By Sabu M. Thampi, El-Sayed M. El-Alfy, Hideyuki Takagi, Selwyn Piramuthu, Thomas Hanne
This ebook encompasses a number of refereed and revised papers of clever Informatics music initially offered on the 3rd overseas Symposium on clever Informatics (ISI-2014), September 24-27, 2014, Delhi, India. The papers chosen for this song conceal a number of clever informatics and similar issues together with sign processing, trend acceptance, snapshot processing, info mining and their purposes.
Read Online or Download Advances in Intelligent Informatics (Advances in Intelligent Systems and Computing, Volume 320) PDF
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Additional resources for Advances in Intelligent Informatics (Advances in Intelligent Systems and Computing, Volume 320)
Prasad, P. P. Sinha The is the output pseudo color image with various extra details which a colored image has, without losing any other information. now can be used in digital color image processing operation which was not possible in earlier case . e. as the reference color image changes the resultant output pseudo color image changes. Therefore, a standard reference color image is chosen where all color shades are present, as in figure 2. In next section experimental results with different domains are presented and analyzed.
This process of quantization converts the input image in such a way that many of the pixel values in the given image become almost similar and thus instead of comparing all the intensity values with the cluster center, only a representative intensity value is compared. , if three intensity values are same then only one pixel is required for comparison. In this work the last three bits of the binary representation is replaced with zeroes. Aggregation is performed by selecting a reference pixel. , top and left) is computed and the reference pixel is replaced with this value.
The tumor region is then extracted using the intensity metric. A contour is evolved over the identified tumor region using Active Contour model (ACM) to extract exact tumor segment. Thirty five features including Gray Level Co-occurrence Matrix (GLCM) features, Gray Level Run Length Matrix features (GLRL), statistical features and shape based features are extracted from the tumor region. Neural network and Support Vector Machine (SVM) classifiers are trained using these features. Results indicate that Support vector machine classifier with quadratic kernel function performs better than Radial Basis Function (RBF) kernel function and neural network classifier with fifty hidden nodes performs better than twenty five hidden nodes.