Important Parameters for Image Color Analysis: An Overview

Gomes, Jefferson de Oliveira; Leta, Fabiana Rodrigues; Costa, Priscila; Baldner, Felipe de Oliveira

doi:10.1007/978-3-642-55131-4_4

Cited by 3 publications

(2 citation statements)

References 12 publications

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“…Therefore, in this study, we propose combining these ubiquitous off-the-shelf components to produce a system that can objectively inspect and classify the colors of products. Digital cameras, on their own, with only red, green, and blue channel filters are often not sufficient to produce environment-independent measurements of surface color [24], [25]. Therefore, we propose making use of the narrowband RGB LED lights at different intensities to measure how the surface color changes.…”

Section: Related Workmentioning

confidence: 99%

Extraction and Classification of Surface Color Information Using Selective LED Illumination and a Statistical Neural Network

Eguchi,

Yamada,

Yeoh

et al. 2024

IEEE Access

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The color of a product is one of the main factors that affect consumer choices. However, it can be difficult to discern the subtle differences in color in order to inspect or classify products without expensive and specialized equipment. In this study, we proposed an approach where standard off-the-shelf components (digital camera and RGB LED lights) can be used to robustly classify products based on their color. By varying the color of the illumination, environment-independent information of the color of the object can be obtained. Then, using a Log-Linearized Gaussian Mixture Neural Network (LLGMN), a neural network based on a statistical model, the objects can be effectively and robustly classified based on their color without requiring a large number of training data. An experiment was performed to verify the effectiveness of this approach. 8 samples with only a subtle difference in color were prepared, and a prototype was developed to be used to classify these 8 samples. We demonstrate that we are able to classify the 8 samples an accuracy of 100% when there was no ambient light and were still able to maintain an accuracy of 94.1% when there was ambient light in the room.

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Section: Related Workmentioning

confidence: 99%

Extraction and Classification of Surface Color Information Using Selective LED Illumination and a Statistical Neural Network

Eguchi,

Yamada,

Yeoh

et al. 2024

IEEE Access

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“…The entropy information is extracted by the image histogram for each change of the external illumination level and the luminance intensity level is calculated directly from pixels with high intensity values in the 'L*' lightness component after converting the RGB (Red, Green, Blue) image to the CIELAB color space which is a common color system in industrial vision, closest to human perception [60], [61]. Converting the images from RGB to CIELAB color space provides a different color space where color is expressed by three values, the Lightness value 'L*', and 'a*', 'b*' that combine the colors red, green, blue and yellow.…”

Section: ) Proposed Quality Metric and Reward Strategymentioning

confidence: 99%

An Autonomous Illumination System for Vehicle Documentation Based on Deep Reinforcement Learning

et al. 2021

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A common problem for machine vision applications is uncontrolled illumination conditions that cause undesired artifacts on sensorial data. For instance, quality inspection using color cameras, while having wide industrial application, requires manual illumination adjustment and is severely affected by external lighting sources and the physical properties of the inspected object. To overcome this problem, we propose an autonomous illumination solution, that adjusts illumination via a Deep Reinforcement Learning (DRL) agent following a goal-oriented reward that takes into account image entropy and specularity. The system is validated in a challenging vehicle documentation use case where vehicle images are captured under various lighting conditions using a camera and an in-house built illumination system. The DRL agent learns to control illumination levels directly from high-dimensional visual inputs by mapping the interactions from the environment to the reward-driven control actions of the illumination system, targeting an optimal illumination zone even under the appearance of abrupt illumination changes in the environment.

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