Abstract:
We propose a novel Convolutional Neural Network (CNN) architecture for joint detection and matching of feature points in images from different sensors using a single forward pass. Unlike classical approaches (SIFT, etc.), the feature detector and descriptor are tightly coupled. Siamese and dual non-weight-sharing CNN subnetworks are used in our method. This allows simultaneous processing and fusion of multimodal image patch joint and disjoint cues. Multiple datasets of multimodal images show that the proposed approach outperforms current state-of-the-art schemes. It outperforms state-of-the-art detectors in repeatable feature point detection across multi-sensor images. To our knowledge, it is the first unified approach for image detection and matching.
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