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Recent Advances in Artificial Intelligence Research and Development
Artificial Intelligence (AI) is a scientific field of longstanding tradition, with origins in the early years of computer science. Today AI has reached a level of maturity that allows us to build highly sophisticated systems which perform very different tasks. Nevertheless, its evolution has opened up a number of new problems, ranging from specific algorithms to system integration, which remain elusive and assure a long life for this research field. Research progress in this area is today an international challenge that must be supported by world-class meetings and organizations, but in spite of this fact, there is also an objective need for meetings and organizations that support and disseminate research at other levels. This book focuses on new and original research on Artificial Intelligence.
Gesture Recognition
This book presents a selection of chapters, written by leading international researchers, related to the automatic analysis of gestures from still images and multi-modal RGB-Depth image sequences. It offers a comprehensive review of vision-based approaches for supervised gesture recognition methods that have been validated by various challenges. Several aspects of gesture recognition are reviewed, including data acquisition from different sources, feature extraction, learning, and recognition of gestures.
Multi-Modal Face Presentation Attack Detection
For the last ten years, face biometric research has been intensively studied by the computer vision community. Face recognition systems have been used in mobile, banking, and surveillance systems. For face recognition systems, face spoofing attack detection is a crucial stage that could cause severe security issues in government sectors. Although effective methods for face presentation attack detection have been proposed so far, the problem is still unsolved due to the difficulty in the design of features and methods that can work for new spoofing attacks. In addition, existing datasets for studying the problem are relatively small which hinders the progress in this relevant domain. In order...
Explainable and Interpretable Models in Computer Vision and Machine Learning
This book compiles leading research on the development of explainable and interpretable machine learning methods in the context of computer vision and machine learning. Research progress in computer vision and pattern recognition has led to a variety of modeling techniques with almost human-like performance. Although these models have obtained astounding results, they are limited in their explainability and interpretability: what is the rationale behind the decision made? what in the model structure explains its functioning? Hence, while good performance is a critical required characteristic for learning machines, explainability and interpretability capabilities are needed to take learning m...
Statistical Machine Learning for Human Behaviour Analysis
This Special Issue focused on novel vision-based approaches, mainly related to computer vision and machine learning, for the automatic analysis of human behaviour. We solicited submissions on the following topics: information theory-based pattern classification, biometric recognition, multimodal human analysis, low resolution human activity analysis, face analysis, abnormal behaviour analysis, unsupervised human analysis scenarios, 3D/4D human pose and shape estimation, human analysis in virtual/augmented reality, affective computing, social signal processing, personality computing, activity recognition, human tracking in the wild, and application of information-theoretic concepts for human behaviour analysis. In the end, 15 papers were accepted for this special issue. These papers, that are reviewed in this editorial, analyse human behaviour from the aforementioned perspectives, defining in most of the cases the state of the art in their corresponding field.
Traffic-Sign Recognition Systems
This work presents a full generic approach to the detection and recognition of traffic signs. The approach is based on the latest computer vision methods for object detection, and on powerful methods for multiclass classification. The challenge was to robustly detect a set of different sign classes in real time, and to classify each detected sign into a large, extensible set of classes. To address this challenge, several state-of-the-art methods were developed that can be used for different recognition problems. Following an introduction to the problems of traffic sign detection and categorization, the text focuses on the problem of detection, and presents recent developments in this field. The text then surveys a specific methodology for the problem of traffic sign categorization – Error-Correcting Output Codes – and presents several algorithms, performing experimental validation on a mobile mapping application. The work ends with a discussion on future research and continuing challenges.
Ensembles in Machine Learning Applications
This book contains the extended papers presented at the 3rd Workshop on Supervised and Unsupervised Ensemble Methods and their Applications (SUEMA) that was held in conjunction with the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML/PKDD 2010, Barcelona, Catalonia, Spain). As its two predecessors, its main theme was ensembles of supervised and unsupervised algorithms – advanced machine learning and data mining technique. Unlike a single classification or clustering algorithm, an ensemble is a group of algorithms, each of which first independently solves the task at hand by assigning a class or cluster label (voting) to instance...
Inpainting and Denoising Challenges
The problem of dealing with missing or incomplete data in machine learning and computer vision arises in many applications. Recent strategies make use of generative models to impute missing or corrupted data. Advances in computer vision using deep generative models have found applications in image/video processing, such as denoising, restoration, super-resolution, or inpainting. Inpainting and Denoising Challenges comprises recent efforts dealing with image and video inpainting tasks. This includes winning solutions to the ChaLearn Looking at People inpainting and denoising challenges: human pose recovery, video de-captioning and fingerprint restoration. This volume starts with a wide review...
Human-Robot Interaction
This book takes the vocal and visual modalities and human-robot interaction applications into account by considering three main aspects, namely, social and affective robotics, robot navigation, and risk event recognition. This book can be a very good starting point for the scientists who are about to start their research work in the field of human-robot interaction.
Artificial Intelligence Research and Development
This book is a collection of 45 accepted papers originally submitted for the 12th International Conference of the Catalan Association for Artificial Intelligence (ACIA). It also includes a brief summary of two papers from invited speakers. The Catalan Association for Artificial Intelligence was founded in 1994 with the aim of fostering cooperation among researchers from the Catalan-speaking AI research community. Collaboration between ACIA members and the wider international AI community has also been wel-established now for many years. The papers in these proceedings reflect this collaboration and include contributions not only from the Catalan-speaking regions of Spain, but also from France and Italy, and from as far afield as Mexico and Australia. Of al the fields in computer science, AI is the one most intertwined with all sorts of disciplines dealt with in the human experience, often employing lessons learnt in one discipline to implement a task in another. The papers in this volume reflect the rich iversity in AI, covering areas such as logics, natural language, machine learning, computer vision, robotics and multi-agent systems.
