3 accepted papers
RGB-based camouflaged object detection struggles in real-world scenarios where color and texture cues are ambiguous. While hyperspectral image offers a powerful alternative by capturing fine-grained spectral signatures, progress in hyperspectral camouflaged object detection (HCOD) has been criticall
Preliminary progress has been made in using deep learning networks for tooth segmentation and numbering, as well as pathological identification in dental panoramic images. However, The publicly available datasets specifically for children’s teeth are very scarce. To address this issue, this paper pr…
Salient Object Detection (SOD) is crucial in computer vision, yet RGB-based methods face limitations in challenging scenes, such as small objects and similar color features. Hyperspectral images provide a promising solution for more accurate Hyperspectral Salient Object Detection (HSOD) by abundant…