The Experts below are selected from a list of 288 Experts worldwide ranked by ideXlab platform
Petra Perner - One of the best experts on this subject based on the ideXlab platform.
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ICCBR - Why Case-Based Reasoning Is Attractive for Image Interpretation
Case-Based Reasoning Research and Development, 2001Co-Authors: Petra PernerAbstract:The development of Image Interpretation systems is concerned with tricky problems such as a limited number of observations, environmental influence, and noise. Recent systems lack robustness, accuracy, and flexibility. The introduction of case-based reasoning (CBR) strategies can help to overcome these drawbacks. The special type of information (i.e., Images) and the problems mentioned above provide special requirements for CBR strategies. In this paper we review what has been achieved so far and research topics concerned with case-based Image Interpretation. We introduce a new approach for an Image Interpretation system and review its components.
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EWCBR - CBR-Based Ultra Sonic Image Interpretation
Lecture Notes in Computer Science, 2000Co-Authors: Petra PernerAbstract:The existing Image Interpretation systems lack robustness and accuracy. They cannot adapt to changing environmental conditions or to new objects. The application of machine learning to Image Interpretation is the next logical step. Our proposed approach aims at the development of dedicated machine learning techniques at all levels of Image Interpretation in a systematic fashion. In this paper we propose a system which uses Case-Based Reasoning (CBR) to optimize Image segmentation at the low level according to changing Image acquisition conditions and Image quality. The intermediate-level unit extracts the case representation used by the high-level unit for further processing. At the high level, CBR is employed to dynamically adapt Image Interpretation.
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Using CBR Learning for the Low-Level and High-Level Unit of an Image Interpretation System
International Conference on Advances in Pattern Recognition, 1999Co-Authors: Petra PernerAbstract:The existing Image Interpretation systems lack robustness and accuracy. They cannot adapt to changing environmental conditions and to new objects. The application of machine learning to Image Interpretation is the next logical step.
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MVA - Ultra Sonic Image Interpretation for Non-Destructive Testing
Journal of Machine Vision and Applications, 1996Co-Authors: Petra PernerAbstract:For Interpretation of ultra sonic Images an Image Interpretation system based on case based reasoning is proposed. We describe the case representation and the case based reasoning process.
Isabelle Bloch - One of the best experts on this subject based on the ideXlab platform.
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Abductive reasoning using tableau methods for high-level Image Interpretation
2015Co-Authors: Yifan Yang, Jamal Atif, Isabelle BlochAbstract:Image Interpretation is a dynamic research domain involving not only the detection of objects in a scene but also the semantic description considering context information in the whole scene. Image Interpretation problem can be formalized as an abductive reasoning problem, i.e. an inference to the best explanation using a background knowledge. In this work, we present a framework using a tableau method for generating and selecting potential explanations of the given Image when the background knowledge is encoded using a description that is able to handle spatial relations.
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fuzzy spatial relation ontology for Image Interpretation
Fuzzy Sets and Systems, 2008Co-Authors: Celine Hudelot, Jamal Atif, Isabelle BlochAbstract:The semantic Interpretation of Images can benefit from representations of useful concepts and the links between them as ontologies. In this paper, we propose an ontology of spatial relations, in order to guide Image Interpretation and the recognition of the structures it contains using structural information on the spatial arrangement of these structures. As an original theoretical contribution, this ontology is then enriched by fuzzy representations of concepts, which define their semantics, and allow establishing the link between these concepts (which are often expressed in linguistic terms) and the information that can be extracted from Images. This contributes to reducing the semantic gap and it constitutes a new methodological approach to guide semantic Image Interpretation. This methodological approach is illustrated on a medical example, dealing with knowledge-based recognition of brain structures in 3D magnetic resonance Images using the proposed fuzzy spatial relation ontology.
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On the interest of spatial relations and fuzzy representations for ontology-based Image Interpretation
Advances in Pattern Recognition, 2006Co-Authors: Isabelle Bloch, Celine Hudelot, Jamal AtifAbstract:In this paper we highlight a few features of the semantic gap problem in Image Interpretation. We show that semantic Image Interpretation can be seen as a symbol grounding problem. In this context, ontologies provide a powerful framework to represent domain knowledge, concepts and their relations, and to reason about them. They are likely to be more and more developed for Image Interpretation. A lot of Image Interpretation systems rely strongly on descriptions of objects through their characteristics such as shape, location, Image intensities. However, spatial relations are very important too and provide a structural description of the Imaged phenomenon, which is often more stable and less prone to variability than pure object descriptions. We show that spatial relations can be integrated in domain ontologies. Because of the intrinsic vagueness we have to cope with, at difierent levels (Image objects, spatial relations, variability, questions to be answered, etc.), fuzzy representations are well adapted and provide a consistent formal framework to address this key issue, as well as the associated reasoning and decision making aspects. Our view is that ontology-based methods can be very useful for Image Interpretation if they are associated to operational models relating the ontology concepts to Image information. In particular, we propose operational models of spatial relations, based on fuzzy representations.
Jing Zhang - One of the best experts on this subject based on the ideXlab platform.
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a markov random field model based approach to Image Interpretation
IEEE Transactions on Pattern Analysis and Machine Intelligence, 1992Co-Authors: J W Modestino, Jing ZhangAbstract:An Image is segmented into a collection of disjoint regions that form the nodes of an adjacency graph, and Image Interpretation is achieved through assigning object labels (or Interpretations) to the segmented regions (or nodes) using domain knowledge, extracted feature measurements, and spatial relationships between the various regions. The Interpretation labels are modeled as a Markov random field (MRF) on the corresponding adjacency graph, and the Image Interpretation problem is then formulated as a maximum a posteriori (MAP) estimation rule, given domain knowledge and region-based measurements. Simulated annealing is used to find this best realization or optimal MAP Interpretation. This approach also provides a systematic method for organizing and representing domain knowledge through appropriate design of the clique functions describing the Gibbs distribution representing the pdf of the underlying MRF. A general methodology is provided for the design of the clique functions. Results of Image Interpretation experiments on synthetic and real-world Images are described. >
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CVPR - A Markov random field model-based approach to Image Interpretation
Proceedings CVPR '89: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 1Co-Authors: J W Modestino, Jing ZhangAbstract:A Markov random field (MRF) model-based approach to automated Image Interpretation is described and demonstrated as a region-based scheme. In this approach, an Image is first segmented into a collection of disjoint regions which form the nodes of an adjacency graph. Image Interpretation is then achieved through assigning object labels, or Interpretations, to the segmented regions, or nodes, using domain knowledge, extracted feature measurements, and spatial relationships between the various regions. The Interpretation labels are modeled as a MRF on the corresponding adjacency graph, and the Image Interpretation problem are formulated as a maximum a posteriori estimation rule. Simulated annealing is used to find the best realization, or optimal Interpretation. Through the MRF model, this approach also provides a systematic method for organizing and representing domain knowledge through the clique functions of the probability density function underlying MRF. Results of Image Interpretation experiments performed on synthetic and real-world Images using this approach are described. >
J W Modestino - One of the best experts on this subject based on the ideXlab platform.
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a markov random field model based approach to Image Interpretation
IEEE Transactions on Pattern Analysis and Machine Intelligence, 1992Co-Authors: J W Modestino, Jing ZhangAbstract:An Image is segmented into a collection of disjoint regions that form the nodes of an adjacency graph, and Image Interpretation is achieved through assigning object labels (or Interpretations) to the segmented regions (or nodes) using domain knowledge, extracted feature measurements, and spatial relationships between the various regions. The Interpretation labels are modeled as a Markov random field (MRF) on the corresponding adjacency graph, and the Image Interpretation problem is then formulated as a maximum a posteriori (MAP) estimation rule, given domain knowledge and region-based measurements. Simulated annealing is used to find this best realization or optimal MAP Interpretation. This approach also provides a systematic method for organizing and representing domain knowledge through appropriate design of the clique functions describing the Gibbs distribution representing the pdf of the underlying MRF. A general methodology is provided for the design of the clique functions. Results of Image Interpretation experiments on synthetic and real-world Images are described. >
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CVPR - A Markov random field model-based approach to Image Interpretation
Proceedings CVPR '89: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 1Co-Authors: J W Modestino, Jing ZhangAbstract:A Markov random field (MRF) model-based approach to automated Image Interpretation is described and demonstrated as a region-based scheme. In this approach, an Image is first segmented into a collection of disjoint regions which form the nodes of an adjacency graph. Image Interpretation is then achieved through assigning object labels, or Interpretations, to the segmented regions, or nodes, using domain knowledge, extracted feature measurements, and spatial relationships between the various regions. The Interpretation labels are modeled as a MRF on the corresponding adjacency graph, and the Image Interpretation problem are formulated as a maximum a posteriori estimation rule. Simulated annealing is used to find the best realization, or optimal Interpretation. Through the MRF model, this approach also provides a systematic method for organizing and representing domain knowledge through the clique functions of the probability density function underlying MRF. Results of Image Interpretation experiments performed on synthetic and real-world Images using this approach are described. >
Koen L. Vincken - One of the best experts on this subject based on the ideXlab platform.
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Medical students' cognitive load in volumetric Image Interpretation
Computers in Human Behavior, 2016Co-Authors: Bobby G. Stuijfzand, Marieke Van Der Schaaf, Femke Kirschner, Cécile J. Ravesloot, Anouk Van Der Gijp, Koen L. VinckenAbstract:Medical Image Interpretation is moving from using 2D- to volumetric Images, thereby changing the cognitive and perceptual processes involved. This is expected to affect medical students' experienced cognitive load, while learning Image Interpretation skills. With two studies this explorative research investigated whether measures inherent to Image Interpretation, i.e. human-computer interaction and eye tracking, relate to cognitive load. Subsequently, it investigated effects of volumetric Image Interpretation on second-year medical students' cognitive load. Study 1 measured human-computer interactions of participants during two volumetric Image Interpretation tasks. Using structural equation modelling, the latent variable 'volumetric Image information' was identified from the data, which significantly predicted self-reported mental effort as a measure of cognitive load. Study 2 measured participants' eye movements during multiple 2D and volumetric Image Interpretation tasks. Multilevel analysis showed that time to locate a relevant structure in an Image was significantly related to pupil dilation, as a proxy for cognitive load. It is discussed how combining human-computer interaction and eye tracking allows for comprehensive measurement of cognitive load. Combining such measures in a single model would allow for disentangling unique sources of cognitive load, leading to recommendations for implementation of volumetric Image Interpretation in the medical education curriculum. Display Omitted Image Interpretation in medicine moved from 2D- to volumetric Images.Cognitive load of students interpreting medical Images affected.Human computer interaction and time to locate relevant area predict cognitive load.Insights useful for avoiding cognitive overload in medical curriculum.
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volumetric and two dimensional Image Interpretation show different cognitive processes in learners
Academic Radiology, 2015Co-Authors: Anouk Van Der Gijp, Marieke Van Der Schaaf, Cécile J. Ravesloot, Koen L. Vincken, Irene C Van Der Schaaf, Josephine C B M Huige, Olle Ten Cate, Jan P J Van SchaikAbstract:Rationale and Objectives In current practice, radiologists interpret digital Images, including a substantial amount of volumetric Images. We hypothesized that Interpretation of a stack of a volumetric data set demands different skills than Interpretation of two-dimensional (2D) cross-sectional Images. This study aimed to investigate and compare knowledge and skills used for Interpretation of volumetric versus 2D Images. Materials and Methods Twenty radiology clerks were asked to think out loud while reading four or five volumetric computed tomography (CT) Images in stack mode and four or five 2D CT Images. Cases were presented in a digital testing program allowing stack viewing of volumetric data sets and changing views and window settings. Thoughts verbalized by the participants were registered and coded by a framework of knowledge and skills concerning three components: perception, analysis, and synthesis. The components were subdivided into 16 discrete knowledge and skill elements. A within-subject analysis was performed to compare cognitive processes during volumetric Image readings versus 2D cross-sectional Image readings. Results Most utterances contained knowledge and skills concerning perception (46%). A smaller part involved synthesis (31%) and analysis (23%). More utterances regarded perception in volumetric Image Interpretation than in 2D Image Interpretation (Median 48% vs 35%; z = −3.9; P < .001). Synthesis was less prominent in volumetric than in 2D Image Interpretation (Median 28% vs 42%; z = −3.9; P < .001). No differences were found in analysis utterances. Conclusions Cognitive processes in volumetric and 2D cross-sectional Image Interpretation differ substantially. Volumetric Image Interpretation draws predominantly on perceptual processes, whereas 2D Image Interpretation is mainly characterized by synthesis. The results encourage the use of volumetric Images for teaching and testing perceptual skills.