Hybrid optimizer for expeditious modeling of virtual urban environments

Tese de mestrado. Engenharia Informática. Faculdade de Engenharia. Universidade do Porto. 200

Efficient Reduced BIAS Genetic Algorithm for Generic Community Detection Objectives

The problem of community structure identification has been an extensively investigated area for biology, physics, social sciences, and computer science in recent years for studying the properties of networks representing complex relationships. Most traditional methods, such as K-means and hierarchical clustering, are based on the assumption that communities have spherical configurations. Lately, Genetic Algorithms (GA) are being utilized for efficient community detection without imposing sphericity. GAs are machine learning methods which mimic natural selection and scale with the complexity of the network. However, traditional GA approaches employ a representation method that dramatically increases the solution space to be searched by introducing redundancies. They also utilize a crossover operator which imposes a linear ordering that is not suitable for community detection. The algorithm presented here is a framework to detect communities for complex biological networks that removes both redundancies and linearity. We also introduce a novel operator, named Gene Repair. This algorithm is unique as it is a flexible community detection technique aimed at maximizing the value of any given mathematical objective for the network. We reduce the memory requirements by representing chromosomes as a 3-dimensional bit array. Furthermore, in order to increase diversity while retaining promising chromosomes, we use natural selection process based on tournament selection with elitism. Additionally, our approach doesn’t require prior information about the number of true communities in the network. We apply our novel algorithm to benchmark datasets and also to a network representing a large cohort of AD cases and controls. By utilizing this efficient and flexible implementation that is cognizant of characteristics for networks representing complex disease genetics, we sift out communities representing patterns of interacting genetic variants that are associated with this enigmatic disease

Biometric Systems

Biometric authentication has been widely used for access control and security systems over the past few years. The purpose of this book is to provide the readers with life cycle of different biometric authentication systems from their design and development to qualification and final application. The major systems discussed in this book include fingerprint identification, face recognition, iris segmentation and classification, signature verification and other miscellaneous systems which describe management policies of biometrics, reliability measures, pressure based typing and signature verification, bio-chemical systems and behavioral characteristics. In summary, this book provides the students and the researchers with different approaches to develop biometric authentication systems and at the same time includes state-of-the-art approaches in their design and development. The approaches have been thoroughly tested on standard databases and in real world applications

Face recognition using improved deep learning neural network

In recent years the importance and need for computer vision systems increased due to security demands, self-driving cars, cell phone logins, forensic identification, banks, etc. In security, the idea is to distinguish individuals correctly by utilizing facial recognition, iris recognition, or other means suitable for identification. Cell phones use face recognition to unlock the screen and authorization. Face recognition systems perform tremendously well, however, they still face challenges of classification. Their major challenge is the ability to identify or recognize individuals in an image or images. The causes of this challenge could be lighting (illumination) conditions, the place or environment where the image is taken and this can be associated with the background environment of the image, posing, and facial gestures or expressions. This study investigates a possible method to bring a solution. The method proposes a combination of the Principal Component Analysis (PCA), K-Means clustering, and Convolutional Neural Network (CNN) for a face recognition system. Firstly, apply PCA to reduce dataset dimensions, enable smaller network usage and training, remove redundancy, maintain quality, and produce Eigenfaces. Secondly, apply PCA output to K-Means clustering to select centres with better characteristics, and produce initial input data for CNN. Lastly, take K-Means clustering output as the input of the CNN and train the network. It is trained and evaluated using the ORL dataset. This dataset comprises 400 different faces with 40 classes of 10 face images per class. The performance of this technique was tested against (PCA), Support Vector Machine (SVM), and K-Nearest Neighbour (KNN). This method’s accuracy after 90 epochs achieved 99% F1-Score, 99% precision, and 99% recall in 463.934 seconds. It outperformed the PCA that obtained 97% F1-Score and KNN with 84% F1-Score during the experiments. Therefore, this method proved to be efficient in identifying faces in the images.School of EngineeringMTech (Electrical Engineering

Automated recognition of individual performers from de-identified video sequences

dentification of individual humans from RGB image data is well-established. However, in many domains, such as in healthcare or applications involving children, ethical issues have been raised around using traditional RGB image data because individuals can be identified from these data. The widespread availability of reliable depth data, and the associated human skeleton data derived from these data, presents an opportunity to differentiate between individuals while potentially avoiding individually identifiable features. Using skeleton data only, we developed a unique 20-dimensional bone segment length feature vector for 1,761 trials (1,759,980 image frames) of data, captured from 14 participants who engaged in a one-hour group intervention playing Xbox One Kinect Bowling twice-weekly for 24 weeks. We then evaluated our novel feature using representative batch processing (k-nearest neighbour) and real-time (multi-layer perceptron) models, validated against manually-labelled ground-truth data. Our results suggest that our skeleton feature can differentiate between instances (i.e., individuals) with an accuracy over all participants of 100% for batch processing and 96.57% in real-time, and deals well with class imbalances. Our results suggest that we can reliably differentiate between individual persons using only skeleton data derived from depth image data in medical research

Monte Carlo Method with Heuristic Adjustment for Irregularly Shaped Food Product Volume Measurement

Volume measurement plays an important role in the production and processing of food products. Various methods have been proposed to measure the volume of food products with irregular shapes based on 3D reconstruction. However, 3D reconstruction comes with a high-priced computational cost. Furthermore, some of the volume measurement methods based on 3D reconstruction have a low accuracy. Another method for measuring volume of objects uses Monte Carlo method. Monte Carlo method performs volume measurements using random points. Monte Carlo method only requires information regarding whether random points fall inside or outside an object and does not require a 3D reconstruction. This paper proposes volume measurement using a computer vision system for irregularly shaped food products without 3D reconstruction based on Monte Carlo method with heuristic adjustment. Five images of food product were captured using five cameras and processed to produce binary images. Monte Carlo integration with heuristic adjustment was performed to measure the volume based on the information extracted from binary images. The experimental results show that the proposed method provided high accuracy and precision compared to the water displacement method. In addition, the proposed method is more accurate and faster than the space carving method

Methods for Automated Neuron Image Analysis

Knowledge of neuronal cell morphology is essential for performing specialized analyses in the endeavor to understand neuron behavior and unravel the underlying principles of brain function. Neurons can be captured with a high level of detail using modern microscopes, but many neuroscientific studies require a more explicit and accessible representation than offered by the resulting images, underscoring the need for digital reconstruction of neuronal morphology from the images into a tree-like graph structure. This thesis proposes new computational methods for automated detection and reconstruction of neurons from fluorescence microscopy images. Specifically, the successive chapters describe and evaluate original solutions to problems such as the detection of landmarks (critical points) of the neuronal tree, complete tracing and reconstruction of the tree, and the detection of regions containing neurons in high-content screens