A comparison of approaches to high-level image interpretation

1988 ◽  
Vol 21 (3) ◽  
pp. 241-259 ◽  
Author(s):  
Andrew M. Wallace
Keyword(s):  
Image Interpretation ◽  
High Level

scholarly journals Can Limited Education of Lung Ultrasound Be Conducted to Medical Students Properly? A Pilot Study

2017 ◽  
Vol 2017 ◽  
pp. 1-6 ◽  
Author(s):  
Jang Sun Lim ◽  
Sanghun Lee ◽  
Han Ho Do ◽  
Kyu Ho Oh
Keyword(s):  
Medical Students ◽  
Medical School ◽  
Image Acquisition ◽  
Image Interpretation ◽  
School Curriculum ◽  
Anterior Chest Wall ◽  
Psychomotor Skills ◽  
Psychomotor Learning ◽  
Learning Domains ◽  
High Level

Objectives. Lung ultrasonography (LUS) is a useful examination to identify lung problems. Unfortunately, there are currently no LUS educational programs for medical students. We designed a brief LUS training course for medical students during the ED rotation. The purpose of training was improving cognitive and psychomotor learning domains, knowledge of ultrasound, knowledge of LUS, image acquisition, and image interpretation. Methods. Forty students in their fourth year of medical school were enrolled in this study. Student achievement was evaluated through examinations of cognitive and psychomotor skills. A survey was administered following the training. Results. The average test result was 42.1 ± 13.7 before training and 82.6 ± 10.7 after training. With respect to the assessment of LUS performance, the acceptable rates for right and left anterior chest wall scanning and right and left posterolateral scanning were 95%, 97.5%, 92.5%, and 100%, respectively. The students felt a high level of confidence in their ability to administer LUS to patients after training and they agreed that inclusion of LUS training in the medical school curriculum is necessary. Conclusion. This study showed that, among the medical students without ultrasound experience, limited LUS education to improve their knowledge, image acquisition, and interpretation ability was successful.

256 Shades of gray: uncertainty and diagnostic error in radiology

Diagnosis ◽  
2017 ◽  
Vol 4 (3) ◽  
pp. 149-157 ◽  
Author(s):  
Michael A. Bruno
Keyword(s):  
Cognitive Processes ◽  
Image Interpretation ◽  
Diagnostic Error ◽  
High Variability ◽  
Radiological Imaging ◽  
Reduction Strategies ◽  
Diagnostic Power ◽  
High Level ◽  
High Uncertainty ◽  
Degree Of Overlap

Abstract Radiologists practice in an environment of extraordinarily high uncertainty, which results partly from the high variability of the physical and technical aspects of imaging, partly from the inherent limitations in the diagnostic power of the various imaging modalities, and partly from the complex visual-perceptual and cognitive processes involved in image interpretation. This paper reviews the high level of uncertainty inherent to the process of radiological imaging and image interpretation vis-à-vis the issue of radiological interpretive error, in order to highlight the considerable degree of overlap that exists between these. The scope of radiological error, its many potential causes and various error-reduction strategies in radiology are also reviewed.

Automatic interpretation strategies for synthetic aperture radar images

1988 ◽  
Vol 324 (1579) ◽  
pp. 409-421 ◽  
Keyword(s):  
Image Interpretation ◽  
Map Matching ◽  
Low Level ◽  
Radar Images ◽  
Automatic Interpretation ◽  
Segmented Image ◽  
Geometrical Matching ◽  
Automatic Methods ◽  
High Level ◽  
Effective Use

Effective use of the large amounts of timely data to be provided by the coming generation of space-based radars will require automatic methods of image interpretation. The key to such interpretation is an image representation, based on low-level operations, which can support the introduction of high-level (rule-based) knowledge. This representation, described in this paper as a segmented image database (SID), is dependent on the performance of the low-level operations (segmentation, edge-detection and thin-line-detection) which generate it. Methods of quantifying the performance of these operations are described. Use of the SID to support classification based on context, and image-map matching that uses image structure, rather than geometrical matching, are demonstrated.

scholarly journals FORMATION OF THE SCENICALLY-SHAPED CULTURE OF FUTURE TEACHERS OF MUSICAL ART: METHODOLOGICAL BENCHMARKS

2018 ◽  
pp. 109-116 ◽  
Author(s):  
Nataliia Kosinska
Keyword(s):  
Aesthetic Experience ◽  
Professional Competence ◽  
Image Interpretation ◽  
Modern Society ◽  
Musical Composition ◽  
Professional Activity ◽  
Future Teachers ◽  
Stage Of Development ◽  
Methodological Approaches ◽  
High Level

It is proved in the article that at the current stage of development of artistic and pedagogical education its traditional principles of development that are oriented at acquiring of knowledge, formation of experiences and skills, range of competences are established. It is defined that the new requirements are imposed on a teacher in the context of innovational approaches as follows: the modern society needs professionals who can perform professional functions and will be ready from the beginning of professional activity to manifest professionalism, competences in formation of the generation with a high level of aesthetic culture, values and ethical orientations. Methodological aspects of formation of scenically-shaped culture of future teachers of the musical art are highlighted. The concepts of methodological approaches in the context of problem formation of scenically-shaped culture of future teachers of the musical art are disclosed. It is defined that among the leading methodological approaches that determine the essence of scenically-shaped culture of a teacher of the musical art are cultural, axiological, competence-based and hermeneutic ones that enables definition of the corresponding professionally- meaningful quality, professional competence that allow a specialist to master the content of a musical composition as a particular cultural phenomenon, to consider it as synthesis of spiritual, emotional and aesthetic experience of humankind on the basis of interpretation of its artistic and sense dimension to build its scenic image and to relay it to the pedagogical, performing and vocal activities. This quality manifests itself in orientation on artistic image interpretation through a scenic image on the basis of universal and national culture experience, personal life and professional experience, value orientation in command of interpretative skills, vocal and acting techniques, pedagogical talent in decoding of shaped system of a musical composition through the mediation of a stage image, manifold quality, structural components of which are motivational and empathetic, cognitive and educational together with creative and active. Keywords: future teachers of the musical art; methodological foundations; scenically-shaped culture; cultural approach; axiological approach; hermeneutical approach; competency-based approach; image.

scholarly journals Pemetaan Tingkatan Cuaca Ekstrim Masing-Masing Kecamatan di Kota Kupang

2021 ◽  
Vol 2 (1) ◽  
pp. 1-10
Author(s):  
Arfita Rahmawati ◽  
Bella Theo Tomi Pamungkas ◽  
Dwi Partini
Keyword(s):  
Natural Disasters ◽  
Image Interpretation ◽  
Extreme Weather ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Descriptive Research ◽  
Analysis Technique ◽  
Quantitative Descriptive ◽  
High Level ◽  
Detrimental Impact

Extreme weather is one of the natural disasters that occur in Kupang City, East Nusa Tenggara Province. Like other natural disasters, extreme weather also has a detrimental impact on people's lives. The purpose of this study was to determine the extent of extreme weather levels in each sub-district in Kupang City with mapping. This research is included in quantitative descriptive research, then the population and the sample used is Kupang City. The data collection method used the Landsat 8 OLI image interpretation and the data trajectory model. The data analysis technique used overlay analysis with the division of three classes of extreme weather levels, low, medium, and high. Based on the research results, it is 79.53% or with an area of 143.399 km2, Kupang is included in the extreme weather area with high criteria. The sub-district has the largest percentage range of high-level extreme weather in Kupang City is the Kota Lama sub-district. Meanwhile, the sub-district has the largest percentage of area to extreme weather with a low level is Alak sub-district.

scholarly journals A Ship Rotation Detection Model in Remote Sensing Images Based on Feature Fusion Pyramid Network and Deep Reinforcement Learning

2018 ◽  
Vol 10 (12) ◽  
pp. 1922 ◽  
Author(s):  
Kun Fu ◽  
Yang Li ◽  
Hao Sun ◽  
Xue Yang ◽  
Guangluan Xu ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Reinforcement Learning ◽  
Feature Fusion ◽  
Image Interpretation ◽  
Remote Sensing Image ◽  
Detection Task ◽  
Ship Detection ◽  
Detection Model ◽  
Efficient Detection ◽  
High Level

Ship detection plays an important role in automatic remote sensing image interpretation. The scale difference, large aspect ratio of ship, complex remote sensing image background and ship dense parking scene make the detection task difficult. To handle the challenging problems above, we propose a ship rotation detection model based on a Feature Fusion Pyramid Network and deep reinforcement learning (FFPN-RL) in this paper. The detection network can efficiently generate the inclined rectangular box for ship. First, we propose the Feature Fusion Pyramid Network (FFPN) that strengthens the reuse of different scales features, and FFPN can extract the low level location and high level semantic information that has an important impact on multi-scale ship detection and precise location of dense parking ships. Second, in order to get accurate ship angle information, we apply deep reinforcement learning to the inclined ship detection task for the first time. In addition, we put forward prior policy guidance and a long-term training method to train an angle prediction agent constructed through a dueling structure Q network, which is able to iteratively and accurately obtain the ship angle. In addition, we design soft rotation non-maximum suppression to reduce the missed ship detection while suppressing the redundant detection boxes. We carry out detailed experiments on the remote sensing ship image dataset, and the experiments validate that our FFPN-RL ship detection model has efficient detection performance.

System architectures for interactive knowledge-based image-interpretation

1988 ◽  
Vol 324 (1579) ◽  
pp. 457-465 ◽  
Keyword(s):  
Software Architecture ◽  
Complete System ◽  
Image Interpretation ◽  
System Architectures ◽  
Low Level ◽  
Knowledge Based ◽  
Systematic Strategy ◽  
High Level ◽  
Interpretation Systems

We discuss hardware and software architecture for automated image-interpretation. The importance of considering the complete system is emphasized leading in particular to the conclusion that high-level and low-level processing are intimately linked. We present arguments to support the idea that automated image-interpretation systems should be knowledge-based and interactive. We attempt to identify the main architectural problems which such systems must address and outline a systematic strategy for acquiring, structuring and using knowledge.

scholarly journals Multi-U-Net: Residual Module under Multisensory Field and Attention Mechanism Based Optimized U-Net for VHR Image Semantic Segmentation

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1794
Author(s):  
Si Ran ◽  
Jianli Ding ◽  
Bohua Liu ◽  
Xiangyu Ge ◽  
Guolin Ma
Keyword(s):  
Image Interpretation ◽  
Semantic Segmentation ◽  
Attention Mechanism ◽  
Data Sets ◽  
Convolutional Network ◽  
Data Set ◽  
Manual Intervention ◽  
Segmentation Methods ◽  
Effective Network ◽  
High Level

As the acquisition of very high resolution (VHR) images becomes easier, the complex characteristics of VHR images pose new challenges to traditional machine learning semantic segmentation methods. As an excellent convolutional neural network (CNN) structure, U-Net does not require manual intervention, and its high-precision features are widely used in image interpretation. However, as an end-to-end fully convolutional network, U-Net has not explored enough information from the full scale, and there is still room for improvement. In this study, we constructed an effective network module: residual module under a multisensory field (RMMF) to extract multiscale features of target and an attention mechanism to optimize feature information. RMMF uses parallel convolutional layers to learn features of different scales in the network and adds shortcut connections between stacked layers to construct residual blocks, combining low-level detailed information with high-level semantic information. RMMF is universal and extensible. The convolutional layer in the U-Net network is replaced with RMMF to improve the network structure. Additionally, the multiscale convolutional network was tested using RMMF on the Gaofen-2 data set and Potsdam data sets. Experiments show that compared to other technologies, this method has better performance in airborne and spaceborne images.

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