INTEGRATION OF IMAGE-DERIVED AND POS-DERIVED FEATURES FOR IMAGE BLUR DETECTION

The image quality plays an important role for Unmanned Aerial Vehicle (UAV)’s applications. The small fixed wings UAV is suffering from the image blur due to the crosswind and the turbulence. Position and Orientation System (POS), which provides the position and orientation information, is installed onto an UAV to enable acquisition of UAV trajectory. It can be used to calculate the positional and angular velocities when the camera shutter is open. This study proposes a POS-assisted method to detect the blur image. The major steps include feature extraction, blur image detection and verification. In feature extraction, this study extracts different features from images and POS. The image-derived features include mean and standard deviation of image gradient. For POS-derived features, we modify the traditional degree-of-linear-blur (blinear) method to degree-of-motion-blur (bmotion) based on the collinear condition equations and POS parameters. Besides, POS parameters such as positional and angular velocities are also adopted as POS-derived features. In blur detection, this study uses Support Vector Machines (SVM) classifier and extracted features (i.e. image information, POS data, blinear and bmotion) to separate blur and sharp UAV images. The experiment utilizes SenseFly eBee UAV system. The number of image is 129. In blur image detection, we use the proposed degree-of-motion-blur and other image features to classify the blur image and sharp images. The classification result shows that the overall accuracy using image features is only 56%. The integration of image-derived and POS-derived features have improved the overall accuracy from 56% to 76% in blur detection. Besides, this study indicates that the performance of the proposed degree-of-motion-blur is better than the traditional degree-of-linear-blur.

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INTEGRATION OF IMAGE-DERIVED AND POS-DERIVED FEATURES FOR IMAGE BLUR DETECTION

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xli-b1-1051-2016 ◽

2016 ◽

Vol XLI-B1 ◽

pp. 1051-1055

Author(s):

Tee-Ann Teo ◽

Kai-Zhi Zhan

Keyword(s):

Feature Extraction ◽

Image Features ◽

Motion Blur ◽

Support Vector ◽

Svm Classifier ◽

Image Detection ◽

Blur Detection ◽

Angular Velocities ◽

Image Blur ◽

Position And Orientation

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Hybrid Tile based Feature Extraction and Support Vector Machine Base Content-Based Image Retrieval System for Medical Application

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.c5433.098319 ◽

2019 ◽

Vol 8 (3) ◽

pp. 3305-3310

Keyword(s):

Support Vector Machine ◽

Feature Extraction ◽

Image Retrieval ◽

Color Image ◽

Medical Application ◽

Recall Rate ◽

Image Features ◽

Support Vector ◽

Svm Classifier ◽

Feature Extraction Method

Through the landing of therapeutic endoscopes, earth perception satellites and individual telephones, content-based picture recovery (CBIR) has concerned critical consideration, activated by its broad applications, e.g., medicinal picture investigation, removed detecting, and individual re-distinguishing proof. Be that as it may, developing successful component extraction is as yet reported as an invigorating issue.In this paper, to overcome the feature extraction problems a hybrid Tile Based Feature Extraction (TBFE) is introduced. The TBFE algorithm is hybrid with the local binary pattern (LBP) and Local derivative pattern (LDP). These hybrid TBFE feature extraction method helps to extract the color image features in automatic manner. Support vector machine (SVM) is used as a classifier in this image retrieval approach to retrieve the images from the database. The hybrid TBFE along with the SVM classifier image retrieval is named as IR-TBFE-SVM. Experiments show that IR-TBFE-SVMdelivers a higher correctness and recall rate than single feature employed retrieval systems, and ownsdecentweight balancing and query efficiency performance.

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Value of radiomics in differential diagnosis of chromophobe renal cell carcinoma and renal oncocytoma

Abdominal Radiology ◽

10.1007/s00261-019-02269-9 ◽

2019 ◽

Vol 45 (10) ◽

pp. 3193-3201 ◽

Cited By ~ 3

Author(s):

Yajuan Li ◽

Xialing Huang ◽

Yuwei Xia ◽

Liling Long

Keyword(s):

Machine Learning ◽

Differential Diagnosis ◽

Cell Carcinoma ◽

Area Under The Curve ◽

Image Features ◽

Renal Tumors ◽

Support Vector ◽

Svm Classifier ◽

Renal Oncocytoma ◽

Lasso Regression

Abstract Purpose To explore the value of CT-enhanced quantitative features combined with machine learning for differential diagnosis of renal chromophobe cell carcinoma (chRCC) and renal oncocytoma (RO). Methods Sixty-one cases of renal tumors (chRCC = 44; RO = 17) that were pathologically confirmed at our hospital between 2008 and 2018 were retrospectively analyzed. All patients had undergone preoperative enhanced CT scans including the corticomedullary (CMP), nephrographic (NP), and excretory phases (EP) of contrast enhancement. Volumes of interest (VOIs), including lesions on the images, were manually delineated using the RadCloud platform. A LASSO regression algorithm was used to screen the image features extracted from all VOIs. Five machine learning classifications were trained to distinguish chRCC from RO by using a fivefold cross-validation strategy. The performance of the classifier was mainly evaluated by areas under the receiver operating characteristic (ROC) curve and accuracy. Results In total, 1029 features were extracted from CMP, NP, and EP. The LASSO regression algorithm was used to screen out the four, four, and six best features, respectively, and eight features were selected when CMP and NP were combined. All five classifiers had good diagnostic performance, with area under the curve (AUC) values greater than 0.850, and support vector machine (SVM) classifier showed a diagnostic accuracy of 0.945 (AUC 0.964 ± 0.054; sensitivity 0.999; specificity 0.800), showing the best performance. Conclusions Accurate preoperative differential diagnosis of chRCC and RO can be facilitated by a combination of CT-enhanced quantitative features and machine learning.

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A Structural Analysis Based Feature Extraction Method for OCR System For Myanmar Printed Document Images

International Journal of Computer Vision and Image Processing ◽

10.4018/ijcvip.2012010102 ◽

2012 ◽

Vol 2 (1) ◽

pp. 16-41 ◽

Cited By ~ 1

Author(s):

Htwe Pa Pa Win ◽

Phyo Thu Thu Khine ◽

Khin Nwe Ni Tun

Keyword(s):

Feature Extraction ◽

Structural Analysis ◽

Character Recognition ◽

Optical Character Recognition ◽

Extraction Method ◽

Recognition Performance ◽

Extraction Methods ◽

Support Vector ◽

Svm Classifier ◽

Feature Extraction Method

This paper proposes a new feature extraction method for off-line recognition of Myanmar printed documents. One of the most important factors to achieve high recognition performance in Optical Character Recognition (OCR) system is the selection of the feature extraction methods. Different types of existing OCR systems used various feature extraction methods because of the diversity of the scripts’ natures. One major contribution of the work in this paper is the design of logically rigorous coding based features. To show the effectiveness of the proposed method, this paper assumed the documents are successfully segmented into characters and extracted features from these isolated Myanmar characters. These features are extracted using structural analysis of the Myanmar scripts. The experimental results have been carried out using the Support Vector Machine (SVM) classifier and compare the pervious proposed feature extraction method.

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Aircraft Type Recognition in Remote Sensing Images Based on Feature Learning with Conditional Generative Adversarial Networks

Remote Sensing ◽

10.3390/rs10071123 ◽

2018 ◽

Vol 10 (7) ◽

pp. 1123 ◽

Cited By ~ 8

Author(s):

Yuhang Zhang ◽

Hao Sun ◽

Jiawei Zuo ◽

Hongqi Wang ◽

Guangluan Xu ◽

...

Keyword(s):

Remote Sensing ◽

Feature Extraction ◽

Loss Function ◽

Extraction Method ◽

Generative Adversarial Networks ◽

Support Vector ◽

Svm Classifier ◽

Feature Extraction Method ◽

Adversarial Networks ◽

Aircraft Type

Aircraft type recognition plays an important role in remote sensing image interpretation. Traditional methods suffer from bad generalization performance, while deep learning methods require large amounts of data with type labels, which are quite expensive and time-consuming to obtain. To overcome the aforementioned problems, in this paper, we propose an aircraft type recognition framework based on conditional generative adversarial networks (GANs). First, we design a new method to precisely detect aircrafts’ keypoints, which are used to generate aircraft masks and locate the positions of the aircrafts. Second, a conditional GAN with a region of interest (ROI)-weighted loss function is trained on unlabeled aircraft images and their corresponding masks. Third, an ROI feature extraction method is carefully designed to extract multi-scale features from the GAN in the regions of aircrafts. After that, a linear support vector machine (SVM) classifier is adopted to classify each sample using their features. Benefiting from the GAN, we can learn features which are strong enough to represent aircrafts based on a large unlabeled dataset. Additionally, the ROI-weighted loss function and the ROI feature extraction method make the features more related to the aircrafts rather than the background, which improves the quality of features and increases the recognition accuracy significantly. Thorough experiments were conducted on a challenging dataset, and the results prove the effectiveness of the proposed aircraft type recognition framework.

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Deep Learning Assisted Neonatal Cry Classification via Support Vector Machine Models

Frontiers in Public Health ◽

10.3389/fpubh.2021.670352 ◽

2021 ◽

Vol 9 ◽

Author(s):

Ashwini K ◽

P. M. Durai Raj Vincent ◽

Kathiravan Srinivasan ◽

Chuan-Yu Chang

Keyword(s):

Neural Network ◽

Machine Learning ◽

Support Vector Machine ◽

Feature Extraction ◽

Deep Learning ◽

Convolutional Neural Network ◽

Support Vector ◽

Svm Classifier ◽

Infant Cry ◽

Learning Techniques

Neonatal infants communicate with us through cries. The infant cry signals have distinct patterns depending on the purpose of the cries. Preprocessing, feature extraction, and feature selection need expert attention and take much effort in audio signals in recent days. In deep learning techniques, it automatically extracts and selects the most important features. For this, it requires an enormous amount of data for effective classification. This work mainly discriminates the neonatal cries into pain, hunger, and sleepiness. The neonatal cry auditory signals are transformed into a spectrogram image by utilizing the short-time Fourier transform (STFT) technique. The deep convolutional neural network (DCNN) technique takes the spectrogram images for input. The features are obtained from the convolutional neural network and are passed to the support vector machine (SVM) classifier. Machine learning technique classifies neonatal cries. This work combines the advantages of machine learning and deep learning techniques to get the best results even with a moderate number of data samples. The experimental result shows that CNN-based feature extraction and SVM classifier provides promising results. While comparing the SVM-based kernel techniques, namely radial basis function (RBF), linear and polynomial, it is found that SVM-RBF provides the highest accuracy of kernel-based infant cry classification system provides 88.89% accuracy.

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Persian Handwritten Digit Recognition Using Combination of Convolutional Neural Network and Support Vector Machine Methods

The International Arab Journal of Information Technology ◽

10.34028/iajit/17/4/16 ◽

2020 ◽

Vol 17 (4) ◽

pp. 572-578

Author(s):

Mohammad Parseh ◽

Mohammad Rahmanimanesh ◽

Parviz Keshavarzi

Keyword(s):

Neural Network ◽

Support Vector Machine ◽

Feature Extraction ◽

Convolutional Neural Network ◽

Recognition Rate ◽

Support Vector ◽

Svm Classifier ◽

Handwritten Digit Recognition ◽

Digit Recognition ◽

Handwritten Digit

Persian handwritten digit recognition is one of the important topics of image processing which significantly considered by researchers due to its many applications. The most important challenges in Persian handwritten digit recognition is the existence of various patterns in Persian digit writing that makes the feature extraction step to be more complicated.Since the handcraft feature extraction methods are complicated processes and their performance level are not stable, most of the recent studies have concentrated on proposing a suitable method for automatic feature extraction. In this paper, an automatic method based on machine learning is proposed for high-level feature extraction from Persian digit images by using Convolutional Neural Network (CNN). After that, a non-linear multi-class Support Vector Machine (SVM) classifier is used for data classification instead of fully connected layer in final layer of CNN. The proposed method has been applied to HODA dataset and obtained 99.56% of recognition rate. Experimental results are comparable with previous state-of-the-art methods

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An Automated Brain Tumour Boundary Detection using Region based Segmentation Technique along with SVM Classifier

International Journal of Engineering and Advanced Technology - Regular Issue ◽

10.35940/ijeat.f8364.088619 ◽

2019 ◽

Vol 8 (6) ◽

pp. 2523-2531

Keyword(s):

Brain Tumour ◽

Brain Area ◽

Boundary Detection ◽

Image Features ◽

Surrounding Tissue ◽

Support Vector ◽

Svm Classifier ◽

Efficient Manner ◽

Segmentation Technique ◽

Automated Method

We presents a fully automated method for an automated brain-tumour boundary detection using region based segmentation technique along with SVM Classifier of Magnetic Resonance Imaging (MRI).The procedure is based on artificial intelligence technique and classification of each super-pixel in MRI. A number of novel image features extraction approaches including intensity-based, texture based, fractal analysis and curvatures are calculated from each super-pixel within the entire brain area in MRI to ensure a robust classification. Brain tumor is the malignant types of cancer and their classification in earlier stage is biggest issue. While curable with early classification is useful, only extremely trained specialists are capable of accurately recognizing the cancer from skin MRI data. As expertise is in limited contribute, an automated systems capable of classifying cancer could save human lives, and also help to reduce unnecessary MRI, and reduce extra costs. On the way to achieve this goal, we proposed a Brain Tumour Detection and Classification System (BTDCS) that combines recent developments in machine learning with Support Vector Machine (SVM) structure, creating hybrid algorithm of threshold based segmentation with Maximally Stable External Regions (MSER) that are capable of segmenting accurate super-pixel region from MRI, as well as analyzing the detected area and surrounding tissue for malignant. Using threshold based segmentation technique, the foreground and background component is separated into two regions. To improve the segmentation results, MSER is used with the novel concept of region detection and feature extraction mechanism. The proposed system is evaluated using the largest publicly accessible standard BRATS 2015 dataset of MRI, containing benign and malignant images. When the evaluation parameters of proposed work is compared with a few other state-of-art methods, the proposed means attains the best performance of 98.2% concerning classification accuracy using only the MSER approach and SVM as classifier. The ultimate aim of this research is to devise an automated experimental approach that can segment the tumor boundary in a fast and efficient manner.

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Efficient Feature Descriptor using Gabor Filter and Principal Component Analysis for Glaucoma Diagnosis

Design Engineering ◽

10.17762/de.vi.7730 ◽

2021 ◽

pp. 6787-6794

Author(s):

Anisha Rebinth, Dr. S. Mohan Kumar

Keyword(s):

Principal Component Analysis ◽

Gabor Filter ◽

Principal Component ◽

Component Analysis ◽

Disease Diagnosis ◽

Image Features ◽

Support Vector ◽

Svm Classifier ◽

Feature Descriptor ◽

Glaucoma Diagnosis

An automated Computer Aided Diagnosis (CAD) system for glaucoma diagnosis using fundus images is developed. The various glaucoma image classification schemes using the supervised and unsupervised learning approaches are reviewed. The research paper involves three stages of glaucoma disease diagnosis. First, the pre-processing stage the texture features of the fundus image is recorded with a two-dimensional Gabor filter at various sizes and orientations. The image features are generated using higher order statistical characteristics, and then Principal Component Analysis (PCA) is used to select and reduce the dimension of the image features. For the performance study, the Gabor filter based features are extracted from the RIM-ONE and HRF database images, and then Support Vector Machine (SVM) classifier is used for classification. Final stage utilizes the SVM classifier with the Radial Basis Function (RBF) kernel learning technique for the efficient classification of glaucoma disease with accuracy 90%.

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Color–Texture Pattern Classification Using Global–Local Feature Extraction, an SVM Classifier, with Bagging Ensemble Post-Processing

Applied Sciences ◽

10.3390/app9153130 ◽

2019 ◽

Vol 9 (15) ◽

pp. 3130 ◽

Cited By ~ 3

Author(s):

Navarro ◽

Perez

Keyword(s):

Feature Extraction ◽

Pattern Classification ◽

Texture Classification ◽

Support Vector ◽

Svm Classifier ◽

Post Processing ◽

Image Partition ◽

Global And Local ◽

Bagging Ensemble ◽

Color Texture

Many applications in image analysis require the accurate classification of complex patterns including both color and texture, e.g., in content image retrieval, biometrics, and the inspection of fabrics, wood, steel, ceramics, and fruits, among others. A new method for pattern classification using both color and texture information is proposed in this paper. The proposed method includes the following steps: division of each image into global and local samples, texture and color feature extraction from samples using a Haralick statistics and binary quaternion-moment-preserving method, a classification stage using support vector machine, and a final stage of post-processing employing a bagging ensemble. One of the main contributions of this method is the image partition, allowing image representation into global and local features. This partition captures most of the information present in the image for colored texture classification allowing improved results. The proposed method was tested on four databases extensively used in color–texture classification: the Brodatz, VisTex, Outex, and KTH-TIPS2b databases, yielding correct classification rates of 97.63%, 97.13%, 90.78%, and 92.90%, respectively. The use of the post-processing stage improved those results to 99.88%, 100%, 98.97%, and 95.75%, respectively. We compared our results to the best previously published results on the same databases finding significant improvements in all cases.

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