scholarly journals AUTOMATED DETECTION OF LUNAR RIDGES BASED ON DEM DATA

2019 ◽  
Vol XLII-2/W13 ◽  
pp. 1431-1435
Author(s):  
M. Peng ◽  
Y. Wang ◽  
Z. Yue ◽  
K. Di
Keyword(s):  
Noise Removal ◽  
Automated Detection ◽  
Morphological Operations ◽  
Irregular Shapes ◽  
Edge Linking ◽  
Lunar Reconnaissance Orbiter ◽  
Phase Symmetry ◽  
Lunar Maria ◽  
Open Edge ◽  
Over Time

<p><strong>Abstract.</strong> Wrinkle ridges are a common feature in the lunar maria and record subsequent contraction of mare infill. Automatic detection of wrinkle ridges is challenging because the ridges are of irregular shapes and many ridges have been eroded and/or degraded over time. The proposed method consists of the following steps. First, as the slope can reflect the gradient changes of the ridge rims to a certain extent, the slope map is generated and converted to a grayscale (0&amp;ndash;255) image. Then the phase symmetry of the slope map is calculated with filter wavelength and filter scales parameters, which reduce the regions into symmetry regions. Next, a regional threshold is applied to limit the ridge candidates. Candidates with values less than the threshold are rejected. Moreover, the images are processed using a series of morphological operations, such as close, open, edge linking and noise removal. Finally, after thresholding the ridge map can be obtained. An experiment was performed using Lunar Reconnaissance Orbiter Camera (LROC) WAC image and topographic data from LOLA, the results demonstrate promising performance with detection percentage from 73 to 90.</p>

Machine Vision System for Automatic Inspection of Surface Defects in Aluminum Die Casting

2006 ◽  
Vol 10 (3) ◽  
pp. 281-286 ◽  
Author(s):  
Yakov Frayman ◽  
◽  
Hong Zheng ◽  
Saeid Nahavandi ◽  
Keyword(s):  
Genetic Algorithm ◽  
Machine Vision ◽  
Defect Detection ◽  
Surface Defects ◽  
Vision System ◽  
Die Casting ◽  
Noise Removal ◽  
Morphological Operations ◽  
Automatic Inspection ◽  
Machine Vision System

A camera based machine vision system for the automatic inspection of surface defects in aluminum die casting is presented. The system uses a hybrid image processing algorithm based on mathematic morphology to detect defects with different sizes and shapes. The defect inspection algorithm consists of two parts. One is a parameter learning algorithm, in which a genetic algorithm is used to extract optimal structuring element parameters, and segmentation and noise removal thresholds. The second part is a defect detection algorithm, in which the parameters obtained by a genetic algorithm are used for morphological operations. The machine vision system has been applied in an industrial setting to detect two types of casting defects: parts mix-up and any defects on the surface of castings. The system performs with a 99% or higher accuracy for both part mix-up and defect detection and is currently used in industry as part of normal production.

An Enhanced Approach for Number Plate Detection and Recognition

2020 ◽  
Vol 13 ◽  
Author(s):  
Diksha Kurchaniya ◽  
Mohd. Aquib Ansari ◽  
Durga Patel
Keyword(s):  
Character Recognition ◽  
High Speed ◽  
Detection System ◽  
Wiener Filter ◽  
Noise Removal ◽  
Motor Vehicles ◽  
Detection Accuracy ◽  
Morphological Operations ◽  
Wide Range ◽  
High Speed Vehicle

Introduction: The number of vehicles is increasing day by day in our life. The vehicle may violate traffic rules and cause accidents. The automatic number plate detection system (ANPR) plays a significant role to identify these vehicles. Number plate detection is very difficult sometimes because each country has its own format for representing the number plate and font types and sizes may also vary for different vehicles. The number of ANPR systems is available nowadays but still, it is a big problem to detect the number plate correctly in various scenarios like high-speed vehicle, number plate language, etc. Methods: In the development of this method, we mainly used wiener filter for noise removal, morphological operations for number plate localization, connected component algorithm for character segmentation, and template based matching for character recognition. Results: Our proposed methodology is providing promising results in terms of detection accuracy. Discussion: The automatic number plate detection system (ANPR) has wide range of applications because the license number is the crucial, commonly putative and essential identifier of motor vehicles. These applications include ticketless parking fee management, parking access automation, car theft prevention, security guide assistance, Motorway Road Tolling, Border Control, Journey Time Measurement, Law Enforcement and many more. Conclusion: In this paper, an enhanced approach of automatic number plate detection system is proposed using some different techniques which not only detect the number plate of the vehicle but also recognize each character present in the detected number plate image.

scholarly journals SVM Classification of MRI Brain Images for Computer-Assisted Diagnosis

2017 ◽  
Vol 7 (5) ◽  
pp. 2555 ◽  
Author(s):  
Madina Hamiane ◽  
Fatema Saeed
Keyword(s):  
Wavelet Transforms ◽  
Noise Removal ◽  
Image Features ◽  
Support Vector ◽  
Computer Assisted ◽  
Discrete Wavelet ◽  
Normal Brain ◽  
Morphological Operations ◽  
Malignant Tumours ◽  
Brain Images

Magnetic Resonance Imaging is a powerful technique that helps in the diagnosis of various medical conditions. MRI Image pre-processing followed by detection of brain abnormalities, such as brain tumors, are considered in this work. These images are often corrupted by noise from various sources. The Discrete Wavelet Transforms (DWT) with details thresholding is used for efficient noise removal followed by edge detection and threshold segmentation of the denoised images. Segmented image features are then extracted using morphological operations. These features are finally used to train an improved Support Vector Machine classifier that uses a Gausssian radial basis function kernel. The performance of the classifier is evaluated and the results of the classification show that the proposed scheme accurately distinguishes normal brain images from the abnormal ones and benign lesions from malignant tumours. The accuracy of the classification is shown to be 100% which is superior to the results reported in the literature.

scholarly journals Brain Tumor Extraction and Classification from MRI Images

2019 ◽  
Vol 8 (5S3) ◽  
pp. 477-481
Keyword(s):  
Image Processing ◽  
Brain Tumor ◽  
Brain Tumour ◽  
Noise Removal ◽  
Input Image ◽  
Svm Classifier ◽  
Morphological Operations ◽  
Tumor Extraction ◽  
Mri Image

This paper proposes a methodology in which detection, extraction and classification of brain tumour is done with the help of a patient’s MRI image. Processing of medical images is currently a huge emerging issue and it has attracted lots of research all over the globe. Several techniques have been developed so far to process the images efficiently and extract out their important features. The paper describes certain strategies including some noise removal filters, grayscaling, segmentation along with morphological operations which are needed to extract out the features from the input image and SVM classifier for classification purpose

scholarly journals MRI brain tumor segmentation: A forthright image processing approach

2020 ◽  
Vol 9 (3) ◽  
pp. 1024-1031
Author(s):  
Noor Elaiza Abd Khalid ◽  
Muhammad Firdaus Ismail ◽  
Muhammad Azri AB Manaf ◽  
Ahmad Firdaus Ahmad Fadzil ◽  
Shafaf Ibrahim
Keyword(s):  
Image Processing ◽  
Brain Tumor ◽  
Edge Detection ◽  
Confusion Matrix ◽  
Noise Removal ◽  
Tumor Segmentation ◽  
Morphological Operations ◽  
Mri Brain ◽  
Processing Approach ◽  
Depth Analysis

Brain tumor is a collection of cells that grow in an abnormal and uncontrollable way. It may affect the regular function of the brain since it grows inside the skull region. As a brain tumor can be possibly led to cancer, early detection in Computed Tomography (CT) or Magnetic Resonance Imaging (MRI) scanned images are crucial. Thus, this paper proposed a forthright image processing approach towards detection and localization of brain tumor region The approach consists of a few stages such as pre-processing, edge detection and segmentation. The pre-processing stage converts the original image into a greyscale image, and noise removal if necessary. Next, the image is enhanced using image enhancement techniques. It is then followed by edge detection using Sobel and Canny algorithms. Finally, the segmentation is applied to highlight the tumor with morphological operations towards the affected region in the MRI images. The in-depth analysis is measured using a confusion matrix. From the results, it signifies that the proposed approach is capable to provide decent segmentation of brain tumor from various MRI brain images.

scholarly journals A missing color area extraction approach from high-resolution statue images for cultural heritage documentation

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Adel Nasri ◽  
XianFeng Huang
Keyword(s):  
High Resolution ◽  
Color Space ◽  
Morphological Operations ◽  
Time Cost ◽  
Hsv Color Space ◽  
Physical Measurements ◽  
High Resolution Imagery ◽  
Digital Documentation ◽  
Rgb Color Space ◽  
Over Time

AbstractAncient statues are usually fragile and have a tendency to deteriorate over time, developing cracks, corrosion, and losing color. Before any intervention on the object of art, a conservator must map degradation and take measurements. Deterioration mapping is an extremely long process, as the conservator or restorer must locate and digitize the damages manually and collect physical measurements from the artwork. Extracting and measuring the deterioration automatically from images is less expensive and aids the digital documentation process, thus reducing the time cost of manual deterioration mapping. In this paper, we propose an effective approach named Missing Color Area Extraction in order to extract and measure missing color areas from high-resolution imagery statues, using a thresholding technique. The conversion from RGB color space to HSV color space is applied, in addition to morphological operations to remove the dust and small objects.

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