scholarly journals Measurement of Vickers Hardness Using Image Processing Techniques. 2nd Report. Application of Image Feature Extraction.

1995 ◽  
Vol 61 (588) ◽  
pp. 1851-1857
Author(s):  
Akira Kato ◽  
Takashi Yoshida
Keyword(s):  
Image Processing ◽  
Feature Extraction ◽  
Vickers Hardness ◽  
Image Feature ◽  
Image Feature Extraction ◽  
Image Processing Techniques ◽  
Processing Techniques

scholarly journals PENGENALAN HURUF LATIN PADA ANAK USIA DINI DENGAN PENERAPAN METODE BACKPROPAGATION

2021 ◽  
Vol 2 (2) ◽  
pp. 130-137
Author(s):  
Slamet Riyadi ◽  
Zilvanhisna Emka Fitri ◽  
Arizal Mujibtamala Nanda Imron
Keyword(s):  
Artificial Intelligence ◽  
Image Processing ◽  
Early Childhood ◽  
Feature Extraction ◽  
Image Processing Techniques ◽  
Total Data ◽  
Backpropagation Method ◽  
Testing Accuracy ◽  
Processing Techniques ◽  
Pixel Mapping

Early childhood has difficulty remembering Latin letters or Roman characters than adults. Some of the factors that cause it are cognitive development, motivation, interest in learning, emotions and environmental factors. To overcome this, an innovative media is needed so that children can easily remember Latin letters. One of the innovative media applies digital image processing techniques and artificial intelligence. The fonts used are 10 types of letter models with image processing techniques such as preprocessing, binaryization, pixel mapping and creating vector as feature extraction.  While the artificial intelligence used is the backpropagation method. The total data is 208 letter images with 625 input features with 500 epochs, the best learning rate used by the system is 0.025 so that the best training accuracy is 93.96% and testing accuracy is 92.31%.

scholarly journals Subject Review: Diagnoses cancer diseases systems for most body's sections using image processing techniques

2021 ◽  
Vol 6 (3) ◽  
pp. 056-062
Author(s):  
Dena Nadir George ◽  
Haitham Salman Chyad ◽  
Raniah Ali Mustafa
Keyword(s):  
Image Processing ◽  
Feature Extraction ◽  
Medical Imaging ◽  
Image Processing Techniques ◽  
Classification Feature ◽  
Segmentation Methods ◽  
Comprehensive Survey ◽  
Segmentation Image ◽  
Processing Techniques ◽  
Cancer Diseases

Medical imaging has become an important part of diagnosing, early detection, and treating cancers. In this paper, a comprehensive survey on various image processing techniques for medical images specifically examined cancer diseases for most body sections. These sections are Bone, Liver, Kidney, Breast, Lung, and Brain. Detection of medical imaging involves different stages such as classification, segmentation, image pre-processing, and feature extraction. With regard to this work, many image processing methods will be studied, over 10 surveys reviewing classification, feature extraction, and segmentation methods utilized image processing for cancer diseases for most body's sections are clearly reviewed.

Radial Moments for Image Retrieval

2017 ◽  
pp. 358-389
Author(s):  
Pooja Sharma
Keyword(s):  
Image Processing ◽  
Feature Extraction ◽  
Image Retrieval ◽  
Performance Measures ◽  
Similarity Measures ◽  
Image Feature ◽  
Content Based Image Retrieval ◽  
Visual Data ◽  
Image Feature Extraction ◽  
Radial Moments

Images have always been considered an effective medium for presenting visual data in numerous applications ranging from industry to academia. Consequently, managing and indexing of images become essential in order to retrieve relevant images effectively and efficiently. Therefore, the proposed chapter aims to elaborate one of the advanced concepts of image processing, i.e., Content Based Image Retrieval (CBIR) and image feature extraction using advanced methods known as radial moments. In this chapter, various radial moments are discussed with their properties. Besides, performance measures and various similarity measures are elaborated in depth. The performance of radial moments is evaluated through an extensive set of experiments on benchmark databases such as Kimia-99, MPEG-7, COIL-100, etc.

scholarly journals Detection of Glaucoma using ORB (Oriented FAST and Rotated BRIEF) Feature Extraction

2021 ◽  
pp. 153-158
Author(s):  
Kazi Safayet Md. Shabbir ◽  
Md. Imteaz Ahmed ◽  
Marzan Alam
Keyword(s):  
Image Processing ◽  
Feature Extraction ◽  
Texture Analysis ◽  
Image Feature ◽  
Image Feature Extraction ◽  
Fundus Camera ◽  
Analysis Methods ◽  
Binary Descriptor ◽  
Descriptor Approach ◽  
Precision And Accuracy

This research was utilized to identify glaucoma, a type of eye illness. This endeavor necessitates the use of pictures from the fundus camera for image processing. This study reflects the effort done to detect glaucoma-affected eyes utilizing image feature extraction using Oriented FAST and Rotated BRIEF (ORB). ORB is a binary descriptor approach that is based on BRIEF and is highly fast. This technique is insensitive to picture noise and is invariant to any rotation. ORB is two orders of magnitude faster than SURF and performs similarly to SIFT. It is more efficient than other texture analysis methods. It is less computationally difficult than other approaches in the literature. This technique extracts features and detects texture by inspecting each pixel of the retina picture. It was trained on 160 fundus pictures of normal and glaucoma-affected retinas. After that, any healthy or glaucoma-affected eye may be easily recognized by obtaining an accurate eye picture. The results reveal that this technique has a precision and accuracy of more than 90%.

scholarly journals CHARACTERISATION OF TABLETS FOR DEFECT IDENTIFICATION USING IMAGE PROCESSING TECHNIQUES FOR QUALITY CONTROL IN PHARMACEUTICAL INDUSTRY

2017 ◽  
Vol 10 (11) ◽  
pp. 270
Author(s):  
Durga Karthik ◽  
Vijayarekha K ◽  
Surya K
Keyword(s):  
Image Processing ◽  
Quality Control ◽  
Feature Extraction ◽  
Pharmaceutical Industry ◽  
Color Variation ◽  
Defect Identification ◽  
Image Processing Techniques ◽  
Manufacturing Line ◽  
Area And Perimeter ◽  
Processing Techniques

  Objective: Our aim is to characterize the parameters for identifying defective tablets from the manufacturing line using image processing techniques.Methods: Manufactured tablets might have defects such as broken chips, missing tablet, and color variation. Images of tablets are captured using machine vision camera. The features are detected using feature extraction for a tablet without defects and are stored in a database. The stored details are used for identifying defective tablets during manufacturing.Results: The characteristics such as color, shape, number of pills, area, and perimeter of the normal tablets without defects were extracted.Conclusion: The defective tablets can be identified by comparing the characteristics stored in the database and can be removed effectively.

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