3 种颜色体系在食品应用中的研究进展

外观颜色是食品的一个重要属性,影响着人们对食品质量的感知。用于描述食品颜色且适于测量颜色差异的Hunter Lab体系、国际照明委员会（Commission International de IEclairage,CIE）LAB体系和CIELCH体系,已被广泛应用于食品工厂的颜色测量中。本文综述了国内外Hunter Lab、CIELAB和CIELCH体系应用于食品颜色研究中的现状,对3 种颜色体系的特性、3 种体系在各类食品颜色研究中的最新进展进行了阐述,并对未来3 种颜色体系应用的发展方向进行展望,以期为食品颜色研究应用提供参考。     

基于视觉感知模型的彩色图像分割框架

图像分割是图像理解和识别的重要基础。目前,彩色图像分割有3个问题有待解决：无法处理有空间频率变化的图像;没有考虑图像的观察条件和颜色细节;对颜色空间的依赖性较大。本文提出了一个基于视觉感知的图像分割框架。此框架利用人眼视觉灵敏度函数模仿人眼的视觉模糊机制,能有效处理有空间频率变化的图像;使用CIECAM02来预测复杂观察条件下的图像色貌;最后应用不同的色差公式实现基于颜色相似性的分割算法。实验结果证明本文框架可以产生比传统算法更适合于人类感知的分割结果。     

Blind image quality assessment in the contourlet domain

No-reference/blind image quality assessment (NR-IQA/BIQA) algorithms play an important role in image evaluation, as they can assess the quality of an image automatically, only using the distorted image whose quality is being assessed. Among the existing NR-IQA/BIQA methods, natural scene statistic (NSS) models which can be expressed in different bandpass domains show good consistency with human subjective judgments of quality.In this paper, we create new ‘quality-aware’ features: the energy differences of the sub-band coefficients across scales via contourlet transform, and propose a new NR-IQA/BIQA model that operates on natural scene statistics in the contourlet domain. Prior to applying the contourlet transform, we apply two preprocessing steps that help to create more information-dense, low-entropy representations. Specifically, we transform the picture into the CIELAB color space and gradient magnitude map. Then, a number of ‘quality-aware’ features are discovered in the contourlet transform domain: the energy of the sub-band coefficients within scales, and the energy differences between scales, as well as measurements of the statistical relationships of pixels across scales. A detailed analysis is conducted to show how different distortions affect the statistical characteristics of these features, and then features are fed to a support vector regression (SVR) model which learns to predict image quality. Experimental results show that the proposed method has high linearity against human subjective perception, and outperforms the state-of-the-art NR-IQA models.     

Color reproduction in virtual lip makeup using a convolutional neural network

Recently, it has become possible to examine the suitability of cosmetic products by virtual makeup techniques so that shoppers can buy products online. The virtual makeup can also be utilized at offline stores to prevent possible sanitation problems associated with swatching. Faithful color reproduction is one of the most important factors in virtual makeup applications. Thus, the color difference between the virtual and real makeup results needs to be minimized. However, most previous studies on virtual makeup focus on the recommendation of makeup style rather than on the accuracy of color reproduction. This article proposes an accurate lipstick color reproduction method based on convolutional neural network. This study indicates that the proposed method using a convolutional neural network results in the minimum value of color difference compared with linear regression and multilayer perceptron algorithms.     

Quantitative representation of floral colors

Human and insect pollinator perceived floral colors of 81 species of angiosperms (flowering plants) from Trivandrum (Kerala, India) was represented using the CIE 1976 L*a*b* color space and color hexagon, respectively. The floral color difference among human perceived red, yellow, and blue‐hued flowers and that of each flower from its respective pure hue was calculated using the CIE ΔE 2000 formula. Human perceived floral color difference values were consistently higher than 3.5, indicating the uniqueness of floral colors. Flowers perceived red and yellow by humans were dominant and of comparable proportions. Insect pollinators perceive most of the flowers as blue‐green. Quantitative representation of human and pollinator perceived floral colors would be invaluable to understand the information broadcasted by flowers. It can form the basis of flower grading in the floriculture industry and underpin objectivity in evolving the framework for national pollinator strategies.     

Performance of two color scales for virgin olive oils: Influence of ripeness,variety, and harvest season

The purpose of this investigation was to compare the performance of the bromthymol blue (BTB) method and the Uniform Oil Color Scale (UOCS) method with different sets of virgin olive oil samples from Andalusia (Spain), namely, 1213 samples from olives at three stages of ripeness, 1008 samples from eight olive varieties, and 1700 samples from olives harvested in four different crop seasons. All oils were extracted in the laboratory by the same procedure. The performance of the two color scales was compared using CIELAB color differences between the oil samples and the nearest standard from each scale. The UOCS performed at least 2.0 times better than the BTB for each of the three stages of olive ripeness, and the difference between the two color scales was statistically significant (P<0.001). The UOCS performed at least 1.3 times better than the BTB for each of the eight olive varieties studied, and the differences between the two color scales were statistically significant (P<0.02). The UOCS also performed at least 1.6 times better than the BTB for each of the four harvests analyzed, and the differences between the two color scales were statistically significant (P<0.001). Using the current oil samples, we could discern no substantial improvements to the UOCS standards.     

Error propagation analysis in color measurement and imaging

We apply multivariate error-propagation analysis to color-signal transformations. Results are given that indicate how linear, matrix, and nonlinear transformations influence the mean, variance, and covariance of color-measurements and color-images. Since many signal processing paths include these steps, the analysis is applicable to color-measurement and imaging systems. Expressions are given that allow image noise or error propagation for a spectrophotometer, colorimeter, or digital camera. In a computed example, error statistics are propagated from tristimulus values to CIELAB coordinates. The resulting signal covariance is interpreted in terms of CIELAB error ellipsoids and the mean value of color-difference measures, and . The application of this analysis to system design is also illustrated by relating a tolerance to equivalent tristimulus-value error statistics. © 1997 John Wiley & Sons, Inc. Col Res Appl, 22, 280–289, 1997     

人眼明度视觉对比敏感度特性测量

目的 研究人眼明度视觉感受野“中心-周边”的拮抗特征。方法 设计心理物理实验,测量视觉感知颜色空间CIELAB明度轴上5个明度样本在11个空间频率上的对比敏感度数据;分别采用一元分析法和主成分分析法对实验采集数据进行分析,对比不同明度视觉、空间频率以及对比敏感度阈值之间的函数关系。结果 一元分析表明,5个明度样本的对比敏感度曲线表现为带通特性,明度值越高带通特性越明显;主成分分析表明,以前2个主成分为新变量,可描述人眼视觉感受野特性。结论 人眼视觉在低频1~6 cpd时表现出了“中心-周边”环绕的阶调传递特征;在高频时人眼明度CSF与亮度CSF相似,随着空间频率的升高,人眼视觉对比敏感度降低。     

Automatic detection of layout of color yarns of yarn‐dyed fabric. Part 2: Region segmentation of double‐system‐Mélange color fabric

To detect the layout of color yarns automatically, a novel projection‐based fabric segmentation method is proposed to segment the double‐system‐mélange color fabric into several regions, which can be seen as single‐system‐mélange color fabrics. This method consists of five main steps: (1) yarn skew detection, (2) fabric image projecting, (3) projection curve smoothing, (4) variance curve calculating, and (5) curve peak confirmation. Based on the acquisition fabric image, the skew angles of warp and weft yarns are detected by Hough transform first. The projection curves of L, a, and b channels in Lab color model are generated and smoothed by Savitzky–Golay filter. The variance curves of L, a, and b are then calculated, and the peaks corresponding to the regional boundaries in each curve are detected. The regional boundaries are confirmed by synthesizing the curve peaks of L, a, and b. The experimental and theoretical analysis proves that the proposed method can segment the double‐system‐mélange color fabric into regions with satisfactory accuracy and good robustness. © 2015 Wiley Periodicals, Inc. Col Res Appl, 41, 626–635, 2016