32 research outputs found
SAR-to-Optical Image Translation via Thermodynamics-inspired Network
Synthetic aperture radar (SAR) is prevalent in the remote sensing field but
is difficult to interpret in human visual perception. Recently, SAR-to-optical
(S2O) image conversion methods have provided a prospective solution for
interpretation. However, since there is a huge domain difference between
optical and SAR images, they suffer from low image quality and geometric
distortion in the produced optical images. Motivated by the analogy between
pixels during the S2O image translation and molecules in a heat field,
Thermodynamics-inspired Network for SAR-to-Optical Image Translation (S2O-TDN)
is proposed in this paper. Specifically, we design a Third-order Finite
Difference (TFD) residual structure in light of the TFD equation of
thermodynamics, which allows us to efficiently extract inter-domain invariant
features and facilitate the learning of the nonlinear translation mapping. In
addition, we exploit the first law of thermodynamics (FLT) to devise an
FLT-guided branch that promotes the state transition of the feature values from
the unstable diffusion state to the stable one, aiming to regularize the
feature diffusion and preserve image structures during S2O image translation.
S2O-TDN follows an explicit design principle derived from thermodynamic theory
and enjoys the advantage of explainability. Experiments on the public SEN1-2
dataset show the advantages of the proposed S2O-TDN over the current methods
with more delicate textures and higher quantitative results
Learning to Generate SAR Images with Adversarial Autoencoder
Deep learning-based synthetic aperture radar (SAR) target recognition often suffers from sparsely distributed training samples and rapid angular variations due to scattering scintillation. Thus, data-driven SAR target recognition is considered a typical few-shot learning (FSL) task. This paper first reviews the key issues of FSL and provides a definition of the FSL task. A novel adversarial autoencoder (AAE) is then proposed as a SAR representation and generation network. It consists of a generator network that decodes target knowledge to SAR images and an adversarial discriminator network that not only learns to discriminate “fake” generated images from real ones but also encodes the input SAR image back to a target knowledge. The discriminator employs progressively expanding convolution layers and a corresponding layer-by-layer training strategy. It uses two cyclic loss functions to enforce consistency between the inputs and outputs. Moreover, rotated cropping is introduced as a mechanism to address the challenge of representing the target orientation. The MSTAR 7-target dataset is used to evaluate
the AAE’s performance, and the results demonstrate its ability to generate SAR images with aspect angular diversity. Using only 90 training samples with at least 25 degrees of orientation interval, the trained AAE is able to generate the remaining 1,748 samples of other orientation angles with an unprecedented level of fidelity. Thus, it can be used for data augmentation in SAR target recognition FSL tasks. Our experimental results show that the AAE could boost the test accuracy by 5.77%
On the use of deep learning for phase recovery
Phase recovery (PR) refers to calculating the phase of the light field from
its intensity measurements. As exemplified from quantitative phase imaging and
coherent diffraction imaging to adaptive optics, PR is essential for
reconstructing the refractive index distribution or topography of an object and
correcting the aberration of an imaging system. In recent years, deep learning
(DL), often implemented through deep neural networks, has provided
unprecedented support for computational imaging, leading to more efficient
solutions for various PR problems. In this review, we first briefly introduce
conventional methods for PR. Then, we review how DL provides support for PR
from the following three stages, namely, pre-processing, in-processing, and
post-processing. We also review how DL is used in phase image processing.
Finally, we summarize the work in DL for PR and outlook on how to better use DL
to improve the reliability and efficiency in PR. Furthermore, we present a
live-updating resource (https://github.com/kqwang/phase-recovery) for readers
to learn more about PR.Comment: 82 pages, 32 figure
Aerial Vehicles
This book contains 35 chapters written by experts in developing techniques for making aerial vehicles more intelligent, more reliable, more flexible in use, and safer in operation.It will also serve as an inspiration for further improvement of the design and application of aeral vehicles. The advanced techniques and research described here may also be applicable to other high-tech areas such as robotics, avionics, vetronics, and space
Digital Interaction and Machine Intelligence
This book is open access, which means that you have free and unlimited access. This book presents the Proceedings of the 9th Machine Intelligence and Digital Interaction Conference. Significant progress in the development of artificial intelligence (AI) and its wider use in many interactive products are quickly transforming further areas of our life, which results in the emergence of various new social phenomena. Many countries have been making efforts to understand these phenomena and find answers on how to put the development of artificial intelligence on the right track to support the common good of people and societies. These attempts require interdisciplinary actions, covering not only science disciplines involved in the development of artificial intelligence and human-computer interaction but also close cooperation between researchers and practitioners. For this reason, the main goal of the MIDI conference held on 9-10.12.2021 as a virtual event is to integrate two, until recently, independent fields of research in computer science: broadly understood artificial intelligence and human-technology interaction
Entropy in Image Analysis II
Image analysis is a fundamental task for any application where extracting information from images is required. The analysis requires highly sophisticated numerical and analytical methods, particularly for those applications in medicine, security, and other fields where the results of the processing consist of data of vital importance. This fact is evident from all the articles composing the Special Issue "Entropy in Image Analysis II", in which the authors used widely tested methods to verify their results. In the process of reading the present volume, the reader will appreciate the richness of their methods and applications, in particular for medical imaging and image security, and a remarkable cross-fertilization among the proposed research areas
Gaze-Based Human-Robot Interaction by the Brunswick Model
We present a new paradigm for human-robot interaction based on social signal processing, and in particular on the Brunswick model. Originally, the Brunswick model copes with face-to-face dyadic interaction, assuming that the interactants are communicating through a continuous exchange of non verbal social signals, in addition to the spoken messages. Social signals have to be interpreted, thanks to a proper recognition phase that considers visual and audio information. The Brunswick model allows to quantitatively evaluate the quality of the interaction using statistical tools which measure how effective is the recognition phase. In this paper we cast this theory when one of the interactants is a robot; in this case, the recognition phase performed by the robot and the human have to be revised w.r.t. the original model. The model is applied to Berrick, a recent open-source low-cost robotic head platform, where the gazing is the social signal to be considered
ATHENA Research Book, Volume 2
ATHENA European University is an association of nine higher education institutions with the mission of promoting excellence in research and innovation by enabling international cooperation. The acronym ATHENA stands for Association of Advanced Technologies in Higher Education. Partner institutions are from France, Germany, Greece, Italy, Lithuania, Portugal and Slovenia: University of Orléans, University of Siegen, Hellenic Mediterranean University, Niccolò Cusano University, Vilnius Gediminas Technical University, Polytechnic Institute of Porto and University of Maribor. In 2022, two institutions joined the alliance: the Maria Curie-Skłodowska University from Poland and the University of Vigo from Spain. Also in 2022, an institution from Austria joined the alliance as an associate member: Carinthia University of Applied Sciences. This research book presents a selection of the research activities of ATHENA University's partners. It contains an overview of the research activities of individual members, a selection of the most important bibliographic works of members, peer-reviewed student theses, a descriptive list of ATHENA lectures and reports from individual working sections of the ATHENA project. The ATHENA Research Book provides a platform that encourages collaborative and interdisciplinary research projects by advanced and early career researchers