scholarly journals An Improved Feedback Network Superresolution on Camera Lens Images for Blind Superresolution

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Yuhao Liu
Keyword(s):  
Real World ◽  
State Of The Art ◽  
Gaussian Kernel ◽  
Network Structures ◽  
Camera Lens ◽  
Feedback Network ◽  
Series Of Experiments ◽  
Blur Kernel ◽  
Image Pairs ◽  
Realistic Images

Most of the recent advances in image superresolution (SR) assume that the blur kernel during downsampling is predefined (e.g., Bicubic or Gaussian kernel), but it is a difficult task to make it suitable for all the realistic images. In this paper, we propose an Improved Superresolution Feedback Network (ISRFN) which is designed free to predefine the downsampling blur kernel by dealing with real-world HR-LR image pairs directly without downsampling process. We propose ISRFN by modifying the layers and network structures of the famous Superresolution Feedback Network (SRFBN). We trained the ISRFN with the Camera Lens Database named City100, which produced the HR and LR on the same lens, respectively, free for downsampling, so our proposed ISRFN is free to estimate the blur kernel. Due to different camera lens (smartphone and DSLR) databases, we perform two series of experiments under two camera lenses-based City100 databases, respectively, to choose the optimum network structures; experiments make it clear that different camera lens-based databases have different optimum network structures. We also compare our two ISRFNs with the state-of-the-art algorithms on performance; experiments show that our proposed ISRFN outperforms other state-of-the-art algorithms.

scholarly journals Appearance-Based Sequential Robot Localization Using a Patchwise Approximation of a Descriptor Manifold

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2483
Author(s):  
Alberto Jaenal ◽  
Francisco-Angel Moreno ◽  
Javier Gonzalez-Jimenez
Keyword(s):  
Gaussian Process ◽  
State Of The Art ◽  
Robot Localization ◽  
Camera Tracking ◽  
Image Descriptors ◽  
Series Of Experiments ◽  
Image Pairs ◽  
Low Dimensional ◽  
The Impact ◽  
White Noise Distribution

This paper addresses appearance-based robot localization in 2D with a sparse, lightweight map of the environment composed of descriptor–pose image pairs. Based on previous research in the field, we assume that image descriptors are samples of a low-dimensional Descriptor Manifold that is locally articulated by the camera pose. We propose a piecewise approximation of the geometry of such Descriptor Manifold through a tessellation of so-called Patches of Smooth Appearance Change (PSACs), which defines our appearance map. Upon this map, the presented robot localization method applies both a Gaussian Process Particle Filter (GPPF) to perform camera tracking and a Place Recognition (PR) technique for relocalization within the most likely PSACs according to the observed descriptor. A specific Gaussian Process (GP) is trained for each PSAC to regress a Gaussian distribution over the descriptor for any particle pose lying within that PSAC. The evaluation of the observed descriptor in this distribution gives us a likelihood, which is used as the weight for the particle. Besides, we model the impact of appearance variations on image descriptors as a white noise distribution within the GP formulation, ensuring adequate operation under lighting and scene appearance changes with respect to the conditions in which the map was constructed. A series of experiments with both real and synthetic images show that our method outperforms state-of-the-art appearance-based localization methods in terms of robustness and accuracy, with median errors below 0.3 m and 6∘.

scholarly journals Extrinsic Camera Calibration with Line-Laser Projection

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1091
Author(s):  
Izaak Van Crombrugge ◽  
Rudi Penne ◽  
Steve Vanlanduit
Keyword(s):  
Camera Calibration ◽  
Real World ◽  
Large Scale ◽  
State Of The Art ◽  
Bundle Adjustment ◽  
Field Of View ◽  
Extrinsic Calibration ◽  
Practical Procedure ◽  
Partial Overlap

Knowledge of precise camera poses is vital for multi-camera setups. Camera intrinsics can be obtained for each camera separately in lab conditions. For fixed multi-camera setups, the extrinsic calibration can only be done in situ. Usually, some markers are used, like checkerboards, requiring some level of overlap between cameras. In this work, we propose a method for cases with little or no overlap. Laser lines are projected on a plane (e.g., floor or wall) using a laser line projector. The pose of the plane and cameras is then optimized using bundle adjustment to match the lines seen by the cameras. To find the extrinsic calibration, only a partial overlap between the laser lines and the field of view of the cameras is needed. Real-world experiments were conducted both with and without overlapping fields of view, resulting in rotation errors below 0.5°. We show that the accuracy is comparable to other state-of-the-art methods while offering a more practical procedure. The method can also be used in large-scale applications and can be fully automated.

scholarly journals A Survey on Bias and Fairness in Machine Learning

2021 ◽  
Vol 54 (6) ◽  
pp. 1-35
Author(s):  
Ninareh Mehrabi ◽  
Fred Morstatter ◽  
Nripsuta Saxena ◽  
Kristina Lerman ◽  
Aram Galstyan
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Real World ◽  
State Of The Art ◽  
Future Directions ◽  
Discriminatory Behavior ◽  
Real World Applications ◽  
Near Future ◽  
Different Sources

With the widespread use of artificial intelligence (AI) systems and applications in our everyday lives, accounting for fairness has gained significant importance in designing and engineering of such systems. AI systems can be used in many sensitive environments to make important and life-changing decisions; thus, it is crucial to ensure that these decisions do not reflect discriminatory behavior toward certain groups or populations. More recently some work has been developed in traditional machine learning and deep learning that address such challenges in different subdomains. With the commercialization of these systems, researchers are becoming more aware of the biases that these applications can contain and are attempting to address them. In this survey, we investigated different real-world applications that have shown biases in various ways, and we listed different sources of biases that can affect AI applications. We then created a taxonomy for fairness definitions that machine learning researchers have defined to avoid the existing bias in AI systems. In addition to that, we examined different domains and subdomains in AI showing what researchers have observed with regard to unfair outcomes in the state-of-the-art methods and ways they have tried to address them. There are still many future directions and solutions that can be taken to mitigate the problem of bias in AI systems. We are hoping that this survey will motivate researchers to tackle these issues in the near future by observing existing work in their respective fields.

The State of the Art of Rubber-Seal Technology

1987 ◽  
Vol 60 (3) ◽  
pp. 381-416 ◽  
Author(s):  
B. S. Nau
Keyword(s):  
State Of The Art ◽  
Test Procedure ◽  
Working Party ◽  
The State ◽  
Supporting Evidence ◽  
Wide Range ◽  
Lip Seals ◽  
Alternative Hypotheses ◽  
Series Of Experiments ◽  
Range Of Values

Abstract The understanding of the engineering fundamentals of rubber seals of all the various types has been developing gradually over the past two or three decades, but there is still much to understand, Tables V–VII summarize the state of the art. In the case of rubber-based gaskets, the field of high-temperature applications has scarcely been touched, although there are plans to initiate work in this area both in the U.S.A. at PVRC, and in the U.K., at BHRA. In the case of reciprocating rubber seals, a broad basis of theory and experiment has been developed, yet it still is not possible to design such a seal from first principles. Indeed, in a comparative series of experiments run recently on seals from a single batch, tested in different laboratories round the world to the same test procedure, under the aegis of an ISO working party, a very wide range of values was reported for leakage and friction. The explanation for this has still to be ascertained. In the case of rotary lip seals, theories and supporting evidence have been brought forward to support alternative hypotheses for lubrication and sealing mechanisms. None can be said to have become generally accepted, and it remains to crystallize a unified theory.

Multi-resolution Visual Positioning and Navigation Technique for Unmanned Aerial System Landing Assistance

2017 ◽  
Vol 70 (6) ◽  
pp. 1276-1292
Author(s):  
Chong Yu ◽  
Jiyuan Cai ◽  
Qingyu Chen
Keyword(s):  
Real World ◽  
State Of The Art ◽  
Unmanned Aerial System ◽  
Detection Accuracy ◽  
Relative Positioning ◽  
Positioning Accuracy ◽  
Visual Positioning ◽  
Positioning Technique ◽  
Technique Comparison ◽  
Resolution Simulation

To achieve more accurate navigation performance in the landing process, a multi-resolution visual positioning technique is proposed for landing assistance of an Unmanned Aerial System (UAS). This technique uses a captured image of an artificial landmark (e.g. barcode) to provide relative positioning information in the X, Y and Z axes, and yaw, roll and pitch orientations. A multi-resolution coding algorithm is designed to ensure the UAS will not lose the detection of the landing target due to limited visual angles or camera resolution. Simulation and real world experiments prove the performance of the proposed technique in positioning accuracy, detection accuracy, and navigation effect. Two types of UAS are used to verify the generalisation of the proposed technique. Comparison experiments to state-of-the-art techniques are also included with the results analysis.

scholarly journals HyP-ABC: A Novel Automated Hyper-Parameter Tuning Algorithm Using Evolutionary Optimization

2021 ◽  
Author(s):  
Leila Zahedi ◽  
Farid Ghareh Mohammadi ◽  
M. Hadi Amini
Keyword(s):  
Parameter Optimization ◽  
Real World ◽  
Optimization Problems ◽  
State Of The Art ◽  
Parameter Tuning ◽  
Gradient Boosting ◽  
Support Vector ◽  
Wide Range ◽  
Extreme Gradient Boosting ◽  
Art Techniques

Machine learning techniques lend themselves as promising decision-making and analytic tools in a wide range of applications. Different ML algorithms have various hyper-parameters. In order to tailor an ML model towards a specific application, a large number of hyper-parameters should be tuned. Tuning the hyper-parameters directly affects the performance (accuracy and run-time). However, for large-scale search spaces, efficiently exploring the ample number of combinations of hyper-parameters is computationally challenging. Existing automated hyper-parameter tuning techniques suffer from high time complexity. In this paper, we propose HyP-ABC, an automatic innovative hybrid hyper-parameter optimization algorithm using the modified artificial bee colony approach, to measure the classification accuracy of three ML algorithms, namely random forest, extreme gradient boosting, and support vector machine. Compared to the state-of-the-art techniques, HyP-ABC is more efficient and has a limited number of parameters to be tuned, making it worthwhile for real-world hyper-parameter optimization problems. We further compare our proposed HyP-ABC algorithm with state-of-the-art techniques. In order to ensure the robustness of the proposed method, the algorithm takes a wide range of feasible hyper-parameter values, and is tested using a real-world educational dataset.

scholarly journals Silica-encapsulated DNA tracers for measuring aerosol distribution dynamics in real-world settings

2021 ◽  
Author(s):  
Anne M Luescher ◽  
Julian Koch ◽  
Wendelin J Stark ◽  
Robert N Grass
Keyword(s):  
Real World ◽  
Complex Dynamics ◽  
Quantitative Polymerase Chain Reaction ◽  
Cost Effective ◽  
Confined Space ◽  
Aerosol Dynamics ◽  
Novel Approach ◽  
Aerosol Distribution ◽  
Wide Range ◽  
Series Of Experiments

Aerosolized particles play a significant role in human health and environmental risk management. The global importance of aerosol-related hazards, such as the circulation of pathogens and high levels of air pollutants, have led to a surging demand for suitable surrogate tracers to investigate the complex dynamics of airborne particles in real-world scenarios. In this study, we propose a novel approach using silica particles with encapsulated DNA (SPED) as a tracing agent for measuring aerosol distribution indoors. In a series of experiments with a portable setup, SPED were successfully aerosolized, re-captured and quantified using quantitative polymerase chain reaction (qPCR). Position-dependency and ventilation effects within a confined space could be shown in a quantitative fashion achieving detection limits below 0.1 ng particles per m3 of sampled air. In conclusion, SPED show promise for a flexible, cost-effective and low-impact characterization of aerosol dynamics in a wide range of settings.

scholarly journals Triple-GAIL: A Multi-Modal Imitation Learning Framework with Generative Adversarial Nets

2020 ◽  
Author(s):  
Cong Fei ◽  
Bin Wang ◽  
Yuzheng Zhuang ◽  
Zongzhang Zhang ◽  
Jianye Hao ◽  
...  
Keyword(s):  
Real World ◽  
Autonomous Vehicles ◽  
Data Augmentation ◽  
State Of The Art ◽  
Robot Learning ◽  
Imitation Learning ◽  
Proper Understanding ◽  
Learning Framework ◽  
Strategy Game ◽  
Real Time Strategy Game

Generative adversarial imitation learning (GAIL) has shown promising results by taking advantage of generative adversarial nets, especially in the field of robot learning. However, the requirement of isolated single modal demonstrations limits the scalability of the approach to real world scenarios such as autonomous vehicles' demand for a proper understanding of human drivers' behavior. In this paper, we propose a novel multi-modal GAIL framework, named Triple-GAIL, that is able to learn skill selection and imitation jointly from both expert demonstrations and continuously generated experiences with data augmentation purpose by introducing an auxiliary selector. We provide theoretical guarantees on the convergence to optima for both of the generator and the selector respectively. Experiments on real driver trajectories and real-time strategy game datasets demonstrate that Triple-GAIL can better fit multi-modal behaviors close to the demonstrators and outperforms state-of-the-art methods.

scholarly journals Sliding-Window Thompson Sampling for Non-Stationary Settings

2020 ◽  
Vol 68 ◽  
pp. 311-364
Author(s):  
Francesco Trovo ◽  
Stefano Paladino ◽  
Marcello Restelli ◽  
Nicola Gatti
Keyword(s):  
Real World ◽  
State Of The Art ◽  
Sliding Window ◽  
Upper Bounds ◽  
Decision Problems ◽  
Sequential Decision ◽  
Thompson Sampling ◽  
The Past ◽  
Real World Applications ◽  
Window Approach

Multi-Armed Bandit (MAB) techniques have been successfully applied to many classes of sequential decision problems in the past decades. However, non-stationary settings -- very common in real-world applications -- received little attention so far, and theoretical guarantees on the regret are known only for some frequentist algorithms. In this paper, we propose an algorithm, namely Sliding-Window Thompson Sampling (SW-TS), for nonstationary stochastic MAB settings. Our algorithm is based on Thompson Sampling and exploits a sliding-window approach to tackle, in a unified fashion, two different forms of non-stationarity studied separately so far: abruptly changing and smoothly changing. In the former, the reward distributions are constant during sequences of rounds, and their change may be arbitrary and happen at unknown rounds, while, in the latter, the reward distributions smoothly evolve over rounds according to unknown dynamics. Under mild assumptions, we provide regret upper bounds on the dynamic pseudo-regret of SW-TS for the abruptly changing environment, for the smoothly changing one, and for the setting in which both the non-stationarity forms are present. Furthermore, we empirically show that SW-TS dramatically outperforms state-of-the-art algorithms even when the forms of non-stationarity are taken separately, as previously studied in the literature.

Augmented Reality–Assisted Dementia Care

2019 ◽  
pp. 71-92
Author(s):  
Mengyu Y. Zhao ◽  
S. K. Ong ◽  
Andrew Y. C. Nee
Keyword(s):  
Augmented Reality ◽  
Motor Skills ◽  
Real World ◽  
Emerging Technologies ◽  
State Of The Art ◽  
Aging Population ◽  
Dementia Care ◽  
High Rate ◽  
The Future ◽  
World Environment

With the increasing aging population, the number of people suffering from dementia continues to grow at a high rate. Emerging technologies have been applied to support the independence of patients with dementia. Among these technologies, augmented reality (AR) technology augments computer-generated virtual content on the real-world environment. There is an increasing interest in the use of AR tools and applications for dementia care as these tools and applications are capable of providing intuitive interaction and reducing the workload of caregivers. This chapter presents a state-of-the-art overview of AR-assisted applications for dementia care. An AR-assisted healthcare exercising system that enhances the user’s motor skills and cognition capability is presented to illustrate the use of AR in dementia care. Furthermore, an outlook of the future of AR-assisted dementia care applications is presented.

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