134 research outputs found
Cascaded Segmentation-Detection Networks for Word-Level Text Spotting
We introduce an algorithm for word-level text spotting that is able to
accurately and reliably determine the bounding regions of individual words of
text "in the wild". Our system is formed by the cascade of two convolutional
neural networks. The first network is fully convolutional and is in charge of
detecting areas containing text. This results in a very reliable but possibly
inaccurate segmentation of the input image. The second network (inspired by the
popular YOLO architecture) analyzes each segment produced in the first stage,
and predicts oriented rectangular regions containing individual words. No
post-processing (e.g. text line grouping) is necessary. With execution time of
450 ms for a 1000-by-560 image on a Titan X GPU, our system achieves the
highest score to date among published algorithms on the ICDAR 2015 Incidental
Scene Text dataset benchmark.Comment: 7 pages, 8 figure
Finding Your Way Back: Comparing Path Odometry Algorithms for Assisted Return.
We present a comparative analysis of inertial-based odometry algorithms for the purpose of assisted return. An assisted return system facilitates backtracking of a path previously taken, and can be particularly useful for blind pedestrians. We present a new algorithm for path matching, and test it in simulated assisted return tasks with data from WeAllWalk, the only existing data set with inertial data recorded from blind walkers. We consider two odometry systems, one based on deep learning (RoNIN), and the second based on robust turn detection and step counting. Our results show that the best path matching results are obtained using the turns/steps odometry system
Semantic Interior Mapology: A Toolbox For Indoor Scene Description From Architectural Floor Plans
We introduce the Semantic Interior Mapology (SIM) toolbox for the conversion
of a floor plan and its room contents (such as furnitures) to a vectorized
form. The toolbox is composed of the Map Conversion toolkit and the Map
Population toolkit. The Map Conversion toolkit allows one to quickly trace the
layout of a floor plan, and to generate a GeoJSON file that can be rendered in
3D using web applications such as Mapbox. The Map Population toolkit takes the
3D scan of a room in the building (acquired from an RGB-D camera), and, through
a semi-automatic process, populates individual objects of interest with a
correct dimension and position in the GeoJSON representation of the building.
SIM is easy to use and produces accurate results even in the case of complex
building layouts.Comment: 9 pages, 12 figure
Automatic Semantic Content Removal by Learning to Neglect
We introduce a new system for automatic image content removal and inpainting.
Unlike traditional inpainting algorithms, which require advance knowledge of
the region to be filled in, our system automatically detects the area to be
removed and infilled. Region segmentation and inpainting are performed jointly
in a single pass. In this way, potential segmentation errors are more naturally
alleviated by the inpainting module. The system is implemented as an
encoder-decoder architecture, with two decoder branches, one tasked with
segmentation of the foreground region, the other with inpainting. The encoder
and the two decoder branches are linked via neglect nodes, which guide the
inpainting process in selecting which areas need reconstruction. The whole
model is trained using a conditional GAN strategy. Comparative experiments show
that our algorithm outperforms state-of-the-art inpainting techniques (which,
unlike our system, do not segment the input image and thus must be aided by an
external segmentation module.)Comment: Accepted to BMVC 2018 as an oral presentatio
- …