scholarly journals High-Brightness Image Enhancement Algorithm

2021 ◽  
Vol 11 (23) ◽  
pp. 11497
Author(s):  
Yifei Wei ◽  
Zhenhong Jia ◽  
Jie Yang ◽  
Nikola K. Kasabov
Keyword(s):  
Real Life ◽  
Objective Evaluation ◽  
Tone Mapping ◽  
High Brightness ◽  
Practical Applications ◽  
Mapping Algorithm ◽  
Video Images ◽  
Application Data ◽  
Human Eyes ◽  
Enhancement Algorithm

In this paper, we introduce a tone mapping algorithm for processing high-brightness video images. This method can maximally recover the information of high-brightness areas and preserve detailed information. Along with benchmark data, real-life and practical application data were taken to test the proposed method. The experimental objects were license plates. We reconstructed the image in the RGB channel, and gamma correction was carried out. After that, local linear adjustment was completed through a tone mapping window to restore the detailed information of the high-brightness region. The experimental results showed that our algorithm could clearly restore the details of high-brightness local areas. The processed image conformed to the visual effect observed by human eyes but with higher definition. Compared with other algorithms, the proposed algorithm has advantages in terms of both subjective and objective evaluation. It can fully satisfy the needs in various practical applications.

Efficient and Adaptive Tone Mapping Algorithm Based on Guided Image Filter

2019 ◽  
Vol 34 (04) ◽  
pp. 2054012 ◽  
Author(s):  
Yuan Jia ◽  
Wenting Zhang
Keyword(s):  
Dynamic Range ◽  
Recognition Rate ◽  
Objective Evaluation ◽  
High Dynamic Range ◽  
Base Layer ◽  
Tone Mapping ◽  
Image Filter ◽  
Mapping Algorithm ◽  
Guided Image Filter ◽  
Hdr Image

The recognition rate of computer vision algorithms is highly dependent on the image quality. To enhance the visual quality of the images captured under high-dynamic range (HDR) scenes, we propose an efficient and adaptive tone mapping algorithm based on guided image filter (GIF). The HDR image is compressed adaptively according to its average luminance. Then we decompose it into a base layer and a detail layer using the guided image filter. We improve the base layer and enhance the detail layer simultaneously, and combine the two layers to get the final low-dynamic range (LDR) image. Since the parameters are linked with image statistics, they adaptively fit to various kinds of images. The objective evaluation results on HDR image sets demonstrate the superiority of our proposed algorithm. Meanwhile, the result of our algorithm can reduce the halo artifacts and preserve more detail by subjective observation.

scholarly journals Research on feeder network design: a case study of feeder service for the port of Kotka

2020 ◽  
Vol 12 (1) ◽  
Author(s):  
Yisong Lin ◽  
Xuefeng Wang ◽  
Hao Hu ◽  
Hui Zhao
Keyword(s):  
Control System ◽  
Numerical Experiment ◽  
Network Design ◽  
Evaluation System ◽  
Real Life ◽  
Objective Evaluation ◽  
Multi Objective Optimization ◽  
Real Life Data ◽  
The Mathematical Model

Abstract By exemplifying the feeder service for the port of Kotka, this study proposed a multi-objective optimization model for feeder network design. Innovative for difference from the single-objective evaluation system, the objective of feeder network design was proposed to include single allocation cost, intra-Europe cargo revenue, equipment balance, sailing cycle, allocation utilization, service route competitiveness, and stability. A three-stage control system was presented, and numerical experiment based on container liner’s real life data was conducted to verify the mathematical model and the control system. The numerical experiment revealed that the three-stage control system is effective and practical, and the research ideas had been applicable with satisfactory effect.

Removing NOx Pollution by Photocatalytic Building Materials in Real-Life: Evaluation of Existing Field Studies

2021 ◽  
Vol 02 ◽  
Author(s):  
Pernille D. Pedersen ◽  
Nina Lock ◽  
Henrik Jensen
Keyword(s):  
Air Pollutants ◽  
Building Materials ◽  
Evaluation Criteria ◽  
Real Life ◽  
Ambient Air ◽  
Well Being ◽  
Good Health ◽  
Objective Evaluation ◽  
Field Studies ◽  
Hazardous Air Pollutants

: The NOx gasses (NO and NO2) are among the most important air pollutants, due to the toxicity of NO2, as well as the role of NOx in the tropospheric oxidation of Volatile Organic Carbons (VOCs), contributing to the formation of other hazardous air pollutants. Air pollution is one of the biggest health threats world-wide, hence reducing NOx levels is an important objective of the UN sustainable development goals, e.g. #3, “Good health and well-being” and #11 “Sustainable cities and communities”. Photocatalysis using TiO2 and light is a promising technique for removing NOx along with other pollutants, as demonstrated on laboratory scale. Furthermore, a long range of real-life test studies of varying scales have been conducted during the past two decades. The results of these studies have been conflicting, with some studies reporting no effect on the ambient air quality and others reporting significant reductions of NOx level. However, the studies are very difficult to compare and assess due to the very different approaches used, which consequently vary in quality. In this review, we aim to develop a set of objective evaluation criteria to assess the quality of the individual studies in order to simplify the interpretation and comparison of the existing studies. Moreover, we propose some guidelines for future test-studies. Furthermore, the approaches and main conclusions from 23 studies are independently assessed and discussed herein.

scholarly journals Development and Application of an Atmospheric Pollutant Monitoring System Based on LoRa—Part I: Design and Reliability Tests

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 3891 ◽  
Author(s):  
Yushuang Ma ◽  
Long Zhao ◽  
Rongjin Yang ◽  
Xiuhong Li ◽  
Qiao Song ◽  
...  
Keyword(s):  
Embedded System ◽  
Communication Technology ◽  
Atmospheric Pollutant ◽  
Practical Applications ◽  
Actual Application ◽  
Pollutant Concentrations ◽  
Application Data ◽  
Software And Hardware ◽  
The Internet Of Things ◽  
Monitoring Platform

At present, as growing importance continues to be attached to atmospheric environmental problems, the demand for real-time monitoring of these problems is constantly increasing. This article describes the development and application of an embedded system for monitoring of atmospheric pollutant concentrations based on LoRa (Long Range) wireless communication technology, which is widely used in the Internet of Things (IoT). The proposed system is realized using a combination of software and hardware and is designed using the concept of modularization. Separation of each function into independent modules allows the system to be developed more quickly and to be applied more stably. In addition, by combining the requirements of the remote atmospheric pollutant concentration monitoring platform with the specific requirements for the intended application environment, the system demonstrates its significance for practical applications. In addition, the actual application data also verifies the sound application prospects of the proposed system.

Feature Subspace Estimation for Resolving Dimensionality Mismatch in Practical Color-Based Face Recognition Systems

2016 ◽  
Vol 30 (04) ◽  
pp. 1656004 ◽  
Author(s):  
Jae Young Choi
Keyword(s):  
Face Recognition ◽  
Color Image ◽  
Recognition Performance ◽  
Real Life ◽  
Practical Applications ◽  
Novel Approach ◽  
Feature Subspace ◽  
New Feature ◽  
Different Color ◽  
Input Level

Recently, considerable research efforts have been devoted to effective utilization of facial color information for improved recognition performance. Of all color-based face recognition (FR) methods, the most widely used approach is a color FR method using input-level fusion. In this method, augmented input vectors of the color images are first generated by concatenating different color components (including both luminance and chrominance information) by column order at the input level and feature subspace is then trained with a set of augmented input vectors. However, in practical applications, a testing image could be captured as a grayscale image, rather than as a color image, mainly caused by different, heterogeneous image acquisition environment. A grayscale testing image causes so-called dimensionality mismatch between the trained feature subspace and testing input vector. Disparity in dimensionality negatively impacts the reliable FR performance and even imposes a significant restriction on carrying out FR operations in practical color FR systems. To resolve the dimensionality mismatch, we propose a novel approach to estimate new feature subspace, suitable for recognizing a grayscale testing image. In particular, new feature subspace is estimated from a given feature subspace created using color training images. The effectiveness of proposed solution has been successfully tested on four public face databases (DBs) such as CMU, FERET, XM2VTSDB, and ORL DBs. Extensive and comparative experiments showed that the proposed solution works well for resolving dimensionality mismatch of importance in real-life color FR systems.

3D Printing Applications in STEM Education

2018 ◽  
pp. 2626-2640
Author(s):  
Norman Gwangwava ◽  
Catherine Hlahla
Keyword(s):  
3D Printing ◽  
Real Life ◽  
Printing Technology ◽  
Web Based ◽  
Practical Applications ◽  
3D Printing Technology ◽  
Lecture Room ◽  
Storage Products ◽  
Educational Toys ◽  
Learning Institutions

Using 3D printing technology in learning institutions brings an industrial experience to learners as well as an exposure to the same cutting-edge technologies encountered in real life careers. The chapter explores 3D printing technology at kindergarten (preschool), in the lecture room (BEng programme), and ready-to-use 3D printed products. In educational toy applications, the effect of poor product designs that do not meet the children's dimensional and safety requirements can lead to injuries, development of musculoskeletal disorders and health problems, some of which may be experienced by the children when they grow up. In order to address the problem of poor design, measurements of anthropometric dimensions from male and female children, aging from 6 to 7 years old were taken and concepts for educational toys were then generated. Other practical applications of the 3D printing technology explored in the chapter are lecture room demonstrations, prototyping of design projects and a web-based mass-customization of office mini-storage products.

An AI Walk from Pharmacokinetics to Marketing

2011 ◽  
pp. 71-75
Author(s):  
José D. Martín-Guerrero ◽  
Emilio Soria-Olivas ◽  
Paulo J.G. Lisboa ◽  
Antonio J. Serrano-López
Keyword(s):  
Finite Impulse Response ◽  
Clustering Algorithms ◽  
Real Life ◽  
Support Vector ◽  
Conditional Heteroskedasticity ◽  
Resonance Theory ◽  
Adaptive Resonance ◽  
Practical Applications ◽  
Life Problems ◽  
Auto Regressive

This work is intended for providing a review of reallife practical applications of Artificial Intelligence (AI) methods. We focus on the use of Machine Learning (ML) methods applied to rather real problems than synthetic problems with standard and controlled environment. In particular, we will describe the following problems in next sections: • Optimization of Erythropoietin (EPO) dosages in anaemic patients undergoing Chronic Renal Failure (CRF). • Optimization of a recommender system for citizen web portal users. • Optimization of a marketing campaign. The choice of these problems is due to their relevance and their heterogeneity. This heterogeneity shows the capabilities and versatility of ML methods to solve real-life problems in very different fields of knowledge. The following methods will be mentioned during this work: • Artificial Neural Networks (ANNs): Multilayer Perceptron (MLP), Finite Impulse Response (FIR) Neural Network, Elman Network, Self-Oganizing Maps (SOMs) and Adaptive Resonance Theory (ART). • Other clustering algorithms: K-Means, Expectation- Maximization (EM) algorithm, Fuzzy C-Means (FCM), Hierarchical Clustering Algorithms (HCA). • Generalized Auto-Regressive Conditional Heteroskedasticity (GARCH). • Support Vector Regression (SVR). • Collaborative filtering techniques. • Reinforcement Learning (RL) methods.

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