Pattern Recognition

A wealth of advanced pattern recognition algorithms are emerging from the interdiscipline between technologies of effective visual features and the human-brain cognition process. Effective visual features are made possible through the rapid developments in appropriate sensor equipments, novel filter designs, and viable information processing architectures. While the understanding of human-brain cognition process broadens the way in which the computer can perform pattern recognition tasks. The present book is intended to collect representative researches around the globe focusing on low-level vision, filter design, features and image descriptors, data mining and analysis, and biologically inspired algorithms. The 27 chapters coved in this book disclose recent advances and new ideas in promoting the techniques, technology and applications of pattern recognition

Ais-Psmaca: Towards Proposing an Artificial Immune System for Strengthening Psmaca: An Automated Protein Structure Prediction using Multiple Attractor Cellular Automata

Predicting the structure of proteins from their amino acid sequences has gained a remarkable attention in recent years. Even though there are some prediction techniques addressing this problem, the approximate accuracy in predicting the protein structure is closely 75%. An automated procedure was evolved with MACA (Multiple Attractor Cellular Automata) for predicting the structure of the protein. Artificial Immune System (AIS-PSMACA) a novel computational intelligence technique is used for strengthening the system (PSMACA) with more adaptability and incorporating more parallelism to the system. Most of the existing approaches are sequential which will classify the input into four major classes and these are designed for similar sequences. AIS-PSMACA is designed to identify ten classes from the sequences that share twilight zone similarity and identity with the training sequences with mixed and hybrid variations. This method also predicts three states (helix, strand, and coil) for the secondary structure. Our comprehensive design considers 10 feature selection methods and 4 classifiers to develop MACA (Multiple Attractor Cellular Automata) based classifiers that are build for each of the ten classes. We have tested the proposed classifier with twilight-zone and 1-high-similarity benchmark datasets with over three dozens of modern competing predictors shows that AIS-PSMACA provides the best overall accuracy that ranges between 80% and 89.8% depending on the dataset

Geographic Vector Agents from Pixels to Intelligent Processing Units

Spatial modelling methods usually utilise pixels and image objects as the fundamental processing unit to address real-world objects (geo-objects) in image space. To do this, both pixel-based and object-based approaches typically employ a linear two-staged workflow of segmentation and classification. Pixel-based methods often segment a classified image to address geo-objects in image space. In contrast, object-based approaches classify a segmented image to determine geo-objects. These methods lack the ability to simultaneously integrate the geometry and theme of geo-objects in image space. This thesis explores Vector Agents (VA) as an automated and intelligent processing unit to directly address real-world objects in the image space. A VA, is an object that can represent (non)dynamic and (ir)regular vector boundaries (Moore, 2011; Hammam et al., 2007). This aim is achieved by modelling geometry, state, and temporal changes of geo-objects in spatial space. To reach this aim, we first defined and formulated the main components of the VA, including geometry, state and neighbourhood, and their respective rules in accordance with the properties of raster datasets (e.g. satellite images), as a representation of a geographical space (the Earth). The geometry of the VA was formulated according to a directional planar graph that includes a set of spatial reasoning relationships and geometric operators, in order to implement a set of dynamic geometric behaviours, such as growing, joining or splitting. Transition rules were defined by using a classifier (e.g. Support Vector Machines (SVMs)), a set of image analysis operators (e.g. edge detection, median filter), and the characteristics of the objects in real world. VAs used the transition rules in order to find and update their states in image space. The proximity between VAs was explicitly formulated according to the minimum distance between VAs in image space. These components were then used to model the main elements of our software agent (e.g. geo-objects), namely sensors, effectors, states, rules and strategies. These elements allow a VA to perceive its environment, change its geometry and interact with other VAs to evolve inconsistency together with their thematic meaning. It also enables VAs to adjust their thematic meaning based on changes in their own attributes and those of their neighbours. We then tested this concept by using the VA to extract geo-objects from different types of raster datasets (e.g. multispectral and hyperspectral images). The results of the VA model confirmed that: (a) The VA is flexible enough to integrate thematic and geometric components of geo-objects in order to extract them directly from image space, and (b) The VA has sufficient capability to be applied in different areas of image analysis. We discuss the limitations of this work and present the possible solutions in the last chapter

Advanced Visual Computing for Image Saliency Detection

Saliency detection is a category of computer vision algorithms that aims to filter out the most salient object in a given image. Existing saliency detection methods can generally be categorized as bottom-up methods and top-down methods, and the prevalent deep neural network (DNN) has begun to show its applications in saliency detection in recent years. However, the challenges in existing methods, such as problematic pre-assumption, inefficient feature integration and absence of high-level feature learning, prevent them from superior performances. In this thesis, to address the limitations above, we have proposed multiple novel models with favorable performances. Specifically, we first systematically reviewed the developments of saliency detection and its related works, and then proposed four new methods, with two based on low-level image features, and two based on DNNs. The regularized random walks ranking method (RR) and its reversion-correction-improved version (RCRR) are based on conventional low-level image features, which exhibit higher accuracy and robustness in extracting the image boundary based foreground / background queries; while the background search and foreground estimation (BSFE) and dense and sparse labeling (DSL) methods are based on DNNs, which have shown their dominant advantages in high-level image feature extraction, as well as the combined strength of multi-dimensional features. Each of the proposed methods is evaluated by extensive experiments, and all of them behave favorably against the state-of-the-art, especially the DSL method, which achieves remarkably higher performance against sixteen state-of-the-art methods (including ten conventional methods and six learning based methods) on six well-recognized public datasets. The successes of our proposed methods reveal more potential and meaningful applications of saliency detection in real-life computer vision tasks