Haralick texture features

Haralick texture features

Haralick's texture features [28] were calculated using the kharalick() function of the cytometry tool box [29] for Khoros (version 2.1 Pro, Khoral Research, Inc., Albuquerque, NM USA; http://www.khoral.com). The basis for these features is the gray-level co-occurrence matrix ( G in Equation 2.6). This matrix is square with dimension Ng, where Ng is the number of gray levels in the image. Element [i,j] of the matrix is generated by counting the number of times a pixel with value i is adjacent to a pixel with value j and then dividing the entire matrix by the total number of such comparisons made. Each entry is therefore considered to be the probability that a pixel with value i will be found adjacent to a pixel of value j

 Haralick texture features_第1张图片 (2.6)

Since adjacency can be defined to occur in each of four directions in a 2D, square pixel image (horizontal, vertical, left and right diagonals - see Figure  2.2 ), four such matrices can be calculated.


   
Figure 2.2: Four directions of adjacency as defined for calculation of the Haralick texture features. The Haralick statistics are calculated for co-occurrence matrices generated using each of these directions of adjacency.
\begin{figure}\begin{center}\includegraphics[width=4in]{haralick_neighbors.eps}\end{center}\end{figure}

Zernike moments through degree 12 were calculated (Znl such that $n\leq 12$ in Equation 2.4) using the code in Section 5.2.1 (p. [*]). Since the moments themselves are complex numbers and are sensitive to rotation of the image, the magnitudes of the moments were used as features (i.e. |Znl|) [21]. This provided 49 descriptive features for each image.

Haralick then described 14 statistics that can be calculated from the co-occurrence matrix with the intent of describing the texture of the image:


Haralick texture features_第2张图片 

Since rotation invariance is a primary criterion for any features used with these images, a kind of invariance was achieved for each of these statistics by averaging them over the four directional co-occurrence matrices. The maximal correlation coefficient was not calculated due to computational instability so there were 13 texture features for each image.


检测语言世界语中文简体中文繁体丹麦语乌克兰语乌兹别克语乌尔都语亚美尼亚语伊博语俄语保加利亚语僧伽罗语克罗地亚语冰岛语加利西亚语加泰罗尼亚语匈牙利语南非祖鲁语卡纳达语印地语印尼巽他语印尼爪哇语印尼语古吉拉特语哈萨克语土耳其语塔吉克语塞尔维亚语塞索托语威尔士语孟加拉语宿务语尼泊尔语巴斯克语布尔语(南非荷兰语)希伯来语希腊语德语意大利语意第绪语拉丁语拉脱维亚语挪威语捷克语斯洛伐克语斯洛文尼亚语斯瓦希里语旁遮普语日语格鲁吉亚语毛利语法语波兰语波斯尼亚语波斯语泰卢固语泰米尔语泰语海地克里奥尔语爱尔兰语爱沙尼亚语瑞典语白俄罗斯语立陶宛语索马里语约鲁巴语缅甸语罗马尼亚语老挝语芬兰语苗语英语荷兰语菲律宾语葡萄牙语蒙古语西班牙语豪萨语越南语阿塞拜疆语阿尔巴尼亚语阿拉伯语韩语马其顿语马尔加什语马拉地语马拉雅拉姆语马来语马耳他语高棉语齐切瓦语
世界语中文简体中文繁体丹麦语乌克兰语乌兹别克语乌尔都语亚美尼亚语伊博语俄语保加利亚语僧伽罗语克罗地亚语冰岛语加利西亚语加泰罗尼亚语匈牙利语南非祖鲁语卡纳达语印地语印尼巽他语印尼爪哇语印尼语古吉拉特语哈萨克语土耳其语塔吉克语塞尔维亚语塞索托语威尔士语孟加拉语宿务语尼泊尔语巴斯克语布尔语(南非荷兰语)希伯来语希腊语德语意大利语意第绪语拉丁语拉脱维亚语挪威语捷克语斯洛伐克语斯洛文尼亚语斯瓦希里语旁遮普语日语格鲁吉亚语毛利语法语波兰语波斯尼亚语波斯语泰卢固语泰米尔语泰语海地克里奥尔语爱尔兰语爱沙尼亚语瑞典语白俄罗斯语立陶宛语索马里语约鲁巴语缅甸语罗马尼亚语老挝语芬兰语苗语英语荷兰语菲律宾语葡萄牙语蒙古语西班牙语豪萨语越南语阿塞拜疆语阿尔巴尼亚语阿拉伯语韩语马其顿语马尔加什语马拉地语马拉雅拉姆语马来语马耳他语高棉语齐切瓦语  
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