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An enhanced segmentation technique and improved support vector machine classifier for facial image recognition

International Journal of Intelligent Computing and Cybernetics 2022年第2期15卷 302-317页

作者： Rangayya Virupakshappa nagabhushan patil Sharnbasva University KalaburagiIndia Poojya Doddappa Appa College of Engineering GulbargaIndia

Purpose-One of the challenging issues in computer vision and pattern recognition is face image *** studies based on face recognition were introduced in the past decades,but it has few classification issues in terms of poor ***,the authors proposed a novel model for face ***/methodology/approach-The proposed method consists of four major sections such as data acquisition,segmentation,feature extraction and ***,the images are transferred into grayscale images,and they pose issues that are eliminated by resizing the input *** contrast limited adaptive histogram equalization(CLAHE)utilizes the image preprocessing step,thereby eliminating unwanted noise and improving the image contrast ***,the active contour and level set-based segmentation(ALS)with neural network(NN)or ALS with NN algorithm is used for facial image ***,the four major kinds of feature descriptors are dominant color structure descriptors,scale-invariant feature transform descriptors,improved center-symmetric local binary patterns(ICSLBP)and histograms of gradients(HOG)are based on clour and texture ***,the support vector machine(SVM)with modified random forest(MRF)model for facial image ***-Experimentally,the proposed method performance is evaluated using different kinds of evaluation criterions such as accuracy,similarity index,dice similarity coefficient,precision,recall and F-score ***,the proposed method offers superior recognition performances than other state-of-art *** face recognition was analyzed with the metrics such as accuracy,precision,recall and F-score and attained 99.2,96,98 and 96%,***/value-The good facial recognition method is proposed in this research work to overcome threat to privacy,violation of rights and provide better security of data.

关键词： Face recognition Active contour and Level set-based segmentation Neural network algorithm Support vector machine Modified random forest classifier

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Ultrasound liver tumor segmentation using adaptively regularized kernel-based fuzzy C means with enhanced level set algorithm

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International Journal of Intelligent Computing and Cybernetics 2022年第3期15卷 438-453页

作者： Deepak S.Uplaonkar Virupakshappa nagabhushan patil Research Scholar in Electrical and Electronics Engineering Department Poojya Doddappa Appa College of EngineeringKalaburagiIndia Computer Science and Engineering Department Sharnbasva UniversityKalaburagiIndia Electrical and Electronics Engineering Department Poojya Doddappa Appa College of EngineeringKalaburagiIndia

Purpose-The purpose of this study is to develop a hybrid algorithm for segmenting tumor from ultrasound images of the ***/methodology/approach-After collecting the ultrasound images,contrast-limited adaptive histogram equalization approach(CLAHE)is applied as preprocessing,in order to enhance the visual quality of the images that helps in better ***,adaptively regularized kernel-based fuzzy C means(ARKFCM)is used to segment tumor from the enhanced image along with local ternary pattern combined with selective level set ***-The proposed segmentation algorithm precisely segments the tumor portions from the enhanced images with lower computation *** proposed segmentation algorithm is compared with the existing algorithms and ground truth values in terms of Jaccard coefficient,dice coefficient,precision,Matthews correlation coefficient,f-score and *** experimental analysis shows that the proposed algorithm achieved 99.18% of accuracy and 92.17% of f-score value,which is better than the existing *** implications-From the experimental analysis,the proposed ARKFCM with enhanced level set algorithm obtained better performance in ultrasound liver tumor segmentation related to graph-based ***,the proposed algorithm showed 3.11% improvement in dice coefficient compared to graph-based ***/value-The image preprocessing is carried out using CLAHE *** preprocessed image is segmented by employing selective level set model and Local Ternary Pattern in ARKFCM *** this research,the proposed algorithm has advantages such as independence of clustering parameters,robustness in preserving the image details and optimal in finding the threshold value that effectively reduces the computational cost.

关键词： Adaptively regularized kernel-based fuzzy C means Contrast-limited adaptive histogram equalization Level set algorithm Liver tumor segmentation Local ternary pattern

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