scholarly journals Automatic feature extraction and matching modelling for highly noise near-equatorial satellite images

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Hayder Dibs ◽  
Hashim Ali Hasab ◽  
Hussein Sabah Jaber ◽  
Nadhir Al-Ansari
Keyword(s):  
Feature Extraction ◽  
Satellite Images ◽  
Near Infrared ◽  
Satellite System ◽  
Absolute Difference ◽  
Scale Invariant ◽  
Frame Size ◽  
Automatic Feature Extraction ◽  
Scale Invariant Feature ◽  
Image Band

AbstractFeature extraction plays an important role in pattern recognition because band-to-band registration and geometric correction from different satellite images have linear image distortion. However, new near-equatorial orbital satellite system (NEqO) images is different because they have nonlinear distortion. Conventional techniques cannot overcome this type of distortion and lead to the extraction of false features and incorrect image matching. This research presents a new method by improving the performance of the Scale-Invariant Feature Transformation (SIFT) with a significantly higher rate of true extracted features and their correct matching. The data in this study were obtained from the RazakSAT satellite covering a part of Penang state, Malaysia. The method consists of many stages: image band selection, image band compression, image sharpening, automatic feature extraction, and applying the sum of absolute difference algorithm with an experimental and empirical threshold. We evaluate a refined features scenario by comparing the result of the original extracted SIFT features with corresponding features of the proposed method. The result indicates accurate and precise performance of the proposed method from removing false SIFT extracted features of satellite images and remain only true SIFT extracted features, that leads to reduce the extracted feature from using three frame size: (1) from 2000 to 750, 552 and 92 for the green and red bands image, (2) from 678 extracted control points to be 193, 228 and 73 between the green and blue bands, and (3) from 1995 extracted CPs to be 656, 733, and 556 between the green and near-infrared bands, respectively.

scholarly journals Dog Identification Method Based on Muzzle Pattern Image

2020 ◽  
Vol 10 (24) ◽  
pp. 8994
Author(s):  
Dong-Hwa Jang ◽  
Kyeong-Seok Kwon ◽  
Jung-Kon Kim ◽  
Ka-Young Yang ◽  
Jong-Bok Kim
Keyword(s):  
Feature Extraction ◽  
Radio Frequency Identification ◽  
Hamming Distance ◽  
Histogram Equalization ◽  
Scale Invariant ◽  
Speeded Up Robust Features ◽  
Identification Method ◽  
Approximate Nearest Neighbors ◽  
Frequency Identification ◽  
Scale Invariant Feature

Currently, invasive and external radio frequency identification (RFID) devices and pet tags are widely used for dog identification. However, social problems such as abandoning and losing dogs are constantly increasing. A more effective alternative to the existing identification method is required and the biometrics can be the alternative. This paper proposes an effective dog muzzle recognition method to identify individual dogs. The proposed method consists of preprocessing, feature extraction, matching, and postprocessing. For preprocessing, proposed resize and histogram equalization are used. For feature extraction algorithm, Scale Invariant Feature Transform (SIFT), Speeded Up Robust Features (SURF), Binary Robust Invariant Scaling Keypoints (BRISK) and Oriented FAST, and Rotated BRIEF (ORB) are applied and compared. For matching, Fast Library for Approximate Nearest Neighbors (FLANN) is used for SIFT and SURF, and hamming distance are used for BRISK and ORB. For postprocessing, two techniques to reduce incorrect matches are proposed. The proposed method was evaluated with 55 dog muzzle pattern images acquired from 11 dogs and 990 images augmented by the image deformation (i.e., angle, illumination, noise, affine transform). The best Equal Error Rate (EER) of the proposed method was 0.35%, and ORB was the most appropriate for the dog muzzle pattern recognition.

scholarly journals RESEARCH ON DIFFERENT FEATURE EXTRACTION AND MAMMOGRAM CLASSIFICATION TECHNIQUES

2019 ◽  
pp. 1-3
Author(s):  
Anita Kaklotar
Keyword(s):  
Feature Extraction ◽  
Association Rule ◽  
Support Vector ◽  
Common Disease ◽  
Extraction Techniques ◽  
Scale Invariant ◽  
Invariant Feature ◽  
Feature Transform ◽  
Scale Invariant Feature ◽  
Mammogram Images

Breast cancer is the primary and the most common disease found among women. Today, mammography is the most powerful screening technique used for early detection of cancer which increases the chance of successful treatment. In order to correctly detect the mammogram images as being cancerous or malignant, there is a need of a classier. With this objective, an attempt is made to analyze different feature extraction techniques and classiers. In the proposed system we rst do the preprocessing of the mammogram images, where the unwanted noise and disturbances in the mammograms are removed. Features are then extracted from the mammogram images using Gray Level Co-Occurrences Matrix (GLCM) and Scale Invariant Feature Transform (SIFT). Finally, the features are classied using classiers like HiCARe (Classier based on High Condence Association Rule Agreements), Support Vector Machine (SVM), Naïve Bayes classier and K-NN Classier. Further we test the images and classify them as benign or malignant class.

scholarly journals SEA ICE DRIFT MONITORING IN THE BOHAI SEA BASED ON GF4 SATELLITE

2018 ◽  
Vol XLII-3 ◽  
pp. 2419-2425 ◽  
Author(s):  
Y. Zhao ◽  
P. Wei ◽  
H. Zhu ◽  
B. Xing
Keyword(s):  
Remote Sensing ◽  
Sea Ice ◽  
Satellite Images ◽  
Bohai Sea ◽  
Scale Invariant ◽  
The Bohai Sea ◽  
Invariant Feature ◽  
Ice Drift ◽  
Sea Ice Drift ◽  
Scale Invariant Feature

The Bohai Sea is the inland sea with the highest latitude in China. In winter, the phenomenon of freezing occurs in the Bohai Sea due to frequent cold wave influx. According to historical records, there have been three serious ice packs in the Bohai Sea in the past 50 years which caused heavy losses to our economy. Therefore, it is of great significance to monitor the drift of sea ice and sea ice in the Bohai Sea. The GF4 image has the advantages of short imaging time and high spatial resolution. Based on the GF4 satellite images, the three methods of SIFT (Scale invariant feature – the transform and Scale invariant feature transform), MCC (maximum cross-correlation method) and sift combined with MCC are used to monitor sea ice drift and calculate the speed and direction of sea ice drift, the three calculation results are compared and analyzed by using expert interpretation and historical statistical data to carry out remote sensing monitoring of sea ice drift results. The experimental results show that the experimental results of the three methods are in accordance with expert interpretation and historical statistics. Therefore, the GF4 remote sensing satellite images have the ability to monitor sea ice drift and can be used for drift monitoring of sea ice in the Bohai Sea.

Human–Computer Interactive Gesture Feature Capture and Recognition in Virtual Reality

2020 ◽  
pp. 106480462092413 ◽  
Author(s):  
Fan Zhang
Keyword(s):  
Neural Network ◽  
Virtual Reality ◽  
Feature Extraction ◽  
Bp Neural Network ◽  
Back Propagation ◽  
Extraction Methods ◽  
Scale Invariant ◽  
Feature Vectors ◽  
Accuracy And Precision ◽  
Scale Invariant Feature

With the development of computer technology, the simulation authenticity of virtual reality technology is getting higher and higher, and the accurate recognition of human–computer interaction gestures is also the key technology to enhance the authenticity of virtual reality. This article briefly introduced three different gesture feature extraction methods: scale invariant feature transform, local binary pattern and histogram of oriented gradients (HOG), and back-propagation (BP) neural network for classifying and recognizing different gestures. The gesture feature vectors obtained by three feature extraction methods were used as input data of BP neural network respectively and were simulated in MATLAB software. The results showed that the information of feature gesture diagram extracted by HOG was the closest to the original one; the BP neural network that applied HOG extracted feature vectors converged to stability faster and had the smallest error when it was stable; in the aspect of gesture recognition, the BP neural network that applied HOG extracted feature vector had higher accuracy and precision and lower false alarm rate.

scholarly journals Sasirangan Motifs Classification using Scale- Invariant Feature Transform (SIFT) and Support Vector Machine (SVM)

2019 ◽  
Vol 280 ◽  
pp. 05023
Author(s):  
Muhammad Alkaff ◽  
Husnul Khatimi ◽  
Nur Lathifah ◽  
Yuslena Sari
Keyword(s):  
Support Vector Machine ◽  
Feature Extraction ◽  
Scale Invariant Feature Transform ◽  
Primary Data ◽  
Support Vector ◽  
Scale Invariant ◽  
Svm Classification ◽  
Invariant Feature ◽  
Feature Transform ◽  
Scale Invariant Feature

Sasirangan is one of the traditional cloth from Indonesia. Specifically, it comes from South Borneo. It has many variations of motifs with a different meaning for each pattern. This paper proposes a prototype of Sasirangan motifs classification using four (4) type of Sasirangan motifs namely Hiris Gagatas, Gigi Haruan, Kulat Kurikit, and Hiris Pudak. We used primary data of Sasirangan images collected from Kampung Sasirangan, Banjarmasin, South Kalimantan. After that, the images are processed using Scale-Invariant Feature Transform (SIFT) to extract its features. Furthermore, the extracted features vectors obtained is classified using the Support Vector Machine (SVM). The result shows that the Scale- Invariant Feature Transform (SIFT) feature extraction with Support Vector Machine (SVM) classification able to classify Sasirangan motifs with an overall accuracy of 95%.

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