Abstract
In the context of mathematical morphology, component-graphs are complex but powerful structures for multi-band image modeling, processing, and analysis. In this work, we propose a novel multi-band object detection method relying on the component-graphs and statistical hypothesis tests. Our analysis shows that component-graphs are better at capturing image structures compared to the classical component-trees, with significantly higher detection capacity. Besides, we introduce two filtering algorithms to identify duplicated and partial nodes in the component-graphs. The proposed method, applied to the detection of sources on astronomical images, demonstrates a significant improvement in detecting faint objects on both multi-band simulated and real astronomical images compared to the state of the art.
Highlights
I N mathematical morphology, component-trees (CTs) and component-graphs (CGs) are classical structures for image modeling and analysis
All CT variants (Min-Tree, Max-Tree [1], [2], Tree of Shape [3]) benefit from efficient construction and filtering algorithms [4], [5]. They have diverse applications related to connected filtering, object detection, and segmentation, but those are limited to single-band image processing
FILTERING THE COMPONENT-GRAPH We introduce Component-Graph Objects (CGO), a method to handle multi-band object detection with CG
Summary
I N mathematical morphology, component-trees (CTs) and component-graphs (CGs) are classical structures for image modeling and analysis. Beyond the classical multivariate extensions of CTs, CGs efficiently capture the whole structural information of multi-band images as directed acyclic graph (DAG) variants. MTObject/Sourcerer has already shown its capability at detecting faint astronomical sources [14] while requiring far less parameter tuning than SExtractor Both methods focus on singleband processing while most optical astronomical surveys are multi-band. To handle such images, that are expected to lead to an increased sensitivity, we propose to generalize the detection method based on statistical testing to CGs. The main challenge is to effectively leverage multi-bands to filter relevant information from the richer component-graph structure. In-depth presentations of the component-graph can be found in [9], [10]
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