scholarly journals Texture classification using gene expression programming

2021 ◽  
Author(s):  
Fereshteh Mahvarsayyad
Keyword(s):  
Gene Expression ◽  
Image Processing ◽  
Computer Vision ◽  
Feature Extraction ◽  
Image Segmentation ◽  
Texture Classification ◽  
Gene Expression Programming ◽  
Simple Arithmetic ◽  
Image Characteristics ◽  
Processing Step

In computer vision, segmentation refers to the process of subdividing a digital image into constituent regions with homogeneity in some image characteristics. Image segmentation is considered as a pre-processing step for object recognition. The problem of segmentation, being one of the most difficult tasks in image processing, gets more complicated in the presence of random textures in the image. This paper focuses on texture classification, which is defined as supervised texture segmentation with prior knowledge of textures in the image. We investigate a classification method using Gene Expression Programming (GEP). It is shown that GEP is capable of evolving accurate classifiers using simple arithmetic operations and direct pixel values without employing complicated feature extraction algorithms. It is also shown that the accuracy of classification is related to the fact that GEP can detect the regularities of texture patterns. As part of this project, we implemented a Photoshop plug-in that uses the evolved classifiers to identify and select target textures in digital images.

scholarly journals Texture classification using gene expression programming

2021 ◽  
Author(s):  
Fereshteh Mahvarsayyad
Keyword(s):  
Gene Expression ◽  
Image Processing ◽  
Computer Vision ◽  
Feature Extraction ◽  
Image Segmentation ◽  
Texture Classification ◽  
Gene Expression Programming ◽  
Simple Arithmetic ◽  
Image Characteristics ◽  
Processing Step

In computer vision, segmentation refers to the process of subdividing a digital image into constituent regions with homogeneity in some image characteristics. Image segmentation is considered as a pre-processing step for object recognition. The problem of segmentation, being one of the most difficult tasks in image processing, gets more complicated in the presence of random textures in the image. This paper focuses on texture classification, which is defined as supervised texture segmentation with prior knowledge of textures in the image. We investigate a classification method using Gene Expression Programming (GEP). It is shown that GEP is capable of evolving accurate classifiers using simple arithmetic operations and direct pixel values without employing complicated feature extraction algorithms. It is also shown that the accuracy of classification is related to the fact that GEP can detect the regularities of texture patterns. As part of this project, we implemented a Photoshop plug-in that uses the evolved classifiers to identify and select target textures in digital images.

scholarly journals Fashion Product Classification through Deep Learning and Computer Vision

2019 ◽  
Vol 9 (7) ◽  
pp. 1385 ◽  
Author(s):  
Luca Donati ◽  
Eleonora Iotti ◽  
Giulio Mordonini ◽  
Andrea Prati
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Feature Extraction ◽  
Deep Learning ◽  
Template Matching ◽  
Learning Approaches ◽  
Visual Classification ◽  
Product Classification ◽  
Processing Techniques

Visual classification of commercial products is a branch of the wider fields of object detection and feature extraction in computer vision, and, in particular, it is an important step in the creative workflow in fashion industries. Automatically classifying garment features makes both designers and data experts aware of their overall production, which is fundamental in order to organize marketing campaigns, avoid duplicates, categorize apparel products for e-commerce purposes, and so on. There are many different techniques for visual classification, ranging from standard image processing to machine learning approaches: this work, made by using and testing the aforementioned approaches in collaboration with Adidas AG™, describes a real-world study aimed at automatically recognizing and classifying logos, stripes, colors, and other features of clothing, solely from final rendering images of their products. Specifically, both deep learning and image processing techniques, such as template matching, were used. The result is a novel system for image recognition and feature extraction that has a high classification accuracy and which is reliable and robust enough to be used by a company like Adidas. This paper shows the main problems and proposed solutions in the development of this system, and the experimental results on the Adidas AG™ dataset.

Development of Cable Inspection System Based on Image Processing

2014 ◽  
Vol 602-605 ◽  
pp. 2199-2204
Author(s):  
Huan Liu ◽  
Chao Tao Liu
Keyword(s):  
Image Processing ◽  
Feature Extraction ◽  
Image Segmentation ◽  
Image Enhancement ◽  
Image Recognition ◽  
Host Computer ◽  
Inspection System ◽  
Energy Distributions ◽  
Single Motor ◽  
Image Acquisition System

A stayed cable inspection system was developed which consists of robot, host computer, cameras and image acquisition system. The robot was driven with single motor and could climb cables of various and variable diameters. Pictures of the cables’ were taken by the robot, and the defects and mars were identified automatically with image recognition. The steps of image recognition includes image de-noising, image enhancement, image segmentation, feature extraction, and recognition with the features of the images’ histogram grayscale distributions and energy distributions.

FEATURE EXTRACTION BY RANK-ORDER FILTERING FOR IMAGE SEGMENTATION

1988 ◽  
Vol 02 (02) ◽  
pp. 301-319 ◽  
Author(s):  
P. ZAMPERONI
Keyword(s):  
Image Processing ◽  
Feature Extraction ◽  
Image Segmentation ◽  
Density Function ◽  
Rank Order ◽  
Experimental Results ◽  
Textural Features ◽  
Segmentation Method ◽  
Unified Approach

The aim of this paper is to outline a unified approach to feature extraction for segmentation purposes by means of the rank-order filtering of grey values in a neighbourhood of each pixel of a digitized image. In the first section an overview of rank-order filtering for image processing is given, and a fast histogram algorithm is proposed. Section 2 deals with the extraction of a “locally most representative grey value”, defined as the maximum of the local histogram density function. In Section 3 several textural features are described, which can be extracted from the local histogram by means of rank-order filtering, and their properties are discussed. Section 4 formulates some general requirements to be met by the process of image segmentation, and describes a method based upon the features introduced in the former sections. In the last section some experimental results applied to aerial views obtained with the segmentation method of Sect. 4 are reported. These test images have been analyzed within the scope of an investigation centered on terrain recognition for agricultural and ecological purposes.

scholarly journals Implementasi Canny Edge Detection Pada Aplikasi Pendeteksi Jalur Lalu Lintas

2021 ◽  
Vol 5 (1) ◽  
pp. 164
Author(s):  
Ratna Salkiawati ◽  
Allan Desi Alexander ◽  
Hendarman Lubis
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Image Segmentation ◽  
Traffic Accident ◽  
Traffic Accidents ◽  
Image Understanding ◽  
Canny Edge Detection ◽  
Traffic Lane ◽  
Sense Of Security ◽  
Canny Edge

Based on the traffic accident report, it was found that there were 41,771 (Forty-one thousand seven hundred and seventy-one) incidents caused by disorderly drivers. (POLRI, 2018). One of these disorders is by driving a motorized vehicle outside the traffic lane. In this study, researchers developed computer vision using sensor methods and image processing. The stages in computer vision are the image acquisition process, the image segmentation process, and the image understanding process. This study aims to develop an application using computer vision to warn drivers of disorderly traffic or to increase the alertness of motorized vehicle drivers by detecting the condition of the driver's path. It is hoped that this research will provide a sense of security for motorized vehicle drivers, as well as provide applications that are expected to increase driver awareness to avoid traffic accidents

scholarly journals A REVIEW ON IMAGE SEGMENTATION USING GPU

2016 ◽  
Vol 15 (10) ◽  
pp. 7160-7163
Author(s):  
Gurpreet Kaur ◽  
Sonika Jindal
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Image Segmentation ◽  
Graphics Processing Unit ◽  
Processing Unit ◽  
System Resources ◽  
Cuda Architecture ◽  
Graphics Processing

Image Segmentations play a heavy role in areas such as computer vision and image processing due to its broad usage and immense applications. Because of the large importance of image segmentation a number of algorithms have been proposed and different approaches have been adopted. Segmentation divides an image into distinct regions containing each pixel with similar attributes. The objective of apportioning is to simplify and/or alter the representation of an image into something that is more meaningful and more comfortable to break down. This paper discusses the various techniques implemented for image segmentation and discusses the various Computations that can be performed on the graphics processing unit (GPU) by means of the CUDA architecture in order to achieve fast performance and increase the utilization of available system resources.

A Segmentation Method of Traffic Marking Based on Average Gray Approximating the Optimal Threshold

2014 ◽  
Vol 556-562 ◽  
pp. 3510-3513 ◽  
Author(s):  
Zu Sheng Chen ◽  
You Fu Wu
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Image Segmentation ◽  
Crucial Role ◽  
Road Traffic ◽  
Image Database ◽  
Identification Problem ◽  
Optimal Threshold ◽  
Segmentation Method ◽  
Local Average

Image segmentation technique was used widely for computer vision and image processing. A robust technique of image segmentation plays a crucial role in identification problem. In this paper, a nonparametric and unsupervised method of automatic threshold for segmenting image was proposed, i.e. the optimal threshold is approximated by global average gray and local average gray, and this method was compared with other methods by using standard image. The experimental results show that our method proposed in this paper is robust. In addition, an image database of road traffic marking (www.ananth.in/RoadMarkingdetection.html) is provided to do this experiment for testing our method, the results show that our method is excellent.

scholarly journals Implementasi Jaringan Saraf Tiruan Sebagai Alat Bantu Deteksi Bakteri Staphylococcus Aureus Pada Sayuran

2020 ◽  
Vol 1 (3) ◽  
pp. 258
Author(s):  
Ali Rahmad Pohan
Keyword(s):  
Neural Networks ◽  
Image Processing ◽  
Staphylococcus Aureus ◽  
Image Segmentation ◽  
Artificial Neural Networks ◽  
Image Quality ◽  
Bacterial Detection ◽  
Image Characteristics ◽  
Reduce Image Quality ◽  
Artificial Neural

This study aims to aid bacterial detection through bacterial imagery in vegetables to help identify Staphylococcus aureus bacteria in vegetables. Input to the software is the image of bacteria in vegetables. Bacterial image is processed by grayscaling, thresholding and image segmentation processing methods so that the image characteristics that represent bacteria in vegetables are obtained. One technique that can be used as a tool to observe Staphylococcus aureus is to use artificial neural networks and combine them with image processing. Artificial neural networks function as information processing by inferring information from data that has been received and as a decision maker for data that has been studied. Image processing is the science of manipulating images, which includes techniques to improve or reduce image quality. The detection process using software that has been built can be done well. The process is carried out by matching the value of the exercise cutra backpropagation vector with the image to be detected.

Role of Clustering Techniques in Effective Image Segmentation

2018 ◽  
pp. 128-160
Author(s):  
Bhavneet Kaur ◽  
Meenakshi Sharma
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Image Segmentation ◽  
Object Detection ◽  
Real Time ◽  
Vision Research ◽  
Medical Equipment ◽  
Additional Advantage ◽  
Clustering Techniques

Image segmentation is gauged as an essential stage of representation in image processing. This process segregates a digitized image into various categorized sections. An additional advantage of distinguishing dissimilar objects can be represented within this state of the art. Numerous image segmentation techniques have been proposed by various researchers, which maintained a smooth and easy timely evaluation. In this chapter, an introduction to image processing along with segmentation techniques, computer vision fundamentals, and its applied applications that will be of worth to the image processing and computer vision research communities has been deeply studied. It aims to interpret the role of various clustering-based image segmentation techniques specifically. Use of the proposed chapter if made in real time can project better outcomes in object detection and recognition, which can then later be applied in numerous applications and devices like in robots, automation, medical equipment, etc. for safety, advancement, and betterment of society.

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