7,204 research outputs found
Recovering 6D Object Pose: A Review and Multi-modal Analysis
A large number of studies analyse object detection and pose estimation at
visual level in 2D, discussing the effects of challenges such as occlusion,
clutter, texture, etc., on the performances of the methods, which work in the
context of RGB modality. Interpreting the depth data, the study in this paper
presents thorough multi-modal analyses. It discusses the above-mentioned
challenges for full 6D object pose estimation in RGB-D images comparing the
performances of several 6D detectors in order to answer the following
questions: What is the current position of the computer vision community for
maintaining "automation" in robotic manipulation? What next steps should the
community take for improving "autonomy" in robotics while handling objects? Our
findings include: (i) reasonably accurate results are obtained on
textured-objects at varying viewpoints with cluttered backgrounds. (ii) Heavy
existence of occlusion and clutter severely affects the detectors, and
similar-looking distractors is the biggest challenge in recovering instances'
6D. (iii) Template-based methods and random forest-based learning algorithms
underlie object detection and 6D pose estimation. Recent paradigm is to learn
deep discriminative feature representations and to adopt CNNs taking RGB images
as input. (iv) Depending on the availability of large-scale 6D annotated depth
datasets, feature representations can be learnt on these datasets, and then the
learnt representations can be customized for the 6D problem
Automated Visual Fin Identification of Individual Great White Sharks
This paper discusses the automated visual identification of individual great
white sharks from dorsal fin imagery. We propose a computer vision photo ID
system and report recognition results over a database of thousands of
unconstrained fin images. To the best of our knowledge this line of work
establishes the first fully automated contour-based visual ID system in the
field of animal biometrics. The approach put forward appreciates shark fins as
textureless, flexible and partially occluded objects with an individually
characteristic shape. In order to recover animal identities from an image we
first introduce an open contour stroke model, which extends multi-scale region
segmentation to achieve robust fin detection. Secondly, we show that
combinatorial, scale-space selective fingerprinting can successfully encode fin
individuality. We then measure the species-specific distribution of visual
individuality along the fin contour via an embedding into a global `fin space'.
Exploiting this domain, we finally propose a non-linear model for individual
animal recognition and combine all approaches into a fine-grained
multi-instance framework. We provide a system evaluation, compare results to
prior work, and report performance and properties in detail.Comment: 17 pages, 16 figures. To be published in IJCV. Article replaced to
update first author contact details and to correct a Figure reference on page
RCDN -- Robust X-Corner Detection Algorithm based on Advanced CNN Model
Accurate detection and localization of X-corner on both planar and non-planar
patterns is a core step in robotics and machine vision. However, previous works
could not make a good balance between accuracy and robustness, which are both
crucial criteria to evaluate the detectors performance. To address this
problem, in this paper we present a novel detection algorithm which can
maintain high sub-pixel precision on inputs under multiple interference, such
as lens distortion, extreme poses and noise. The whole algorithm, adopting a
coarse-to-fine strategy, contains a X-corner detection network and three
post-processing techniques to distinguish the correct corner candidates, as
well as a mixed sub-pixel refinement technique and an improved region growth
strategy to recover the checkerboard pattern partially visible or occluded
automatically. Evaluations on real and synthetic images indicate that the
presented algorithm has the higher detection rate, sub-pixel accuracy and
robustness than other commonly used methods. Finally, experiments of camera
calibration and pose estimation verify it can also get smaller re-projection
error in quantitative comparisons to the state-of-the-art.Comment: 15 pages, 8 figures and 4 tables. Unpublished further research and
experiments of Checkerboard corner detection network CCDN (arXiv:2302.05097)
and application exploration for robust camera calibration
(https://ieeexplore.ieee.org/abstract/document/9428389
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