5,859 research outputs found
Blending Learning and Inference in Structured Prediction
In this paper we derive an efficient algorithm to learn the parameters of
structured predictors in general graphical models. This algorithm blends the
learning and inference tasks, which results in a significant speedup over
traditional approaches, such as conditional random fields and structured
support vector machines. For this purpose we utilize the structures of the
predictors to describe a low dimensional structured prediction task which
encourages local consistencies within the different structures while learning
the parameters of the model. Convexity of the learning task provides the means
to enforce the consistencies between the different parts. The
inference-learning blending algorithm that we propose is guaranteed to converge
to the optimum of the low dimensional primal and dual programs. Unlike many of
the existing approaches, the inference-learning blending allows us to learn
efficiently high-order graphical models, over regions of any size, and very
large number of parameters. We demonstrate the effectiveness of our approach,
while presenting state-of-the-art results in stereo estimation, semantic
segmentation, shape reconstruction, and indoor scene understanding
Structured Light-Based 3D Reconstruction System for Plants.
Camera-based 3D reconstruction of physical objects is one of the most popular computer vision trends in recent years. Many systems have been built to model different real-world subjects, but there is lack of a completely robust system for plants. This paper presents a full 3D reconstruction system that incorporates both hardware structures (including the proposed structured light system to enhance textures on object surfaces) and software algorithms (including the proposed 3D point cloud registration and plant feature measurement). This paper demonstrates the ability to produce 3D models of whole plants created from multiple pairs of stereo images taken at different viewing angles, without the need to destructively cut away any parts of a plant. The ability to accurately predict phenotyping features, such as the number of leaves, plant height, leaf size and internode distances, is also demonstrated. Experimental results show that, for plants having a range of leaf sizes and a distance between leaves appropriate for the hardware design, the algorithms successfully predict phenotyping features in the target crops, with a recall of 0.97 and a precision of 0.89 for leaf detection and less than a 13-mm error for plant size, leaf size and internode distance
Adversarial Deep Structured Nets for Mass Segmentation from Mammograms
Mass segmentation provides effective morphological features which are
important for mass diagnosis. In this work, we propose a novel end-to-end
network for mammographic mass segmentation which employs a fully convolutional
network (FCN) to model a potential function, followed by a CRF to perform
structured learning. Because the mass distribution varies greatly with pixel
position, the FCN is combined with a position priori. Further, we employ
adversarial training to eliminate over-fitting due to the small sizes of
mammogram datasets. Multi-scale FCN is employed to improve the segmentation
performance. Experimental results on two public datasets, INbreast and
DDSM-BCRP, demonstrate that our end-to-end network achieves better performance
than state-of-the-art approaches.
\footnote{https://github.com/wentaozhu/adversarial-deep-structural-networks.git}Comment: Accepted by ISBI2018. arXiv admin note: substantial text overlap with
arXiv:1612.0597
Linear vs Nonlinear Extreme Learning Machine for Spectral-Spatial Classification of Hyperspectral Image
As a new machine learning approach, extreme learning machine (ELM) has
received wide attentions due to its good performances. However, when directly
applied to the hyperspectral image (HSI) classification, the recognition rate
is too low. This is because ELM does not use the spatial information which is
very important for HSI classification. In view of this, this paper proposes a
new framework for spectral-spatial classification of HSI by combining ELM with
loopy belief propagation (LBP). The original ELM is linear, and the nonlinear
ELMs (or Kernel ELMs) are the improvement of linear ELM (LELM). However, based
on lots of experiments and analysis, we found out that the LELM is a better
choice than nonlinear ELM for spectral-spatial classification of HSI.
Furthermore, we exploit the marginal probability distribution that uses the
whole information in the HSI and learn such distribution using the LBP. The
proposed method not only maintain the fast speed of ELM, but also greatly
improves the accuracy of classification. The experimental results in the
well-known HSI data sets, Indian Pines and Pavia University, demonstrate the
good performances of the proposed method.Comment: 13 pages,8 figures,3 tables,articl
Learning sparse representations of depth
This paper introduces a new method for learning and inferring sparse
representations of depth (disparity) maps. The proposed algorithm relaxes the
usual assumption of the stationary noise model in sparse coding. This enables
learning from data corrupted with spatially varying noise or uncertainty,
typically obtained by laser range scanners or structured light depth cameras.
Sparse representations are learned from the Middlebury database disparity maps
and then exploited in a two-layer graphical model for inferring depth from
stereo, by including a sparsity prior on the learned features. Since they
capture higher-order dependencies in the depth structure, these priors can
complement smoothness priors commonly used in depth inference based on Markov
Random Field (MRF) models. Inference on the proposed graph is achieved using an
alternating iterative optimization technique, where the first layer is solved
using an existing MRF-based stereo matching algorithm, then held fixed as the
second layer is solved using the proposed non-stationary sparse coding
algorithm. This leads to a general method for improving solutions of state of
the art MRF-based depth estimation algorithms. Our experimental results first
show that depth inference using learned representations leads to state of the
art denoising of depth maps obtained from laser range scanners and a time of
flight camera. Furthermore, we show that adding sparse priors improves the
results of two depth estimation methods: the classical graph cut algorithm by
Boykov et al. and the more recent algorithm of Woodford et al.Comment: 12 page
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