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Machine learning builds models of the world using training data from the application domain and prior knowledge about the problem. The models are later applied to future data in order to estimate the current state of the world. An implied assumption is that the future is stochastically similar to the past. The approach fails when the system encounters situations that are not anticipated from the past experience. In contrast, successful natural organisms identify new unanticipated stimuli and situations and frequently generate appropriate responses. The observation described above lead to the initiation of the DIRAC EC project in 2006. In 2010 a workshop was held, aimed to bring together researchers and students from different disciplines in order to present and discuss new approaches for identifying and reacting to unexpected events in information-rich environments. This book includes a summary of the achievements of the DIRAC project in chapter 1, and a collection of the papers presented in this workshop in the remaining parts.
This book constitutes the refereed proceedings of the 6th International Conference on Computer Vision Systems, ICVS 2008, held in Santorini, Greece, May 12-15, 2008. The 23 revised papers presented together with 30 poster presentations and 2 invited papers were carefully reviewed and selected from 128 submissions. The papers are organized in topical sections on cognitive vision, monitor and surveillance, computer vision architectures, calibration and registration object recognition and tracking, learning, human machine interaction as well as cross modal systems.
This book constitutes the refereed proceedings of the 11th International Conference on Text, Speech and Dialogue, TSD 2008, held in Brno, Czech Republic, September 8-12, 2008. The 79 revised full papers presented together with 4 invited papers were carefully reviewed and selected from 173 submissions. The topics of the conference include, but are not limited to, text corpora and tagging; transcription problems in spoken corpora; sense disambiguation; links between text and speech oriented systems; parsing issues; parsing problems in spoken texts; multi-lingual issues; multi-lingual dialogue systems; information retrieval and information extraction; text/topic summarization; machine translation; semantic networks and ontologies; semantic web; speech modeling; speech segmentation; speech recognition; search in speech for IR and IE; text-to-speech synthesis; dialogue systems; development of dialogue strategies; prosody in dialogues; emotions and personality modeling; user modeling; knowledge representation in relation to dialogue systems; assistive technologies based on speech and dialogue; applied systems and software; facial animation; and visual speech synthesis
In natural environments, the auditory system is typically confronted with a mixture of sounds originating from different sound sources. As sounds spread over time, the auditory system has to continuously decompose competing sounds into distinct meaningful auditory objects or “auditory streams” referring to certain sound sources. This decomposition work, which was termed by Albert Bregman as “Auditory scene analysis” (ASA), involves two kinds of grouping to be done. Grouping based on simultaneous cues, such as harmonicity and on sequential cues, such as similarity in acoustic features over time. Understanding how the brain solves these tasks is a fundamental challenge facing auditory scientist. In recent years, the topic of ASA was broadly investigated in different fields of auditory research, including a wide range of methods, studies in different species, and modeling. Despite the advance in understanding ASA, it still proves to be a major challenge for auditory research. This includes verifying whether experimental findings are transferable to more realistic auditory scenes. A central approach in understanding ASA is the use of certain stimulus parameters that produce an ambiguous percept. The advantage of such an approach is that different perceptual organizations can be studied without varying physical stimulus parameters. Additionally, the perception of ambiguous stimuli can be volitionally controlled by intention or task. By using this one can mirror real hearing situations where listeners intent to identify and to localize auditory sources. Recently it was also found that in classical auditory streaming sequences perceptual ambiguity was not restricted to but was observed over a broad range of stimulus parameters. The proposed Research Topic pursues to bring together scientist in the different fields of auditory research whose work addresses the issue of perceptual ambiguity. Researchers were welcome to contribute experimental reports, computational modeling, and reviews that consider auditory ambiguity in its modality specific characteristics as well as in comparison to visual ambiguous figures. The overall goal of contributions was to consider the experimental findings from the perspective of real auditory scenes. In a broader sense, the Research Topic was open for contributions which are related to the issue of active listening in complex scenes.
This book constitutes the refereed proceedings of the 8th International Conference on Brain Inspired Cognitive Systems, BICS 2016, held in Beijing, China, in November 2016. The 32 full papers presented were carefully reviewed and selected from 43 submissions. They discuss the emerging areas and challenges, present the state of the art of brain-inspired cognitive systems research and applications in diverse fields by covering many topics in brain inspired cognitive systems related research including biologically inspired systems, cognitive neuroscience, models consciousness, and neural computation.
This timely and authoritative volume explores the bidirectional relationship between images and locations. The text presents a comprehensive review of the state of the art in large-scale visual geo-localization, and discusses the emerging trends in this area. Valuable insights are supplied by a pre-eminent selection of experts in the field, into a varied range of real-world applications of geo-localization. Topics and features: discusses the latest methods to exploit internet-scale image databases for devising geographically rich features and geo-localizing query images at different scales; investigates geo-localization techniques that are built upon high-level and semantic cues; describes methods that perform precise localization by geometrically aligning the query image against a 3D model; reviews techniques that accomplish image understanding assisted by the geo-location, as well as several approaches for geo-localization under practical, real-world settings.
Intelligent Speech Signal Processing investigates the utilization of speech analytics across several systems and real-world activities, including sharing data analytics, creating collaboration networks between several participants, and implementing video-conferencing in different application areas. Chapters focus on the latest applications of speech data analysis and management tools across different recording systems. The book emphasizes the multidisciplinary nature of the field, presenting different applications and challenges with extensive studies on the design, development and management of intelligent systems, neural networks and related machine learning techniques for speech signal processing.