetic and real-world datasets