Qualitative Spatial Reasoning with Directional and Topological Relations

Nam, Sangha; Kim, In-Cheol

doi:10.1155/2015/902043

Cited by 4 publications

(2 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The object projection is an annotation method to determine the direction of the spatial relationship for the main object and its reference object using directional relations [43], [44]. With this method, the position of the main object is determined by projecting its position based on the reference object.…”

Section: Image Annotation Based On Object Projectionmentioning

confidence: 99%

Chest X-Ray Image Annotation based on Spatial Relationship Feature Extraction

Saad,

Mohsin,

Hamid

et al. 2023

AETiC

View full text Add to dashboard Cite

Digital imaging has become an essential element in every medical institution. Therefore, medical image retrieval such as chest X-ray (CXR) must be improved via novel feature extraction and annotation activities before they are stored into image databases. To date, many methods have been introduced to annotate medical images using spatial relationships after these features are extracted. However, the annotation performance for each method is inconsistent and does not show promising achievement to retrieve images. It is noticed that each method is still struggling with at least two big problems. Firstly, the recommended annotation model is weak because the method does not consider the object shape and rely on gross object shape estimation. Secondly, the suggested annotation model can only be functional for simple object placement. As a result, it is difficult to determine the spatial relationship feature after they are extracted to annotate images accurately. Hence, this study aims to propose a new model to annotate nodule location within lung zone for CXR image with extracted spatial relationship feature to improve image retrieval. In order to achieve the aim, a methodology that consists of six phases of CXR image annotation using the extracted spatial relationship features is introduced. This comprehensive methodology covers all cycles for image annotation tasks starting from image pre-processing until determination of spatial relationship features for the lung zone in the CXR. The outcome from applying the methodology also enables us to produce a new semi-automatic annotation system named CHEXRIARS which acts as a tool to annotate the extracted spatial relationship features in CXR images. The CHEXRIARS performance is tested using a retrieval test with two common tests namely the precision and recall (PNR). Apart from CHEXRIARS, three other annotation methods that are object slope, object projection and comparison of region boundaries are also included in the retrieval performance test. Overall, the CHEXRIARS interpolated PNR curve shows the best shape because it is the closest curve approaching the value of 1 on the X-axis and Y-axis. Meanwhile the value of area under curve for CHEXRIARS also revealed that this system attained the highest score at 0.856 as compared to the other three annotation methods. The outcome from the retrieval performance test indicated that the proposed annotation model has produced outstanding outcome and improved the image retrieval.

show abstract

Section: Image Annotation Based On Object Projectionmentioning

confidence: 99%

Chest X-Ray Image Annotation based on Spatial Relationship Feature Extraction

Saad,

Mohsin,

Hamid

et al. 2023

AETiC

View full text Add to dashboard Cite

show abstract

“…path consistency algorithms). Nam and Kim (2015) presented an algorithm working on a mixture of 9 cone-shaped directional (CSD−9) relations and 8 region connection calculus (RCC8) topological relations. To achieve highly efficient reasoning methods, continuous improvement of the efficiency of these reasoning algorithms is essential.…”

Section: Open Accessmentioning

confidence: 99%

Qualitative spatial reasoning on topological relations by combining the semantic web and constraint satisfaction

Wang

Qiao

Liu

et al. 2018

Geo-spatial Information Science

View full text Add to dashboard Cite

Qualitative spatial reasoning on topological relations can extract hidden spatial knowledge from qualitatively described topological information, which is of significant importance for decisionmaking and query optimization in spatial analysis. Qualitative reasoning on spatial topological information based on semantic knowledge and reasoning rules is an efficient means of reducing both the known relations and the corresponding rules, which can result in enhanced reasoning performance. This paper proposes a qualitative reasoning method for spatial topological relations based on the semantic description of reasoning rules and constraint set. Combined with knowledge from the Semantic Web, the proposed method can easily extract potential spatial results consistent with both unique and non-unique rules. The Constraint-Satisfactionbased approach, describing constraint set with semantic expressions, is then used together with an improved path consistency algorithm to verify the consistency of the unique-rules-based and non-unique-rules-based reasoning results. The verification can eliminate certain reasoning results to ensure the reliability of the final results. Thus, the task of qualitative spatial reasoning on topological relations is completed.

show abstract

Detailed model of topological and metric relationships between a line and region

2019

Arab J Geosci

View full text Add to dashboard Cite

Qualitative Spatial Reasoning with Directional and Topological Relations

Cited by 4 publications

References 14 publications

Chest X-Ray Image Annotation based on Spatial Relationship Feature Extraction

Chest X-Ray Image Annotation based on Spatial Relationship Feature Extraction

Qualitative spatial reasoning on topological relations by combining the semantic web and constraint satisfaction

Detailed model of topological and metric relationships between a line and region

Contact Info

Product

Resources

About