As three-dimensional microscopy becomes commonplace in biological research, there is an increasing need for researchers to be able to view experimental image stacks in a natural three-dimensional viewing context. Through stereoscopy and motion tracking, commercial virtual reality headsets provide a solution to this important visualization challenge by allowing researchers to view volumetric objects in an entirely intuitive fashion. With this motivation, we present DIVA, a user-friendly software tool that automatically creates detailed three-dimensional reconstructions of raw experimental image stacks that are integrated in virtual reality. In DIVA's immersive virtual environment, users can view, manipulate and perform volumetric measurements on their microscopy images as they would to real physical objects. In contrast to similar solutions, our software provides high-quality volume rendering with native TIFF file com-

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As three-dimensional microscopy becomes commonplace in biological research, there is an increasing need for researchers to be able to view experimental image stacks in a natural three-dimensional viewing context. Through stereoscopy and motion tracking, commercial virtual reality headsets provide a solution to this important visualization challenge by allowing researchers to view volumetric objects in an entirely intuitive fashion. With this motivation, we present DIVA, a user-friendly software tool that automatically creates detailed three-dimensional reconstructions of raw experimental image stacks that are integrated in virtual reality. In DIVA's immersive virtual environment, users can view, manipulate and perform volumetric measurements on their microscopy images as they would to real physical objects. In contrast to similar solutions, our software provides high-quality volume rendering with native TIFF file com-! Maxime Dahan died on the 28 th of July, 2018. patibility. We benchmark the software with diverse image types including those generated by confocal, light-sheet and electron microscopy. DIVA is available at https://diva.pasteur.fr and will be regularly updated.

The performance of the tracking algorithm relies heavily on the target structural information accuracy. In this paper, we propose a robust object tracking method based on the log-Gabor texture and color histogram. Our hypothesis is that by adding log-Gabor filter to color features, and then embedded it in the mean shift framework, tracking performances will notably enhance. Compared with several methods of state-of-the-art mean shift trackers, our approach extracts the target information efficiently. Experimental results on various challenging videos show that the proposed method improves the tracking with fewer mean shift iterations. 2 CONVENTIONAL MEAN SHIFT ALGORITHM 2.1 Target Representation In the mean shift algorithm, the target model is

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