scholarly journals Image Processing-Based Spall Object Detection Using Gabor Filter, Texture Analysis, and Adaptive Moment Estimation (Adam) Optimized Logistic Regression Models

2020 ◽  
Vol 2020 ◽  
pp. 1-16
Author(s):  
Nhat-Duc Hoang
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Logistic Regression ◽  
Gabor Filter ◽  
Region Of Interest ◽  
Principal Component ◽  
Detection Accuracy ◽  
Learning Approaches ◽  
Logistic Regression Models ◽  
Moment Estimation

This study aims at proposing a computer vision model for automatic recognition of localized spall objects appearing on surfaces of reinforced concrete elements. The new model is an integration of image processing techniques and machine learning approaches. The Gabor filter supported by principal component analysis and k-means clustering is used for identifying the region of interest within an image sample. The binary gradient contour, gray level co-occurrence matrix, and color channels’ statistical measurements are employed to compute the texture of the extracted region of interest. Based on the computed texture-based features, the logistic regression model trained by the state-of-the-art adaptive moment estimation (Adam) is utilized to establish a decision boundary that delivers predictions on the status of “nonlocalized spall” and “localized spall.” Experimental results demonstrate that the newly developed model is able to achieve good detection accuracy with classification accuracy rate = 85.32%, precision = 0.86, recall = 0.79, negative predictive value = 0.85, and F1 score = 0.82. Thus, the proposed computer vision model can be helpful to assist decision makers in the task of the periodic survey of structure heath condition.

Randomized Hough Transform

2011 ◽  
pp. 1343-1350 ◽  
Author(s):  
Lei Xu ◽  
Erkki Oja
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Hough Transform ◽  
Random Sampling ◽  
Computational Cost ◽  
Detection Accuracy ◽  
The Past ◽  
Randomized Hough Transform ◽  
Dynamic Storage ◽  
High Computational Cost

Proposed in 1962, the Hough transform (HT) has been widely applied and investigated for detecting curves, shapes, and motions in the fields of image processing and computer vision. However, the HT has several shortcomings, including high computational cost, low detection accuracy, vulnerability to noise, and possibility of missing objects. Many efforts target at solving some of the problems for decades, while the key idea remains more or less the same. Proposed in 1989 and further developed thereafter, the Randomized Hough Transform (RHT) manages to considerably overcome these shortcomings via innovations on the fundamental mechanisms, with random sampling in place of pixel scanning, converging mapping in place of diverging mapping, and dynamic storage in place of accumulation array. This article will provides an overview on advances and applications of RHT in the past one and half decades.

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.

Research of Image Processing Algorithm Based on Region of Interest

2013 ◽  
Vol 798-799 ◽  
pp. 814-817
Author(s):  
Fang Wang
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Visual Attention ◽  
Region Of Interest ◽  
Processing Algorithm ◽  
Human Information Processing ◽  
Visual Attention Model ◽  
Attention Model ◽  
Starting Point ◽  
Image Zooming

With the further development of modern scientific study, it promotes the research of the image based on region of interest. By doing these studies, it satisfies the pressing needs in many fields such as military, production and living areas, etc. meanwhile, it is also the key problem in the fields of computer vision, image processing, artificial intelligence, video communication. Visual attention plays a very important role in the human information processing of the psychological adjustment mechanism. It is a conscious activity which chooses the useful information from large amounts of information. It owns the high efficiency and reliability in the process of human visual perception. Visual attention model, which is based on the visual attention and combined with the computer vision, builds a spatial feature of visual attention architecture. It is helpful not only to find out the visual cognition rule, but also to solve the problem of interested area selection and focus on improving the efficiency of the computer image processing. It has important application value in areas such as image extraction and image zooming. The paper has carried out the deeply study in the interested image region. With the improved visual attention model as a starting point, it combines with graph processing algorithm. And it uses the image extraction algorithm and image zooming algorithm to improve the visual attention model and detect the interested area.

scholarly journals A NOVEL METHOD FOR INSPECTION DEFECTS IN COMMERCIAL EGGS USING COMPUTER VISION

2021 ◽  
Vol XLIII-B2-2021 ◽  
pp. 809-816
Author(s):  
Y. M. Valencia ◽  
J. J. Majin ◽  
V. B. Taveira ◽  
J. D. Salazar ◽  
M. E. Stivanello ◽  
...  
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Visual Inspection ◽  
Working Hours ◽  
Inspection System ◽  
Learning Approaches ◽  
Visual Fatigue ◽  
Long Working Hours ◽  
Novel Method ◽  
Classical Image

Abstract. The objective of this work is to compare the use of classical image processing approaches with deep learning approaches in a visual inspection system for defects in commercial eggs. Currently, many industries perform the detection of defects in eggs manually, this implies a large number of workers with long working hours who are exposed to visual fatigue and physical and mental discomfort. As a solution, this work proposes to develop an automatic inspection technique for defects in eggs using computer vision, capable of being operable in the industry. Different image processing approaches were evaluated in order to determine the best solution in terms of performance and processing time.

scholarly journals Ball Classification through Object Detection using Deep Learning for Handball

Mekatronika ◽  
2020 ◽  
Vol 2 (2) ◽  
pp. 49-54
Author(s):  
Arzielah Ashiqin Alwi ◽  
Ahmad Najmuddin Ibrahim ◽  
Muhammad Nur Aiman Shapiee ◽  
Muhammad Ar Rahim Ibrahim ◽  
Mohd Azraai Mohd Razman ◽  
...  
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Deep Learning ◽  
Performance Analysis ◽  
Object Detection ◽  
High Speed ◽  
Detection Accuracy ◽  
Narrow Angle ◽  
Target Locations ◽  
Ball Detection

Dynamic gameplay, fast-paced and fast-changing gameplay, where angle shooting (top and bottom corner) has the best chance of a good goal, are the main aspects of handball. When it comes to the narrow-angle area, the goalkeeper has trouble blocked the goal. Therefore, this research discusses image processing to investigate the shooting precision performance analysis to detect the ball's accuracy at high speed. In the handball goal, the participants had to complete 50 successful shots at each of the four target locations. Computer vision will then be implemented through a camera to identify the ball, followed by determining the accuracy of the ball position of floating, net tangle and farthest or smallest using object detection as the accuracy marker. The model will be trained using Deep Learning (DL)  models of YOLOv2, YOLOv3, and Faster R-CNN and the best precision models of ball detection accuracy were compared. It was found that the best performance of the accuracy of the classifier Faster R-CNN produces 99% for all ball positions.

A Scientometric Analysis and a Review on Current Literature of Computer Vision Applications

2020 ◽  
pp. 104-117
Author(s):  
Osman Hürol Türkakın
Keyword(s):  
Machine Learning ◽  
Image Processing ◽  
Computer Vision ◽  
Current Literature ◽  
Machine Learning Techniques ◽  
Scientometric Analysis ◽  
Learning Approaches ◽  
Learning Techniques ◽  
Computer Vision Applications ◽  
Processing Techniques

Computer vision methods are wide-spread techniques mostly used for detecting cracks on structural components, extracting information from traffic flows, and analyzing safety in construction processes. In recent years, with increasing usage of machine learning techniques, computer vision applications are supported by machine learning approaches. So, several studies were conducted using machine learning techniques to apply image processing. As a result, this chapter offers a scientometric analysis for investigating current literature of image processing studies for civil engineering field in order to track the scientometric relationship between machine learning and image processing techniques.

scholarly journals Detecção de Roubo de Computadores em Laboratório Usando Visão Computacional

2020 ◽  
Author(s):  
Gabriel Andrade Cordeiro ◽  
Giovani Grockotzki ◽  
Itamar Junior de Azevedo ◽  
João Mantovani ◽  
Matheus Henrique da Silva Santos ◽  
...  
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Real Time ◽  
Video Analysis ◽  
Region Of Interest ◽  
Background Information ◽  
Laboratory Monitoring ◽  
Exit Door ◽  
Definition Of ◽  
Computer Labs

Computer theft in computer labs causes academic damage to coursesthat require this resource and ends up directly harming students. Inthis context, this paper describes a methodology applied to detectcomputer removal through video analysis in real-time. For eachframe, image processing and computer vision techniques were used,subtracting background information, binarization, segmentationof the region of interest and definition of contours. The case studywas developed at a Brazilian university. For theft detection, it wasconsidered a black computer tower case carried by people leavingthe laboratory. Monitoring is carried out by a camera positioned infront of the lab exit door. The software developed alerts a suspiciousactivity that may indicate a possible computer theft.

scholarly journals Machine learning techniques to derive bioclimatic classifications for Colombia

2021 ◽  
Author(s):  
Richard Rios ◽  
Elkin A. Noguera-Urbano ◽  
Jairo Espinosa ◽  
Jose Manuael Ochoa
Keyword(s):  
Machine Learning ◽  
Principal Component Analysis ◽  
Logistic Regression ◽  
Regression Models ◽  
Principal Component ◽  
Component Analysis ◽  
Machine Learning Techniques ◽  
Study Region ◽  
Logistic Regression Models ◽  
Learning Techniques

Bioclimatic classifications seek to divide a study region into geographic areas with similar bioclimatic characteristics. In this study we proposed two bioclimatic classifications for Colombia using machine learning techniques. We firstly characterized the precipitation space of Colombia using principal component analysis. Based on Lang classification, we then projected all background sites in the precipitation space with their corresponding categories. We sequentially fit logistic regression models to re-classify all background sites in the precipitation space with six redefined Lang categories. New categories were the used to define a new modified Lang and Caldas-Lang classifications.

A Hybrid Approach for Plant Leaf Disease Detection and Classification Using Digital Image Processing Methods

2020 ◽  
pp. 002072092095312
Author(s):  
Anusha Rao ◽  
S.B. Kulkarni
Keyword(s):  
Image Processing ◽  
Feature Extraction ◽  
Digital Image Processing ◽  
Digital Image ◽  
Gabor Filter ◽  
Hybrid Approach ◽  
Project Based Learning ◽  
Detection Accuracy ◽  
Plant Leaf ◽  
Leaf Disease

Detection of plant leaf disease has been considered an interesting research field which is helpful to improve the crop and fruit yield. Computer vision and machine learning based approaches have gained huge attraction in digital image processing field. Several visual computing based techniques have been presented in the past for early prediction of plant leaf diseases. However, detection accuracy is still considered as a challenging task. Hence, in order to overcome this issue, we introduce a novel hybrid approach carried out in three forms. During the first phase, image enhancement and image conversion scheme are incorporated, which helps to overcome the low-illumination and noise related issues. In the next phase, a combined feature extraction technique is developed by using GLCM, Complex Gabor filter, Curvelet and image moments. Finally, a Neuro-Fuzzy Logic classifier is trained with the extracted features. The proposed approach is implemented using MATLAB simulation tool where PlantVillage Database is considered for analysis. The average detection accuracy has been obtained as more than 90% for 2 test cases which shows that the proposed combination of feature extraction and image pre-processing process is able to obtain improved classification accuracy. This work is useful for the students of UG/PG programme to carry out Project-based learning.

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

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