Computer Science

Permanent URI for this collectionhttps://hdl.handle.net/10679/43

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Browsing by Institution Author "ATEŞ, Hasan Fehmi"

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    Improved homographic adaptation for keypoint generation in cross-spectral registration of thermal and optical imagery
    (SPIE, 2023) Yağmur, İsmail Can; Ateş, Hasan Fehmi; Electrical & Electronics Engineering; ATEŞ, Hasan Fehmi; Yağmur, İsmail Can
    Autonomous navigation is an important area of research for aerial vehicles. Visual odometry and simultaneous localization and mapping algorithms are critical for the three-dimensional understanding of the environment. For that purpose, consistent multi-spectral maps of the environment should be generated. Existing pixel-based image registration methods are accurate but too slow to operate in real-time. Recently deep learning is used to develop feature-based data-driven methods for generating interest points and associated descriptors for registering multi-spectral image pairs. These methods are fast and perform better than existing methods for optical images. However, the results are less convincing for thermal image registration. In this work, we propose an improved multi-spectral homographic adaptation technique to generate highly repeatable ground truth interest points that are invariant across viewpoint changes in both spectra. These interest points are used to train the MultiPoint image registration network. Simulation results show that our improved model outperforms existing techniques for feature-based image alignment of optical and thermal images.
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    Swin transformer based siamese network for thermal and optical image registration
    (IEEE, 2023) Elsaeidy, M.; Yağmur, İsmail Can; Ateş, Hasan Fehmi; Güntürk, B. K.; Electrical & Electronics Engineering; ATEŞ, Hasan Fehmi; Yağmur, İsmail Can
    The process of multi-modal image registration is fundamental in remote sensing and visual navigation applications. However, existing image registration methods that are designed for single modality images do not provide satisfactory results when applied to multi-modal image registration. In this research, our objective is to achieve highly accurate alignment of both infrared and optical (visible range) images. To accomplish this goal, we explore the effectiveness of the Swin Transformer encoder and cosine loss in enhancing the keypoint-based image registration process. Simulation results show the improvement achieved in multi-modal registration by using a transformer based Siamese network.