scholarly journals Deep Neural Models for Illumination Estimation and Relighting: A Survey

2021 ◽  
Author(s):  
Farshad Einabadi ◽  
Jean‐Yves Guillemaut ◽  
Adrian Hilton
Keyword(s):  
Neural Models ◽  
Illumination Estimation

Extended Corrected-Moments Illumination Estimation

2016 ◽  
Vol 2016 (20) ◽  
pp. 1-8 ◽  
Author(s):  
Xiaochuan Chen ◽  
MarkS. Drew ◽  
Ze-Nian Li ◽  
GrahamD. Finlayson
Keyword(s):  
Illumination Estimation

Non-Linear Masking based Contrast Enhancement via Illumination Estimation

2018 ◽  
Vol 2018 (13) ◽  
pp. 389-1-389-5
Author(s):  
Soonyoung Hong ◽  
Minsub Kim ◽  
Moon Gi Kang
Keyword(s):  
Contrast Enhancement ◽  
Illumination Estimation ◽  
Non Linear

A Flying Gray Ball Multi-illuminant Image Dataset for Color Research

2020 ◽  
Vol 64 (5) ◽  
pp. 50411-1-50411-8
Author(s):  
Hoda Aghaei ◽  
Brian Funt
Keyword(s):  
Machine Vision ◽  
Spectral Reflectance ◽  
Color Constancy ◽  
Ground Truth ◽  
Practical Approach ◽  
Post Processing ◽  
Illumination Estimation ◽  
Processing Step ◽  
Image Dataset

Abstract For research in the field of illumination estimation and color constancy, there is a need for ground-truth measurement of the illumination color at many locations within multi-illuminant scenes. A practical approach to obtaining such ground-truth illumination data is presented here. The proposed method involves using a drone to carry a gray ball of known percent surface spectral reflectance throughout a scene while photographing it frequently during the flight using a calibrated camera. The captured images are then post-processed. In the post-processing step, machine vision techniques are used to detect the gray ball within each frame. The camera RGB of light reflected from the gray ball provides a measure of the illumination color at that location. In total, the dataset contains 30 scenes with 100 illumination measurements on average per scene. The dataset is available for download free of charge.

DSNet: Deep Shadow Network for Illumination Estimation

2021 ◽  
Author(s):  
Yuan Xiong ◽  
Hongrui Chen ◽  
Jingru Wang ◽  
Zhe Zhu ◽  
Zhong Zhou
Keyword(s):  
Illumination Estimation

Towards corpus and model: Hierarchical structured-attention-based features for Indonesian named entity recognition

2021 ◽  
pp. 1-12
Author(s):  
Yingwen Fu ◽  
Nankai Lin ◽  
Xiaotian Lin ◽  
Shengyi Jiang
Keyword(s):  
Language Processing ◽  
State Of The Art ◽  
Named Entity Recognition ◽  
Entity Recognition ◽  
Language Models ◽  
Neural Models ◽  
Performance Models ◽  
Named Entity ◽  
High Resource ◽  
Benchmark Datasets

Named entity recognition (NER) is fundamental to natural language processing (NLP). Most state-of-the-art researches on NER are based on pre-trained language models (PLMs) or classic neural models. However, these researches are mainly oriented to high-resource languages such as English. While for Indonesian, related resources (both in dataset and technology) are not yet well-developed. Besides, affix is an important word composition for Indonesian language, indicating the essentiality of character and token features for token-wise Indonesian NLP tasks. However, features extracted by currently top-performance models are insufficient. Aiming at Indonesian NER task, in this paper, we build an Indonesian NER dataset (IDNER) comprising over 50 thousand sentences (over 670 thousand tokens) to alleviate the shortage of labeled resources in Indonesian. Furthermore, we construct a hierarchical structured-attention-based model (HSA) for Indonesian NER to extract sequence features from different perspectives. Specifically, we use an enhanced convolutional structure as well as an enhanced attention structure to extract deeper features from characters and tokens. Experimental results show that HSA establishes competitive performance on IDNER and three benchmark datasets.

Investigating the Neural Models for Irony Detection on Turkish Informal Texts

2020 ◽  
Author(s):  
Yesim Cemek ◽  
Cenk Cidecio ◽  
Asli Umay Ozturk ◽  
Recep Firat Cekinel ◽  
Pinar Karagoz
Keyword(s):  
Neural Models

scholarly journals Assessment of the Content of Dry Matter and Dry Organic Matter in Compost with Neural Modelling Methods

Agriculture ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 307
Author(s):  
Dawid Wojcieszak ◽  
Maciej Zaborowicz ◽  
Jacek Przybył ◽  
Piotr Boniecki ◽  
Aleksander Jędruś
Keyword(s):  
Image Analysis ◽  
Organic Matter ◽  
Dry Matter ◽  
Neural Model ◽  
Original Method ◽  
Neural Models ◽  
Test Error ◽  
Effective Assessment ◽  
Modelling Methods

Neural image analysis is commonly used to solve scientific problems of biosystems and mechanical engineering. The method has been applied, for example, to assess the quality of foodstuffs such as fruit and vegetables, cereal grains, and meat. The method can also be used to analyse composting processes. The scientific problem lets us formulate the research hypothesis: it is possible to identify representative traits of the image of composted material that are necessary to create a neural model supporting the process of assessment of the content of dry matter and dry organic matter in composted material. The effect of the research is the identification of selected features of the composted material and the methods of neural image analysis resulted in a new original method enabling effective assessment of the content of dry matter and dry organic matter. The content of dry matter and dry organic matter can be analysed by means of parameters specifying the colour of compost. The best developed neural models for the assessment of the content of dry matter and dry organic matter in compost are: in visible light RBF 19:19-2-1:1 (test error 0.0922) and MLP 14:14-14-11-1:1 (test error 0.1722), in mixed light RBF 30:30-8-1:1 (test error 0.0764) and MLP 7:7-9-7-1:1 (test error 0.1795). The neural models generated for the compost images taken in mixed light had better qualitative characteristics.

Analog Gross Fault Identification in RF Circuits Using Neural Models and Constrained Parameter Extraction

2019 ◽  
Vol 67 (6) ◽  
pp. 2143-2150 ◽  
Author(s):  
Andres Viveros-Wacher ◽  
Jose Ernesto Rayas-Sanchez ◽  
Zabdiel Brito-Brito
Keyword(s):  
Rf Circuits ◽  
Parameter Extraction ◽  
Fault Identification ◽  
Neural Models
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