4,717 research outputs found
Learning and Matching Multi-View Descriptors for Registration of Point Clouds
Critical to the registration of point clouds is the establishment of a set of
accurate correspondences between points in 3D space. The correspondence problem
is generally addressed by the design of discriminative 3D local descriptors on
the one hand, and the development of robust matching strategies on the other
hand. In this work, we first propose a multi-view local descriptor, which is
learned from the images of multiple views, for the description of 3D keypoints.
Then, we develop a robust matching approach, aiming at rejecting outlier
matches based on the efficient inference via belief propagation on the defined
graphical model. We have demonstrated the boost of our approaches to
registration on the public scanning and multi-view stereo datasets. The
superior performance has been verified by the intensive comparisons against a
variety of descriptors and matching methods
Online Mutual Foreground Segmentation for Multispectral Stereo Videos
The segmentation of video sequences into foreground and background regions is
a low-level process commonly used in video content analysis and smart
surveillance applications. Using a multispectral camera setup can improve this
process by providing more diverse data to help identify objects despite adverse
imaging conditions. The registration of several data sources is however not
trivial if the appearance of objects produced by each sensor differs
substantially. This problem is further complicated when parallax effects cannot
be ignored when using close-range stereo pairs. In this work, we present a new
method to simultaneously tackle multispectral segmentation and stereo
registration. Using an iterative procedure, we estimate the labeling result for
one problem using the provisional result of the other. Our approach is based on
the alternating minimization of two energy functions that are linked through
the use of dynamic priors. We rely on the integration of shape and appearance
cues to find proper multispectral correspondences, and to properly segment
objects in low contrast regions. We also formulate our model as a frame
processing pipeline using higher order terms to improve the temporal coherence
of our results. Our method is evaluated under different configurations on
multiple multispectral datasets, and our implementation is available online.Comment: Preprint accepted for publication in IJCV (December 2018
Single-Shot Clothing Category Recognition in Free-Configurations with Application to Autonomous Clothes Sorting
This paper proposes a single-shot approach for recognising clothing
categories from 2.5D features. We propose two visual features, BSP (B-Spline
Patch) and TSD (Topology Spatial Distances) for this task. The local BSP
features are encoded by LLC (Locality-constrained Linear Coding) and fused with
three different global features. Our visual feature is robust to deformable
shapes and our approach is able to recognise the category of unknown clothing
in unconstrained and random configurations. We integrated the category
recognition pipeline with a stereo vision system, clothing instance detection,
and dual-arm manipulators to achieve an autonomous sorting system. To verify
the performance of our proposed method, we build a high-resolution RGBD
clothing dataset of 50 clothing items of 5 categories sampled in random
configurations (a total of 2,100 clothing samples). Experimental results show
that our approach is able to reach 83.2\% accuracy while classifying clothing
items which were previously unseen during training. This advances beyond the
previous state-of-the-art by 36.2\%. Finally, we evaluate the proposed approach
in an autonomous robot sorting system, in which the robot recognises a clothing
item from an unconstrained pile, grasps it, and sorts it into a box according
to its category. Our proposed sorting system achieves reasonable sorting
success rates with single-shot perception.Comment: 9 pages, accepted by IROS201
Recognising the Clothing Categories from Free-Configuration Using Gaussian-Process-Based Interactive Perception
In this paper, we propose a Gaussian Process- based interactive perception approach for recognising highly- wrinkled clothes. We have integrated this recognition method within a clothes sorting pipeline for the pre-washing stage of an autonomous laundering process. Our approach differs from reported clothing manipulation approaches by allowing the robot to update its perception confidence via numerous interactions with the garments. The classifiers predominantly reported in clothing perception (e.g. SVM, Random Forest) studies do not provide true classification probabilities, due to their inherent structure. In contrast, probabilistic classifiers (of which the Gaussian Process is a popular example) are able to provide predictive probabilities. In our approach, we employ a multi-class Gaussian Process classification using the Laplace approximation for posterior inference and optimising hyper-parameters via marginal likelihood maximisation. Our experimental results show that our approach is able to recognise unknown garments from highly-occluded and wrinkled con- figurations and demonstrates a substantial improvement over non-interactive perception approaches
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