A methodology for photometric validation in vehicles visual interactive systems

Faria, Alexandre Wagner Chagas; Menotti, David; Pappa, Gisele L.; Lara, Daniel S. D.; Araújo, Arnaldo de Albuquerque

doi:10.1016/j.eswa.2011.09.126

Cited by 5 publications

(3 citation statements)

References 8 publications

(10 reference statements)

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“…Extending beyond simulation, some researchers opt to pilot their artificial intelligence tools in either a mock environment or ‘true’ environment to assess postdevelopment performance and conduct validation [13]. Although this offers the potential for gathering robust data points for validation and is useful for applications such as autonomous driving, it remains difficult to apply as the sole method to healthcare applications, given the inherent risk of harm to human patients [23]. It would be unethical, for example, to trial an artificial intelligence system designed to determine whether patients with diabetic macular edema should receive an injection of antivascular endothelial growth factor or not, given that human assessors could potentially further minimize harm.…”

Section: Current Approaches To Algorithm Validationmentioning

confidence: 99%

“…healthcare applications, given the inherent risk of harm to human patients [23]. It would be unethical, for example, to trial an artificial intelligence system designed to determine whether patients with diabetic macular edema should receive an injection of antivascular endothelial growth factor or not, given that human assessors could potentially further minimize harm.…”

Section: Key Pointsmentioning

confidence: 99%

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Toward safer ophthalmic artificial intelligence via distributed validation on real-world data

Nath

Rahimy

Kras

et al. 2023

Current Opinion in Ophthalmology

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Purpose of review The current article provides an overview of the present approaches to algorithm validation, which are variable and largely self-determined, as well as solutions to address inadequacies. Recent findings In the last decade alone, numerous machine learning applications have been proposed for ophthalmic diagnosis or disease monitoring. Remarkably, of these, less than 15 have received regulatory approval for implementation into clinical practice. Although there exists a vast pool of structured and relatively clean datasets from which to develop and test algorithms in the computational ‘laboratory’, real-world validation remains key to allow for safe, equitable, and clinically reliable implementation. Bottlenecks in the validation process stem from a striking paucity of regulatory guidance surrounding safety and performance thresholds, lack of oversight on critical postdeployment monitoring and context-specific recalibration, and inherent complexities of heterogeneous disease states and clinical environments. Implementation of secure, third-party, unbiased, pre and postdeployment validation offers the potential to address existing shortfalls in the validation process. Summary Given the criticality of validation to the algorithm pipeline, there is an urgent need for developers, machine learning researchers, and end-user clinicians to devise a consensus approach, allowing for the rapid introduction of safe, equitable, and clinically valid machine learning implementations.

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Section: Current Approaches To Algorithm Validationmentioning

confidence: 99%

Section: Key Pointsmentioning

confidence: 99%

Toward safer ophthalmic artificial intelligence via distributed validation on real-world data

Nath

Rahimy

Kras

et al. 2023

Current Opinion in Ophthalmology

View full text Add to dashboard Cite

show abstract

“…For example, Brahme and Joshi introduced the standard of measuring and evaluating uniformity of cluster using CCD camera [1]. Faria et al provides photometric validation guideline using images taken by digital camera with varying its aperture [2]. Both researchers pointed out that the harmony of interior lighting could increase user satisfaction.…”

Section: Introductionmentioning

confidence: 99%

A method proposal for evaluating color tolerance in viewing multiple white points focusing on the vehicle instrument panels

Lee¹,

Kim²,

Suk³

2022

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Although chromatic adaptation eases us to adopt our vision to whites, viewing more than two substantially different white balances costs perceptual workload and appeals to poor quality control. This study proposed a method for evaluating the color tolerance of light modules using a uniformity analyzer focusing on the instrument panels in passenger cars, two premium line-up vehicles from Hyundai and Mercedes Benz. Using a luminance uniformity analyzer, we captured three main lighting regions in their instrument panels: clusters, steering wheel, and center console. Based on u' and v' values, we identified and compared the chromaticity coordinates of the white lighting components. The measurement-based judgment supports the manufacturer in achieving the quality objectively and consistently.

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Reading on eInk and Backlit LED – The Influence of Positive and Negative Contrast on Eye Movements

Bochud

Garbely

2013

Lecture Notes in Computer Science

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A methodology for photometric validation in vehicles visual interactive systems

Cited by 5 publications

References 8 publications

Toward safer ophthalmic artificial intelligence via distributed validation on real-world data

Toward safer ophthalmic artificial intelligence via distributed validation on real-world data

A method proposal for evaluating color tolerance in viewing multiple white points focusing on the vehicle instrument panels

Reading on eInk and Backlit LED – The Influence of Positive and Negative Contrast on Eye Movements

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