Application of image analysis to optimization of the bread-making process based on the acceptability of the crust color

Castro, Wilson; Oblitas, Jimy; Chuquizuta, Tony; Ávila-George, Himer

doi:10.1016/j.jcs.2017.02.002

Cited by 38 publications

(21 citation statements)

References 29 publications

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“…Similar approach was also introduced by Castro et al (2017) who applied RSM to optimize the bread‐making process in CIELAB color space. The difference between our study and Castro et al, was, in our study, L * a * b * was applied as independent variables to predict CSI and RT while Castro et al applied L * a * b * as dependent variables. Using a similar approach, our study aims to enhance the undetectability of military camouflage based on variations of L *, a *, and b * in CIELAB color space.…”

Section: Methodsmentioning

confidence: 99%

“…It is mostly applied when single or multiple responses of interest are influenced by several variables and the objectives are to optimize this response . Recent color research has explored the applications of RSM particularly in food packing material and even color optimization in the bread‐making process …”

Section: Introductionmentioning

confidence: 99%

“…22 Recent color research has explored the applications of RSM particularly in food packing material 23 and even color optimization in the bread-making process. 24 The purpose of this study is to propose a new and practical approach for enhancing the selected military camouflage with relatively few human subject tests by utilizing RSM of %L*, %a*, and %b* on a selected military camouflage. Two responses were collected during human visual experiment, CSI and RT.…”

Section: Introductionmentioning

confidence: 99%

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Optimization of color design for military camouflage in CIELAB color space

Lin

Prasetyo

Siswanto

et al. 2019

Color Research & Application

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The assessment of military camouflage is a key consideration in the modern military field. Traditionally, the assessment relies on traditional human visual detection tests because a large scale multi‐level and multi‐factor experiments are time‐ and resource‐consuming. One aspect of camouflage assessment, to which this current study pertains, entails improving upon or “enhancing” an existing or “selected” design. The current study presents a new and practical approach for enhancing the selected military camouflage by utilizing response surface methodology (RSM) of %L*, %a*, and %b* in CIELAB color space. Ten participants were recruited to evaluate 35 variations of %L*, %a*, and %b* on camouflage similarity index (CSI) and reaction time (RT). Based on RSM, the optimum combination occurs at L*: 61.4966, a*: −5.6505, and b*: 10.5114. In addition, a predictive algorithm to calculate the optimum shift of %L*, %a*, and %b* from the original camouflage to the improved camouflage derived from RSM is also proposed. The optimum shift occurs at −25%L*, −55%a*, and + 80%b*. In the end, a new design guideline is proposed for the enhancement of selected military camouflage, which adopts the present study's research findings.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Optimization of color design for military camouflage in CIELAB color space

Lin

Prasetyo

Siswanto

et al. 2019

Color Research & Application

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show abstract

“…In this study, the authors applied image analysis by using the XYZ color model components and described the process as a function of baking temperature and water activity. Castro et al [3] apply a system for obtaining, processing and analyzing images for tracking the baking process. Measurements are in the Lab color model.…”

Section: Related Workmentioning

confidence: 99%

Modeling of changes in spectral characteristics of bread crust during baking

Zlatev¹,

Kazakova²,

Topuzova³

2017

ARTTE

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“…Moreover, color has been closely associated with various quality factors (ripeness, variety, and desirability) and food safety. Therefore, color is an essential classification element for most food products Castro et al (2017); Oliveira et al (2016); Avila et al (2015); Wu and Sun (2013).…”

Section: Introductionmentioning

confidence: 99%

Using machine learning techniques and different color spaces for the classification of Cape gooseberry (Physalis peruviana L.) fruits according to ripeness level

Castro

Oblitas

De-la-Torre

et al. 2019

Preprint

Self Cite

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The classification of fresh fruits according to their visual ripeness is typically a subjective and tedious task; consequently, there is growing interest in the use of non-contact techniques to automate this process. Machine learning techniques such as artificial neural networks, support vector machines, decision trees, and K-nearest neighbor algorithms have been successfully applied for classification problems in the literature, particularly for images of fruit. However, the particularities of each classification problem make it difficult, if not impossible, to select a general technique that is applicable to all types of fruit. In this paper, combinations of four machine learning techniques and three color spaces (RGB, HSV, and L*a*b*) were evaluated with regard to their ability to classify Cape gooseberry fruits. To this end, 925 Cape gooseberry fruit samples were collected, and each fruit was manually classified into one of seven different classes according to its level of ripeness. The color values of each fruit image in the three color spaces and their corresponding ripening stages were organized for training and validation following a 5-fold cross-validation strategy in an iterative process repeated 100 times. According to the results, the classification of Cape gooseberry fruits by their ripeness level was sensitive to both the color space and the classification technique used. The models based on the L*a*b* color space and the SVM classifier showed the highest f-measure regardless of the color space, and the principal component analysis combination of color spaces improved the performance of the models at the expense of an increased complexity.

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Application of image analysis to optimization of the bread-making process based on the acceptability of the crust color

Cited by 38 publications

References 29 publications

Optimization of color design for military camouflage in CIELAB color space

Optimization of color design for military camouflage in CIELAB color space

Modeling of changes in spectral characteristics of bread crust during baking

Using machine learning techniques and different color spaces for the classification of Cape gooseberry (Physalis peruviana L.) fruits according to ripeness level

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