Search Results - (( parameter segmentation using algorithm ) OR ( image classification using algorithm ))
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1
Automatic Segmentation and Classification of Skin Lesions in Dermoscopic Images
Published 2024“…This proposed classifier achieved 97.9% classification accuracy on the ISIC dataset. In the third classification algorithm, hybrid features are extracted using AlexNet and VGG-16 through a transfer learning approach where parameter manipulation is implemented to simplify the network. …”
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2
Improvement on rooftop classification of worldview-3 imagery using object-based image analysis
Published 2019“…The accuracy of classification result using OBIA is insufficient to depend on the segmentation parameters, the selection of features, and the existence of spectrally mixed objects. …”
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3
Detection of corneal arcus using rubber sheet and machine learning methods
Published 2019“…The elements extracted from the confusion matrix parameters (i.e. accuracy, specificity, sensitivity, AUC, precision and f-score) are used in benchmarking the optimal performance of classification algorithms. …”
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4
Non-invasive gliomas grading using swarm intelligence algorithm / Muhammad Harith Ramli
Published 2017“…The basic feature extraction of minimum, maximum and mean of gray level values are used as the parameter to develop the prototype. Swarm intelligence (SI) algorithm is implemented because there are lot of previous works which prove that the SI is good for segmentation and classification. …”
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5
Extremal region detection and selection with fuzzy encoding for food recognition
Published 2019“…The first algorithm locates interest points in food images using an MSER. …”
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6
Extremal region selection for MSER detection in food recognition
Published 2021“…UECFOOD-100 and UNICT-FD1200 are the two food datasets used to benchmark the proposed algorithm. The results of this research have found that the ERS algorithm by using optimum parameters and thresholds, be able to reduce the number of extremal regions with sustained classification performance.…”
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7
Building detection using object-based Image analysis (OBIA) and machine learning (ML) algorithms / Hanani Mohd Shahar
Published 2020“…Thus, by enhancing the classification techniques in OBIA, building extraction accuracy using ML algorithms for medium resolution images can be improved and the expenses also can be reduced indirectly.…”
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8
Face expression recognition using artificial neural network (ANN) / Mazuraini Ghani
Published 2005“…This project is all about implementing the back-propagation neural network algorithm in classification of face expression. This project has 3 objectives. …”
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9
Extremal Region Selection for MSER Detection in Food Recognition
Published 2021“…UECFOOD-100 and UNICT-FD1200 are the two food datasets used to benchmark the proposed algorithm. The results of this research have found that the ERS algorithm by using optimum parameters and thresholds, be able to reduce the number of extremal regions with sustained classification performance.…”
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10
HEP-2 CELL IMAGES CLASSIFICATION BASED ON STATISTICAL TEXTURE ANALYSIS AND FUZZY LOGIC
Published 2014“…The extracted features will then be used as an input parameter to classify the staining pattern of the HEp-2 cell images by using Fuzzy Logic. …”
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Final Year Project -
11
Feature extraction and classification :a case study of classifying a simulated digital mammogram images using self-organizing maps (som)
Published 2007“…This feature extraction technique can be used to find five parameters which are the size, intensity, centroid X, centroid Y and region distribution of segmented regions . …”
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12
Quantifying forest disturbance using LiDAR data and time series Landsat images / Syaza Rozali
Published 2021“…Mcnemar‘s test (p-value <0.05) for SpectralLandsat + HeightALS using Random Forest classifier is 0.03. All objectives were achieved successfully and the findings shows that; 1) the higher the resolution of the fusion image, the higher the number of the scale parameter will be used in multi-resolution segmentation; 2) the accuracy of classification was improved when combining LiDAR and Landsat image and 3) quantifying the forest disturbance can be performed using NDVI, CHM and DI information.…”
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A joint Bayesian optimization for the classification of fine spatial resolution remotely sensed imagery using object-based convolutional neural networks
Published 2022“…A Bayesian technique was used to find the best parameters for the multiresolution segmentation (MRS) algorithm while the CNN model learns the image features at different layers, achieving joint optimization. …”
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15
Digital segmentation of skin diseases / Hadzli Hashim and Razali Abdul Hadi
Published 2004“…RGB colour variegations are useful features used by the domain's experts in their morphological learning method for skin disease classification. …”
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Research Reports -
16
Hybridization of SLIC and extra tree for object based image analysis in extracting shoreline from medium resolution satellite images
Published 2018“…Thus, the object-based approach is proposed using a combination of segmentation algorithms, namely Felzenswalb, Quickshift, and SLIC, together with 15 machine learning classifiers, to classify segmented images of Langkawi Island. …”
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Optimized techniques for landslide detection and characteristics using LiDAR data
Published 2018“…The segmentation process was optimized using Fuzzy-based Segmentation Parameter. …”
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18
Local gray level S-curve transformation – A generalized contrast enhancement technique for medical images
Published 2017“…The proposed technique can be used as a preprocessing tool for effective segmentation and classification of tissue structures in medical images. © 2017 Elsevier Ltd…”
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A Semi-Automatic Approach for Thermographic Inspection of Electrical Installations Within Buildings
Published 2012“…The classification accuracy of multilayered perceptron networks are also compared with discriminant analysis classifier and it is found that the multilayered perceptron network using Levenberg–Marquardt algorithm gives the best testing performance. …”
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Development of computer aided design system based on artificial neural network for macular hole detection
Published 2021“…There are browse image, pre-processing, segmentation, feature extraction and lastly classification steps. …”
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