12,558 research outputs found
Constrained Manifold Learning for Hyperspectral Imagery Visualization
Displaying the large number of bands in a hyper- spectral image (HSI) on a
trichromatic monitor is important for HSI processing and analysis system. The
visualized image shall convey as much information as possible from the original
HSI and meanwhile facilitate image interpretation. However, most existing
methods display HSIs in false color, which contradicts with user experience and
expectation. In this paper, we propose a visualization approach based on
constrained manifold learning, whose goal is to learn a visualized image that
not only preserves the manifold structure of the HSI but also has natural
colors. Manifold learning preserves the image structure by forcing pixels with
similar signatures to be displayed with similar colors. A composite kernel is
applied in manifold learning to incorporate both the spatial and spectral
information of HSI in the embedded space. The colors of the output image are
constrained by a corresponding natural-looking RGB image, which can either be
generated from the HSI itself (e.g., band selection from the visible
wavelength) or be captured by a separate device. Our method can be done at
instance-level and feature-level. Instance-level learning directly obtains the
RGB coordinates for the pixels in the HSI while feature-level learning learns
an explicit mapping function from the high dimensional spectral space to the
RGB space. Experimental results demonstrate the advantage of the proposed
method in information preservation and natural color visualization
Hypergraph p-Laplacian Regularization for Remote Sensing Image Recognition
It is of great importance to preserve locality and similarity information in
semi-supervised learning (SSL) based applications. Graph based SSL and manifold
regularization based SSL including Laplacian regularization (LapR) and
Hypergraph Laplacian regularization (HLapR) are representative SSL methods and
have achieved prominent performance by exploiting the relationship of sample
distribution. However, it is still a great challenge to exactly explore and
exploit the local structure of the data distribution. In this paper, we present
an effect and effective approximation algorithm of Hypergraph p-Laplacian and
then propose Hypergraph p-Laplacian regularization (HpLapR) to preserve the
geometry of the probability distribution. In particular, p-Laplacian is a
nonlinear generalization of the standard graph Laplacian and Hypergraph is a
generalization of a standard graph. Therefore, the proposed HpLapR provides
more potential to exploiting the local structure preserving. We apply HpLapR to
logistic regression and conduct the implementations for remote sensing image
recognition. We compare the proposed HpLapR to several popular manifold
regularization based SSL methods including LapR, HLapR and HpLapR on UC-Merced
dataset. The experimental results demonstrate the superiority of the proposed
HpLapR.Comment: 9 pages, 6 figure
Scalable low dimensional manifold model in the reconstruction of noisy and incomplete hyperspectral images
We present a scalable low dimensional manifold model for the reconstruction
of noisy and incomplete hyperspectral images. The model is based on the
observation that the spatial-spectral blocks of a hyperspectral image typically
lie close to a collection of low dimensional manifolds. To emphasize this, the
dimension of the manifold is directly used as a regularizer in a variational
functional, which is solved efficiently by alternating direction of
minimization and weighted nonlocal Laplacian. Unlike general 3D images, the
same similarity matrix can be shared across all spectral bands for a
hyperspectral image, therefore the resulting algorithm is much more scalable
than that for general 3D data. Numerical experiments on the reconstruction of
hyperspectral images from sparse and noisy sampling demonstrate the superiority
of our proposed algorithm in terms of both speed and accuracy
Tensor Representation and Manifold Learning Methods for Remote Sensing Images
One of the main purposes of earth observation is to extract interested
information and knowledge from remote sensing (RS) images with high efficiency
and accuracy. However, with the development of RS technologies, RS system
provide images with higher spatial and temporal resolution and more spectral
channels than before, and it is inefficient and almost impossible to manually
interpret these images. Thus, it is of great interests to explore automatic and
intelligent algorithms to quickly process such massive RS data with high
accuracy. This thesis targets to develop some efficient information extraction
algorithms for RS images, by relying on the advanced technologies in machine
learning. More precisely, we adopt the manifold learning algorithms as the
mainline and unify the regularization theory, tensor-based method, sparse
learning and transfer learning into the same framework. The main contributions
of this thesis are as follows.Comment: 7 page
Invertible generative models for inverse problems: mitigating representation error and dataset bias
Trained generative models have shown remarkable performance as priors for
inverse problems in imaging -- for example, Generative Adversarial Network
priors permit recovery of test images from 5-10x fewer measurements than
sparsity priors. Unfortunately, these models may be unable to represent any
particular image because of architectural choices, mode collapse, and bias in
the training dataset. In this paper, we demonstrate that invertible neural
networks, which have zero representation error by design, can be effective
natural signal priors at inverse problems such as denoising, compressive
sensing, and inpainting. Given a trained generative model, we study the
empirical risk formulation of the desired inverse problem under a
regularization that promotes high likelihood images, either directly by
penalization or algorithmically by initialization. For compressive sensing,
invertible priors can yield higher accuracy than sparsity priors across almost
all undersampling ratios, and due to their lack of representation error,
invertible priors can yield better reconstructions than GAN priors for images
that have rare features of variation within the biased training set, including
out-of-distribution natural images. We additionally compare performance for
compressive sensing to unlearned methods, such as the deep decoder, and we
establish theoretical bounds on expected recovery error in the case of a linear
invertible model.Comment: Camera ready version for ICML 2020, paper 265
Realtime State Estimation with Tactile and Visual Sensing for Inserting a Suction-held Object
We develop a real-time state estimation system to recover the pose and
contact formation of an object relative to its environment. In this paper, we
focus on the application of inserting an object picked by a suction cup into a
tight space, an enabling technology for robotic packaging.
We propose a framework that fuses force and visual sensing for improved
accuracy and robustness. Visual sensing is versatile and non-intrusive, but
suffers from occlusions and limited accuracy, especially for tasks involving
contact. Tactile sensing is local, but provides accuracy and robustness to
occlusions. The proposed algorithm to fuse them is based on iSAM, an on-line
optimization technique, which we use to incorporate kinematic measurements from
the robot, contact geometry of the object and the container, and visual
tracking. In this paper, we generalize previous results in planar settings to a
3D task with more complex contact interactions. A key challenge in using force
sensing is that we do not observe contact point locations directly. We propose
a data-driven method to infer the contact formation, which is then used in
real-time by the state estimator. We demonstrate and evaluate the algorithm in
a setup instrumented to provide groundtruth.Comment: 8 pages, 10 figures, submitted to IROS 201
Perceptual Visual Interactive Learning
Supervised learning methods are widely used in machine learning. However, the
lack of labels in existing data limits the application of these technologies.
Visual interactive learning (VIL) compared with computers can avoid semantic
gap, and solve the labeling problem of small label quantity (SLQ) samples in a
groundbreaking way. In order to fully understand the importance of VIL to the
interaction process, we re-summarize the interactive learning related
algorithms (e.g. clustering, classification, retrieval etc.) from the
perspective of VIL. Note that, perception and cognition are two main visual
processes of VIL. On this basis, we propose a perceptual visual interactive
learning (PVIL) framework, which adopts gestalt principle to design interaction
strategy and multi-dimensionality reduction (MDR) to optimize the process of
visualization. The advantage of PVIL framework is that it combines computer's
sensitivity of detailed features and human's overall understanding of global
tasks. Experimental results validate that the framework is superior to
traditional computer labeling methods (such as label propagation) in both
accuracy and efficiency, which achieves significant classification results on
dense distribution and sparse classes dataset
Deep Generative Adversarial Networks for Compressed Sensing Automates MRI
Magnetic resonance image (MRI) reconstruction is a severely ill-posed linear
inverse task demanding time and resource intensive computations that can
substantially trade off {\it accuracy} for {\it speed} in real-time imaging. In
addition, state-of-the-art compressed sensing (CS) analytics are not cognizant
of the image {\it diagnostic quality}. To cope with these challenges we put
forth a novel CS framework that permeates benefits from generative adversarial
networks (GAN) to train a (low-dimensional) manifold of diagnostic-quality MR
images from historical patients. Leveraging a mixture of least-squares (LS)
GANs and pixel-wise cost, a deep residual network with skip
connections is trained as the generator that learns to remove the {\it
aliasing} artifacts by projecting onto the manifold. LSGAN learns the texture
details, while controls the high-frequency noise. A multilayer
convolutional neural network is then jointly trained based on diagnostic
quality images to discriminate the projection quality. The test phase performs
feed-forward propagation over the generator network that demands a very low
computational overhead. Extensive evaluations are performed on a large
contrast-enhanced MR dataset of pediatric patients. In particular, images rated
based on expert radiologists corroborate that GANCS retrieves high contrast
images with detailed texture relative to conventional CS, and pixel-wise
schemes. In addition, it offers reconstruction under a few milliseconds, two
orders of magnitude faster than state-of-the-art CS-MRI schemes
Multi-view Vector-valued Manifold Regularization for Multi-label Image Classification
In computer vision, image datasets used for classification are naturally
associated with multiple labels and comprised of multiple views, because each
image may contain several objects (e.g. pedestrian, bicycle and tree) and is
properly characterized by multiple visual features (e.g. color, texture and
shape). Currently available tools ignore either the label relationship or the
view complementary. Motivated by the success of the vector-valued function that
constructs matrix-valued kernels to explore the multi-label structure in the
output space, we introduce multi-view vector-valued manifold regularization
(MVMR) to integrate multiple features. MVMR exploits
the complementary property of different features and discovers the intrinsic
local geometry of the compact support shared by different features under the
theme of manifold regularization. We conducted extensive experiments on two
challenging, but popular datasets, PASCAL VOC' 07 (VOC) and MIR Flickr (MIR),
and validated the effectiveness of the proposed MVMR for image
classification
Composite Kernel Local Angular Discriminant Analysis for Multi-Sensor Geospatial Image Analysis
With the emergence of passive and active optical sensors available for
geospatial imaging, information fusion across sensors is becoming ever more
important. An important aspect of single (or multiple) sensor geospatial image
analysis is feature extraction - the process of finding "optimal" lower
dimensional subspaces that adequately characterize class-specific information
for subsequent analysis tasks, such as classification, change and anomaly
detection etc. In recent work, we proposed and developed an angle-based
discriminant analysis approach that projected data onto subspaces with maximal
"angular" separability in the input (raw) feature space and Reproducible Kernel
Hilbert Space (RKHS). We also developed an angular locality preserving variant
of this algorithm. In this letter, we advance this work and make it suitable
for information fusion - we propose and validate a composite kernel local
angular discriminant analysis projection, that can operate on an ensemble of
feature sources (e.g. from different sources), and project the data onto a
unified space through composite kernels where the data are maximally separated
in an angular sense. We validate this method with the multi-sensor University
of Houston hyperspectral and LiDAR dataset, and demonstrate that the proposed
method significantly outperforms other composite kernel approaches to sensor
(information) fusion
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