Feature Extraction & Image Processing for Computer Vision

2012 ◽  
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Feature Extraction

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.

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.

Hair Detection in Mushroom Based on Image Processing

2015 ◽  
Vol 713-715 ◽  
pp. 402-405
Author(s):  
Zhan Si Deng ◽  
Tong Qiang Li
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Feature Extraction ◽  
High Speed ◽  
Detection System ◽  
Target Identification ◽  
Accurate Method ◽  
Inspection System ◽  
Extraction Techniques ◽  
On Line

Nowadays,artificial recognition is widely used in the mushroom inspection system, however, it depends on subjective judgment of inspectors.Therefore,the testing personnel's experience, technology and other factors will affect the objectivity and accuracy of test results.Commodity inspection system need a high-speed, objective and accurate method for the on-line hair detection in the mushroom.On the basis of summary of domestic and foreign research, this paper studies the target identification and feature extraction techniques based on computer vision, conducts a feasibility study for the real-time hair detection system.

scholarly journals Feature Extraction of Real-Time Image Using SIFT Algorithm

2020 ◽  
Vol 4 (3) ◽  
Author(s):  
Nellutla Sasikala ◽  
V. Swathipriya ◽  
M. Ashwini ◽  
V. Preethi ◽  
A. Pranavi ◽  
...  
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Feature Extraction ◽  
Stereo Matching ◽  
Vital Role ◽  
Sift Algorithm ◽  
Real Time Image ◽  
Point Detection ◽  
Performance Of Algorithm ◽  
Time Image

This paper deals with image processing and feature extraction. Feature extraction plays a vital role in the field of image processing. There exist different image pre-processing approaches for feature extraction such as binarization, thresholding, resizing, normalisation so on...Then after these techniques are applied to obtain high clarity images. In Feature extraction object recognition and stereo matching are at the base of many computer vision problems. The descriptor generator module is changed for increasing the performance of algorithm. SIFT algorithm consist of two modules such as key point detection module and descriptor generation module. When compared to recent solution, the descriptor generation module speed is fifteen times faster and the time for feature extraction is also reduced.

Modern Technology for Image processing and Computer vision -A Review

2018 ◽  
Vol 1 (2) ◽  
pp. 17-23
Author(s):  
Takialddin Al Smadi
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Augmented Reality ◽  
Video Compression ◽  
Video Processing ◽  
Vision System ◽  
Citrus Fruit ◽  
Modern Technology ◽  
Image And Video Processing ◽  
Depth Video

This survey outlines the use of computer vision in Image and video processing in multidisciplinary applications; either in academia or industry, which are active in this field.The scope of this paper covers the theoretical and practical aspects in image and video processing in addition of computer vision, from essential research to evolution of application.In this paper a various subjects of image processing and computer vision will be demonstrated ,these subjects are spanned from the evolution of mobile augmented reality (MAR) applications, to augmented reality under 3D modeling and real time depth imaging, video processing algorithms will be discussed to get higher depth video compression, beside that in the field of mobile platform an automatic computer vision system for citrus fruit has been implemented ,where the Bayesian classification with Boundary Growing to detect the text in the video scene. Also the paper illustrates the usability of the handed interactive method to the portable projector based on augmented reality.   © 2018 JASET, International Scholars and Researchers Association

Pattern Recognition for Classification and Matching of Car Tires

2005 ◽  
Vol 33 (1) ◽  
pp. 2-17 ◽  
Author(s):  
D. Colbry ◽  
D. Cherba ◽  
J. Luchini
Keyword(s):  
Computer Vision ◽  
Feature Extraction ◽  
Pattern Matching ◽  
Fourier Transforms ◽  
Two Dimensions ◽  
Multiple Sources ◽  
Matching Process ◽  
Visual Searches ◽  
New Feature ◽  
2D Fft

Abstract Commercial databases containing images of tire tread patterns are currently used by product designers, forensic specialists and product application personnel to identify whether a given tread pattern matches an existing tire. Currently, this pattern matching process is almost entirely manual, requiring visual searches of extensive libraries of tire tread patterns. Our work explores a first step toward automating this pattern matching process by building on feature analysis techniques from computer vision and image processing to develop a new method for extracting and classifying features from tire tread patterns and automatically locating candidate matches from a database of existing tread pattern images. Our method begins with a selection of tire tread images obtained from multiple sources (including manufacturers' literature, Web site images, and Tire Guides, Inc.), which are preprocessed and normalized using Two-Dimensional Fast Fourier Transforms (2D-FFT). The results of this preprocessing are feature-rich images that are further analyzed using feature extraction algorithms drawn from research in computer vision. A new, feature extraction algorithm is developed based on the geometry of the 2D-FFT images of the tire. The resulting FFT-based analysis allows independent classification of the tire images along two dimensions, specifically by separating “rib” and “lug” features of the tread pattern. Dimensionality of (0,0) indicates a smooth treaded tire with no pattern; dimensionality of (1,0) and (0,1) are purely rib and lug tires; and dimensionality of (1,1) is an all-season pattern. This analysis technique allows a candidate tire to be classified according to the features of its tread pattern, and other tires with similar features and tread pattern classifications can be automatically retrieved from the database.

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