A Color Constancy Algorithm Using Photodetector Characteristics of a Camera for Indoor Scenes

2016 ◽  
pp. 511-519
Author(s):  
Ue-Hwan Kim ◽  
Jong-Hwan Kim
Keyword(s):  
Color Constancy ◽  
Indoor Scenes

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.

Abstraction and Understanding of Indoor Scenes from Single-View RGB-D Scanning Data

2018 ◽  
Vol 30 (6) ◽  
pp. 1046
Author(s):  
Yuliang Sun ◽  
Yongwei Miao ◽  
Lijie Yu ◽  
Pajarola Renato
Keyword(s):  
Single View ◽  
Indoor Scenes

Segmentation for Indoor Scenes Based on DBSCAN Clustering

2019 ◽  
Vol 31 (7) ◽  
pp. 1183
Author(s):  
Mengdi Liu ◽  
Xiao Pan ◽  
Shanshan Gao ◽  
Shiqing Xin ◽  
Yuanfeng Zhou
Keyword(s):  
Dbscan Clustering ◽  
Indoor Scenes

Contribution of the marginal peripheral retina to color constancy: Evidence obtained due to contact lens with implanted occluder

2019 ◽  
Vol 33 (2) ◽  
pp. 113-123
Author(s):  
G. I. Rozhkova ◽  
E. N. Iomdina ◽  
O. M. Selina ◽  
A. V. Belokopytov ◽  
P. P. Nikolayev
Keyword(s):  
Contact Lens ◽  
Color Constancy ◽  
Peripheral Retina

Color Constancy

2017 ◽  
Author(s):  
Joshua Gert
Keyword(s):  
Visual Representation ◽  
Color Constancy ◽  
Color Appearance ◽  
Fine Grained ◽  
Hybrid View

This chapter presents an account of color constancy that explains a well-known division in the data from color-constancy experiments: So-called “paper matches” exhibit a much higher level of constancy than so-called “hue-saturation matches.” It argues that the visual representation of objective color is the representation of something associated with a function from viewing circumstances to color appearances. Thus, a relatively robust constancy in the representation of objective color is perfectly consistent with a relatively less robust level of constancy in color appearance. The account also endorses Hilbert’s idea that we can represent the color of the illumination on a surface as well as the color of the surface itself. Finally, the chapter addresses an objection to the hybrid view that notes our capacity to make very fine-grained distinctions between the objective colors of surfaces.

Improving Color Constancy by Photometric Edge Weighting

2012 ◽  
Vol 34 (5) ◽  
pp. 918-929 ◽  
Author(s):  
A. Gijsenij ◽  
T. Gevers ◽  
J. van de Weijer
Keyword(s):  
Color Constancy ◽  
Edge Weighting

SiFi

2021 ◽  
Vol 2 (3) ◽  
pp. 1-21
Author(s):  
Deke Guo ◽  
Xiaoqiang Teng ◽  
Yulan Guo ◽  
Xiaolei Zhou ◽  
Zhong Liu
Keyword(s):  
Rapid Development ◽  
Estimation Method ◽  
Likelihood Estimation ◽  
Location Based Services ◽  
Shopping Mall ◽  
Inference Problem ◽  
Indoor Location ◽  
Label Semantics ◽  
Geometrical Constraints ◽  
Indoor Scenes

Due to the rapid development of indoor location-based services, automatically deriving an indoor semantic floorplan becomes a highly promising technique for ubiquitous applications. To make an indoor semantic floorplan fully practical, it is essential to handle the dynamics of semantic information. Despite several methods proposed for automatic construction and semantic labeling of indoor floorplans, this problem has not been well studied and remains open. In this article, we present a system called SiFi to provide accurate and automatic self-updating service. It updates semantics with instant videos acquired by mobile devices in indoor scenes. First, a crowdsourced-based task model is designed to attract users to contribute semantic-rich videos. Second, we use the maximum likelihood estimation method to solve the text inferring problem as the sequential relationship of texts provides additional geometrical constraints. Finally, we formulate the semantic update as an inference problem to accurately label semantics at correct locations on the indoor floorplans. Extensive experiments have been conducted across 9 weeks in a shopping mall with more than 250 stores. Experimental results show that SiFi achieves 84.5% accuracy of semantic update.

scholarly journals Semantic Mapping for Mobile Robots in Indoor Scenes: A Survey

Information ◽  
2021 ◽  
Vol 12 (2) ◽  
pp. 92
Author(s):  
Xiaoning Han ◽  
Shuailong Li ◽  
Xiaohui Wang ◽  
Weijia Zhou
Keyword(s):  
Mobile Robots ◽  
Semantic Information ◽  
Semantic Mapping ◽  
Human Beings ◽  
Future Directions ◽  
Essential Requirement ◽  
Semantic Map ◽  
Semantic Maps ◽  
Great Progress ◽  
Indoor Scenes

Sensing and mapping its surroundings is an essential requirement for a mobile robot. Geometric maps endow robots with the capacity of basic tasks, e.g., navigation. To co-exist with human beings in indoor scenes, the need to attach semantic information to a geometric map, which is called a semantic map, has been realized in the last two decades. A semantic map can help robots to behave in human rules, plan and perform advanced tasks, and communicate with humans on the conceptual level. This survey reviews methods about semantic mapping in indoor scenes. To begin with, we answered the question, what is a semantic map for mobile robots, by its definitions. After that, we reviewed works about each of the three modules of semantic mapping, i.e., spatial mapping, acquisition of semantic information, and map representation, respectively. Finally, though great progress has been made, there is a long way to implement semantic maps in advanced tasks for robots, thus challenges and potential future directions are discussed before a conclusion at last.

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