Feature Extraction Methods for Character Recognition

Not Include

Intelligent Sensors for Human Motion Analysis

The book, "Intelligent Sensors for Human Motion Analysis," contains 17 articles published in the Special Issue of the Sensors journal. These articles deal with many aspects related to the analysis of human movement. New techniques and methods for pose estimation, gait recognition, and fall detection have been proposed and verified. Some of them will trigger further research, and some may become the backbone of commercial systems

Image and Video Forensics

Nowadays, images and videos have become the main modalities of information being exchanged in everyday life, and their pervasiveness has led the image forensics community to question their reliability, integrity, confidentiality, and security. Multimedia contents are generated in many different ways through the use of consumer electronics and high-quality digital imaging devices, such as smartphones, digital cameras, tablets, and wearable and IoT devices. The ever-increasing convenience of image acquisition has facilitated instant distribution and sharing of digital images on digital social platforms, determining a great amount of exchange data. Moreover, the pervasiveness of powerful image editing tools has allowed the manipulation of digital images for malicious or criminal ends, up to the creation of synthesized images and videos with the use of deep learning techniques. In response to these threats, the multimedia forensics community has produced major research efforts regarding the identification of the source and the detection of manipulation. In all cases (e.g., forensic investigations, fake news debunking, information warfare, and cyberattacks) where images and videos serve as critical evidence, forensic technologies that help to determine the origin, authenticity, and integrity of multimedia content can become essential tools. This book aims to collect a diverse and complementary set of articles that demonstrate new developments and applications in image and video forensics to tackle new and serious challenges to ensure media authenticity

Ubiquitous Technologies for Emotion Recognition

Emotions play a very important role in how we think and behave. As such, the emotions we feel every day can compel us to act and influence the decisions and plans we make about our lives. Being able to measure, analyze, and better comprehend how or why our emotions may change is thus of much relevance to understand human behavior and its consequences. Despite the great efforts made in the past in the study of human emotions, it is only now, with the advent of wearable, mobile, and ubiquitous technologies, that we can aim to sense and recognize emotions, continuously and in real time. This book brings together the latest experiences, findings, and developments regarding ubiquitous sensing, modeling, and the recognition of human emotions

Identification d'espèces végétales par une description géométrique locale d'images de feuilles

Plant species identification, usually performed by specialists, is based on the observation of its organs and mostly on visual criteria. Thanks to its ease of acquisition, the leaf is the most used organ. In addition, it contains important information on the taxonomy of the plant. This enables the use of computer vision and pattern recognition techniques for developing an automatic recognition process of the plant species from a leaf image. We introduce a new approach to identify plant species, based on the description of the following leaf characteristics : its shape, its salient points and its venation. First, the shape of the leaf is represented by local descriptors associated to a set of points sampled on the contour. Different multi-scale triangular representations are introduced and compared. To describe the salient points of the leaf, we propose a shape context based representation. Finally, the venation is extracted by detecting elementary linear structures with morphological tools. The venation network is described by its main directions and its spatial distribution in the context of the leaf boundary. A local matching method is used for all descriptors. Evaluations, conducted on six publicly available plant identification benchmarks, show that our approaches identify the plant species of the leaf in most of the cases and that the late fusion of the proposed descriptors improves the identification process.Il est nécessaire de reconnaître les espèces végétales afin de préserver la biodiversité des écosystèmes. L’identification d’une plante, habituellement effectuée par les experts, se base sur l’observation de ses organes et en majeure partie sur des critères visuels. La feuille est l’organe le plus utilisé grâce à sa facilité d’acquisition. De plus, celle-ci contient une information importante sur la taxonomie de la plante. Ceci permet d’envisager d’utiliser l’analyse d’images pour élaborer un processus de reconnaissance automatique de l’espèce végétale à partir de la donnée d’une image de feuille. Nous introduisons une nouvelle approche d’identification d’espèces végétales, basée sur la description des caractères foliaires suivants : la forme, les points saillants et la nervation. En premier lieu, la forme de la feuille est représentée par des descripteurs locaux associés aux points échantillonnés sur le contour. Différentes représentations triangulaires multi-échelle sont introduites et comparées. Pour décrire les points saillants de la feuille, nous proposons une représentation dérivée du contexte de forme (Shape Context). Finalement, la nervation est extraite par la détection de structures linéaires élémentaires avec des outils morphologiques. Le réseau de nervures extrait est décrit par ses directions principales et sa répartition spatiale dans le contexte de la surface de la feuille.Pour tous les descripteurs, une méthode de mise en correspondance locale est utilisée. Des évaluations, menées sur six bases de feuilles publiques, montrent que nos approches permettent généralement d’identifier l’espèce végétale de la feuille et que la fusion tardivedes descripteurs augmente la précision de l’identification