Species classification of aquatic plants using GRNN and BPNN

Computer-aided plant species identification acts significantly on plant digital museum system and systematic botany, which is the groundwork for research and development of plants. This work presents a method for plant species identification using the images of flowers. It focuses on the stable feature extraction of flowers such as color, texture and shape features. Color-based segmentation using k-means clustering is used to extract the color features. Texture segmentation using texture filter is used to segment the image and obtain texture features. Sobel, Prewitt and Robert operators are used to extract the boundary of image and to obtain the shape features. From 405 images of flowers, color, texture and shape features are extracted. Classification of the plants into dry land plants and aquatic plants, the aquatic plant species into wet and marsh aquatic plants, wet aquatic plants into Iridaceae and Epilobium family and marsh aquatic plants into Malvaceae and Onagraceae family, the Iridaceae family is again classified into Babiana and Crocus species, the family Epilobium into Canum and Hirsutum, the family Malvaceae into Mallow and Pavonia, the family Onagraceae into Fuschia and Ludwigia species are done using general regression neural network and backpropagation neural network classifiers.     

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.     

融合颜色与纹理的复杂场景下的服装图像分割算法

针对复杂场景下拍摄到的服装图像的分割问题,提出一种基于先验知识的融合颜色和纹理特征的无监督分割算法。首先利用块截断编码思想将传统的三维颜色空间截断成为六维空间,得到更为精细的颜色特征,并结合改进的局部二值模式纹理特征实现对图像的特征描述;然后根据目标区域和背景区域在图像中出现的统计规律,提出了一种基于先验知识的两分法来对图像进行分割。由于对图像做了分块处理,因此在子图像块的基础上进行的图像分割将更加高效。实验表明,设计的算法能快速有效地将目标区域从各类不同的复杂场景中分割出来,且整个过程无须人工设定任何参数,对后续的图像理解和图像检索具有重要意义。     

基于多尺度特征融合的SAR图像分割

由于存在相干斑噪声的影响,给SAR图像分割造成很大的困难,提出一种基于多尺度特征融合的SAR图像分割方法。该方法利用快速离散curvelet变换提取图像的纹理特征,利用平稳小波变换提取图像的统计特征,将两种多尺度特征融合成高维的特征向量,采用模糊C均值聚类的方法进行分割。在仿真SAR图像和真实SAR图像的分割实验结果表明,提出的方法优于单独采用小波变换进行SAR图像分割的方法,在消除均质区内碎块的同时,使得边界更为精准和平滑。     

Robust image segmentation using resampling and shape constraints

Automated segmentation of images has been considered an important intermediate processing task to extract semantic meaning from pixels. We propose an integrated approach for image segmentation based on a generative clustering model combined with coarse shape information and robust parameter estimation. The sensitivity of segmentation solutions to image variations is measured by image resampling. Shape information is included in the inference process to guide ambiguous groupings of color and texture features. Shape and similarity-based grouping information is combined into a semantic likelihood map in the framework of Bayesian statistics. Experimental evidence shows that semantically meaningful segments are inferred even when image data alone gives rise to ambiguous segmentations.     

结合纹理特征改进的GBIS图像分割方法

针对GBIS(efficient graph-based image segmentation)方法在分割含有较丰富纹理信息的图像时, 分割效果不理想的问题, 在L*a*b*彩色空间下, 结合图像的纹理特征, 提出了一种改进GBIS图像分割方法, 记为IGBIS(improved efficient graph-based image segmentation)。该方法首先将图像由RGB空间转换到L*a*b*颜色空间; 接着, 结合L*a*b*彩色空间, 对GBIS方法中的权值函数作了改进, 引入了一个常数s, 用于控制相邻像素之间颜色的差异程度; 然后, 用熵的方法来获取L*a*b*彩色图像的纹理特征; 最后, 结合图像的纹理信息, 改变了GBIS方法中的区域合并条件, 得到最终的分割结果。实验证明, 与原算法相比, 该方法在分割精度与分割质量上有了很大程度的提高。IGBIS有效地抑制了彩色图像在分割中存在的过分割现象, 并适合于含有丰富纹理的彩色图像。     

Color image segmentation using automatic pixel classification with support vector machine

Automatic segmentation of images is a very challenging fundamental task in computer vision and one of the most crucial steps toward image understanding. In this paper, we present a color image segmentation using automatic pixel classification with support vector machine (SVM). First, the pixel-level color feature is extracted in consideration of human visual sensitivity for color pattern variations, and the image pixel's texture feature is represented via steerable filter. Both the pixel-level color feature and texture feature are used as input of SVM model (classifier). Then, the SVM model (classifier) is trained by using fuzzy c-means clustering (FCM) with the extracted pixel-level features. Finally, the color image is segmented with the trained SVM model (classifier). This image segmentation not only can fully take advantage of the local information of color image, but also the ability of SVM classifier. Experimental evidence shows that the proposed method has a very effective segmentation results and computational behavior, and decreases the time and increases the quality of color image segmentation in compare with the state-of-the-art segmentation methods recently proposed in the literature.     

基于训练样本自动选取的SVM彩色图像分割方法

图像分割是模式识别、图像理解、计算机视觉等领域的重要研究内容。基于支持向量机((Support Vcctor Ma- chine, SVM)的方法现已广泛应用于图像分割,但其在训练样本的选取上大多是人工选择,这降低了图像分割的自适 应性,且影响了SVM的分类性能。提出一种基于训练样本自动选取的SVM彩色图像分割方法,算法首先使用模糊 C均值(Fuzzy C-Mcans, FCM)聚类算法自动获取训练样本,然后分别提取图像颜色特征和纹理特征,将其作为SVM 模型训练样本的特征属性进行训练,最后用训练好的分类器对图像进行分割。实验结果表明,提出的方法可取得很好 的分割结果。     

基于图像边缘与玻璃属性约束的窗户检测

窗户检测是建筑物图像理解的重要内容之一。为此提出了一种基于图像边缘与玻璃 属性约束的窗户检测方法。首先,将图像边缘作为窗户的初始定位,然后分别在边缘两侧提取局 部颜色和纹理特征,并将二者的差异性作为识别窗户的特征,最后,先采用Mahalanobis 距离分 类器进行局部含窗区域判决,再利用图像分割算法提取窗户的整体区域。实验表明,提出的算法 能在窗户形状结构未知的情况下,实现对建筑物表面的窗户有效检测。     

一种综合颜色和纹理特征的图像检索算法

对图像颜色特征和纹理特征进行了研究。在图像颜色特征方面,利用人类的视觉特性,对图像不同分块的主色进行确认和加权处理,获得加权主色颜色特征;在纹理特征方面,利用统计法和结构法构造灰度-差分基元共生矩阵来提取纹理特征。在此基础上,通过高斯归一化方法将颜色特征和纹理特征进行综合,形成最后的图像检索特征,并给出了利用该特征的图像检索算法。实验结果表明,所提出的灰度-差分基元共生矩阵特征提取较传统的灰度共生矩阵特征更加精细,在此基础上综合利用颜色和纹理特征的图像检索方法具有更好的检索精度。     

利用风格迁移和特征点的多模态图像配准算法

由于多模态遥感图像在光谱成份上存在巨大的差异,传统图像配准算法在该类图像的配准中正确率非常低.针对这一难题,提出了一种利用风格迁移和特征点的图像配准算法.首先,利用卷积神经网络对基准图像的风格特征以及待配准图像的内容特征进行抽取并重新组合,得到一幅与基准图像差异性较小的生成图像;其次,通过图像分割的方法分离出待配准图像...     

基于色彩分割与体态纹理分析的车牌定位方法

介绍了一种基于色彩分割、体态分析及数学形态学纹理分析的车牌定位方法。该方法利用车牌与背景的色彩特征．在HSV空间内根据车牌颜色的三分量取值范围和色彩距离阚值进行色彩分割以初步过滤背景。对分割后得到的与车牌底色相符合的连通区域再根据车牌体态特征和车牌字符纹理特征，应用数学形态学处理及连通域体态分析等手段，逐步过滤干扰区域，最终定位正确的车牌位置。该方法充分考虑了车牌与背景的色彩、体态及纹理特征的差别，实验证明在复杂背景下具有很强的适应性与鲁棒性。     

Segmentation of psoriasis vulgaris images using multiresolution-based orthogonal subspace techniques.

In this paper, a method is proposed for the segmentation of color images using a multiresolution-based signature subspace classifier (MSSC) with application to psoriasis images. The essential techniques consist of feature extraction and image segmentation (classification) methods. In this approach, the fuzzy texture spectrum and the two-dimensional fuzzy color histogram in the hue-saturation space are first adopted as the feature vector to locate homogeneous regions in the image. Then these regions are used to compute the signature matrices for the orthogonal subspace classifier to obtain a more accurate segmentation. To reduce the computational requirement, the MSSC has been developed. In the experiments, the method is quantitatively evaluated by using a similarity function and compared with the well-known LS-SVM method. The results show that the proposed algorithm can effectively segment psoriasis images. The proposed approach can also be applied to general color texture segmentation applications.     

NeTra: A toolbox for navigating large image databases

We present here an implementation of NeTra, a prototype image retrieval system that uses color, texture, shape and spatial location information in segmented image regions to search and retrieve similar regions from the database. A distinguishing aspect of this system is its incorporation of a robust automated image segmentation algorithm that allows object- or region-based search. Image segmentation significantly improves the quality of image retrieval when images contain multiple complex objects. Images are segmented into homogeneous regions at the time of ingest into the database, and image attributes that represent each of these regions are computed. In addition to image segmentation, other important components of the system include an efficient color representation, and indexing of color, texture, and shape features for fast search and retrieval. This representation allows the user to compose interesting queries such as “retrieve all images that contain regions that have the color of object A, texture of object B, shape of object C, and lie in the upper of the image”, where the individual objects could be regions belonging to different images. A Java-based web implementation of NeTra is available at http://vivaldi.ece.ucsb.edu/Netra.     

基于K均值聚类和粒子群优化的多核SVM图像分割

图像分割是图像理解和计算机视觉的重要内容.针对单核SVM在进行图像分割过程中不能兼顾分割精度高和泛化性能好的问题,提出一种基于K均值聚类和优化多核SVM的图像分割算法.该算法首先运用K均值聚类算法自动选取训练样本,然后提取其颜色特征和纹理特征作为训练样本的特征属性,并使用其对构造的多核SVM分割模型进行训练,最后用粒子群优化算法对多核核参数、惩罚因子以及核权重系数联合寻优,使生成的多核SVM具有更好的分割性能.实验结果表明,本文方法在有效提取图像目标细节的同时,获得了更高的分割精度,与基于单核的SVM分割模型相比,具有更强的泛化能力.     

MDS-based segmentation model for the fusion of contour and texture cues in natural images

In this paper, we present an original image segmentation model based on a preliminary spatially adaptive non-linear data dimensionality reduction step integrating contour and texture cues. This new dimensionality reduction model aims at converting an input texture image into a noisy color image in order to greatly simplify its subsequent segmentation. In this latter de-texturing model, the (spatially adaptive) non-local constraints based on edge and contour cues allows us to efficiently regularize the reduced data (or the resulting de-textured color image) and to efficiently combine inhomogeneous region and edge based features in a data fusion/reduction model used as pre-processing step for a final segmentation task. In addition, a set of color/texture and edge-based adaptive spatial continuity constraints is imposed during the segmentation step. These improvements lead to an appealing and powerful two-step adaptive segmentation model, integrating contour and texture cues. Extensive experimental evaluation on the Berkeley image segmentation database demonstrates the efficiency of this hybrid segmentation model in terms of classification accuracy of pairwise pixels in the resulting segmentation map and in the precision–recall framework widespread used for evaluating contour detectors.     

颜色、纹理、形状及相关反馈在图像检索中的应用

图像数据库的不断庞大使基于内容的图像检索成为研究热点,目前主要集中于底层特征的相似度匹配。文章重点介绍了颜色特征中的分块主色法,纹理特征中的灰度共生矩阵法和形状特征中的小波变换及不变矩法。在利用单一特征检索的基础上,该文提出了一种综合利用上述三种特征共同进行检索的方法。同时,还将相关反馈技术融合到算法中,通过权值矩阵的正负调整及三种特征系数的调整来提高检索准确率。由实验数据表明,文中的方法是很有效的。     

Color image segmentation using pixel wise support vector machine classification

Image segmentation is an important tool in image processing and can serve as an efficient front end to sophisticated algorithms and thereby simplify subsequent processing. In this paper, we present a color image segmentation using pixel wise support vector machine (SVM) classification. Firstly, the pixel-level color feature and texture feature of the image, which is used as input of SVM model (classifier), are extracted via the local homogeneity model and Gabor filter. Then, the SVM model (classifier) is trained by using FCM with the extracted pixel-level features. Finally, the color image is segmented with the trained SVM model (classifier). This image segmentation not only can fully take advantage of the local information of color image, but also the ability of SVM classifier. Experimental evidence shows that the proposed method has a very effective segmentation results and computational behavior, and decreases the time and increases the quality of color image segmentation in comparison with the state-of-the-art segmentation methods recently proposed in the literature.