Computational Attention Towards Attentive Computers.pdf

Computational Attention Towards Attentive Computers

Matei Mancas

Consciously or unconsciously, humans always pay attention to a wide variety of stimuli. Attention is part of daily life and it is the first step to understanding. The proposed thesis deals with a computational approach to the human attentional mechanism and with its possible applications mainly in the field of computer vision. In a first stage, the text introduces a rarity-based three-level attention model handling monodimensional signals as well as images or video sequences. The concept of attention is defined as the transformation of a huge acquired unstructured data set into a smaller structured one while preserving the information : the attentional mechanism turns rough data into intelligence. Afterwards, several applications are described in the fields of machine vision, signal coding and enhancement, medical imaging, event detection and so on. These applications not only show the applicability of the proposed computational attention method, but they also support the idea that similarly to the fact that attention is the beginning of intelligence in humans, computational attention may be the starting point of artificial intelligence in engineering applications. Several databases containing different kinds of signals were used to test the model and its applications : audio signals of natural complex ambiences and events, real-life video sequences as well as simulated sequences and finally natural scenes, textured or synthetic images. Results are presented in a clear and comprehensive way within each application providing the relevance of the use of the computational attention model. Finally, a large discussion is opened based on the theoretical and practical achievements and future extensions are proposed.

Machines and mindlessness: Social responses to computers. ... [Image: Frintrop: Attentive Robots, In: From Human Attention to Computational Attention: A ...

9.61 MB Taille du fichier
9782874630996 ISBN
Libre PRIX
Computational Attention Towards Attentive Computers.pdf


PC et Mac

Lisez l'eBook immédiatement après l'avoir téléchargé via "Lire maintenant" dans votre navigateur ou avec le logiciel de lecture gratuit Adobe Digital Editions.

iOS & Android

Pour tablettes et smartphones: notre application de lecture tolino gratuite

eBook Reader

Téléchargez l'eBook directement sur le lecteur dans la boutique ou transférez-le avec le logiciel gratuit Sony READER FOR PC / Mac ou Adobe Digital Editions.


Après la synchronisation automatique, ouvrez le livre électronique sur le lecteur ou transférez-le manuellement sur votre appareil tolino à l'aide du logiciel gratuit Adobe Digital Editions.

Notes actuelles

Sofya Voigtuh

Computational Attention Towards Attentive …

Mattio Müllers

Keywords: computational attention, saliency, rarity, audio event, tumour 1 Introduction The human visual system (HVS) is a topic of increasing importance in computer vision research since Hubel’s work [1]. Mimicking some of the processes done by our visual system may help to improve the existing computer vision systems. Besides, there are evidences than some mechanisms that are involved in

Noels Schulzen

Computational Attention Towards Attentive …

Jason Leghmann

The publication of the large-scale attention dataset SALICON [26] has contributed to a big progress of deep saliency predic- tion models and several new ... Laminar Computing that clarify the global organization of brain dynamics and ... events before they occur, and to focus attention upon ex- pected objects and ... emotional learning; breakdowns in attentive vigilance dur- ing autism, medial ...

Jessica Kolhmann

Towards Attentive Robots - Welcome to iLab! Towards Attentive Robots Simone Frintrop Cognitive Vision Group Institute of Computer Science III University of Bonn, Germany. Human Attention Why are there attentional mechanisms in the human brain? • Deal with the computational complexity: – Not enough neurons to process everything (~10 8 bits/second) – Many problems in vision are NP-hard (Tsotsos: Analyzing vision at the complexity