Search Results - (( feature classification problems algorithm ) OR ( based segmentation method algorithm ))
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UNSUPERVISED SEGMENTATION OF CORAL REEF IMAGES BY USING COLOR AND TEXTURE FEATURES
Published 2022“…However, the major problem of JSEG algorithm is over-segmentation. The unsupervised image segmentation method groups local pixels that are homogeneous in low-level features into non-overlapped larger regions that may potentially correspond to objects or their parts without any training examples. …”
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Thesis -
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Edge detection and contour segmentation for fruit classification in natural environment / Khairul Adilah Ahmad
Published 2018“…This reserarch adapted a methodology of computer vision and algorithms that exploit image segmentation, feature extraction and fuzzy classification to guide the research activities. …”
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Extremal region detection and selection with fuzzy encoding for food recognition
Published 2019“…The performance of algorithms was measured based on classification accuracy, error rate, and precision and recall. …”
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Quranic diacritic and character segmentation and recognition using flood fill and k-nearest neighbors algorithm
Published 2019“…The diacritic detections are performed using a region-based algorithm with 89% accuracy and 95% improved by using flood fill segmentations method. 2DMED feature extraction accuracy was 90% for diacritics and 96% improved by applied CNN. …”
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Detection of corneal arcus using rubber sheet and machine learning methods
Published 2019“…The benchmark of the classification algorithm for CA is needed to analyze the optimal output of the algorithm. …”
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Geometric Feature Extraction for Identification and Classification of Overlapping Cells for Leukaemia
Published 2022“…This paper describes the study of overlapping leukaemia cells based on geometric features for identification and classification. …”
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Color Image Segmentation Based on Bayesian Theorem for Mobile Robot Navigation
Published 2009“…Experimental results show that the proposed algorithm works better than other two methods in terms of classifier accuracy with result of more than 99 percent successful segmentation of desired color in varying illumination. …”
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Geometric Feature Extraction for Identification and Classification of the Overlapping Cells for Leukaemia
Published 2018“…The average percentage of cell counting had tested by the proposed method. Extended-Minima Transform Watershed Segmentation Algorithm had successful increasing 36.89% of accuracy to WBC segmentation. …”
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Early detection of spots high water saturation for landslide prediction using thermal imaging analysis
“…There are three segmentation algorithm used in this study such as HSV, K-Means and Feature Matching. …”
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Improved Genetic Algorithm Multilayer Perceptron Network For Data Classification
Published 2017“…The performance of improved GA has been evaluated using highly complicated and multimodal benchmark test functions and compared with the standard GA. Based on the occurrences of the best result obtained by an algorithm across different test functions; it is proven that the proposed method outperforms standard GA. …”
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Early detection of high water saturation spots for landslide prediction using thermal image analysis
Published 2018“…There are three segmentation algorithm used in this study which are HSV, K-Means and Feature Matching. …”
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A New Approach in a Gray-Level Image Contrast Enhancement by using Fuzzy Logic Technique
Published 2015“…Fuzzy Logic technique represents a new approach for gray level image contrast enhancement. The image contrast problem is one of the main problems that confront the researchers in the field of digital image processing, such as in the biomedical image processing like X-Ray and MRI image segmentation for disease classification. …”
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An automated multimodal white matter hyperintensities identification in MRI brain images using image processing / Iza Sazanita Isa
Published 2018“…In parallel, the automated performance and manual delineation for WMH identification is validated to determine the degree of similarity between both the methods. In addition, this research also proposes to classify the WMH severity based on the features of segmented WMH. …”
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Book Section -
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An automated multimodal white matter hyperintensities identification in MRI brain images using image processing / Iza Sazanita Isa
Published 2018“…In parallel, the automated performance and manual delineation for WMH identification is validated to determine the degree of similarity between both the methods. In addition, this research also proposes to classify the WMH severity based on the features of segmented WMH. …”
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The Role of Machine Learning and Deep Learning Approaches for the Detection of Skin Cancer
Published 2023“…Most of the techniques found in this survey address these two problems. Some of the methods also categorize the type of cancer too.…”
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FEATURES EXTRACTION OF HEP-2 IMMUNOFLUORESCENCE PATTERNS BASED ON TEXTURE AND REGION OF INTEREST TECHNIQUES
Published 2013“…To differentiate between one pattern from another, image classification is done by evaluating the properties of internal image from features extraction and a boundary is drawn between Centromere and Nucleolar pattern. …”
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Final Year Project -
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Analytical framework for predicting online purchasing behavior in Malaysia using a machine learning approach
Published 2025“…The descriptive analysis examines purchasing behavior through correlation and regression analyses, while the predictive model uses decision trees (J48, Random Tree, REPTree), rule-based algorithms (JRip, OneR, PART), and clustering (K-Means) to identify patterns and predict trends. …”
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Formulating new enhanced pattern classification algorithms based on ACO-SVM
Published 2013“…ACO originally deals with discrete optimization problem.In applying ACO for solving SVM model selection problem which are continuous variables, there is a need to discretize the continuously value into discrete values.This discretization process would result in loss of some information and hence affects the classification accuracy and seeking time.In this algorithm we propose to solve SVM model selection problem using IACOR without the need to discretize continuous value for SVM.The second algorithm aims to simultaneously solve SVM model selection problem and selects a small number of features.SVM model selection and selection of suitable and small number of feature subsets must occur simultaneously because error produced from the feature subset selection phase will affect the values of SVM model selection and result in low classification accuracy.In this second algorithm we propose the use of IACOMV to simultaneously solve SVM model selection problem and features subset selection.Ten benchmark datasets were used to evaluate the proposed algorithms.Results showed that the proposed algorithms can enhance the classification accuracy with small size of features subset.…”
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