Object Detection in Images Based on Homogeneous Region Segmentation

dc.contributor.authorAmrane, Abdesalam
dc.contributor.authorMeziane, Abdelkrim
dc.contributor.authorBoulkrinat, Nour El Houda
dc.date.accessioned2023-10-04T11:59:05Z
dc.date.available2023-10-04T11:59:05Z
dc.date.issued2018
dc.description.abstractImage segmentation for object detection is one of the most fundamental problems in computer vision, especially in object-region extraction task. Most popular approaches in the segmentation/object detection tasks use sliding-window or super-pixel labeling methods. The first method suffers from the number of window proposals, whereas the second suffers from the over-segmentation problem. To overcome these limitations, we present two strategies: the first one is a fast algorithm based on the region growing method for segmenting images into homogeneous regions. In the second one, we present a new technique for similar region merging, based on a three similarity measures, and computed using the region adjacency matrix. All of these methods are evaluated and compared to other state-of-the-art approaches that were applied on the Berkeley image database. The experimentations yielded promising results and would be used for future directions in our work.
dc.identifier.doihttps://doi.org/10.1007/978-3-319-92058-0_31
dc.identifier.isbn978-3-319-92057-3
dc.identifier.issn0302-9743
dc.identifier.urihttps://dl.cerist.dz/handle/CERIST/981
dc.publisherSpringer
dc.relation.ispartofseriesRecent Trends and Future Technology in Applied Intelligence ; 31st International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2018, Montreal, QC, Canada, June 25-28, 2018, Proceedings
dc.relation.pages327-333
dc.relation.placeMontreal, QC, Canada
dc.structureSystèmes et Documents Multimédia Structurés (SDMS)
dc.subjectRegion proposal
dc.subjectRegion growing
dc.subjectRegion merging
dc.subjectImage segmentation
dc.titleObject Detection in Images Based on Homogeneous Region Segmentation
dc.typeConference paper
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