Toward a Unified Color Space for Perception-Based Image Processing

Lissner, Ingmar; Urban, Philipp

doi:10.1109/tip.2011.2163522

Cited by 101 publications

(48 citation statements)

References 34 publications

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“…Lab2000HL color space, having two color axes as much independent as possible of each other, and having color value differences of ideal linearity with respect to human perception, has many advantages and has proved its success in various applications [30,31,32,33]. In this study, in order to increase the success rate, spatial and temporal consistency features are used in background subtraction with GMM.…”

Section: Resultsmentioning

confidence: 99%

“…The transformation of the CIELAB (L * a * b * ) color space into a Euclidean space with respect to the Lab2000 (L * 00 ,a * 00 ,b * 00 ) color-difference formula has been described in [33]. According to this paper, Ψ 00 transformation performed by one-dimensional look-up Fig 1. Detailed information about this color space and calculation procedure can be found in [32,33]. RGB to Lab2000HL conversion code is available at [31].…”

Section: Lab2000hl Color Spacementioning

confidence: 99%

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Moving Object Detection using Lab2000HL Color Space with Spatial and Temporal Smoothing

Balcilar¹,

Amasyalı²,

Sonmez³

2014

Appl. Math. Inf. Sci.

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Abstract:In order to detect moving objects such as vehicles in motorways, background subtraction techniques are commonly used. This is completely solved problem for static backgrounds. However, real-world problems contain many non-static components such as waving sea, camera oscillations, and sudden changes in daylight. Gaussian Mixture Model (GMM) is statistical based background subtraction method, in which values of each pixels features are represented with a few normal distributions, partially overcame such problems at least. To improve performance of GMM model, using spatial and temporal features in Lab2000HL color space which have linear hue band, is proposed in this study. The spatial and temporal features performed by using spatial low-pass filter and temporal kalman filter respectively. As a performance metric, the area under the Precision Recall (PR) curve is used. In addition to videos existing in the I2R dataset, a new dataset which images gained from traffic surveillance cameras placed over the entrance of the Istanbul FSM Bridge at different times of the day used for compare proposed method against other well-known GMM version. According to our tests proposed method has been more successful to the other methods in most cases.

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Section: Resultsmentioning

confidence: 99%

Section: Lab2000hl Color Spacementioning

confidence: 99%

Moving Object Detection using Lab2000HL Color Space with Spatial and Temporal Smoothing

Balcilar¹,

Amasyalı²,

Sonmez³

2014

Appl. Math. Inf. Sci.

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“…The HSV, CIE Lab and CIE Luv colour spaces represent colours based on human perception. Furthermore, CIE Lab and CIE Luv are approximately perceptually uniform colour spaces and can simplify the identification of colour properties, as it is easy to maintain colour-difference ratios [29]. Six statistical measures, i.e., average, variance, standard deviation, minimum and maximum colours, and colour skewness, are computed for each colour channel in the region of the lesion using the aforementioned four-colour spaces that correspond to 12 channels.…”

Section: Feature Extraction and Data Pre-processingmentioning

confidence: 99%

Skin lesion computational diagnosis of dermoscopic images: Ensemble models based on input feature manipulation

Oliveira

Pereira

Tavares

2017

Computer Methods and Programs in Biomedicine

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Background and Objectives:The number of deaths worldwide due to melanoma has risen in recent times, in part because melanoma is the most aggressive type of skin cancer. Computational systems have been developed to assist dermatologists in early diagnosis of skin cancer, or even to monitor skin lesions. However, there still remains a challenge to improve classifiers for the diagnosis of such skin lesions. The main objective of this article is to evaluate different ensemble classification models based on input feature manipulation to diagnose skin lesions. Methods: Input feature manipulation processes are based on feature subset selections from shape properties, colour variation and texture analysis to generate diversity for the ensemble models. Three subset selection models are presented here: 1) a subset selection model based on specific feature groups, 2) a correlation-based subset selection model, and 3) a subset selection model based on feature selection algorithms. Each ensemble classification model is generated using an optimum-path forest classifier and integrated with a majority voting strategy. The proposed models were applied on a set of 1104 dermoscopic images using a crossvalidation procedure. Results: The best results were obtained by the first ensemble classification model that generates a feature subset ensemble based on specific feature groups. The skin lesion diagnosis computational system achieved 94.3% accuracy, 91.8% sensitivity and 96.7% specificity. Conclusions:The input feature manipulation process based on specific feature subsets generated the greatest diversity for the ensemble classification model with very promising results.

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“…A recent paper [8] uses a grid optimisation approach to find colour spaces with better perceptual uniformity. Besides perceptual uniformity it is also required that the colour attributes lightness, chroma, and hue are easily obtained.…”

Section: Introductionmentioning

confidence: 99%

The metric of colour space

Gravesen

2015

Graphical Models

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The space of colours is a fascinating space. It is a real vector space, but no matter what inner product you put on the space the resulting Euclidean distance does not corresponds to human perception of difference between colours.In 1942 MacAdam performed the first experiments on colour matching and found the MacAdam ellipses which are often interpreted as defining the metric tensor at their centres. An important question is whether it is possible to define colour coordinates such that the Euclidean distance in these coordinates correspond to human perception.Using cubic splines to represent the colour coordinates and an optimisation approach we find new colour coordinates that make the MacAdam ellipses closer to uniform circles than the existing standards.

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Toward a Unified Color Space for Perception-Based Image Processing

Cited by 101 publications

References 34 publications

Moving Object Detection using Lab2000HL Color Space with Spatial and Temporal Smoothing

Moving Object Detection using Lab2000HL Color Space with Spatial and Temporal Smoothing

Skin lesion computational diagnosis of dermoscopic images: Ensemble models based on input feature manipulation

The metric of colour space

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