Advances in Image Processing, Analysis and Recognition Technology

For many decades, researchers have been trying to make computers’ analysis of images as effective as the system of human vision is. For this purpose, many algorithms and systems have previously been created. The whole process covers various stages, including image processing, representation and recognition. The results of this work can be applied to many computer-assisted areas of everyday life. They improve particular activities and provide handy tools, which are sometimes only for entertainment, but quite often, they significantly increase our safety. In fact, the practical implementation of image processing algorithms is particularly wide. Moreover, the rapid growth of computational complexity and computer efficiency has allowed for the development of more sophisticated and effective algorithms and tools. Although significant progress has been made so far, many issues still remain, resulting in the need for the development of novel approaches

Data Hiding and Its Applications

Data hiding techniques have been widely used to provide copyright protection, data integrity, covert communication, non-repudiation, and authentication, among other applications. In the context of the increased dissemination and distribution of multimedia content over the internet, data hiding methods, such as digital watermarking and steganography, are becoming increasingly relevant in providing multimedia security. The goal of this book is to focus on the improvement of data hiding algorithms and their different applications (both traditional and emerging), bringing together researchers and practitioners from different research fields, including data hiding, signal processing, cryptography, and information theory, among others

MECA: Mathematical Expression Based Post Publication Content Analysis

Mathematical expressions (ME) are critical abstractions for technical publications. While the sheer volume of technical publications grows in time, few ME centric applications have been developed due to the steep gap between the typesetting data in post-publication digital documents and the high-level technical semantics. With the acceleration of the technical publications every year, word-based information analysis technologies are inadequate to enable users in discovery, organizing, and interrelating technical work efficiently and effectively. This dissertation presents a modeling framework and the associated algorithms, called the mathematical-centered post-publication content analysis (MECA) system to address several critical issues to build a layered solution architecture for recovery of high-level technical information. Overall, MECA is consisted of four layers of modeling work, starting from the extraction of MEs from Portable Document Format (PDF) files. Specifically, a weakly-supervised sequential typesetting Bayesian model is developed by using a concise font-value based feature space for Bayesian inference of ME vs. words for the rendering units separated by space. A Markov Random Field (MRF) model is designed to merge and correct the MEs identified from the rendering units, which are otherwise prone to fragmentation of large MEs. At the next layer, MECA aims at the recovery of ME semantics. The first step is the ME layout analysis to disambiguate layout structures based on a Content-Constrained Spatial (CCS) global inference model to overcome local errors. It achieves high accuracy at low computing cost by a parametric lognormal model for the feature distribution of typographic systems. The ME layout is parsed into ME semantics with a three-phase processing workflow to overcome a variety of semantic ambiguities. In the first phase, the ME layout is linearized into a token sequence, upon which the abstract syntax tree (AST) is constructed in the second phase using probabilistic context-free grammar. Tree rewriting will transform the AST into ME objects in the third phase. Built upon the two layers of ME extraction and semantics modeling work, next we explore one of the bonding relationships between words and MEs: ME declarations, where the words and MEs are respectively the qualitative and quantitative (QuQn) descriptors of technical concepts. Conventional low-level PoS tagging and parsing tools have poor performance in the processing of this type of mixed word-ME (MWM) sentences. As such, we develop an MWM processing toolkit. A semi-automated weakly-supervised framework is employed for mining of declaration templates from a large amount of unlabeled data so that the templates can be used for the detection of ME declarations. On the basis of the three low-level content extraction and prediction solutions, the MECA system can extract MEs, interpret their mathematical semantics, and identify their bonding declaration words. By analyzing the dependency among these elements in a paper, we can construct a QuQn map, which essentially represents the reasoning flow of a paper. Three case studies are conducted for QuQn map applications: differential content comparison of papers, publication trend generation, and interactive mathematical learning. Outcomes from these studies suggest that MECA is a highly practical content analysis technology based on a theoretically sound framework. Much more can be expanded and improved upon for the next generation of deep content analysis solutions

Fine Art Pattern Extraction and Recognition

This is a reprint of articles from the Special Issue published online in the open access journal Journal of Imaging (ISSN 2313-433X) (available at: https://www.mdpi.com/journal/jimaging/special issues/faper2020)

A Defense of Pure Connectionism

Connectionism is an approach to neural-networks-based cognitive modeling that encompasses the recent deep learning movement in artificial intelligence. It came of age in the 1980s, with its roots in cybernetics and earlier attempts to model the brain as a system of simple parallel processors. Connectionist models center on statistical inference within neural networks with empirically learnable parameters, which can be represented as graphical models. More recent approaches focus on learning and inference within hierarchical generative models. Contra influential and ongoing critiques, I argue in this dissertation that the connectionist approach to cognitive science possesses in principle (and, as is becoming increasingly clear, in practice) the resources to model even the most rich and distinctly human cognitive capacities, such as abstract, conceptual thought and natural language comprehension and production. Consonant with much previous philosophical work on connectionism, I argue that a core principle—that proximal representations in a vector space have similar semantic values—is the key to a successful connectionist account of the systematicity and productivity of thought, language, and other core cognitive phenomena. My work here differs from preceding work in philosophy in several respects: (1) I compare a wide variety of connectionist responses to the systematicity challenge and isolate two main strands that are both historically important and reflected in ongoing work today: (a) vector symbolic architectures and (b) (compositional) vector space semantic models; (2) I consider very recent applications of these approaches, including their deployment on large-scale machine learning tasks such as machine translation; (3) I argue, again on the basis mostly of recent developments, for a continuity in representation and processing across natural language, image processing and other domains; (4) I explicitly link broad, abstract features of connectionist representation to recent proposals in cognitive science similar in spirit, such as hierarchical Bayesian and free energy minimization approaches, and offer a single rebuttal of criticisms of these related paradigms; (5) I critique recent alternative proposals that argue for a hybrid Classical (i.e. serial symbolic)/statistical model of mind; (6) I argue that defending the most plausible form of a connectionist cognitive architecture requires rethinking certain distinctions that have figured prominently in the history of the philosophy of mind and language, such as that between word- and phrase-level semantic content, and between inference and association

Machine Learning Methods with Noisy, Incomplete or Small Datasets

In many machine learning applications, available datasets are sometimes incomplete, noisy or affected by artifacts. In supervised scenarios, it could happen that label information has low quality, which might include unbalanced training sets, noisy labels and other problems. Moreover, in practice, it is very common that available data samples are not enough to derive useful supervised or unsupervised classifiers. All these issues are commonly referred to as the low-quality data problem. This book collects novel contributions on machine learning methods for low-quality datasets, to contribute to the dissemination of new ideas to solve this challenging problem, and to provide clear examples of application in real scenarios

Entropy in Image Analysis II

Image analysis is a fundamental task for any application where extracting information from images is required. The analysis requires highly sophisticated numerical and analytical methods, particularly for those applications in medicine, security, and other fields where the results of the processing consist of data of vital importance. This fact is evident from all the articles composing the Special Issue "Entropy in Image Analysis II", in which the authors used widely tested methods to verify their results. In the process of reading the present volume, the reader will appreciate the richness of their methods and applications, in particular for medical imaging and image security, and a remarkable cross-fertilization among the proposed research areas

Exploiting Spatio-Temporal Coherence for Video Object Detection in Robotics

This paper proposes a method to enhance video object detection for indoor environments in robotics. Concretely, it exploits knowledge about the camera motion between frames to propagate previously detected objects to successive frames. The proposal is rooted in the concepts of planar homography to propose regions of interest where to find objects, and recursive Bayesian filtering to integrate observations over time. The proposal is evaluated on six virtual, indoor environments, accounting for the detection of nine object classes over a total of ∼ 7k frames. Results show that our proposal improves the recall and the F1-score by a factor of 1.41 and 1.27, respectively, as well as it achieves a significant reduction of the object categorization entropy (58.8%) when compared to a two-stage video object detection method used as baseline, at the cost of small time overheads (120 ms) and precision loss (0.92).</p

Tracking the Temporal-Evolution of Supernova Bubbles in Numerical Simulations

The study of low-dimensional, noisy manifolds embedded in a higher dimensional space has been extremely useful in many applications, from the chemical analysis of multi-phase flows to simulations of galactic mergers. Building a probabilistic model of the manifolds has helped in describing their essential properties and how they vary in space. However, when the manifold is evolving through time, a joint spatio-temporal modelling is needed, in order to fully comprehend its nature. We propose a first-order Markovian process that propagates the spatial probabilistic model of a manifold at fixed time, to its adjacent temporal stages. The proposed methodology is demonstrated using a particle simulation of an interacting dwarf galaxy to describe the evolution of a cavity generated by a Supernov

Atti del IX Convegno Annuale dell'Associazione per l'Informatica Umanistica e la Cultura Digitale (AIUCD). La svolta inevitabile: sfide e prospettive per l'Informatica Umanistica

Proceedings of the IX edition of the annual AIUCD conferenc