scholarly journals 3D Transparent Object Detection and Reconstruction Based on Passive Mode Single-Pixel Imaging

Sensors ◽  
2020 ◽  
Vol 20 (15) ◽  
pp. 4211 ◽  
Author(s):  
Anumol Mathai ◽  
Ningqun Guo ◽  
Dong Liu ◽  
Xin Wang
Keyword(s):  
Object Detection ◽  
Cost Effective ◽  
Surface Shape ◽  
Practical Applications ◽  
Pixel Detectors ◽  
Reconstruction Methods ◽  
Transparent Objects ◽  
Transparent Object ◽  
Single Pixel ◽  
Insight Into

Transparent object detection and reconstruction are significant, due to their practical applications. The appearance and characteristics of light in these objects make reconstruction methods tailored for Lambertian surfaces fail disgracefully. In this paper, we introduce a fixed multi-viewpoint approach to ascertain the shape of transparent objects, thereby avoiding the rotation or movement of the object during imaging. In addition, a simple and cost-effective experimental setup is presented, which employs two single-pixel detectors and a digital micromirror device, for imaging transparent objects by projecting binary patterns. In the system setup, a dark framework is implemented around the object, to create shades at the boundaries of the object. By triangulating the light path from the object, the surface shape is recovered, neither considering the reflections nor the number of refractions. It can, therefore, handle transparent objects with a relatively complex shape with the unknown refractive index. The implementation of compressive sensing in this technique further simplifies the acquisition process, by reducing the number of measurements. The experimental results show that 2D images obtained from the single-pixel detectors are better in quality with a resolution of 32×32. Additionally, the obtained disparity and error map indicate the feasibility and accuracy of the proposed method. This work provides a new insight into 3D transparent object detection and reconstruction, based on single-pixel imaging at an affordable cost, with the implementation of a few numbers of detectors.

Non-line-of-sight transparent object detection and reconstruction based on passive single-pixel imaging

2021 ◽  
Vol 18 (2) ◽  
pp. 025204
Author(s):  
Mengdi Li ◽  
Anumol Mathai ◽  
Xiping Xu ◽  
Xin Wang ◽  
Yue Pan ◽  
...  
Keyword(s):  
Object Detection ◽  
Line Of Sight ◽  
Transparent Object ◽  
Single Pixel ◽  
Non Line Of Sight

scholarly journals Chemical Modification of Combusted Coal Gangue for U(VI) Adsorption: Towards a Waste Control by Waste Strategy

2021 ◽  
Vol 13 (15) ◽  
pp. 8421
Author(s):  
Yuan Gao ◽  
Jiandong Huang ◽  
Meng Li ◽  
Zhongran Dai ◽  
Rongli Jiang ◽  
...  
Keyword(s):  
Structural Analysis ◽  
High Efficiency ◽  
Low Cost ◽  
Chemical Activation ◽  
Cost Effective ◽  
Coal Gangue ◽  
Order Kinetic ◽  
Practical Applications ◽  
Detailed Structural Analysis ◽  
Alkaline Conditions

Uranium mining waste causes serious radiation-related health and environmental problems. This has encouraged efforts toward U(VI) removal with low cost and high efficiency. Typical uranium adsorbents, such as polymers, geopolymers, zeolites, and MOFs, and their associated high costs limit their practical applications. In this regard, this work found that the natural combusted coal gangue (CCG) could be a potential precursor of cheap sorbents to eliminate U(VI). The removal efficiency was modulated by chemical activation under acid and alkaline conditions, obtaining HCG (CCG activated with HCl) and KCG (CCG activated with KOH), respectively. The detailed structural analysis uncovered that those natural mineral substances, including quartz and kaolinite, were the main components in CCG and HCG. One of the key findings was that kalsilite formed in KCG under a mild synthetic condition can conspicuous enhance the affinity towards U(VI). The best equilibrium adsorption capacity with KCG was observed to be 140 mg/g under pH 6 within 120 min, following a pseudo-second-order kinetic model. To understand the improved adsorption performance, an adsorption mechanism was proposed by evaluating the pH of uranyl solutions, adsorbent dosage, as well as contact time. Combining with the structural analysis, this revealed that the uranyl adsorption process was mainly governed by chemisorption. This study gave rise to a utilization approach for CCG to obtain cost-effective adsorbents and paved a novel way towards eliminating uranium by a waste control by waste strategy.

scholarly journals A Survey on Deep Learning Based Methods and Datasets for Monocular 3D Object Detection

Electronics ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 517
Author(s):  
Seong-heum Kim ◽  
Youngbae Hwang
Keyword(s):  
Deep Learning ◽  
Object Detection ◽  
Low Cost ◽  
Detection Methods ◽  
Future Research ◽  
3D Object ◽  
Practical Applications ◽  
Depth Sensors ◽  
Significant Research ◽  
3D Object Detection

Owing to recent advancements in deep learning methods and relevant databases, it is becoming increasingly easier to recognize 3D objects using only RGB images from single viewpoints. This study investigates the major breakthroughs and current progress in deep learning-based monocular 3D object detection. For relatively low-cost data acquisition systems without depth sensors or cameras at multiple viewpoints, we first consider existing databases with 2D RGB photos and their relevant attributes. Based on this simple sensor modality for practical applications, deep learning-based monocular 3D object detection methods that overcome significant research challenges are categorized and summarized. We present the key concepts and detailed descriptions of representative single-stage and multiple-stage detection solutions. In addition, we discuss the effectiveness of the detection models on their baseline benchmarks. Finally, we explore several directions for future research on monocular 3D object detection.

scholarly journals Specular Reflection Detection and Inpainting in Transparent Object through MSPLFI

2021 ◽  
Vol 13 (3) ◽  
pp. 455
Author(s):  
Md Nazrul Islam ◽  
Murat Tahtali ◽  
Mark Pickering
Keyword(s):  
Light Field ◽  
Mean Squared Error ◽  
Specular Reflection ◽  
Similarity Index ◽  
Detection Algorithm ◽  
Color Channel ◽  
Absolute Deviation ◽  
Significant Information ◽  
Transparent Objects ◽  
Transparent Object

Multispectral polarimetric light field imagery (MSPLFI) contains significant information about a transparent object’s distribution over spectra, the inherent properties of its surface and its directional movement, as well as intensity, which all together can distinguish its specular reflection. Due to multispectral polarimetric signatures being limited to an object’s properties, specular pixel detection of a transparent object is a difficult task because the object lacks its own texture. In this work, we propose a two-fold approach for determining the specular reflection detection (SRD) and the specular reflection inpainting (SRI) in a transparent object. Firstly, we capture and decode 18 different transparent objects with specularity signatures obtained using a light field (LF) camera. In addition to our image acquisition system, we place different multispectral filters from visible bands and polarimetric filters at different orientations to capture images from multisensory cues containing MSPLFI features. Then, we propose a change detection algorithm for detecting specular reflected pixels from different spectra. A Mahalanobis distance is calculated based on the mean and the covariance of both polarized and unpolarized images of an object in this connection. Secondly, an inpainting algorithm that captures pixel movements among sub-aperture images of the LF is proposed. In this regard, a distance matrix for all the four connected neighboring pixels is computed from the common pixel intensities of each color channel of both the polarized and the unpolarized images. The most correlated pixel pattern is selected for the task of inpainting for each sub-aperture image. This process is repeated for all the sub-aperture images to calculate the final SRI task. The experimental results demonstrate that the proposed two-fold approach significantly improves the accuracy of detection and the quality of inpainting. Furthermore, the proposed approach also improves the SRD metrics (with mean F1-score, G-mean, and accuracy as 0.643, 0.656, and 0.981, respectively) and SRI metrics (with mean structural similarity index (SSIM), peak signal-to-noise ratio (PSNR), mean squared error (IMMSE), and mean absolute deviation (MAD) as 0.966, 0.735, 0.073, and 0.226, respectively) for all the sub-apertures of the 18 transparent objects in MSPLFI dataset as compared with those obtained from the methods in the literature considered in this paper. Future work will exploit the integration of machine learning for better SRD accuracy and SRI quality.

scholarly journals Enhanced Thermoelectric Performance of n-Type Bi2Se3 Nanosheets through Sn Doping

Nanomaterials ◽  
2021 ◽  
Vol 11 (7) ◽  
pp. 1827
Author(s):  
Mengyao Li ◽  
Yu Zhang ◽  
Ting Zhang ◽  
Yong Zuo ◽  
Ke Xiao ◽  
...  
Keyword(s):  
Figure Of Merit ◽  
Cost Effective ◽  
Low Grade ◽  
Hot Press ◽  
Processing Technologies ◽  
Sn Doping ◽  
Practical Applications ◽  
Alternative Materials ◽  
The Cost ◽  
Low Grade Heat

The cost-effective conversion of low-grade heat into electricity using thermoelectric devices requires developing alternative materials and material processing technologies able to reduce the currently high device manufacturing costs. In this direction, thermoelectric materials that do not rely on rare or toxic elements such as tellurium or lead need to be produced using high-throughput technologies not involving high temperatures and long processes. Bi2Se3 is an obvious possible Te-free alternative to Bi2Te3 for ambient temperature thermoelectric applications, but its performance is still low for practical applications, and additional efforts toward finding proper dopants are required. Here, we report a scalable method to produce Bi2Se3 nanosheets at low synthesis temperatures. We studied the influence of different dopants on the thermoelectric properties of this material. Among the elements tested, we demonstrated that Sn doping resulted in the best performance. Sn incorporation resulted in a significant improvement to the Bi2Se3 Seebeck coefficient and a reduction in the thermal conductivity in the direction of the hot-press axis, resulting in an overall 60% improvement in the thermoelectric figure of merit of Bi2Se3.

Colour imaging with single-pixel detectors

2013 ◽  
Vol 7 (12) ◽  
pp. 943-943 ◽  
Author(s):  
Noriaki Horiuchi
Keyword(s):  
Pixel Detectors ◽  
Single Pixel

Dangerous Scenes Recognition During Hoisting Based on Faster Region-Based Convolutional Neural Network

2018 ◽  
Author(s):  
Hongguo Su ◽  
Mingyuan Zhang ◽  
Shengyuan Li ◽  
Xuefeng Zhao
Keyword(s):  
Neural Network ◽  
Object Detection ◽  
Convolutional Neural Network ◽  
Image Classification ◽  
State Of The Art ◽  
Spatial Location ◽  
Average Precision ◽  
Practical Applications ◽  
Security Enhancement ◽  
Human Interactions

In the last couple of years, advancements in the deep learning, especially in convolutional neural networks, proved to be a boon for the image classification and recognition tasks. One of the important practical applications of object detection and image classification can be for security enhancement. If dangerous objects or scenes can be identified automatically, then a lot of accidents can be prevented. For this purpose, in this paper we made use of state-of-the-art implementation of Faster Region-based Convolutional Neural Network (Faster R-CNN) based on the monitoring video of hoisting sites to train a model to detect the dangerous object and the worker. By extracting the locations of them, object-human interactions during hoisting, mainly for changes in their spatial location relationship, can be understood whereby estimating whether the scene is safe or dangerous. Experimental results showed that the pre-trained model achieved good performance with a high mean average precision of 97.66% on object detection and the proposed method fulfilled the goal of dangerous scenes recognition perfectly.

Market Opportunity Analysis in Thailand: Case of Individual Power Sources by Thermoelectric-Generator Technology for Portable Electronics

2019 ◽  
Vol 16 (03) ◽  
pp. 1950027
Author(s):  
Surapree Maolikul ◽  
Thira Chavarnakul ◽  
Somchai Kiatgamolchai
Keyword(s):  
Thermoelectric Generator ◽  
Electrical Power ◽  
Technology Commercialization ◽  
Market Opportunity ◽  
Business Research ◽  
Power Sources ◽  
Practical Applications ◽  
Portable Electronics ◽  
Decision Factors ◽  
Insight Into

Thermoelectrics, an energy-conversion technology, has been developed for its potential to support portable electronics with an innovative power source. Primarily focusing on the metropolitan market in Thailand, the study, thus, aimed at the market insight into portable electronics users’ characteristics and opinions of thermoelectric-generator (TEG) technology commercialization. The business research was conducted to analyze their behaviors for power-supply lacking problems, encountering heat or cold sources, purchasing decision for a TEG-based charger and key decision factors. For practical applications, an innovative TEG-based charger should be more flexible by harnessing various heat or cold sources from ambient situations to generate electrical power.

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