一种融合颜色和空间信息的区域生长算法

该文提出了一种基于图像颜色和局部空间信息的种子区域生长算法,并用于彩色图像分割。该算法首先根据相对欧式距离使用均值聚类算法对图像进行颜色量化,形成图像的初始分割结果,然后通过计算局部颜色散度,进行分级区域合并,最后,利用形态学相关算法对分割区域的边缘进行平滑。实验表明,该算法能得到与人类视觉判断相一致的有意义区域的分割。     

种子区域生长技术在彩色图像分割中的应用

提出一种基于种子区域生长(Seeded Region Growing,SRG)技术的彩色图像分割方法.该算法利用L*a*b*颜色空间的象素与其邻域的颜色差异及相对欧式距离自动选择种子;应用SRG技术由已知的种子生长出初始分割区域;根据融合了颜色空间和邻接关系的区域距离对初始区域进行分级合并.算法克服了传统区域生长方法不能自动选择种子且容易导致过分割的局限性.将新的分割方法应用到彩色图像,并得到与视觉判断相一致的有意义的分割结果.实验结果显示了所提出的方法对于不同自然彩色图像分割的有效性与适应性.     

光突发包在高生存性弹性分组环上的传输实现

提出了一种基于图像颜色和空间信息的彩色图像分割算法。该算法首先根据所提出的颜色粗糙度概念对图像进行颜色量化,并在此基础上使用增量式的区域生长算法发现颜色相近的像素之间的空间连通,形成图像的初始分割区域。然后,根据融合了颜色和空间信息的区域距离,对初始分割区域进行分级合并,直到系统满足了所提出的停止区域合并的准则。最后,利用形态学的有关算法对分割区域的边缘进行平滑。实验证明,算法的分割结果与人的主观视觉感知具有良好的一致性。     

一种融合颜色和空间信息的彩色图像分割算法

提出了一种基于图像颜色和空间信息的彩色图像分割算法.该算法首先根据所提出的颜色粗糙度概念对图像进行颜色量化,并在此基础上使用增量式的区域生长算法发现颜色相近的像素之间的空间连通性,形成图像的初始分割区域.然后,根据融合了颜色和空间信息的区域距离,对初始分割区域进行分级合并,直到系统满足了所提出的停止区域合并的准则.最后,利用形态学的有关算法对分割区域的边缘进行平滑.实验证明,算法的分割结果与人的主观视觉感知具有良好的一致性.     

基于区域生长的彩色图像分割算法

针对传统种子区域生长算法在分割具有复杂纹理的彩色图像中存在的问题,提出一种改进的种子区域生长算法,该算法在YCbCr颜色空间中进行,采用离散余弦变换提取图像纹理特征值,进行自动种子及种子区域的生长,并用区域合并改善过度分割。实验结果表明,该算法能有效提高图像分割的精确性。     

基于改进分水岭算法的熏烤肉图像分割

针对传统分水岭算法容易产生过度分割的问题,提出一种改进分水岭算法,并用来分割熏烤肉表面颜色。算法先对熏烤肉原始图像作滤波预处理,然后作传统分水岭变换,对产生的过度分割区域,在RGB颜色空间中进行自动种子选取及种子区域生长,最后对剩余小区域进行合并得到分割图像。实验表明,该方法减少了过度分割现象,成功地分割熏烤肉表面颜色,为之后的分析工作奠定了基础。     

基于NBS颜色距离及半监督聚类的织物图像分割

为提高纺织CAD技术,对织物图像的分割进行了研究。依据半监督聚类理论,提出了一种基于HLC颜色空间的以NBS颜色距离为优化准则的半监督聚类的织物图像分割算法。算法利用有限的人工信息,即在织物图像上点击有限的几个点以标识相应区域之间的关系,从而得到满足用户给定限制的织物图像分割结果。算法首先对织物图像进行量化转换处理,而后在HLC色彩空间中集成先验的分割信息进行色彩聚类。实验结果表明,该算法在织物图像分割中是一种可行的方法。     

基于MSP-ROA边缘检测和区域合并的图像组合分割方法

提出一种基于MSP-ROA边缘检测和区域合并的图像组合分割算法，算法中对边缘检测的结果做种子生长、标注和区域填充，并根据相似性准则对填充的初始分割结果进行相邻区域的合并处理，最终得到同质性和连通性都较好的图像分割结果。     

基于Hessian矩阵和RSF模型的CT图像淋巴结分割

临床上医生分割淋巴结主要依靠手动,针对手动分割淋巴结的缺点和局限,本文提出一种基于Hessian矩阵和区域扩展拟合水平集模型(Region-Scalable Fitting,RSF)的淋巴结自动分割算法。该算法首先利用Hessian矩阵对CT图像中的淋巴结进行增强,并得到淋巴结粗略轮廓,然后把该粗略轮廓作为RSF模型的初始轮廓,并利用RSF模型对初始轮廓进行演化以实现淋巴结的有效分割。将该方法应用于6个病例的CT淋巴结图像中,初步实验结果与医生手动分割结果对比,平均重叠率93.3%,平均Hausdorff距离为3.8 mm。     

结合聚类与改进分水岭算法的彩色图像分割

针对传统分水岭算法产生严重的过分割问题,提出了一种聚类和改进分水岭算法结合的彩色图像分割算法.该算法首先利用聚类算法在HSV颜色空间将特征相似的像素归为一类,然后对分水岭算法产生的分割区域进行种子区域生长,并利用区域合并将剩余的小区域进行合并,从而完成了对彩色图像的分割.实验证明该算法减少了分水岭算法的过分割现象,提高...     

Color Image Segmentation for Multimedia Applications

Image segmentation is crucial for multimedia applications. Multimedia databases utilize segmentation for the storage and indexing of images and video. Image segmentation is used for object tracking in the new MPEG-7 video compression standard. It is also used in video conferencing for compression and coding purposes. These are only some of the multimedia applications in image segmentation. It is usually the first task of any image analysis process, and thus, subsequent tasks rely heavily on the quality of segmentation. The proposed method of color image segmentation is very effective in segmenting a multimedia-type image into regions. Pixels are first classified as either chromatic or achromatic depending on their HSI color values. Next, a seed determination algorithm finds seed pixels that are in the center of regions. These seed pixels are used in the region growing step to grow regions by comparing these seed pixels to neighboring pixels using the cylindrical distance metric. Merging regions that are similar in color is a final means used for segmenting the image into even smaller regions.     

基于矢量量化和区域生长的彩色图像分割新算法

针对光照变化和阴影对图像分割的不利影响问题,提出了一种基于矢量量化和区域生长的彩色图像分割新算法。该算法不仅考虑了彩色图像的颜色信息,而且也考虑了彩色图像的空间信息。该算法首先利用一种修改的GLA算法对彩色图像进行量化,并根据彩色图像量化的结果选取种子像素;然后基于矢量角相似性准则,并结合像素空间邻接信息,对每一个种子像素进行区域生长;最后利用模糊C-M eans算法来对未能归类的剩余像素进行分类。实验表明,该算法不仅可以在很大程度上克服光照变化及阴影对图像分割的不利影响,而且分割结果与人的主观视觉感知具有良好的一致性。     

基于半监督的超像素谱聚类彩色图像分割算法

近年来谱聚类算法被广泛应用于图像分割领域,而相似性矩阵的构造是谱聚类算法的关键步骤。 针对传统谱聚类算法计算复杂度高难以应用到大规模图像分割处理的问题,提出了基于半监督的超像素谱聚类彩色图像分割算法。该算法利用超像素将彩色图像进行预分割,利用用户提供的少量标记信息构造预分割区域的基于半监督的模糊相似性测度,利用该相似性测度构造预分隔区域的相似性矩阵并通过规范切图谱划分准则对预分割区域进行划分得到最终的图像分割结果。由于少量标记信息和模糊理论的引入,提高了传统谱聚类的分割性能,对比实验也表明该算法在分割效果和计算复杂度上都有较大的改善。     

Segmentation of small objects in color images

A method for effective segmentation of small objects in color images is presented. It can be used jointly with region growing algorithms. Segmentation of small objects in color images is a difficult problem because their boundaries are close to each other. The proposed algorithm accurately determines the location of the boundary points of closely located small objects and finds the skeletons (seed regions) of those objects. The method makes use of conditions obtained by analyzing the change of color characteristics of the edge pixels along the direction that is orthogonal to the boundaries of adjacent objects. These conditions are generalized for the case of the well-known class of color images having misregistration artifacts. If high-quality seed regions are available, the final segmentation can be performed using one of the region growing methods. The segmentation algorithm based on the proposed method was tested using a large number of color images, and it proved to be very efficient.     

基于局部同态性的自适应彩色图像分割方法

提出了一种基于局部同态性和空间邻接关系的彩色图像分割方法。首先利用局部同态性得到H图像来确定种子区域,进而进行区域生长形成图像的初始分割区域。然后利用颜色和空间邻接关系确定的区域距离进行区域合并,直到满足所给的停止区域合并的规则为止。实验结果表明了该方法的有效性。     

Color image segmentation based on multiobjective artificial bee colony optimization

This paper presents a new color image segmentation method based on a multiobjective optimization algorithm, named improved bee colony algorithm for multi-objective optimization (IBMO). Segmentation is posed as a clustering problem through grouping image features in this approach, which combines IBMO with seeded region growing (SRG). Since feature extraction has a crucial role for image segmentation, the presented method is firstly focused on this manner. The main features of an image: color, texture and gradient magnitudes are measured by using the local homogeneity, Gabor filter and color spaces. Then SRG utilizes the extracted feature vector to classify the pixels spatially. It starts running from centroid points called as seeds. IBMO determines the coordinates of the seed points and similarity difference of each region by optimizing a set of cluster validity indices simultaneously in order to improve the quality of segmentation. Finally, segmentation is completed by merging small and similar regions. The proposed method was applied on several natural images obtained from Berkeley segmentation database. The robustness of the proposed ideas was showed by comparison of hand-labeled and experimentally obtained segmentation results. Besides, it has been seen that the obtained segmentation results have better values than the ones obtained from fuzzy c-means which is one of the most popular methods used in image segmentation, non-dominated sorting genetic algorithm II which is a state-of-the-art algorithm, and non-dominated sorted PSO which is an adapted algorithm of PSO for multi-objective optimization.     

Color texture segmentation based on image pixel classification

Image segmentation partitions an image into nonoverlapping regions, which ideally should be meaningful for a certain purpose. Thus, image segmentation plays an important role in many multimedia applications. In recent years, many image segmentation algorithms have been developed, but they are often very complex and some undesired results occur frequently. By combination of Fuzzy Support Vector Machine (FSVM) and Fuzzy C-Means (FCM), a color texture segmentation based on image pixel classification is proposed in this paper. Specifically, we first extract the pixel-level color feature and texture feature of the image via the local spatial similarity measure model and localized Fourier transform, which is used as input of FSVM model (classifier). We then train the FSVM model (classifier) by using FCM with the extracted pixel-level features. Color image segmentation can be then performed through the trained FSVM model (classifier). Compared with three other segmentation algorithms, the results show that the proposed algorithm is more effective in color image segmentation.