A new method for automatic salient object segmentation is presented.Salient object segmentation is an important research area in the field of object recognition,image retrieval,image editing,scene reconstruction,and 2...A new method for automatic salient object segmentation is presented.Salient object segmentation is an important research area in the field of object recognition,image retrieval,image editing,scene reconstruction,and 2D/3D conversion.In this work,salient object segmentation is performed using saliency map and color segmentation.Edge,color and intensity feature are extracted from mean shift segmentation(MSS)image,and saliency map is created using these features.First average saliency per segment image is calculated using the color information from MSS image and generated saliency map.Then,second average saliency per segment image is calculated by applying same procedure for the first image to the thresholding,labeling,and hole-filling applied image.Thresholding,labeling and hole-filling are applied to the mean image of the generated two images to get the final salient object segmentation.The effectiveness of proposed method is proved by showing 80%,89%and 80%of precision,recall and F-measure values from the generated salient object segmentation image and ground truth image.展开更多
Color quantization is bound to lose spatial information of color distribution. If too much necessary spatial distribution information of color is lost in JSEG, it is difficult or even impossible for JSEG to segment im...Color quantization is bound to lose spatial information of color distribution. If too much necessary spatial distribution information of color is lost in JSEG, it is difficult or even impossible for JSEG to segment image correctly. Enlightened from segmentation based on fuzzy theories, soft class-map is constracted to solve that problem. The definitions of values and other related ones are adjusted according to the soft class-map. With more detailed values obtained from soft class map, more color distribution information is preserved. Experiments on a synthetic image and many other color images illustrate that JSEG with soft class-map can solve efficiently the problem that in a region there may exist color gradual variation in a smooth transition. It is a more robust method especially for images which haven＇ t been heavily blurred near boundaries of underlying regions.展开更多
An improved approach for J-value segmentation （JSEG） is presented for unsupervised color image segmentation. Instead of color quantization algorithm, an automatic classification method based on adaptive mean shift ...An improved approach for J-value segmentation （JSEG） is presented for unsupervised color image segmentation. Instead of color quantization algorithm, an automatic classification method based on adaptive mean shift （AMS） based clustering is used for nonparametric clustering of image data set. The clustering results are used to construct Gaussian mixture modelling （GMM） of image data for the calculation of soft J value. The region growing algorithm used in JSEG is then applied in segmenting the image based on the multiscale soft J-images. Experiments show that the synergism of JSEG and the soft classification based on AMS based clustering and GMM overcomes the limitations of JSEG successfully and is more robust.展开更多
To improve the segmentation quality and efficiency of color image,a novel approach which combines the advantages of the mean shift(MS) segmentation and improved ant clustering method is proposed.The regions which can ...To improve the segmentation quality and efficiency of color image,a novel approach which combines the advantages of the mean shift(MS) segmentation and improved ant clustering method is proposed.The regions which can preserve the discontinuity characteristics of an image are segmented by MS algorithm,and then they are represented by a graph in which every region is represented by a node.In order to solve the graph partition problem,an improved ant clustering algorithm,called similarity carrying ant model(SCAM-ant),is proposed,in which a new similarity calculation method is given.Using SCAM-ant,the maximum number of items that each ant can carry will increase,the clustering time will be effectively reduced,and globally optimized clustering can also be realized.Because the graph is not based on the pixels of original image but on the segmentation result of MS algorithm,the computational complexity is greatly reduced.Experiments show that the proposed method can realize color image segmentation efficiently,and compared with the conventional methods based on the image pixels,it improves the image segmentation quality and the anti-interference ability.展开更多
茶毫是红茶外形品质的重要评价指标,当前主要依赖于专业人员的感官评价,主观性强且评语抽象,缺乏客观化、数字化的品质评价手段。为构建茶毫品质数字化评价方法,采集3个不同茶毫品质等级的祁门红茶样品图像,采用HSV彩色图像分割技术对...茶毫是红茶外形品质的重要评价指标,当前主要依赖于专业人员的感官评价,主观性强且评语抽象,缺乏客观化、数字化的品质评价手段。为构建茶毫品质数字化评价方法,采集3个不同茶毫品质等级的祁门红茶样品图像,采用HSV彩色图像分割技术对感兴趣区域(Region of interest,ROI)提取HSV颜色空间分量特征,构建分割指数(Segmentation index,SI)检索得到茶毫、茶身和阴影的最佳分割阈值,采用掩膜法和像素点判别对图像分割效果进行定性和定量评价,并构建茶毫比例量化方法。结果表明,茶毫、茶身和阴影区域的平均分割准确率达到了98.70%,进一步通过茶毫比例量化结果获得祁门红茶3个茶毫品质等级(“显毫”“多毫”和“少毫”)的推荐毫量比例阈值。不同毫量梯度拼配茶样的线性回归分析(R2=0.958,P<0.01)及滇红、金骏眉的泛化应用效果表明,构建的茶毫品质数字化评价方法在不同毫量区间和不同红茶类别上具有较好的适应性。展开更多
准确识别彩色番茄果实成熟状态是实现机器人高效分类采摘的基础。该研究针对彩色番茄颜色多样、背景复杂、成熟度检测精度不高等问题,以多粒度理论为基础,提出一种“先分割,后检测”U-YOLOv8n细粒度的彩色番茄检测方法。首先,利用U-Net...准确识别彩色番茄果实成熟状态是实现机器人高效分类采摘的基础。该研究针对彩色番茄颜色多样、背景复杂、成熟度检测精度不高等问题,以多粒度理论为基础,提出一种“先分割,后检测”U-YOLOv8n细粒度的彩色番茄检测方法。首先,利用U-Net对彩色番茄串进行分割,将VGG16作为U-Net的主干网络,增强模型特征提取能力;引入全局注意力机制(global attention mechanism,GAM),以提升模型分割性能。对感兴趣区域(region of interest,ROI)进行分割,减少复杂背景对第二阶段检测任务的干扰。其次,以YOLOv8n为基准模型,构建C2f_MS模块,提升模型多尺度特征提取能力;采用SCDown模块替换部分Conv,降低计算冗余的同时,保留细节空间信息;去除小目标检测头及部分Neck层结构,降低计算负荷。结果表明,改进后U-Net的平均像素精确度(mean pixel accuracy,MPA)和平均交并比(mean intersection over union,MIoU)分别达94.35%和88.98%。改进后YOLOv8n的精确度、均值平均精度(m AP_(0.5))分别达93.5%和95.6%,相较于YOLOv8n分别提高了3.1和0.5个百分点;针对较难识别的半熟期,精确率、均值平均精度分别达91.8%和92.4%,较YOLOv8n提高了5.7和1.3个百分点;轻量化方面,较YOLOv8n计算量和参数量分别降低了31%和14%,帧率(frames per second,FPS)提高了41%。该方法可有效完成复杂背景下的彩色番茄成熟度检测任务,为彩色番茄的成熟度分级和智能采摘提供技术支持。展开更多
文摘A new method for automatic salient object segmentation is presented.Salient object segmentation is an important research area in the field of object recognition,image retrieval,image editing,scene reconstruction,and 2D/3D conversion.In this work,salient object segmentation is performed using saliency map and color segmentation.Edge,color and intensity feature are extracted from mean shift segmentation(MSS)image,and saliency map is created using these features.First average saliency per segment image is calculated using the color information from MSS image and generated saliency map.Then,second average saliency per segment image is calculated by applying same procedure for the first image to the thresholding,labeling,and hole-filling applied image.Thresholding,labeling and hole-filling are applied to the mean image of the generated two images to get the final salient object segmentation.The effectiveness of proposed method is proved by showing 80%,89%and 80%of precision,recall and F-measure values from the generated salient object segmentation image and ground truth image.
文摘Color quantization is bound to lose spatial information of color distribution. If too much necessary spatial distribution information of color is lost in JSEG, it is difficult or even impossible for JSEG to segment image correctly. Enlightened from segmentation based on fuzzy theories, soft class-map is constracted to solve that problem. The definitions of values and other related ones are adjusted according to the soft class-map. With more detailed values obtained from soft class map, more color distribution information is preserved. Experiments on a synthetic image and many other color images illustrate that JSEG with soft class-map can solve efficiently the problem that in a region there may exist color gradual variation in a smooth transition. It is a more robust method especially for images which haven＇ t been heavily blurred near boundaries of underlying regions.
文摘An improved approach for J-value segmentation （JSEG） is presented for unsupervised color image segmentation. Instead of color quantization algorithm, an automatic classification method based on adaptive mean shift （AMS） based clustering is used for nonparametric clustering of image data set. The clustering results are used to construct Gaussian mixture modelling （GMM） of image data for the calculation of soft J value. The region growing algorithm used in JSEG is then applied in segmenting the image based on the multiscale soft J-images. Experiments show that the synergism of JSEG and the soft classification based on AMS based clustering and GMM overcomes the limitations of JSEG successfully and is more robust.
基金Project(60874070) supported by the National Natural Science Foundation of China
文摘To improve the segmentation quality and efficiency of color image,a novel approach which combines the advantages of the mean shift(MS) segmentation and improved ant clustering method is proposed.The regions which can preserve the discontinuity characteristics of an image are segmented by MS algorithm,and then they are represented by a graph in which every region is represented by a node.In order to solve the graph partition problem,an improved ant clustering algorithm,called similarity carrying ant model(SCAM-ant),is proposed,in which a new similarity calculation method is given.Using SCAM-ant,the maximum number of items that each ant can carry will increase,the clustering time will be effectively reduced,and globally optimized clustering can also be realized.Because the graph is not based on the pixels of original image but on the segmentation result of MS algorithm,the computational complexity is greatly reduced.Experiments show that the proposed method can realize color image segmentation efficiently,and compared with the conventional methods based on the image pixels,it improves the image segmentation quality and the anti-interference ability.
文摘茶毫是红茶外形品质的重要评价指标,当前主要依赖于专业人员的感官评价,主观性强且评语抽象,缺乏客观化、数字化的品质评价手段。为构建茶毫品质数字化评价方法,采集3个不同茶毫品质等级的祁门红茶样品图像,采用HSV彩色图像分割技术对感兴趣区域(Region of interest,ROI)提取HSV颜色空间分量特征,构建分割指数(Segmentation index,SI)检索得到茶毫、茶身和阴影的最佳分割阈值,采用掩膜法和像素点判别对图像分割效果进行定性和定量评价,并构建茶毫比例量化方法。结果表明,茶毫、茶身和阴影区域的平均分割准确率达到了98.70%,进一步通过茶毫比例量化结果获得祁门红茶3个茶毫品质等级(“显毫”“多毫”和“少毫”)的推荐毫量比例阈值。不同毫量梯度拼配茶样的线性回归分析(R2=0.958,P<0.01)及滇红、金骏眉的泛化应用效果表明,构建的茶毫品质数字化评价方法在不同毫量区间和不同红茶类别上具有较好的适应性。
文摘准确识别彩色番茄果实成熟状态是实现机器人高效分类采摘的基础。该研究针对彩色番茄颜色多样、背景复杂、成熟度检测精度不高等问题,以多粒度理论为基础,提出一种“先分割,后检测”U-YOLOv8n细粒度的彩色番茄检测方法。首先,利用U-Net对彩色番茄串进行分割,将VGG16作为U-Net的主干网络,增强模型特征提取能力;引入全局注意力机制(global attention mechanism,GAM),以提升模型分割性能。对感兴趣区域(region of interest,ROI)进行分割,减少复杂背景对第二阶段检测任务的干扰。其次,以YOLOv8n为基准模型,构建C2f_MS模块,提升模型多尺度特征提取能力;采用SCDown模块替换部分Conv,降低计算冗余的同时,保留细节空间信息;去除小目标检测头及部分Neck层结构,降低计算负荷。结果表明,改进后U-Net的平均像素精确度(mean pixel accuracy,MPA)和平均交并比(mean intersection over union,MIoU)分别达94.35%和88.98%。改进后YOLOv8n的精确度、均值平均精度(m AP_(0.5))分别达93.5%和95.6%,相较于YOLOv8n分别提高了3.1和0.5个百分点;针对较难识别的半熟期,精确率、均值平均精度分别达91.8%和92.4%,较YOLOv8n提高了5.7和1.3个百分点;轻量化方面,较YOLOv8n计算量和参数量分别降低了31%和14%,帧率(frames per second,FPS)提高了41%。该方法可有效完成复杂背景下的彩色番茄成熟度检测任务,为彩色番茄的成熟度分级和智能采摘提供技术支持。
