Fast Local Laplacian-Based Steerable and Sobel Filters Integrated with Adaptive Boosting Classification Tree for Automatic Recognition of Asphalt Pavement Cracks

Effective road maintenance requires adequate periodic surveys of asphalt pavement condition. The manual process of pavement assessment is labor intensive and time-consuming. This study proposes an alternative for automating the periodic surveys of pavement condition by means of image processing and machine learning. Advanced image processing techniques including fast local Laplacian filter, Sobel filter, steerable filter, and projection integral are employed for image enhancement and analysis to extract useful features from digital images. Based on the features produced by these image processing techniques, adaptive boosting classification tree is used to perform pavement crack recognition tasks. A dataset of image samples consisting of five classes (alligator crack, diagonal crack, longitudinal crack, noncrack, and transverse crack) has been collected to construct and verify the performance of the adaptive boosting classification tree. The experimental results show that the proposed approach has achieved a high crack classification accuracy which is roughly 90%. Therefore, the newly developed model is a promising alternative to help transportation agencies in pavement condition evaluation.

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Classification of Asphalt Pavement Cracks Using Laplacian Pyramid-Based Image Processing and a Hybrid Computational Approach

Computational Intelligence and Neuroscience ◽

10.1155/2018/1312787 ◽

2018 ◽

Vol 2018 ◽

pp. 1-16 ◽

Cited By ~ 5

Author(s):

Nhat-Duc Hoang

Keyword(s):

Image Processing ◽

Computational Intelligence ◽

Asphalt Pavement ◽

Classification Model ◽

Laplacian Pyramid ◽

Pavement Condition ◽

Crack Patterns ◽

Image Processing Techniques ◽

Processing Techniques

To improve the efficiency of the periodic surveys of the asphalt pavement condition, this study puts forward an intelligent method for automating the classification of pavement crack patterns. The new approach relies on image processing techniques and computational intelligence algorithms. The image processing techniques of Laplacian pyramid and projection integral are employed to extract numerical features from digital images. Least squares support vector machine (LSSVM) and Differential Flower Pollination (DFP) are the two computational intelligence algorithms that are employed to construct the crack classification model based on the extracted features. LSSVM is employed for data classification. In addition, the model construction phase of LSSVM requires a proper setting of the regularization and kernel function parameters. This study relies on DFP to fine-tune these two parameters of LSSVM. A dataset consisting of 500 image samples and five class labels of alligator crack, diagonal crack, longitudinal crack, no crack, and transverse crack has been collected to train and verify the established approach. The experimental results show that the Laplacian pyramid is really helpful to enhance the pavement images and reveal the crack patterns. Moreover, the hybridization of LSSVM and DFP, named as DFP-LSSVM, used with the Laplacian pyramid at the level 4 can help us to achieve the highest classification accuracy rate of 93.04%. Thus, the new hybrid approach of DFP-LSSVM is a promising tool to assist transportation agencies in the task of pavement condition surveying.

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AN EFFICIENT METHOD TO AUTOMATE POTHOLE DETECTION IN ROAD SURFACE USING IOT

International Journal of Engineering Applied Sciences and Technology ◽

10.33564/ijeast.2021.v05i10.025 ◽

2021 ◽

Vol 5 (10) ◽

Author(s):

Ajith Kumar B ◽

Vignesh G ◽

Anbumani A.

Keyword(s):

Image Processing ◽

Digital Image Processing ◽

Digital Image ◽

Road Surface ◽

Detection Accuracy ◽

Road Maintenance ◽

Application System ◽

Image Processing Techniques ◽

Processing Techniques ◽

Pothole Detection

With the development of information technology, the digital image processing has the characteristics of strong permeability, large use of action and good comprehensive benefits. A road maintenance pothole detection is one of the important tasks. A road surface modelling or road image analysis is generally come from computer vision approaches. However, these two categories were always used independently. Furthermore, the accuracy of the pothole detection is not satisfactory. These challenges promote the development of a better application to detect potholes, cracks using the digital image processing like segmentation, extraction, recognition, and morphology from the images of road surface by using image processing. We are proposing an application system with efficient digital image processing techniques to improve the accuracy and consistency of obtaining accurate shapes of potholes and topologies, etc. The successful detection accuracy is around 98.7% and the overall pixel-level accuracy is approximately 99.6%. By using the digital image processing techniques, the detected potholes and cracks are updated to the web server by using IOT device.

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Three-Dimensional Structure of Rotavirus-Fab Complex

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100179944 ◽

1990 ◽

Vol 48 (1) ◽

pp. 238-239

Author(s):

B.V.V. Prasad ◽

E. Marietta ◽

J.W. Burns ◽

M.K. Estes ◽

W. Chiu

Keyword(s):

Image Processing ◽

Smooth Surface ◽

Three Dimensional ◽

Outer Shell ◽

Dimensional Structure ◽

Structural Studies ◽

Cell Penetration ◽

Electron Cryomicroscopy ◽

Image Processing Techniques ◽

Processing Techniques

Rotaviruses are spherical, double-shelled particles. They have been identified as a major cause of infantile gastroenteritis worldwide. In our earlier studies we determined the three-dimensional structures of double-and single-shelled simian rotavirus embedded in vitreous ice using electron cryomicroscopy and image processing techniques to a resolution of 40Å. A distinctive feature of the rotavirus structure is the presence of 132 large channels spanning across both the shells at all 5- and 6-coordinated positions of a T=13ℓ icosahedral lattice. The outer shell has 60 spikes emanating from its relatively smooth surface. The inner shell, in contrast, exhibits a bristly surface made of 260 morphological units at all local and strict 3-fold axes (Fig.l).The outer shell of rotavirus is made up of two proteins, VP4 and VP7. VP7, a glycoprotein and a neutralization antigen, is the major component. VP4 has been implicated in several important functions such as cell penetration, hemagglutination, neutralization and virulence. From our earlier studies we had proposed that the spikes correspond to VP4 and the rest of the surface is composed of VP7. Our recent structural studies, using the same techniques, with monoclonal antibodies specific to VP4 have established that surface spikes are made up of VP4.

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A Survey on Detection and Classification of Brain Tumor Using Image Processing Techniques

International Journal of Scientific Research in Computer Science Engineering and Information Technology ◽

10.32628/cseit2063211 ◽

2020 ◽

pp. 967-973

Author(s):

V. Deepika ◽

T. Rajasenbagam

Keyword(s):

Image Processing ◽

Brain Tumor ◽

Soft Tissues ◽

Tumor Detection ◽

Tumor Classification ◽

Normal Brain ◽

Image Processing Techniques ◽

Normal Brain Function ◽

Processing Techniques ◽

The Brain

A brain tumor is an uncontrolled growth of abnormal brain tissue that can interfere with normal brain function. Although various methods have been developed for brain tumor classification, tumor detection and multiclass classification remain challenging due to the complex characteristics of the brain tumor. Brain tumor detection and classification are one of the most challenging and time-consuming tasks in the processing of medical images. MRI (Magnetic Resonance Imaging) is a visual imaging technique, which provides a information about the soft tissues of the human body, which helps identify the brain tumor. Proper diagnosis can prevent a patient's health to some extent. This paper presents a review of various detection and classification methods for brain tumor classification using image processing techniques.

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