An Image Feature Extraction and Image Representation Strategies for the Analysis of Image Processing

2017 ◽  
Vol 11 (2) ◽  
pp. 642
Author(s):  
P V Kumaraguru ◽  
V J Chakravarthy
Keyword(s):  
Image Processing ◽  
Feature Extraction ◽  
Image Representation ◽  
Image Feature ◽  
Image Feature Extraction

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 Slope Collapse Detection Based on Image Processing

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Xin-Dai An ◽  
Xiang-Wen Xie ◽  
Di Wu ◽  
Ke-Feng Song
Keyword(s):  
Image Processing ◽  
Feature Extraction ◽  
Image Recognition ◽  
Motion Detection ◽  
Detection Algorithm ◽  
Image Feature ◽  
Moving Targets ◽  
Image Feature Extraction ◽  
Slope Collapse ◽  
River Embankment

In this paper, we study a task of slope collapse detection (SCD) for river embankment and formulate it as the tasks of motion detection and image recognition. Specifically, we introduce an SCD method based on motion detection and image recognition technologies to help inspector attendants detect the slope collapse. In this method, we use the foreground motion detection algorithm to identify the slope collapse of the scene of the river embankment. Since the moving targets in the foreground may not only be the slope collapse but also maybe some biology, we further use the image feature extraction and image recognition technology to recognize the foreground motion area, thus eliminating the influence of the biology on the detection results. Experimental results on the relevant scene data show that the proposed method can identify the slope collapse in real-time, and can effectively eliminate the motion interference of the biology, which has a high practical value.

scholarly journals Review of local binary pattern operators in image feature extraction

2020 ◽  
Vol 19 (1) ◽  
pp. 23
Author(s):  
Shihab Hamad Khaleefah ◽  
Salama A. Mostafa ◽  
Aida Mustapha ◽  
Mohammad Faidzul Nasrudin
Keyword(s):  
Image Processing ◽  
Pattern Recognition ◽  
Feature Extraction ◽  
Local Binary Pattern ◽  
Image Feature ◽  
Image Feature Extraction ◽  
Classification Image ◽  
Current Trends ◽  
Segmentation Pattern ◽  
Information Image

<span>With the substantial expansion of image information, image processing and computer vision have significant roles in several applications, including image classification, image segmentation, pattern recognition, and image retrieval. An important feature that has been applied in many image applications is texture. Texture is the characteristic of a set of pixels that form an image. Therefore, analyzing texture has a significant impact on segmenting an image or detecting important portions of an image. This paper provides a review on LBP and its modifications. The aim of this review is to show the current trends for using, modifying and adapting LBP in the domain of image processing.</span>

Radial Moments for Image Retrieval

2018 ◽  
pp. 2420-2451
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 A Study on Various Image Feature Extraction Techniques

2019 ◽  
pp. 151-157
Author(s):  
Merllin Ann George ◽  
L. C. Manikandan
Keyword(s):  
Image Processing ◽  
Feature Extraction ◽  
Character Recognition ◽  
Quantitative Information ◽  
Image Feature ◽  
Extraction Techniques ◽  
Unique Form ◽  
Image Feature Extraction ◽  
Time Consumption ◽  
Matrix Vector

Feature Extraction is the technique of extracting quantitative information from a image. Feature plays a very important role in the area of image processing. The purpose of feature extraction technique in image processing is to represent the image in its compact and unique form of single values or matrix vector. Feature extraction techniques are helpful in various image processing applications e.g. character recognition. As features define the behavior of an image, they show its place in terms of storage taken, efficiency in classification and obviously in time consumption also. The aim of this paper is to give the overview of image feature extraction techniques for young learners and researchers.

scholarly journals Homology-Based Image Processing for Automatic Classification of Histopathological Images of Lung Tissue

Cancers ◽  
2021 ◽  
Vol 13 (6) ◽  
pp. 1192
Author(s):  
Mizuho Nishio ◽  
Mari Nishio ◽  
Naoe Jimbo ◽  
Kazuaki Nakane
Keyword(s):  
Machine Learning ◽  
Image Processing ◽  
Feature Extraction ◽  
Automatic Classification ◽  
Image Feature ◽  
Image Feature Extraction ◽  
The Public ◽  
Cad System ◽  
Histopathological Images

The purpose of this study was to develop a computer-aided diagnosis (CAD) system for automatic classification of histopathological images of lung tissues. Two datasets (private and public datasets) were obtained and used for developing and validating CAD. The private dataset consists of 94 histopathological images that were obtained for the following five categories: normal, emphysema, atypical adenomatous hyperplasia, lepidic pattern of adenocarcinoma, and invasive adenocarcinoma. The public dataset consists of 15,000 histopathological images that were obtained for the following three categories: lung adenocarcinoma, lung squamous cell carcinoma, and benign lung tissue. These images were automatically classified using machine learning and two types of image feature extraction: conventional texture analysis (TA) and homology-based image processing (HI). Multiscale analysis was used in the image feature extraction, after which automatic classification was performed using the image features and eight machine learning algorithms. The multicategory accuracy of our CAD system was evaluated in the two datasets. In both the public and private datasets, the CAD system with HI was better than that with TA. It was possible to build an accurate CAD system for lung tissues. HI was more useful for the CAD systems than TA.

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