8,003 research outputs found
Large scale evaluation of local image feature detectors on homography datasets
We present a large scale benchmark for the evaluation of local feature
detectors. Our key innovation is the introduction of a new evaluation protocol
which extends and improves the standard detection repeatability measure. The
new protocol is better for assessment on a large number of images and reduces
the dependency of the results on unwanted distractors such as the number of
detected features and the feature magnification factor. Additionally, our
protocol provides a comprehensive assessment of the expected performance of
detectors under several practical scenarios. Using images from the
recently-introduced HPatches dataset, we evaluate a range of state-of-the-art
local feature detectors on two main tasks: viewpoint and illumination invariant
detection. Contrary to previous detector evaluations, our study contains an
order of magnitude more image sequences, resulting in a quantitative evaluation
significantly more robust to over-fitting. We also show that traditional
detectors are still very competitive when compared to recent deep-learning
alternatives.Comment: Accepted to BMVC 201
A practical multirobot localization system
We present a fast and precise vision-based software intended for multiple robot localization. The core component of the software is a novel and efficient algorithm for black and white pattern detection. The method is robust to variable lighting conditions, achieves sub-pixel precision and its computational complexity is independent of the processed image size. With off-the-shelf computational equipment and low-cost cameras, the core algorithm is able to process hundreds of images per second while tracking hundreds of objects with a millimeter precision. In addition, we present the method's mathematical model, which allows to estimate the expected localization precision, area of coverage, and processing speed from the camera's intrinsic parameters and hardware's processing capacity. The correctness of the presented model and performance of the algorithm in real-world conditions is verified in several experiments. Apart from the method description, we also make its source code public at \emph{http://purl.org/robotics/whycon}; so, it can be used as an enabling technology for various mobile robotic problems
Fast and Accurate Algorithm for Eye Localization for Gaze Tracking in Low Resolution Images
Iris centre localization in low-resolution visible images is a challenging
problem in computer vision community due to noise, shadows, occlusions, pose
variations, eye blinks, etc. This paper proposes an efficient method for
determining iris centre in low-resolution images in the visible spectrum. Even
low-cost consumer-grade webcams can be used for gaze tracking without any
additional hardware. A two-stage algorithm is proposed for iris centre
localization. The proposed method uses geometrical characteristics of the eye.
In the first stage, a fast convolution based approach is used for obtaining the
coarse location of iris centre (IC). The IC location is further refined in the
second stage using boundary tracing and ellipse fitting. The algorithm has been
evaluated in public databases like BioID, Gi4E and is found to outperform the
state of the art methods.Comment: 12 pages, 10 figures, IET Computer Vision, 201
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