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Borehole Imaging by Applying 3D Visual SLAM to Borehole Images Acquired in Forward Vision Camera System
Abstract: In the drilling survey, there is a technique called borehole imaging, which examines the
internal ground structure and the presence or absence of faults based on the unfolded panorama created by processing the borehole images taken by a camera. When we investigate the underground to prevent disasters such as landslides, it is desirable to use a portable and simple system including a monocular camera because we set up the system on the mountain slope. The problem with the monocular camera system is that the camera moves off the hole center and rotates around the hole axis. In order to generate the accurate unfolded images, it is necessary to obtain information about the accurate center and orientation of the camera moving inside the hole. For this purpose, we have developed a system to simultaneously estimate the camera pose and the hole center by applying SLAM technology to forward-looking images acquired in a forward vision camera system.
Keywords: borehole imaging, panorama, visual SLAM, forward-looking images, monocular camera, optical flow, disaster prevention engineering, drilling survey.
A Visual Search System Powered by Locality Sensitive Hashing
Abstract: This paper presents an end-to-end large―scale visual search system. Several challenges were met during its development: dealing with heptamerous image data, indexing large―scale images for massive data updating, training deep learning models for effective feature representation without a lot of manual work, improving the latency and providing accurate results. We assessed our system on a publicly available dataset consisting of flowers commonly available in the UK. This paper describes our implementation in great details as well as our lessons learnt during the building of such a large-scale system. We used locality sensitive hashing to perform the nearest neighbor search and given the data size, we relied on the random projection technique. In order to represent the image in a smaller dimension we used Bit-ResNet as the underlying model given its fine-tuning setup. Extensive experiments show that our system provides satisfactory results.
Keywords: Additional Key Words and Phrases: datasets, neural networks, visual search, locality sensitive hashing