Assessing Gray Scale Grades for Color Change of Dyed Fabrics via Image Processing Technology

Gray scale grades for color change of dyed fabrics are assessed via image processing technology.Digital images of groups of specimens are obtained,cropped,and saved as JPEG format.Relationships between gray scale grades for color change and the corresponding color differences calculated via image processing technology are investigated,compared with those obtained from high accurate computer color matching system.Results show that the new method is acceptable with an accuracy of 92.0% when the grading errors are of not more than one grade.

An anti-aliasing filtering of quantum images in spatial domain using a pyramid structure

As a part of quantum image processing,quantum image filtering is a crucial technology in the development of quantum computing.Low-pass filtering can effectively achieve anti-aliasing effects on images.Currently,most quantum image filterings are based on classical domains and grayscale images,and there are relatively fewer studies on anti-aliasing in the quantum domain.This paper proposes a scheme for anti-aliasing filtering based on quantum grayscale and color image scaling in the spatial domain.It achieves the effect of anti-aliasing filtering on quantum images during the scaling process.First,we use the novel enhanced quantum representation(NEQR)and the improved quantum representation of color images(INCQI)to represent classical images.Since aliasing phenomena are more pronounced when images are scaled down,this paper focuses only on the anti-aliasing effects in the case of reduction.Subsequently,we perform anti-aliasing filtering on the quantum representation of the original image and then use bilinear interpolation to scale down the image,achieving the anti-aliasing effect.The constructed pyramid model is then used to select an appropriate image for upscaling to the original image size.Finally,the complexity of the circuit is analyzed.Compared to the images experiencing aliasing effects solely due to scaling,applying anti-aliasing filtering to the images results in smoother and clearer outputs.Additionally,the anti-aliasing filtering allows for manual intervention to select the desired level of image smoothness.

Recognition of Greenhouse Cucumber Disease Based on Image Processing Technology

This paper mainly studies the disease of cucumber downy mildew, powdery mildew and anthracnose leaf image processing and recognition technologies. Application of median filtering method of filtering noise, leaf spot disease of cucumber leaf color range segmentation part extract color feature parameters of the lesion site, characteristic parameters of the shape;extraction texture parameters by using gray level co-occurrence matrix. Based on the shortest distance methods to identify diseases of images. The experimental result showed that the current method on disease recognition accuracy rates more than 96%.

Feature fusion method for edge detection of color images

A novel feature fusion method is proposed for the edge detection of color images. Except for the typical features used in edge detection, the color contrast similarity and the orientation consistency are also selected as the features. The four features are combined together as a parameter to detect the edges of color images. Experimental results show that the method can inhibit noisy edges and facilitate the detection for weak edges. It has a better performance than conventional methods in noisy environments.

Fast color transfer from multiple images

Color transfer between images uses the statistics information of image effectively. We present a novel approach of local color transfer between images based on the simple statistics and locally linear embedding. A sketching interface is proposed for quickly and easily specifying the color correspondences between target and source image. The user can specify the corre- spondences of local region using scribes, which more accurately transfers the target color to the source image while smoothly preserving the boundaries, and exhibits more natural output results. Our algorithm is not restricted to one-to-one image color transfer and can make use of more than one target images to transfer the color in different regions in the source image. Moreover, our algorithm does not require to choose the same color style and image size between source and target images. We propose the sub-sampling to reduce the computational load. Comparing with other approaches, our algorithm is much better in color blending in the input data. Our approach preserves the other color details in the source image. Various experimental results show that our approach specifies the correspondences of local color region in source and target images. And it expresses the intention of users and generates more actual and natural results of visual effect.

Intelligent Deep Learning Based Disease Diagnosis Using Biomedical Tongue Images

The rapid development of biomedical imaging modalities led to its wide application in disease diagnosis.Tongue-based diagnostic procedures are proficient and non-invasive in nature to carry out secondary diagnostic processes ubiquitously.Traditionally,physicians examine the characteristics of tongue prior to decision-making.In this scenario,to get rid of qualitative aspects,tongue images can be quantitatively inspected for which a new disease diagnosis model is proposed.This model can reduce the physical harm made to the patients.Several tongue image analytical methodologies have been proposed earlier.However,there is a need exists to design an intelligent Deep Learning(DL)based disease diagnosis model.With this motivation,the current research article designs an Intelligent DL-basedDisease Diagnosis method using Biomedical Tongue Images called IDLDD-BTI model.The proposed IDLDD-BTI model incorporates Fuzzy-based Adaptive Median Filtering(FADM)technique for noise removal process.Besides,SqueezeNet model is employed as a feature extractor in which the hyperparameters of SqueezeNet are tuned using Oppositional Glowworm Swarm Optimization(OGSO)algorithm.At last,Weighted Extreme Learning Machine(WELM)classifier is applied to allocate proper class labels for input tongue color images.The design of OGSO algorithm for SqueezeNet model shows the novelty of the work.To assess the enhanced diagnostic performance of the presented IDLDD-BTI technique,a series of simulations was conducted on benchmark dataset and the results were examined in terms of several measures.The resultant experimental values highlighted the supremacy of IDLDD-BTI model over other state-of-the-art methods.

Hierarchical Image Segmentation Using a Combined Geometrical and Feature Based Approach

This paper presents a fully automatic segmentation algorithm based on geometrical and local attributes of color images. This method incorporates a hierarchical assessment scheme into any general segmentation algorithm for which the segmentation sensitivity can be changed through parameters. The parameters are varied to create different segmentation levels in the hierarchy. The algorithm examines the consistency of segments based on local features and their relationships with each other, and selects segments at different levels to generate a final segmentation. This adaptive parameter variation scheme provides an automatic way to set segmentation sensitivity parameters locally according to each region's characteristics instead of the entire image. The algorithm does not require any training dataset. The geometrical attributes can be defined by a shape prior for specific applications, i.e. targeting objects of interest, or by one or more general constraint(s) such as boundaries between regions for non-specific applications. Using mean shift as the general segmentation algorithm, we show that our hierarchical approach generates segments that satisfy geometrical properties while conforming with local properties. In the case of using a shape prior, the algorithm can cope with partial occlusions. Evaluation is carried out on the Berkeley Segmentation Dataset and Benchmark (BSDS300) (general natural images) and on geo-spatial images (with specific shapes of interest). The F-measure for our proposed algorithm, i.e. the harmonic mean between precision and recall rates, is 64.2% on BSDS300, outperforming the same segmentation algorithm in its standard non-hierarchical variant.

Estimation of Low Nutrients in Tomato Crops Through the Analysis of Leaf Images Using Machine Learning

Tomato crops are considered the most important agricultural products worldwide.However,the quality of tomatoes depends mainly on the nutrient levels.Visual inspection is made by farmers to anticipate the nutrient deficiency of the plants.Recently,precision agriculture has explored opportunities to automate nutrient level monitoring.Previous work has demonstrated that a convolutional neural network is able to estimate low nutrients in tomato plants using images of their leaves.However,the performance of the convolutional neural network was not adequate.Thus,this work proposes a novel convolutional neural network-based classifier,namely,CNN+AHN,for estimating low nutrients in tomato crops using an image of the tomato leaves.The CNN+AHN incorporates a set of convolutional layers as the feature extraction part,and a supervised learning method called artificial hydrocarbon network as the dense layer.Different combinations of the architecture of CNN+AHN were examined.Experimental results showed that our best CNN+AHN classifier is able to estimate low nutrients in tomato plants with an accuracy of 95.57%and F1-score of 95.75%,outperforming the literature.

Novel method for the visual navigation path detection of jujube harvester autopilot based on image processing

To realize automatic harvesting of the jujube,the jujube harvester was designed and manufactured.For achieving the jujube harvester autopilot,a novel algorithm for visual navigation path detection was proposed.The centerline of tree row lines was taken as the navigation path.The method included four main parts:image preprocessing,image segmentation,tree row lines access,and navigation path access.The methods of threshold segmentation,noise removal,and border smoothing were utilized on the image in Lab color space for the image segmentation.The least square method was employed to fit the tree row lines,and the centerline was obtained as the navigation path.Experimental results indicated that the average false detection rate was 3.98%,and the average detection speed was 41 fps.The algorithm meets the requirements of the jujube harvester autopilot in terms of accuracy and speed.It also can lay the foundation for accomplishing the jujube harvester vision-based autopilot.

茶叶智能化拼配匀堆除杂生产线控制系统设计

为提高红茶生产工艺水平和产能,通过变频器、PLC、触摸屏及IPC的电气集成,搭建了一套茶叶生产线分布式控制系统,完成了PLC控制程序、现场控制级触摸屏HMI和IPC监控软件的设计,实现了红茶生产线的全过程自动控制、实时数据采集、生产工艺参数设置、历史数据查询及故障自动报警等功能。该系统具有红茶生产线设备的管控一体化功能,满足了现场生产信息可视化监控的要求,智能化程度高,整体技术水平先进。

养殖水体中校色卡识别与色彩变化分析

水下环境中颜色变化是一种常见现象,研究其变化特征可应用于水下图像处理和水产养殖生物监测等。利用校色卡分析不同水深颜色变化的特点,实验中首先对图像进行大小归一化及颜色空间转换;其次用图像与标准色块对比计算差值图像;然后进行轮廓提取并筛选出可能的色块加入候选列表,通过候选列表中的色块计算出原图色卡的位置;再通过仿射变换将原图色卡提取出来;最后,通过比较不同水深图像中色卡色块的颜色值,分析水下环境中的颜色变化。实验结果表明,该方法能够有效地检测和分析水下环境中的色卡,在0~3 m的浅水域中检测准确率达到100%,在3~10 m的中层水域中检测准确率达到93%,在大于10 m背景较为复杂深水域中提取准确率达到73.7%,在占用极小内存空间的情况下达到了较高的识别准确率;通过水下颜色分析得出:随着水深的增加,红色光的吸收和衰减最明显,蓝色光的吸收和衰减也比较多,而绿色光的影响较小。研究内容为利用水下摄影研究水产养殖生物行为提供了参考方法。

基于改进HSV空间的机器视觉花生霉变检测方法

花生霉变产生的黄曲霉毒素具有强致癌性,严重影响食品安全。为精准快速的识别霉变花生,提出一种基于机器视觉的霉变花生检测方法。首先对花生图像进行双边滤波降噪,然后将图像转为色调、饱和度、亮度(HSV)空间,通过在色调、饱和度空间内提取的霉变颜色范围叠加亮度空间的开运算处理结果来实现对霉变花生的识别检测。实验结果表明,该方法对于霉变花生的识别精度达到95.3%,处理单帧花生图像耗时为0.6s,通过与其它算法对比,该方法具有快速、准确率高等优点,可以满足霉变花生的实时检测,对花生霉变的分级处理也更加实用。

语义驱动的颜色恒常决策算法

不同颜色恒常性算法适用于不同场景下的图像,算法融合是扩展颜色恒常性算法适用范围常用的方法之一,而现有融合性算法在算法选择依据上忽略了语义信息在图像纹理特征描述中的作用,导致光源估计时的精度不高。针对该问题,提出一种语义驱动的颜色恒常决策算法。首先,利用PSPNet(Pyramid Scene Parsing Network)模型对经过一阶灰度边缘算法(1st Gray Edge)偏色预处理后的目标图像进行场景语义分割,并计算场景中各个语义类别的占比;其次,根据语义类别及占比在已训练的决策集合中寻找相似的参考图像,并使用欧氏距离计算两者的语义相似度;最后,将语义相似度与基于多维欧氏空间确定的阈值进行判别,根据判别结果选择合适算法为目标图像实行偏色校正。在Color Checker和NUS-8 camera两种数据集中的实验结果表明,所提算法光源估计角度误差较单一算法均大幅度下降,且较同类型融合性算法分别下降14.02%和8.17%,提高了光源估计的鲁棒性和准确度。

基于图像处理的数码印花棉织物色差检测

织物色差等级评定目前主要依赖于人工对比标准样卡进行判定,其等级判断易受人为主观因素影响。为对棉织物色差进行客观、全局化评定,文中提出3种基于OpenCV的织物色差评定方法:首先在D_(65)光源下获取标准样卡的不同色差等级灰度图像,使用高斯滤波对图像进行降噪,然后分别采用MSE均方误差、PSNR峰值信噪比、SSIM结构相似性指数算法对标准样卡灰度图像进行量化处理,建立并寻找最佳模型。为验证模型的准确性,采用数码印花对经过不同前处理工艺的棉织物喷印染色作为测试样本,通过模型得到色差等级,并与人工评定色差结果对比。结果表明:3种图像处理评定织物色差模型的评定结果均与人工评定具有高度一致性,图像处理方法更加简洁、客观和高效。

基于阈值识别的温室黄瓜整体自动落蔓装置研究

针对长季节黄瓜作物人工落蔓强度大、成本高和效率低等问题,设计一种基于冠层高度阈值识别的温室黄瓜整体自动落蔓装置。为保证黄瓜冠层特征提取的正确率,对8:00、12:00和18:00实时采集的黄瓜冠层图像进行预处理,基于Otsu算法获得平滑黄瓜冠层二值图像。采用霍夫变换算法识别黄瓜冠层顶端绳束,并利用统计学方法归纳出感兴趣区域中HSV色彩空间绿色部分各分量取值范围,研究感兴趣区域中绿色像素点占总像素点的比值变化规律。为防止黄瓜卷须缠绕秧蔓冠层顶端水平绳束,以黄瓜植株高度184~185 cm为落蔓控制目标,确定感兴趣区域绿色像素占比的平均值0.162~0.182为整体落蔓装置的落蔓阈值范围。结果表明,植株高度184~185 cm和高于185 cm落蔓次数占总试验次数比例分别为90%和10%。高于185 cm落蔓组中,最高植株距离顶端绳束(195 cm)的距离为9.2 cm,无黄瓜卷须缠绕黄瓜秧蔓冠层顶端水平绳束现象,采用感兴趣区域绿色像素点占比确定黄瓜落蔓阈值方法,能够满足黄瓜生产实际要求。

Adaptive Segmentation for Unconstrained Iris Recognition

In standard iris recognition systems,a cooperative imaging framework is employed that includes a light source with a near-infrared wavelength to reveal iris texture,look-and-stare constraints,and a close distance requirement to the capture device.When these conditions are relaxed,the system’s performance significantly deteriorates due to segmentation and feature extraction problems.Herein,a novel segmentation algorithm is proposed to correctly detect the pupil and limbus boundaries of iris images captured in unconstrained environments.First,the algorithm scans the whole iris image in the Hue Saturation Value(HSV)color space for local maxima to detect the sclera region.The image quality is then assessed by computing global features in red,green and blue(RGB)space,as noisy images have heterogeneous characteristics.The iris images are accordingly classified into seven categories based on their global RGB intensities.After the classification process,the images are filtered,and adaptive thresholding is applied to enhance the global contrast and detect the outer iris ring.Finally,to characterize the pupil area,the algorithm scans the cropped outer ring region for local minima values to identify the darkest area in the iris ring.The experimental results show that our method outperforms existing segmentation techniques using the UBIRIS.v1 and v2 databases and achieved a segmentation accuracy of 99.32 on UBIRIS.v1 and an error rate of 1.59 on UBIRIS.v2.

基于改进生成对抗网络的图像风格迁移算法

图像风格迁移是图像处理领域的研究热点,但目前风格迁移模型存在生成图像细节模糊、风格纹理的色彩效果较差以及模型参数过多等问题。文中提出了一种基于改进循环一致性生成对抗网络的图像风格迁移方法,通过加入Ghost卷积模块和反残差改进模块来优化生成器网络结构,以此降低模型参数量和计算成本。同时能增强网络的特征提取能力,在损失函数中加入内容风格损失项、颜色重建损失项和映射一致性损失项来改善模型的生成能力,提升生成图像质量。实验结果表明,所提改进方法具有较强的风格迁移能力,有效增强了生成图像的内容细节和风格纹理的色彩效果,显著提升了图像质量,模型性能也得到了改善。

迎接“AI+设计”:以彩色化技术研究进展为例

目的以彩色化技术为主要研究内容,从人工智能结合设计的角度重点阐述了彩色化研究的技术特点及适用场景,结合人工智能背景对智能设计发展趋势进行思考和总结。方法首先,对人工智能彩色化研究的技术需求、应用场景进行融合分析,推导人工智能与设计创作结合的可能性;其次,对彩色化传统方法与人工智能基础技术进行调研,以交互设计的理念为灵感启发,将人工智能的彩色化方法分为显式交互彩色化和隐式交互彩色化并进行归纳和总结;最后讨论研究彩色化技术的应用对象、预处理步骤和实践体验。结果总结概括彩色化技术结合人工智能设计的发展趋势与未来技术重点。结论“AI+设计”的趋势背景下,以彩色化为代表的人工智能数字图像处理技术正成为设计师的辅助工具,在科技、艺术、影视、人文等领域展现出交叉融合的大趋势。

解码色彩:剖析数字图像处理对色彩模型的多维抉择

随着人工智能的全面爆发,如何将图像数字化也成为热点话题。在编程语言中处理颜色时,会遇到专业的术语(颜色模型、颜色空间和颜色配置文件)来描述颜色在色谱中的位置。研究旨在探究在数字图像处理中选择最佳色彩模型的难点,并分析不同色彩模型之间的差异。研究得出结论,选择最佳色彩模型仍然是彩色图像处理中的挑战之一。设计研究者需要根据具体的应用场景和需求,仔细评估不同色彩模型的特点,并选择最适合的模型。此外,未来的发展趋势包括结合人工智能、大数据和交互式技术,进一步提升色彩模型的应用效果。

HSI色系下的国画颜色再现技术研究

为实现国画作品的数字化重建,提出一套HSI色系下的国画颜色再现方案。基于国画材质反射特性对颜色信息特性加以分析,发现在实际操作中可以采用亮度匹配的方式实现国画作品的数字化再现,最后针对该颜色再现方法进行对比实验。经实验研究发现,所提出的国画颜色再现技术能够实现较高精度的色彩重建,重建效果十分接近拍摄效果。