Ultraviolet Radiation Transmission in Building’s Fenestration: Part II, Exploring Digital Imaging, UV Photography, Image Processing, and Computer Vision Techniques
Abstract
:1. Introduction
- Explore digital imaging and ultraviolet image capture process and enabling technologies.
- Identify the present application of UV radiation detection using digital cameras.
- Discuss the Computer Vision process and integration of image analysis for improving raw data, enhancing visual inspection, and enabling more reliable measurements and assessments.
- Explore existing image pixel transformation equations and the mathematical relationships to image data conversion.
2. Methodology
3. Digital Imaging and UV Photography
3.1. Digital Image Acquisition
3.2. UV Imaging
4. Status of UV Radiation Detection Using Cameras
5. Image Analysis
5.1. Image Preprocessing and Digital Processing: A Comprehensive Examination
Image De-Noising
5.2. Analyzing UV Images: Computational Tools, Adaptive Thresholding, and Programming Libraries
5.3. Expanding the Boundaries of Image Analysis: An Exploration of Computer Vision Techniques
5.4. Image Segmentation Overview
Techniques | Advantages | Disadvantages | Authors | New Proposed Technique |
---|---|---|---|---|
Histogram-based threshold | Simple and fast | Sensitive to noise and illumination changes | [127,128] | Otsu–Thresholding [129] |
K means segmentation | Easy to implement and interpret. Tighter clusters than hierarchical methods. | Requires prior knowledge of a number of clusters and initial centroids. | [130] | FCM with advanced optimization techniques [129] |
Watershed segmentation | Effective for separating touching objects | Prone to over-segmentation and noise sensitivity | [128] | Marker Controlled Watershed Segmentation [129] |
Neural networks approach | High accuracy and flexibility | Require large amounts of training data and computational resources | [128,130] | Deep learning techniques [129] |
Region-based segmentation | Robust to noise and intensity variations | May fail to detect boundaries or merge regions incorrectly | [130] | Hemitropic region-growing algorithm [129] |
6. Existing Pixels Transformation Equations
7. Discussion
- Cost-effective and accessible UV detection: Digital imaging methods, particularly those utilizing cameras, have made UV detection more cost-effective and accessible [22,28]. High-resolution cameras can be used to measure UV radiation with potential in fenestration glazing, eliminating the need for expensive spectrophotometers or radiometers.
- Non-invasive and real-time analysis: Digital imaging techniques provide a non-invasive approach to analyzing and allowing for real-time assessment of images [36,108]. This non-destructive approach enables more efficient monitoring and decision-making without damaging the glazing materials and also ensures an increase in the sample size of measurements [37].
- Advanced image processing techniques: The development of advanced image processing algorithms and techniques will improve the accuracy and precision of UV radiation detection [121,133,142]. These techniques, such as segmentation and pixel transformation equations, provide better insights into the complex relationship between pixel values and UV radiation [2,139].
8. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Camera Type | Sensitivity | Lens | Saved Format | External Source | UV | Processing Software | Analysis File Format | Domain | Author |
---|---|---|---|---|---|---|---|---|---|
Nikon Coolpix 5400 | - | fisheye lens | RadianceRGBE and LogLuv TIFF | Natural sunlight, warm yellow light, and MH and HPS lamp | N | Photosphere | RadianceRGBE | HDR Imaging | [10] |
Canon EOS 6D | ISO6400 | Rayfact 105 mm f4.5 UV lens and a Micro Nikkor 105 mm f4 | RAW&JPEG | Y (Xenon lamp) | Y | RawDigger x64 and Excel 2010 | RAW | - | [30] |
Ultraviolet (UV) 1-CCD camera (SONY, XC- EU50 | - | Pentax, B2528-UV | RAW | N and National, PRF-500WB | Y | pixel-segmented Algorithms | RAW | Ultraviolet imaging-based machine vision system | [34] |
Canon, Power shot A-80 | - | - | RAW | N and National, PRF-500WB | N | pixel-segmented Algorithms | RAW | imaging-based machine vision system | [34] |
Canon EOS Rebel T2i DSLR digital camera | - | - | - | 1000 W tungsten halogen lamp | N | MATLAB (MathWorks, Natick, MA, USA) software, R2014a version | - | Agricultural | [35] |
DSLR camera, Nikon D100 | - | F Micro-Nikkor 60 mm f/2.8D lens | - | Two tungsten lamps | N | MATLAB® customized software | - | Granite-forming minerals | [36] |
Power Shot G3 (Canon, Japan) | - | - | TIFF | Four fluorescent lamps (Philips, Natural Daylight) | N | Canon Remote Capture Software (version 2.7.0) | TIFF | Agricultural | [37] |
MS3100 (Duncan Technologies, Inc., CA, USA) | - | 7 mm focal length lens with f-stop of 3.5 | - | Halogen lamp | MATLAB (MathWorks Inc., MA, USA) toolbox | - | Agricultural | [38] | |
CANON EOS 450D | 100 | EF-S18-55 mm f 3.5–5.6 IS | RAW | Four fluorescent lamps (Philips Master Graphica TLD 965 | N | Adobe Photoshop CS3 software for image analysis | RAW | Agricultural | [41] |
Device | Special Band Pass | Band Pass Type | Camera Settings | Application | Reference | Wavelength | Price ($) |
---|---|---|---|---|---|---|---|
Sony Xperia Z1 | Y | UG11 broadband transmission filter with the KG05 infrared blocking filter | 5248 × 3936 pixels 20.7MP an exposure time of 0.125 s. | Determine how thick optical materials affect the camera’s ability to measure and monitor UVA light in places where direct illumination is blocked. | [2] | - | 530 |
LG L3 smartphone | Y | CVI Melles Griot | - | Evaluated the direct sun clear sky irradiances from narrowband direct sun smartphone-derived images | [20] | 340 and 380 nm. | 123.58 |
Samsung Galaxy SII (camera model GT-19100), iPhone5, and Nokia Lumia 800 | Samsung f/2.6 exposure time 1/17; iPhone f/2.4 exposure time 1/15 or 1/16; Nokia f/2.2 exposure time 1/14; | Described how smartphone cameras react to ultraviolet B radiation and show that they can sense this radiation without extra equipment. | [22] | 280 to 320 nm | - | ||
Canon EOS 6D | Y | LaLaU UV pass filter | Rayfact 105 mm f4.5 UV lens | Imaging of Vase under UV Light | [30] | 320–400 nm | |
Samsung Galaxy 5 | Y | CVI Melles Griot | - | To characterize the ultraviolet A (UVA; 320–400 nm) response of a consumer complementary metal oxide semiconductor (CMOS)-based smartphone image sensor in a controlled laboratory environment | [21] | 380 and 340 nm | 70 |
Sony Xperia Z1 | Y | Solar Light Inc | 7.487 mm lens, 21 MP | To characterize the photobiological important direct UVB solar irradiances at 305 nm in clear sky conditions at high air masses. | [58] | 305 nm | 530 |
DSLR (Canon EOS Rebel XTi 400D) | Y | Lifepixel | F 2.8, ISO 1800, shutter speed 1.2 s | To determine if skin cancer-prone facial regions are ineffectively covered | [61] | - | 899 |
Alta U260 cameras | Y | Asahi Spectra 10 nm FWHM XBPA310 and XBPA330 | f25 mm, 16 bit 512 × 512 pixel | Imaging the sulphur dioxide flux distribution of the fumarolic field of La Fossa crater | [62] | - | 5800 |
Polaroid CU5, Faraghan Medical Camera Systems | Y | N/A | 35-mm single-lens | Used UV photography to highlight the damage to facial skin caused by the previous UV exposure | [63] | - | 39 |
Raspberry Pi camera module | Y | UV transmissive AR-coated plano-convex lens | F9-12 mm, 10-bit images, at an initial resolution of 1392 × 1040 | Using low-cost UV cameras to measure how much sulphur dioxide comes out of volcanoes with UV light | [64] | 320 and 330 nm | 500 |
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Onatayo, D.A.; Srinivasan, R.S.; Shah, B. Ultraviolet Radiation Transmission in Building’s Fenestration: Part II, Exploring Digital Imaging, UV Photography, Image Processing, and Computer Vision Techniques. Buildings 2023, 13, 1922. https://doi.org/10.3390/buildings13081922
Onatayo DA, Srinivasan RS, Shah B. Ultraviolet Radiation Transmission in Building’s Fenestration: Part II, Exploring Digital Imaging, UV Photography, Image Processing, and Computer Vision Techniques. Buildings. 2023; 13(8):1922. https://doi.org/10.3390/buildings13081922
Chicago/Turabian StyleOnatayo, Damilola Adeniyi, Ravi Shankar Srinivasan, and Bipin Shah. 2023. "Ultraviolet Radiation Transmission in Building’s Fenestration: Part II, Exploring Digital Imaging, UV Photography, Image Processing, and Computer Vision Techniques" Buildings 13, no. 8: 1922. https://doi.org/10.3390/buildings13081922