CN110108362A - The adaptive online test method of color difference and device based on SLIC super-pixel segmentation - Google Patents

The adaptive online test method of color difference and device based on SLIC super-pixel segmentation Download PDF

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CN110108362A
CN110108362A CN201910306411.6A CN201910306411A CN110108362A CN 110108362 A CN110108362 A CN 110108362A CN 201910306411 A CN201910306411 A CN 201910306411A CN 110108362 A CN110108362 A CN 110108362A
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姚克明
崔祥顺
魏来
王小兰
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Jiangsu University of Technology
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    • GPHYSICS
    • G01MEASURING; TESTING
    • G01JMEASUREMENT OF INTENSITY, VELOCITY, SPECTRAL CONTENT, POLARISATION, PHASE OR PULSE CHARACTERISTICS OF INFRARED, VISIBLE OR ULTRAVIOLET LIGHT; COLORIMETRY; RADIATION PYROMETRY
    • G01J3/00Spectrometry; Spectrophotometry; Monochromators; Measuring colours
    • G01J3/46Measurement of colour; Colour measuring devices, e.g. colorimeters
    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
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    • G06COMPUTING; CALCULATING OR COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00Image analysis
    • G06T7/90Determination of colour characteristics
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01JMEASUREMENT OF INTENSITY, VELOCITY, SPECTRAL CONTENT, POLARISATION, PHASE OR PULSE CHARACTERISTICS OF INFRARED, VISIBLE OR ULTRAVIOLET LIGHT; COLORIMETRY; RADIATION PYROMETRY
    • G01J3/00Spectrometry; Spectrophotometry; Monochromators; Measuring colours
    • G01J3/46Measurement of colour; Colour measuring devices, e.g. colorimeters
    • G01J2003/467Colour computing
    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00Indexing scheme for image analysis or image enhancement
    • G06T2207/30Subject of image; Context of image processing
    • G06T2207/30108Industrial image inspection
    • G06T2207/30124Fabrics; Textile; Paper

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Abstract

The present invention provides a kind of adaptive online test method of color difference based on SLIC super-pixel segmentation and devices, and wherein method includes: to carry out Image Acquisition to sample to be detected by online linear array CCD camera;The image collected is subjected to gaussian filtering process and is transformed into and device-independent uniform color space;Image is pre-processed using super-pixel segmentation method;Colorimetry is carried out using CIELDE2000 colour difference formula, obtains calculated result;Checkout result is compared with preset color difference threshold, exports comparison result.To plain color cloth acetes chinensis low efficiency existing for solving in current industry, the not high situation of the degree of automation saves many operands, greatly improves detection efficiency.

Description

基于SLIC超像素分割的色差自适应在线检测方法及装置Chromatic difference adaptive online detection method and device based on SLIC superpixel segmentation

技术领域technical field

本发明涉及基于机器视觉对色差检测的技术领域,尤其涉及一种基于SLIC超像素分割的色差自适应在线检测方法及装置。The present invention relates to the technical field of color difference detection based on machine vision, in particular to a color difference adaptive online detection method and device based on SLIC superpixel segmentation.

背景技术Background technique

对纺织品而言,颜色作为一个重要特征,丰富了其外观。织物染色质量的好坏也是评价纺织产品质量的一个关键因素。因此,纺织品色差在线检测在纺织行业中具有重要的意义。色差反映了两个染色织物样本在彩度、明度和色调三个色彩值的综合表现颜色感知差异,是检验产品的重要参数之一。企业在实际生产过程中会以客户提供的色板为标准,生产符合客户需求的纺织产品。客户在验收纺织产品染色质量是否达标时,会对比产品与原有样本的色差和光泽,两者完全一致则认为纺织产品颜色符合要求。For textiles, color is an important feature that enriches its appearance. The quality of fabric dyeing is also a key factor in evaluating the quality of textile products. Therefore, the online detection of textile color difference is of great significance in the textile industry. The color difference reflects the comprehensive color perception difference of two dyed fabric samples in the three color values of chroma, lightness and hue, and is one of the important parameters for testing products. In the actual production process, the enterprise will use the color palette provided by the customer as the standard to produce textile products that meet the customer's needs. When customers check whether the dyeing quality of textile products meets the standard, they will compare the color difference and gloss of the product with the original sample. If the two are completely consistent, it is considered that the color of the textile product meets the requirements.

尽管通过工业技术的发展引进了一些先进的技术设备,但印染织物色差的检测水平与国外发达国家相比仍然存在较大的差距。仍然存在许多企业不能完全实现对纺织品样本进行在线方式的检测。因此研究一种全新的自动化检测系统对纺织等相关企业适应竞争越发激烈的现代化市场,有着重要的意义和实用价值。Although some advanced technical equipment has been introduced through the development of industrial technology, there is still a large gap in the detection level of the color difference of printed and dyed fabrics compared with foreign developed countries. There are still many enterprises that cannot fully realize the online detection of textile samples. Therefore, it is of great significance and practical value to study a new automatic detection system for textile and other related enterprises to adapt to the increasingly competitive modern market.

发明内容Contents of the invention

本发明旨在提供一种克服上述问题之一或者至少部分地解决上述任一问题的基于SLIC超像素分割的色差自适应在线检测方法及装置。The present invention aims to provide a color difference adaptive online detection method and device based on SLIC superpixel segmentation that overcomes one of the above problems or at least partially solves any of the above problems.

为达到上述目的,本发明的技术方案具体是这样实现的:In order to achieve the above object, the technical solution of the present invention is specifically realized in the following way:

本发明的一个方面提供了一种基于SLIC超像素分割的色差自适应在线检测方法及装置,包括:通过在线线阵CCD相机对待检测样本进行图像采集;将采集得到的图像进行高斯滤波处理并转换到与设备无关的均匀色彩空间;采用超像素分割法对图像进行预处理;采用CIELDE2000色差公式进行色差计算,得到计算结果;将结算结果与预设的色差阈值进行比对,输出比对结果。One aspect of the present invention provides a color difference adaptive online detection method and device based on SLIC superpixel segmentation, including: collecting images of the samples to be detected through an online line array CCD camera; performing Gaussian filtering on the collected images and converting them to To a uniform color space that has nothing to do with the device; preprocess the image with the superpixel segmentation method; use the CIELDE2000 color difference formula to calculate the color difference, and get the calculation result; compare the settlement result with the preset color difference threshold, and output the comparison result.

其中,将采集得到的图像进行高斯滤波处理并转换到与设备无关的均匀色彩空间包括:将采集得到的图像进行裁剪,获得待处理图像;根据照明体和仰角的不同设置转换参数;对待处理图像进行高斯滤波处理,并进行gamma校正;对校正后的图像根据转换参数进行色彩空间转换。Among them, performing Gaussian filtering on the collected image and converting it to a uniform color space that has nothing to do with the device includes: cutting the collected image to obtain the image to be processed; setting conversion parameters according to different lighting objects and elevation angles; the image to be processed Perform Gaussian filter processing and gamma correction; perform color space conversion on the corrected image according to the conversion parameters.

其中,采用超像素分割法对图像进行预处理包括:将图像分割成预设个数的超像素,在每个超像素内设置种子点;计算每个种子点3×3邻域内的所有像素的梯度值,移动种子点到梯度值最小的位置记为新的种子点;以新的种子点为中心的2S×2S的区域内搜索相似像素点,进行聚类,并为相似像素点分配类标签;对每个搜索到的相似像素点进行距离度量,确定每个相似像素点到种子点的颜色距离和空间距离;确定图像中每一个超像素的颜色特征值、每个普通像素的颜色特征值以及图像的全部超像素颜色特征值。Among them, using the superpixel segmentation method to preprocess the image includes: dividing the image into a preset number of superpixels, setting seed points in each superpixel; calculating Gradient value, move the seed point to the position with the smallest gradient value and record it as a new seed point; search for similar pixels in the 2S×2S area centered on the new seed point, perform clustering, and assign class labels to similar pixels ;Measure the distance of each searched similar pixel point, determine the color distance and spatial distance from each similar pixel point to the seed point; determine the color feature value of each superpixel in the image, and the color feature value of each ordinary pixel and all superpixel color feature values of the image.

其中,采用CIELDE2000色差公式进行色差计算,得到计算结果包括:计算L*a*b*色彩空间中的明度L、色度a、色度b、心里彩度Cab;计算L′,a′b′,色调h′ab和CIEL*a*b*色彩空间中a*轴的调节因子G;计算明度差ΔL、彩度差ΔCab、色相差ΔH′ab;计算权重函数SL,SC,SH和旋转函数RT,RC;确定校正系数KL,KC,KH;通过公式计算获得计算结果。Among them, the CIELDE2000 color difference formula is used to calculate the color difference, and the calculation results include: calculating the lightness L, chroma a, chroma b, and heart chroma C ab in the L*a*b* color space; calculating L', a'b ', the hue h' ab and the adjustment factor G of the a* axis in the CIEL*a*b* color space; calculate the lightness difference ΔL, the chroma difference ΔC ab , the hue difference ΔH'ab; calculate the weight function S L , S C , S H and rotation functions R T , R C ; determine the correction coefficients K L , K C , K H ; through the formula compute to obtain the computed result.

本发明另另一方面提供了一种基于SLIC超像素分割的色差自适应在线检测装置,包括:采集模块,用于通过在线线阵CCD相机对待检测样本进行图像采集;转换模块,用于将采集得到的图像进行高斯滤波处理并转换到与设备无关的均匀色彩空间;预处理模块,用于采用超像素分割法对图像进行预处理;计算模块,用于采用CIELDE2000色差公式进行色差计算,得到计算结果;比对模块,用于将结算结果与预设的色差阈值进行比对,输出比对结果。Another aspect of the present invention provides a chromatic aberration adaptive online detection device based on SLIC superpixel segmentation, including: an acquisition module, which is used to collect images of samples to be detected through an online linear array CCD camera; a conversion module, which is used to collect The obtained image is processed by Gaussian filtering and converted to a device-independent uniform color space; the preprocessing module is used to preprocess the image using the superpixel segmentation method; the calculation module is used to calculate the color difference using the CIELDE2000 color difference formula to obtain the calculated Result; a comparison module, configured to compare the settlement result with a preset color difference threshold, and output the comparison result.

其中,转换模块具体通过如下方式将采集得到的图像进行高斯滤波处理并转换到与设备无关的均匀色彩空间:转换模块,具体用于将采集得到的图像进行裁剪,获得待处理图像;根据照明体和仰角的不同设置转换参数;对待处理图像进行高斯滤波处理,并进行gamma校正;对校正后的图像根据转换参数进行色彩空间转换。Among them, the conversion module specifically performs Gaussian filter processing on the collected images and converts them to a uniform color space that has nothing to do with the device through the following methods: the conversion module is specifically used to crop the collected images to obtain images to be processed; Different settings of conversion parameters and elevation angles; Gaussian filtering and gamma correction are performed on the image to be processed; color space conversion is performed on the corrected image according to the conversion parameters.

其中,预处理模块具体通过如下方式采用超像素分割法对图像进行预处理:预处理模块,具体用于将图像分割成预设个数的超像素,在每个超像素内设置种子点;计算每个种子点3×3邻域内的所有像素的梯度值,移动种子点到梯度值最小的位置记为新的种子点;以新的种子点为中心的2S×2S的区域内搜索相似像素点,进行聚类,并为相似像素点分配类标签;对每个搜索到的相似像素点进行距离度量,确定每个相似像素点到种子点的颜色距离和空间距离;确定图像中每一个超像素的颜色特征值、每个普通像素的颜色特征值以及图像的全部超像素颜色特征值。Wherein, the preprocessing module specifically uses the superpixel segmentation method to preprocess the image in the following manner: the preprocessing module is specifically used to divide the image into a preset number of superpixels, and set a seed point in each superpixel; calculate The gradient values of all pixels in the 3×3 neighborhood of each seed point, move the seed point to the position with the smallest gradient value and record it as a new seed point; search for similar pixels in the 2S×2S area centered on the new seed point , perform clustering, and assign class labels to similar pixels; perform distance measurement on each searched similar pixel, and determine the color distance and spatial distance from each similar pixel to the seed point; determine each superpixel in the image The color eigenvalues of , the color eigenvalues of each ordinary pixel, and the color eigenvalues of all superpixels of the image.

其中,计算模块具体通过如下方式采用CIELDE2000色差公式进行色差计算,得到计算结果:计算模块,具体用于计算L*a*b*色彩空间中的明度L、色度a、色度b、心里彩度Cab;计算L′,a′b′,色调h′ab和CIEL*a*b*色彩空间中a*轴的调节因子G;计算明度差ΔL、彩度差ΔCab、色相差ΔH′ab;计算权重函数SL,SC,SH和旋转函数RT,RC;确定校正系数KL,KC,KH;通过公式计算获得计算结果。Among them, the calculation module specifically uses the CIELDE2000 color difference formula to calculate the color difference in the following manner, and obtains the calculation result: the calculation module is specifically used to calculate the lightness L, chroma a, chroma b, and heart color in the L*a*b* color space. degree C ab ; calculate L', a'b', hue h' ab and adjustment factor G of a* axis in CIEL*a*b* color space; calculate lightness difference ΔL, chroma difference ΔC ab , hue difference ΔH'ab; Calculate the weight function S L , S C , S H and the rotation function R T , R C ; determine the correction coefficient K L , K C , K H ; through the formula compute to obtain the computed result.

由此可见,通过本发明实施例提供的一种基于SLIC超像素分割的色差自适应在线检测方法及装置,解决了目前行业内存在的对素色布匹色差检测效率低,自动化程度不高的局面,节省了很多的运算量,大大提高检测效率。It can be seen that, through the SLIC superpixel segmentation-based color difference adaptive online detection method and device provided by the embodiment of the present invention, it solves the situation in the current industry that the color difference detection efficiency of plain cloth is low and the degree of automation is not high. , which saves a lot of computation and greatly improves the detection efficiency.

附图说明Description of drawings

为了更清楚地说明本发明实施例的技术方案,下面将对实施例描述中所需要使用的附图作简单地介绍,显而易见地,下面描述中的附图仅仅是本发明的一些实施例,对于本领域的普通技术人员来讲,在不付出创造性劳动的前提下,还可以根据这些附图获得其他附图。In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the accompanying drawings that need to be used in the description of the embodiments. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention. For Those of ordinary skill in the art can also obtain other drawings based on these drawings on the premise of not paying creative work.

图1为本发明实施例提供的基于SLIC超像素分割的色差自适应在线检测方法的流程图;Fig. 1 is the flowchart of the color difference adaptive online detection method based on SLIC superpixel segmentation provided by the embodiment of the present invention;

图2为本发明实施例提供的基于SLIC超像素分割的色差自适应在线检测方法的一种具体流程图;Fig. 2 is a specific flow chart of the chromatic aberration adaptive online detection method based on SLIC superpixel segmentation provided by the embodiment of the present invention;

图3为本发明实施例提供的基于SLIC超像素分割的色差自适应在线检测装置的结构示意图。FIG. 3 is a schematic structural diagram of a color difference adaptive online detection device based on SLIC superpixel segmentation provided by an embodiment of the present invention.

具体实施方式Detailed ways

下面将参照附图更详细地描述本公开的示例性实施例。虽然附图中显示了本公开的示例性实施例,然而应当理解,可以以各种形式实现本公开而不应被这里阐述的实施例所限制。相反,提供这些实施例是为了能够更透彻地理解本公开,并且能够将本公开的范围完整的传达给本领域的技术人员。Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be limited by the embodiments set forth herein. Rather, these embodiments are provided for more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

图1示出了本发明实施例提供的基于SLIC超像素分割的色差自适应在线检测方法的流程图,参见图1,本发明实施例提供的基于SLIC超像素分割的色差自适应在线检测方法,包括:Fig. 1 shows the flow chart of the chromatic aberration adaptive online detection method based on SLIC superpixel segmentation provided by the embodiment of the present invention, referring to Fig. 1, the chromatic aberration adaptive online detection method based on SLIC superpixel segmentation provided by the embodiment of the present invention, include:

S1,通过在线线阵CCD(Charge Coupled Device,电荷藕合器件图像传感器)相机对待检测样本进行图像采集。S1, image acquisition of the sample to be detected is performed by an online linear array CCD (Charge Coupled Device, charge coupled device image sensor) camera.

具体地,待检测样本为素色布匹,本发明提供的检测方法是为素色布匹的色差实现的检测。Specifically, the sample to be tested is a plain cloth, and the detection method provided by the present invention is a detection of the color difference of the plain cloth.

S2,将采集得到的图像进行高斯滤波处理并转换到与设备无关的均匀色彩空间。S2. Perform Gaussian filter processing on the acquired image and convert it into a device-independent uniform color space.

作为本发明实施例的一个可选实施方式,本步骤具体可以包括:将采集得到的图像进行裁剪,获得待处理图像;根据照明体和仰角的不同设置转换参数;对待处理图像进行高斯滤波处理,并进行gamma校正;对校正后的图像根据转换参数进行色彩空间转换。As an optional implementation of the embodiment of the present invention, this step may specifically include: clipping the collected image to obtain the image to be processed; setting conversion parameters according to different illumination bodies and elevation angles; performing Gaussian filtering on the image to be processed, And perform gamma correction; perform color space conversion on the corrected image according to the conversion parameters.

具体实施时,可以通过如下方式执行将采集得到的图像进行高斯滤波处理并转换到与设备无关的均匀色彩空间的步骤:During specific implementation, the steps of performing Gaussian filtering on the acquired image and converting it to a device-independent uniform color space can be performed in the following manner:

S21,先对采集到的图像进行高斯线性平滑滤波处理,通过公式进行高斯线性滤波处理,其中σ表示高斯分布参数,σ值最终会决定滤波器的宽度,σ值与平滑成都成正相关性。S21, first perform Gaussian linear smoothing filter processing on the collected images, and use the formula Gaussian linear filtering is performed, where σ represents the parameters of the Gaussian distribution, the value of σ will ultimately determine the width of the filter, and the value of σ is positively correlated with smoothing.

S22:将对处理后的图像从RGB转换到CIELAB空间:S22: convert the processed image from RGB to CIELAB space:

具体利用如下公式:Specifically use the following formula:

其中本步骤中的三刺激值Xn、Yn、Zn根据照明体和仰角有六种模式可以选择,参数表如下:Among them, the tristimulus values X n , Y n , and Z n in this step have six modes to choose from according to the illumination body and elevation angle, and the parameter table is as follows:

其中,若X/Xn、Y/Yn、Z/Zn的值大于 Among them, if the value of X/X n , Y/Y n , Z/Z n is greater than

或者,更为具体的,可以通过如下方式执行将采集得到的图像进行高斯滤波处理并转换到与设备无关的均匀色彩空间的步骤:Or, more specifically, the step of performing Gaussian filtering on the acquired image and converting it to a device-independent uniform color space can be performed as follows:

S21:把获取的原始RGB图像进行裁剪为400×400像素大小的待处理图像。S21: Crop the acquired original RGB image into an image to be processed with a size of 400×400 pixels.

S22:对检测系统照明光环环境进行系统预设:不同的光源与照明角度在后期色彩空间转换参数有一定的影响,因此需要根据目前标定的六种常会模式进行预选,并设定。其中,六种常会模式可以参见表1。S22: System preset for the lighting halo environment of the detection system: Different light sources and lighting angles have certain influence on the color space conversion parameters in the later stage, so it needs to be pre-selected and set according to the six regular meeting modes currently calibrated. Among them, the six regular meeting modes can be found in Table 1.

S23:对上述400×400像素的图像使用高斯滤波器进行平滑图像和抑制噪声处理,然后对其进行gamma校正,为了改善彩色图像的对比度,从而会避免后期SLIC超像素分割产生的大量微小区域;高斯滤波就是对整幅图像进行加权平均的过程,每一个像素点的值,都由其本身和邻域内的其他像素值经过加权平均后得到。高斯滤波的具体操作是:用一个模板(或称卷积、掩模)扫描图像中的每一个像素,用模板确定的邻域内像素的加权平均灰度值去替代模板中心像素点的值用。高斯平滑滤波器对于抑制服从正态分布的噪声非常有效。S23: Use a Gaussian filter to smooth the image and suppress noise on the above-mentioned 400×400 pixel image, and then perform gamma correction on it, in order to improve the contrast of the color image, thereby avoiding a large number of small areas generated by SLIC superpixel segmentation in the later stage; Gaussian filtering is a process of weighted average of the entire image, and the value of each pixel is obtained by weighted average of itself and other pixel values in the neighborhood. The specific operation of Gaussian filtering is: use a template (or convolution, mask) to scan each pixel in the image, and use the weighted average gray value of the pixels in the neighborhood determined by the template to replace the value of the pixel in the center of the template. Gaussian smoothing filters are very effective at suppressing noise that follows a normal distribution.

其中,gamma校正主要是对原始的RGB图像非线性化,通过下面的公式对红绿蓝三通道进行转换;Among them, gamma correction is mainly to nonlinearize the original RGB image, and convert the red, green and blue three channels through the following formula;

其中,r,g,b三通道的取值范围均为[0,255]。Among them, the value ranges of the r, g, and b channels are all [0, 255].

S24:把经过以上步骤处理后的400×400像素的图像进行色彩空间转换,RGB-XYZ-CIELAB。S24: Perform color space conversion on the 400×400 pixel image processed through the above steps, RGB-XYZ-CIELAB.

若X/Xn、Y/Yn、Z/Zn的值大于 If the value of X/X n , Y/Y n , Z/Z n is greater than

其中Xn、Yn、Zn取值根据步骤S22中设定的值。The values of X n , Y n , and Z n are according to the values set in step S22.

S3,采用超像素分割法对图像进行预处理。S3, the image is preprocessed using a superpixel segmentation method.

作为本发明实施例的一个可选实施方式,本步骤具体可以包括:将图像分割成预设个数的超像素,在每个超像素内设置种子点;计算每个种子点3×3邻域内的所有像素的梯度值,移动种子点到梯度值最小的位置记为新的种子点;以新的种子点为中心的2S×2S的区域内搜索相似像素点,进行聚类,并为相似像素点分配类标签;对每个搜索到的相似像素点进行距离度量,确定每个相似像素点到种子点的颜色距离和空间距离;确定图像中每一个超像素的颜色特征值、每个普通像素的颜色特征值以及图像的全部超像素颜色特征值。As an optional implementation of the embodiment of the present invention, this step may specifically include: dividing the image into a preset number of superpixels, setting seed points in each superpixel; calculating Gradient values of all pixels, move the seed point to the position with the smallest gradient value and record it as a new seed point; search for similar pixel points in the 2S×2S area centered on the new seed point, perform clustering, and create similar pixels Point assignment class label; measure the distance of each searched similar pixel point, determine the color distance and spatial distance from each similar pixel point to the seed point; determine the color feature value of each super pixel in the image, each ordinary pixel and all superpixel color feature values of the image.

具体实施时,可以通过如下方式执行采用超像素分割法对图像进行预处理的步骤:During specific implementation, the step of using the superpixel segmentation method to preprocess the image can be performed in the following manner:

S31:初始化种子点。按照设置的超像素个数,在图像内均匀地分配种子点。假设图片有M个像素点,预设分割为K个相同尺寸的超像素,那么每个超像素的大小为M/K,相邻种子点的距离近似为 S31: Initialize the seed point. According to the set number of superpixels, the seed points are evenly distributed in the image. Assuming that the picture has M pixels, and the preset is divided into K superpixels of the same size, then the size of each superpixel is M/K, and the distance between adjacent seed points is approximately

S32:计算该种子点3×3邻域内的所有像素的梯度值,移动种子点到梯度值最小的位置,记为新的种子点。由此可以避免种子点落在轮廓边界附近,影响聚类效果。S32: Calculate the gradient values of all pixels in the 3×3 neighborhood of the seed point, move the seed point to the position with the smallest gradient value, and record it as a new seed point. This can prevent the seed point from falling near the contour boundary and affect the clustering effect.

S33:再以种子点为中心的2S×2S的区域内搜索相似像素点,进行聚类,并且为其分配类标签。S33: Search for similar pixel points in a 2S×2S region centered on the seed point, perform clustering, and assign class labels to them.

S34:对每个搜索到的像素点进行距离度量,包括每个像素点到种子点的颜色距离和空间距离,计算方法如下: S34: Measure the distance of each searched pixel point, including the color distance and spatial distance from each pixel point to the seed point, the calculation method is as follows:

公式中,dlab为颜色距离,ds为空间距离,S′为两个像素的距离度量,D为种子点间的距离,m为平衡参数。In the formula, d lab is the color distance, d s is the space distance, S′ is the distance measure between two pixels, D is the distance between seed points, and m is the balance parameter.

S35:设分割好的图像中每一个超像素的颜色特征值为Vi=(L,B,A),i=1,2,3,...,K,每个普通像素的颜色特征值为vi=(l,a,b),j=1,2,3,...,M/K,则整幅图像的超像素颜色特征值用一维向量[Vi=(L,A,B)T],i=1,2,3,...,K来表示。S35: Set the color feature value of each superpixel in the segmented image V i = (L, B, A), i=1, 2, 3, ..., K, the color feature value of each common pixel For v i = (l, a, b), j = 1, 2, 3, ..., M/K, then The superpixel color feature values of the entire image are represented by a one-dimensional vector [V i =(L, A, B) T ], i=1, 2, 3, . . . , K.

S4,采用CIELDE2000色差公式进行色差计算,得到计算结果。S4, using the CIELDE2000 color difference formula to calculate the color difference, and obtain the calculation result.

作为本发明实施例的一个可选实施方式,本步骤具体可以包括:计算L*a*b*色彩空间中的明度L、色度a、色度b、心里彩度Cab;计算L′,a′b′,色调h′ab和CIEL*a*b*色彩空间中a*轴的调节因子G;计算明度差ΔL、彩度差ΔCab、色相差ΔH′ab;计算权重函数SL,SC,SH和旋转函数RT,RC;确定校正系数KL,KC,KH;通过公式计算获得计算结果。As an optional implementation of the embodiment of the present invention, this step may specifically include: calculating lightness L, chroma a, chroma b, and heart chroma C ab in the L*a*b* color space; calculating L', a'b', hue h' ab and adjustment factor G of a* axis in CIEL*a*b* color space; calculate lightness difference ΔL, chroma difference ΔC ab , hue difference ΔH'ab; calculate weight function S L , S C , S H and rotation functions R T , R C ; determine the correction coefficients K L , K C , K H ; through the formula compute to obtain the computed result.

具体实施时,可以通过如下方式执行采用CIELDE2000色差公式进行色差计算,得到计算结果的步骤:During specific implementation, the steps of using the CIELDE2000 color difference formula to calculate the color difference and obtain the calculation result can be performed in the following manner:

具体可以使用如下公式进行色差计算:Specifically, the following formula can be used for color difference calculation:

式中计算步骤如下:The calculation steps in the formula are as follows:

S41:计算L*a*b*色彩空间中的明度L、色度a、色度b、心里彩度CabS41: Calculate lightness L, chroma a, chroma b, and heart chroma C ab in the L*a*b* color space.

S42:计算L′,a′b′,色调h′ab和CIEL*a*b*色彩空间中a*轴的调节因子G。S42: Calculate L', a'b ', hue h'ab and the adjustment factor G of the a* axis in the CIEL*a*b* color space.

式中为待测图像Cab1与标准图像Cab2的算术平均值。In the formula is the arithmetic mean of the test image C ab1 and the standard image C ab2 .

S43:计算明度差ΔL、彩度差ΔCab、色相差ΔH′abS43: Calculate lightness difference ΔL, chroma difference ΔC ab , and hue difference ΔH′ ab .

S44:计算权重函数SL,SC,SH和旋转函数RT,RCS44: Calculate weighting functions S L , S C , S H and rotation functions R T , R C .

S45:其中KL,KC,KH是根据实际使用条件确定的校正系数。在CIE标准观测条件下,可以设定默认值为KL=KC=KH=1。S45: where K L , K C , and K H are correction coefficients determined according to actual usage conditions. Under CIE standard observation conditions, the default value can be set as K L =K C =K H =1.

S5,将结算结果与预设的色差阈值进行比对,输出比对结果。S5, comparing the settlement result with the preset color difference threshold, and outputting the comparison result.

具体地,将计算结果与预先在系统内设定的阀值进行比较,如果符合要求,则判断产品达标合格,如果不符合要求,则判断产品不达标不合格。即如果计算结果符合要求,表明待检测样本符合要求,即色差符合要求,如果计算结果不符合要求,表明待检测样本不符合要求,即色差不符合要求。Specifically, the calculation result is compared with the preset threshold value in the system. If it meets the requirements, it is judged that the product meets the standard, and if it does not meet the requirements, it is judged that the product does not meet the standard. That is, if the calculation result meets the requirements, it indicates that the sample to be tested meets the requirements, that is, the color difference meets the requirements, and if the calculation result does not meet the requirements, it indicates that the sample to be tested does not meet the requirements, that is, the color difference does not meet the requirements.

由此可见,针对布匹印色过程中,布匹色差在线检测准确率低、速度慢的问题,提出本发明的检测方法。在采集待检测物品,对图像进行预处理,基于超像素的思想,采用简单线性迭代聚类(SLIC,simple linear iterative clustering)算法,对具有相似特征的相邻像素进行聚类,形成结构紧凑、近似均匀的像素块,每个像素块即为一个超像素。用超像素代替像素块内多个相似像素,分别提取标准图像和待检测图像的颜色特征。使用合适色差公式进行色差计算。该方法在保证检测结果准确率的基础上,能够有效地减少数据计算量,提高检测效率。It can be seen that, aiming at the problem of low accuracy and slow speed of on-line detection of cloth color difference in the cloth color printing process, the detection method of the present invention is proposed. After collecting the items to be detected and preprocessing the image, based on the idea of superpixels, a simple linear iterative clustering (SLIC, simple linear iterative clustering) algorithm is used to cluster adjacent pixels with similar characteristics to form a compact, Approximately uniform pixel blocks, each pixel block is a superpixel. Superpixels are used to replace multiple similar pixels in the pixel block, and the color features of the standard image and the image to be detected are extracted respectively. Use the appropriate color difference formula for color difference calculation. On the basis of ensuring the accuracy of the detection results, the method can effectively reduce the amount of data calculation and improve the detection efficiency.

以下,以图2为例,具体说明本发明实施例提供的基于SLIC超像素分割的色差自适应在线检测方法,但本发明并不局限于此:Hereinafter, taking Fig. 2 as an example, the color difference adaptive online detection method based on SLIC superpixel segmentation provided by the embodiment of the present invention is specifically described, but the present invention is not limited thereto:

CCD相机对待检测样本(纯色布匹)采集图像;The CCD camera collects images of the sample to be tested (solid color cloth);

设定光源条件;Set the light source conditions;

高斯滤波器对采集的图像进行处理;The Gaussian filter is used to process the collected images;

对处理后的图像进行超像素分割;Perform superpixel segmentation on the processed image;

提取超像素颜色特征;Extract superpixel color features;

CIEDE2000色差公式计算色差;CIEDE2000 color difference formula to calculate color difference;

将计算得到的色差值与预设阈值进行比较,并判断待检测样本是否合格。Compare the calculated color difference value with the preset threshold, and judge whether the sample to be tested is qualified.

由此可见,通过本发明实施例提供的基于SLIC超像素分割的色差自适应在线检测方法,融合了多种情境下光源条件与角度的检测环境,适用范围比较广,自动化程度相对较高,可以满足当前绝大多数素色布匹的检测要求。且本发明实施例提供的检测方法与当前应用较多的逐像素色差检测方法相比,更具有速度快,精准度好,提升了检测效率的效果。It can be seen that the color difference adaptive online detection method based on SLIC superpixel segmentation provided by the embodiment of the present invention integrates the detection environment of light source conditions and angles in various scenarios, and has a wide range of applications and a relatively high degree of automation. Meet the testing requirements of most of the current plain fabrics. Moreover, the detection method provided by the embodiment of the present invention has the advantages of faster speed, better accuracy, and improved detection efficiency compared with the current pixel-by-pixel color difference detection method that is widely used.

图3示出了本发明实施例提供的基于SLIC超像素分割的色差自适应在线检测装置的结构示意图,该基于SLIC超像素分割的色差自适应在线检测装置应用于上述基于SLIC超像素分割的色差自适应在线检测方法,以下仅对基于SLIC超像素分割的色差自适应在线检测装置的结构进行简要说明,其他未尽事宜,请参照上述基于SLIC超像素分割的色差自适应在线检测方法的相关说明,在此不再赘述。参见图3,本发明实施例提供的基于SLIC超像素分割的色差自适应在线检测装置,包括:Fig. 3 shows a schematic structural diagram of a chromatic aberration adaptive online detection device based on SLIC superpixel segmentation provided by an embodiment of the present invention, which is applied to the above-mentioned chromatic aberration based on SLIC superpixel segmentation Adaptive online detection method. The following is only a brief description of the structure of the chromatic aberration adaptive online detection device based on SLIC superpixel segmentation. For other unfinished matters, please refer to the above-mentioned related descriptions of the chromatic aberration adaptive online detection method based on SLIC superpixel segmentation. , which will not be repeated here. Referring to Fig. 3, the chromatic aberration adaptive online detection device based on SLIC superpixel segmentation provided by the embodiment of the present invention includes:

采集模块301,用于通过在线线阵CCD相机对待检测样本进行图像采集;The collection module 301 is used to collect images of samples to be detected by an online linear array CCD camera;

转换模块302,用于将采集得到的图像进行高斯滤波处理并转换到与设备无关的均匀色彩空间;A conversion module 302, configured to perform Gaussian filter processing on the acquired image and convert it to a device-independent uniform color space;

预处理模块303,用于采用超像素分割法对图像进行预处理;A preprocessing module 303, configured to preprocess the image using a superpixel segmentation method;

计算模块304,用于采用CIELDE2000色差公式进行色差计算,得到计算结果;Calculation module 304, for adopting CIELDE2000 color difference formula to carry out color difference calculation, obtain calculation result;

比对模块305,用于将结算结果与预设的色差阈值进行比对,输出比对结果。The comparison module 305 is configured to compare the settlement result with the preset color difference threshold, and output the comparison result.

作为本发明实施例的一个可选实施方式,转换模块302具体通过如下方式将采集得到的图像进行高斯滤波处理并转换到与设备无关的均匀色彩空间:转换模块302,具体用于将采集得到的图像进行裁剪,获得待处理图像;根据照明体和仰角的不同设置转换参数;对待处理图像进行高斯滤波处理,并进行gamma校正;对校正后的图像根据转换参数进行色彩空间转换。As an optional implementation of the embodiment of the present invention, the conversion module 302 performs Gaussian filter processing on the acquired image and converts it to a device-independent uniform color space in the following manner: the conversion module 302 is specifically used to convert the acquired image The image is cropped to obtain the image to be processed; the conversion parameters are set according to the different illumination bodies and elevation angles; the image to be processed is processed by Gaussian filtering and gamma correction; the corrected image is converted into color space according to the conversion parameters.

作为本发明实施例的一个可选实施方式,预处理模块303具体通过如下方式采用超像素分割法对图像进行预处理:预处理模块303,具体用于将图像分割成预设个数的超像素,在每个超像素内设置种子点;计算每个种子点3×3邻域内的所有像素的梯度值,移动种子点到梯度值最小的位置记为新的种子点;以新的种子点为中心的2S×2S的区域内搜索相似像素点,进行聚类,并为相似像素点分配类标签;对每个搜索到的相似像素点进行距离度量,确定每个相似像素点到种子点的颜色距离和空间距离;确定图像中每一个超像素的颜色特征值、每个普通像素的颜色特征值以及图像的全部超像素颜色特征值。As an optional implementation of the embodiment of the present invention, the preprocessing module 303 specifically uses the superpixel segmentation method to preprocess the image in the following manner: the preprocessing module 303 is specifically used to divide the image into a preset number of superpixels , set a seed point in each superpixel; calculate the gradient value of all pixels in the 3×3 neighborhood of each seed point, move the seed point to the position with the smallest gradient value and record it as a new seed point; use the new seed point as Search for similar pixels in the central 2S×2S area, perform clustering, and assign class labels to similar pixels; measure the distance of each searched similar pixel, and determine the color of each similar pixel to the seed point Distance and spatial distance; determine the color feature value of each superpixel in the image, the color feature value of each ordinary pixel, and the color feature value of all superpixels in the image.

作为本发明实施例的一个可选实施方式,计算模块304具体通过如下方式采用CIELDE2000色差公式进行色差计算,得到计算结果:计算模块304,具体用于计算L*a*b*色彩空间中的明度L、色度a、色度b、心里彩度Cab;计算L′,a′b′,色调h′ab和CIEL*a*b*色彩空间中a*轴的调节因子G;计算明度差ΔL、彩度差ΔCab、色相差ΔH′ab;计算权重函数SL,SC,SH和旋转函数RT,RC;确定校正系数KL,KC,KH;通过公式计算获得计算结果。As an optional implementation of the embodiment of the present invention, the calculation module 304 specifically uses the CIELDE2000 color difference formula to perform color difference calculation in the following manner to obtain the calculation result: the calculation module 304 is specifically used to calculate the lightness in the L*a*b* color space L, chroma a, chroma b, heart chroma C ab ; calculate L', a'b', hue h' ab and adjustment factor G of a* axis in CIEL*a *b * color space; calculate lightness difference ΔL, chroma difference ΔC ab , hue difference ΔH′ ab ; calculate weight functions S L , S C , S H and rotation functions R T , R C ; determine correction coefficients K L , K C , K H ; through the formula compute to obtain the computed result.

由此可见,通过本发明实施例提供的基于SLIC超像素分割的色差自适应在线检测装置,融合了多种情境下光源条件与角度的检测环境,适用范围比较广,自动化程度相对较高,可以满足当前绝大多数素色布匹的检测要求。且本发明实施例提供的检测方法与当前应用较多的逐像素色差检测方法相比,更具有速度快,精准度好,提升了检测效率的效果。It can be seen that the color difference adaptive online detection device based on SLIC superpixel segmentation provided by the embodiment of the present invention integrates the detection environment of light source conditions and angles in various scenarios, and has a wide range of applications and a relatively high degree of automation. Meet the testing requirements of most of the current plain fabrics. Moreover, the detection method provided by the embodiment of the present invention has the advantages of faster speed, better accuracy, and improved detection efficiency compared with the current pixel-by-pixel color difference detection method that is widely used.

以上仅为本申请的实施例而已,并不用于限制本申请。对于本领域技术人员来说,本申请可以有各种更改和变化。凡在本申请的精神和原理之内所作的任何修改、等同替换、改进等,均应包含在本申请的权利要求范围之内。The above are only examples of the present application, and are not intended to limit the present application. For those skilled in the art, various modifications and changes may occur in this application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included within the scope of the claims of the present application.

Claims (8)

1.一种基于SLIC超像素分割的色差自适应在线检测方法,其特征在于,包括:1. A color difference adaptive online detection method based on SLIC superpixel segmentation, characterized in that, comprising: 通过在线线阵CCD相机对待检测样本进行图像采集;Image acquisition of the sample to be tested through the online linear array CCD camera; 将采集得到的图像进行高斯滤波处理并转换到与设备无关的均匀色彩空间;Gaussian filtering is performed on the collected image and converted to a uniform color space that has nothing to do with the device; 采用超像素分割法对所述图像进行预处理;The image is preprocessed by using a superpixel segmentation method; 采用CIELDE2000色差公式进行色差计算,得到计算结果;Use the CIELDE2000 color difference formula to calculate the color difference and get the calculation result; 将所述结算结果与预设的色差阈值进行比对,输出比对结果。The settlement result is compared with the preset color difference threshold, and the comparison result is output. 2.根据权利要求1所述的方法,其特征在于,所述将采集得到的图像进行高斯滤波处理并转换到与设备无关的均匀色彩空间包括:2. The method according to claim 1, wherein said performing Gaussian filter processing on the collected image and converting it to a device-independent uniform color space comprises: 将采集得到的图像进行裁剪,获得待处理图像;Crop the collected image to obtain the image to be processed; 根据照明体和仰角的不同设置转换参数;Set the conversion parameters according to the different lighting bodies and elevation angles; 对所述待处理图像进行高斯滤波处理,并进行gamma校正;Performing Gaussian filter processing on the image to be processed, and performing gamma correction; 对校正后的图像根据所述转换参数进行色彩空间转换。Perform color space conversion on the corrected image according to the conversion parameters. 3.根据权利要求1所述的方法,其特征在于,所述采用超像素分割法对所述图像进行预处理包括:3. method according to claim 1, is characterized in that, described adopting superpixel segmentation method to carry out preprocessing to described image comprises: 将所述图像分割成预设个数的超像素,在每个超像素内设置种子点;The image is divided into a preset number of superpixels, and a seed point is set in each superpixel; 计算每个所述种子点3×3邻域内的所有像素的梯度值,移动所述种子点到梯度值最小的位置记为新的种子点;Calculate the gradient values of all pixels in the 3×3 neighborhood of each seed point, move the seed point to the position with the smallest gradient value and record it as a new seed point; 以所述新的种子点为中心的2S×2S的区域内搜索相似像素点,进行聚类,并为所述相似像素点分配类标签;Search for similar pixel points in the 2S×2S area centered on the new seed point, perform clustering, and assign class labels to the similar pixel points; 对每个搜索到的相似像素点进行距离度量,确定每个所述相似像素点到种子点的颜色距离和空间距离;Perform distance measurement on each searched similar pixel point, and determine the color distance and spatial distance from each similar pixel point to the seed point; 确定所述图像中每一个超像素的颜色特征值、每个普通像素的颜色特征值以及所述图像的全部超像素颜色特征值。The color feature value of each superpixel in the image, the color feature value of each common pixel, and the color feature values of all superpixels in the image are determined. 4.根据权利要求1所述的方法,其特征在于,所述采用CIELDE2000色差公式进行色差计算,得到计算结果包括:4. method according to claim 1, is characterized in that, described adopting CIELDE2000 color difference formula to carry out color difference calculation, obtains calculation result and comprises: 计算L*a*b*色彩空间中的明度L、色度a、色度b、心里彩度CabCalculate lightness L, chroma a, chroma b, heart chroma C ab in the L*a*b* color space; 计算L′,a′b′,色调h′ab和CIEL*a*b*色彩空间中a*轴的调节因子G;Compute L', a'b ', hue h'ab and the adjustment factor G for the a* axis in the CIEL*a*b* color space; 计算明度差ΔL、彩度差ΔCab、色相差ΔH′abCalculate lightness difference ΔL, chroma difference ΔC ab , hue difference ΔH′ ab ; 计算权重函数SL,SC,SH和旋转函数RT,RCCalculation of weight functions S L , S C , S H and rotation functions R T , R C ; 确定校正系数KL,KC,KHDetermine the correction coefficients K L , K C , K H ; 通过公式计算获得计算结果。by formula compute to obtain the computed result. 5.一种基于SLIC超像素分割的色差自适应在线检测装置,其特征在于,包括:5. A color difference adaptive online detection device based on SLIC superpixel segmentation, characterized in that, comprising: 采集模块,用于通过在线线阵CCD相机对待检测样本进行图像采集;The acquisition module is used for image acquisition of the sample to be detected by an online linear array CCD camera; 转换模块,用于将采集得到的图像进行高斯滤波处理并转换到与设备无关的均匀色彩空间;A conversion module, configured to perform Gaussian filtering on the acquired image and convert it to a device-independent uniform color space; 预处理模块,用于采用超像素分割法对所述图像进行预处理;A preprocessing module, configured to preprocess the image using a superpixel segmentation method; 计算模块,用于采用CIELDE2000色差公式进行色差计算,得到计算结果;The calculation module is used to calculate the color difference by using the CIELDE2000 color difference formula to obtain the calculation result; 比对模块,用于将所述结算结果与预设的色差阈值进行比对,输出比对结果。The comparison module is used to compare the settlement result with the preset color difference threshold and output the comparison result. 6.根据权利要求5所述的装置,其特征在于,所述转换模块具体通过如下方式将采集得到的图像进行高斯滤波处理并转换到与设备无关的均匀色彩空间:6. The device according to claim 5, wherein the conversion module performs Gaussian filtering on the collected image and converts it to a device-independent uniform color space in the following manner: 所述转换模块,具体用于将采集得到的图像进行裁剪,获得待处理图像;根据照明体和仰角的不同设置转换参数;对所述待处理图像进行高斯滤波处理,并进行gamma校正;对校正后的图像根据所述转换参数进行色彩空间转换。The conversion module is specifically used to cut the collected image to obtain the image to be processed; set the conversion parameters according to the different illumination body and elevation angle; perform Gaussian filter processing on the image to be processed, and perform gamma correction; The resulting image undergoes color space conversion according to the conversion parameters. 7.根据权利要求5所述的装置,其特征在于,所述预处理模块具体通过如下方式采用超像素分割法对所述图像进行预处理:7. The device according to claim 5, wherein the preprocessing module specifically adopts a superpixel segmentation method to preprocess the image in the following manner: 所述预处理模块,具体用于将所述图像分割成预设个数的超像素,在每个超像素内设置种子点;计算每个所述种子点3×3邻域内的所有像素的梯度值,移动所述种子点到梯度值最小的位置记为新的种子点;以所述新的种子点为中心的2S×2S的区域内搜索相似像素点,进行聚类,并为所述相似像素点分配类标签;对每个搜索到的相似像素点进行距离度量,确定每个所述相似像素点到种子点的颜色距离和空间距离;确定所述图像中每一个超像素的颜色特征值、每个普通像素的颜色特征值以及所述图像的全部超像素颜色特征值。The preprocessing module is specifically used to divide the image into a preset number of superpixels, set a seed point in each superpixel; calculate the gradient of all pixels in the 3×3 neighborhood of each of the seed points value, move the seed point to the position with the smallest gradient value and record it as a new seed point; search for similar pixel points in the 2S×2S area centered on the new seed point, perform clustering, and Assigning class labels to pixel points; performing distance measurement on each searched similar pixel point, determining the color distance and spatial distance from each similar pixel point to the seed point; determining the color feature value of each superpixel in the image , the color feature value of each common pixel and all superpixel color feature values of the image. 8.根据权利要求5所述的装置,其特征在于,所述计算模块具体通过如下方式采用CIELDE2000色差公式进行色差计算,得到计算结果:8. device according to claim 5, is characterized in that, described calculation module specifically adopts CIELDE2000 color difference formula to carry out color difference calculation in the following manner, obtains calculation result: 所述计算模块,具体用于计算L*a*b*色彩空间中的明度L、色度a、色度b、心里彩度Cab;计算L′,a′b′,色调h′ab和CIEL*a*b*色彩空间中a*轴的调节因子G;计算明度差ΔL、彩度差ΔCab、色相差ΔH′ab;计算权重函数SL,SC,SH和旋转函数RT,RC;确定校正系数KL,KC,KH;通过公式计算获得计算结果。The calculation module is specifically used to calculate lightness L, chroma a, chroma b, heart chroma C ab in the L*a*b* color space; calculate L', a'b', hue h' ab and The adjustment factor G of the a* axis in the CIEL*a*b* color space; calculate the lightness difference ΔL, the chroma difference ΔC ab , the hue difference ΔH′ ab ; calculate the weight function S L , S C , S H and the rotation function R T , R C ; determine the correction coefficients K L , K C , K H ; through the formula compute to obtain the computed result.
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