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ABSTRACT: In this article, a conceptual framework developed to acquire expert knowledge from eye-tracking data of skilled individuals
is presented. Domain-specific knowledge is acquired from the visual behaviour of subjects whose eye movements are recorded
while solving complex visual tasks. It is argued that relevant insights into the cognitive strategies followed by the observers
to solve the visual search tasks may be gained by analysing the eye-tracking data in generic feature spaces, which are at
the basis of the selected scheme for knowledge representation. In this context, a feature space is a domain in which each
dimension is defined as a mathematical construct, which may correspond to perceptually meaningful visual cues and which can
take either numerical or categorical values. A special case of such feature spaces is the spatial domain in which the spatial
coordinates of the gaze points define the dimensions of such domain. In the proposed conceptual framework, the definition
of similarities between visual search patterns is essential to characterise the stereotypical visual behaviour of a group
of observers, and thus expert knowledge. Furthermore, since knowledge representation is closely related to the feature domain
in which the search is analysed, feature relevance measures become central to knowledge gathering, and the main aspects regarding
their definition are discussed in this work. Following a detailed presentation of the conceptual framework, a practical application
dealing with expert knowledge gathering in lung radiology is shown both as a proof of concept and also to illustrate a particular
functional implementation of the framework.
KeywordsVisual attention–Eye movements–Feature domain–Bottom-up and top-down processes–Visual saliency
Cognitive Computation 04/2012; 3(1):206-222. · 1.00 Impact Factor
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Cognitive Computation. 01/2011; 3:206-222.
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ABSTRACT: Haemodynamics, and in particular wall shear stress, is thought to play a critical role in the progression and rupture of intracranial
aneurysms. A novel method is presented that combines image-based wall motion estimation obtained through non-rigid registration
with computational fluid dynamics (CFD) simulations in order to provide realistic intra-aneurysmal flow patterns and understand
the effects of deforming walls on the haemodynamic patterns. In contrast to previous approaches, which assume rigid walls
or ad hoc elastic parameters to perform the CFD simulations, wall compliance has been included in this study through the imposition
of measured wall motions. This circumvents the difficulties in estimating personalized elasticity properties. Although variations
in the aneurysmal haemodynamics were observed when incorporating the wall motion, the overall characteristics of the wall
shear stress distribution do not seem to change considerably. Further experiments with more cases will be required to establish
the clinical significance of the observed variations.
09/2006: pages 438-445;
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ABSTRACT: Eye movements provide important insight into the cognitive processes underlying the visual search tasks. For image understanding, although the visual search patterns of different observers while studying the same scene bear some common characteristics, the idiosyncrasy associated with individual observers provides both research opportunities and challenges. The aim of this paper is to study the spatial characteristics of visual search, together with the intrinsic visual features of the fixation points for comparing different visual search strategies. An analysis framework based on earth mover's distance (EMD) in normalized anatomical space is proposed, and the results are demonstrated with high resolution computed tomography (HRCT) images of the lungs. The study shows that through the effective use of both spatial and feature space representation, it is possible to untangle what appear to be uncorrelated fixation distribution patterns to reveal common visual search behaviors.
IEEE Transactions on Medical Imaging 09/2006; 25(8):1011-21. · 3.64 Impact Factor
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ABSTRACT: Haemodynamics, and in particular wall shear stress, is thought to play a critical role in the progression and rupture of intracranial aneurysms. A novel method is presented that combines image-based wall motion estimation obtained through non-rigid registration with computational fluid dynamics (CFD) simulations in order to provide realistic intra-aneurysmal flow patterns and understand the effects of deforming walls on the haemodynamic patterns. In contrast to previous approaches, which assume rigid walls or ad hoc elastic parameters to perform the CFD simulations, wall compliance has been included in this study through the imposition of measured wall motions. This circumvents the difficulties in estimating personalized elasticity properties. Although variations in the aneurysmal haemodynamics were observed when incorporating the wall motion, the overall characteristics of the wall shear stress distribution do not seem to change considerably. Further experiments with more cases will be required to establish the clinical significance of the observed variations.
Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention. 02/2006; 9(Pt 2):438-45.
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ABSTRACT: This paper presents a new framework for capturing intrinsic visual search behavior of different observers in image understanding by analysing saccadic eye movements in feature space. The method is based on the information theory for identifying salient image features based on which visual search is performed. We demonstrate how to obtain feature space fixation density functions that are normalized to the image content along the scan paths. This allows a reliable identification of salient image features that can be mapped back to spatial space for highlighting regions of interest and attention selection. A two-color conjunction search experiment has been implemented to illustrate the theoretical framework of the proposed method including feature selection, hot spot detection, and back-projection. The practical value of the method is demonstrated with computed tomography image of centrilobular emphysema, and we discuss how the proposed framework can be used as a basis for decision support in medical image understanding.
IEEE Transactions on Medical Imaging 10/2003; 22(9):1152-62. · 3.64 Impact Factor
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ABSTRACT: This paper presents a new method of knowledge gathering for decision support in image understanding based on information extracted from the dynamics of saccadic eye movements. The framework involves the construction of a generic image feature extraction library, from which the feature extractors that are most relevant to the visual assessment by domain experts are determined automatically through factor analysis. The dynamics of the visual search are analyzed by using the Markov model for providing training information to novices on how and where to look for image features. The validity of the framework has been evaluated in a clinical scenario whereby the pulmonary vascular distribution on Computed Tomography images was assessed by experienced radiologists as a potential indicator of heart failure. The performance of the system has been demonstrated by training four novices to follow the visual assessment behavior of two experienced observers. In all cases, the accuracy of the students improved from near random decision making (33%) to accuracies ranging from 50% to 68%.
IEEE Transactions on Medical Imaging 08/2002; 21(7):741-54. · 3.64 Impact Factor
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Laura. Dempere-Marco
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ABSTRACT: "2004" Thesis (Ph.D.)--University of London, 2004. Includes bibliographical references (leave 232-241) Photocopy.
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ABSTRACT: Extensive research into the role of saccadic eye movements in human visual perception has been carried out for many years. Although the search patterns of different observers while studying the same image bear some common characteristics, there are often variations in the temporal order in which fixation points are viewed. During visual search for a defined target, there is evidence for both parallel search, with which all objects are processed concurrently, and for sequential search, in which several fixation points are found leading to the target. In this article, we present a review of both theoretical and experimental research directed towards better understanding of the underlying mechanisms of visual search. We begin by looking at the basic dynamics of saccadic eye movements and some of the major psychophysical models that have been developed over the years. An overview of the practical applications and future trends of visual search is then provided. Visual search is a common task that people perform throughout their daily life, and the number of applications inspired by the human visual search mechanism is potentially large. The purpose of this paper is to highlight some of the key opportunities for the image and vision computing community and promote further interactions between biological and computational vision research.
Image and Vision Computing.
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ABSTRACT: This paper presents a computational method of feature evaluation for modeling saliency in visual scenes. This is highly relevant in visual search studies since visual saliency is at the basis of visual attention deployment. Visual saliency can also become important in computer vision applications as it can be used to reduce the computational requirements by permitting processing only in those regions of the scenes containing relevant information. The method is based on Bayesian theory to describe the interaction between top-down and bottom-up information. Unlike other approaches, it evaluates and selects visual features before saliency estimation. This can reduce the complexity and, potentially, improve the accuracy of the saliency computation. To this end, we present an algorithm for feature evaluation and selection. A two-color conjunction search experiment has been applied to illustrate the theoretical framework of the proposed model. The practical value of the method is demonstrated with video segmentation of instruments in a laparoscopic cholecystectomy operation.
Pattern Recognition.
