Visual inputs are one of the key sources from which humans perceive the environment and 'understand' what is happening. Artificial systems perceive the visual inputs as digital images. The images need to be processed and analysed. Within the human brain, processing of visual inputs and subsequent development of perception is one of its major functionalities. In this paper we present part of our...

This dissertation delves into the use of textual metadata for image understanding. We seek to exploit this additional textual information as weak supervision to improve the learning of recognition models. There is a recent and growing interest for methods that exploit such data because they can potentially alleviate the need for manual annotation, which is a costly and time-consuming process. W...

The Medical Image Understanding and Analysis (MIUA) conference series which began in Oxford in 1997 has become the main UK meeting for discussion and dissemination of research progress in image analysis applied to medicine and the biological sciences. In July 2008 we had the pleasure of hosting the 12th MIUA at the University of Dundee. The conference programme included fifty-one full papers fr...

Media for content analysis can include any recorded communication from papyrus to podcast, published or unpublished. Approaches to studying media content are almost as diverse as the approaches to studying human communication behavior. In a general sense, any study of media content is a content analysis, but in communication research the term usually implies a quantitative approach and a series...

This paper focuses on recognizing image concepts by introducing the ISTOP model. The model parses the images from scene to object's parts by using a context sensitive grammar. Since there is a gap between the scene and object levels, this grammar proposes the “Visual Term” level to bridge the gap. Visual term is a higher concept level than the object level representing a few co-occurring object...

With the advance of the Internet and the increasing accessibility of computing resources, humans and computer systems are now brought together in powerful new ways. In this paper, we propose a humancentered computing framework to harness the essential characteristics of both humans and computers for achieving collaborative image understanding (i.e., training large numbers of inter-related class...

Document image understanding denotes the recognition of semantically relevant components in the layout extracted from a document image. This recognition process is based on some visual models, whose manual specification can be a highly demanding task. In order to automatically acquire these models, we propose the application of machine learning techniques. In this paper, problems raised by poss...

Quantitative microscopy has been extensively used in biomedical research and has provided significant insights into structure and dynamics at the cell and tissue level. The entire procedure of quantitative microscopy is comprised of specimen preparation, light absorption/reflection/emission from the specimen, microscope optical processing, optical/electrical conversion by a camera or detector, ...

This survey covers the historical development and current state of the art in image understanding for iris biometrics. Most research publications can be categorized as making their primary contribution to one of the four major modules in iris biometrics: image acquisition, iris segmentation, texture analysis and matching of texture representations. Other important research includes experimental...

This paper proposes a short overview of models and methods based on fuzzy sets for image processing and image understanding, from low level to higher level interpretation. Recent trends are highlighted. © 2015 Elsevier B.V. All rights reserved.