Dataset Information

Development of a computer vision system to estimate the colour indices of Kinnow mandarins.

ABSTRACT: Maturity of a citrus fruit is generally expressed by a numerical value called citrus colour index (CCI). The success of methods employed in estimating the maturity depends on the cultivar and climatic conditions of growing regions. In this work, an image processing based method using CIELAB color model has been developed to estimate the CCI of Kinnow mandarin fruits. A polynomial transformation based camera characterization method was employed to reduce the number of transformations required for RGB to L?a?b? colour space transformation, which resulted into a colour difference of 2.191 with CIELAB ?E? 2000 colour difference formula. In order to analyse the performance of this method, linear regression and partial least square (PLS) models were built on a dataset of 271 Kinnow fruit images wherein spectrophotometer was used for the validation of computed CCI values. The proposed method achieved a high adjusted R2 value of 0.9660 using PLS regression, which ascertain the feasibility of image processing based system in estimating the maturity of Kinnow fruits. Additionally, a correlation analysis between colour coordinates and physicochemical properties was conducted to analyze the relation between the fruit's external peel colour and its internal characteristics.

SUBMITTER: Hadimani L

PROVIDER: S-EPMC6443734 | biostudies-literature | 2019 Apr

REPOSITORIES: biostudies-literature

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Development of a computer vision system to estimate the colour indices of Kinnow mandarins.

Hadimani Lingaraj L Mittal Neerja N

Journal of food science and technology 20190213 4

Maturity of a citrus fruit is generally expressed by a numerical value called citrus colour index (CCI). The success of methods employed in estimating the maturity depends on the cultivar and climatic conditions of growing regions. In this work, an image processing based method using CIELAB color model has been developed to estimate the CCI of Kinnow mandarin fruits. A polynomial transformation based camera characterization method was employed to reduce the number of transformations required for ...[more]

PMID: 30996464

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Project description:Alcohol consumption among young adults is widely accepted in modern society and may be the starting point for abusive use of alcohol at later stages of life. Chronic alcohol exposure can lead to visual function impairment. In the present study, we investigated the spatial luminance contrast sensitivity, colour arrangement ability, and colour discrimination thresholds on young adults that weekly consume alcoholic beverages without clinical concerns. Twenty-four young adults were evaluated by an ophthalmologist and performed three psychophysical tests to evaluate their vision functions. We estimated the spatial luminance contrast sensitivity function at 11 spatial frequencies ranging from 0.1 to 30 cycles/degree. No difference in contrast sensitivity was observed comparing alcohol consumers and control subjects. For the evaluation of colour vision, we used the Farnsworth-Munsell 100 hue test (FM 100 test) to test subject's ability to perform a colour arrangement task and the Mollon-Reffin test (MR test) to measure subject's colour discrimination thresholds. Alcohol consumers made more mistakes than controls in the FM100 test, and their mistakes were diffusely distributed in the FM colour space without any colour axis preference. Alcohol consumers also performed worse than controls in the MR test and had higher colour discrimination thresholds compared to controls around three different reference points of a perceptually homogeneous colour space, the CIE 1976 chromaticity diagram. There was no colour axis preference in the threshold elevation observed among alcoholic subjects. Young adult weekly alcohol consumers showed subclinical colour vision losses with preservation of spatial luminance contrast sensitivity. Adolescence and young adult age are periods of important neurological development and alcohol exposure during this period of life might be responsible for deficits in visual functions, especially colour vision that is very sensitive to neurotoxicants.

Project description:Extracting meaning from a dynamic and variable flow of incoming information is a major goal of both natural and artificial intelligence. Computer vision (CV) guided by deep learning (DL) has made significant strides in recognizing a specific identity despite highly variable attributes. This is the same challenge faced by the nervous system and partially addressed by the concept cells-neurons exhibiting selective firing in response to specific persons/places, described in the human medial temporal lobe (MTL) ⁠. Yet, access to neurons representing a particular concept is limited due to these neurons' sparse coding. It is conceivable, however, that the information required for such decoding is present in relatively small neuronal populations. To evaluate how well neuronal populations encode identity information in natural settings, we recorded neuronal activity from multiple brain regions of nine neurosurgical epilepsy patients implanted with depth electrodes, while the subjects watched an episode of the TV series "24". First, we devised a minimally supervised CV algorithm (with comparable performance against manually-labeled data) to detect the most prevalent characters (above 1% overall appearance) in each frame. Next, we implemented DL models that used the time-varying population neural data as inputs and decoded the visual presence of the four main characters throughout the episode. This methodology allowed us to compare "computer vision" with "neuronal vision"-footprints associated with each character present in the activity of a subset of neurons-and identify the brain regions that contributed to this decoding process. We then tested the DL models during a recognition memory task following movie viewing where subjects were asked to recognize clip segments from the presented episode. DL model activations were not only modulated by the presence of the corresponding characters but also by participants' subjective memory of whether they had seen the clip segment, and by the associative strengths of the characters in the narrative plot. The described approach can offer novel ways to probe the representation of concepts in time-evolving dynamic behavioral tasks. Further, the results suggest that the information required to robustly decode concepts is present in the population activity of only tens of neurons even in brain regions beyond MTL.

Dataset Information

Development of a computer vision system to estimate the colour indices of Kinnow mandarins.

Publications

Development of a computer vision system to estimate the colour indices of Kinnow mandarins.

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