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Surface concrete cracks detection and segmentation using transfer learning and multi-resolution image processing

STRUCTURES 2023年 54卷 386-398页

作者： Iraniparast, Mostafa Ranjbar, Sajad Rahai, Mohammad Nejad, Fereidoon Moghadas Amirkabir Univ Technol Tehran Polytech Dept Civil & Environm Engn Tehran Iran Sharif Univ Technol Dept Civil & Environm Engn Tehran Iran

Surface crack detection must be precise and efficient for the structural health monitoring (SHM) of concrete structures. The traditional techniques of crack detection entail human visual examination. These procedures are labor-intensive, expensive, and inefficient. In order to forestall potentially hazardous circumstances caused by concrete surfaces that have deteriorated, it is vital to have an autonomous concrete crack inspection system. Crack detection and segmentation are the two main components of automatic crack inspection. In this research, deep convolutional neural networks (DCNN) and transfer learning (TL) methods are employed for crack detection in concrete structure images, also multiresolution image analysis based on the wavelet transform is the main process in the crack segmentation. The concrete crack dataset is randomly selected from two prevalent concrete crack datasets for developing the proposed method. The results indicated that DCNN classifier models provide good performance with F1-score ranging from 94.5 to 99.6 percent. In addition, the proposed multiresolution image analysis can segment crack pixels with 95.25 percent for the F1-score and shows acceptable and stable performance regarding various segmentation performance metrics.

关键词： Concrete structures Automatic crack detection Segmentation Transfer learning multiresolution image analysis

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COMBINING BACKGROUND SUBTRACTION AND TEMPORAL PERSISTENCY IN PEDESTRIAN DETECTION FROM STATIC VIDEOS

COMBINING BACKGROUND SUBTRACTION AND TEMPORAL PERSISTENCY IN...

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20th IEEE International Conference on image Processing (ICIP)

作者： Jiang, Zhengqiang Huynh, Du Q. Moran, William Challa, Subhash Univ Western Australia Sch Comp Sci & Software Engn Perth WA 6009 Australia Univ Melbourne Def Sci Inst Melbourne Vic 3010 Australia Univ Melbourne Victoria Res Labs NICTA Melbourne Vic 3010 Australia

ISBN: (纸本)9781479923410

This paper presents a method that incorporates background subtraction and temporal persistency with the HOG pedestrian detector to detect pedestrians from videos captured by a fixed camera. We use a codebook based method and interpolation to extract a series of foreground sub-images for the HOG detector. This allows the detector to focus on pedestrian detection on smaller image regions and thereby reduce its computational cost and lower its false positive error rate. We employ a temporal persistency constraint to overcome problems that may arise from background subtraction. Compared to other state-of-art techniques, the performance of our method is pleasantly promising.

关键词： Pedestrian detection background subtraction temporal persistency multiresolution image analysis surveillance applications

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COMBINING BACKGROUND SUBTRACTION AND TEMPORAL PERSISTENCY IN PEDESTRIAN DETECTION FROM STATIC VIDEOS

COMBINING BACKGROUND SUBTRACTION AND TEMPORAL PERSISTENCY IN...

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IEEE International Conference on image Processing

作者： Zhengqiang Jiang Du Q. Huynh William Moran Subhash Challa School of Computer Science and Software Engineering The University of Western Australia Perth Australia Defence Science Institute The University of Melbourne Australia NICTA Victoria Research Labs The University of Melbourne Australia

ISBN: (纸本)9781479923427

关键词： Pedestrian detection background subtraction temporal persistency multiresolution image analysis surveillance applications Subtraction Videotapes Pedestrians region of an image detector Pedestrian detector Error analysis

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Improving 2-DE gel image denoising using contourlets

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PROTEOMICS 2009年第15期9卷 3877-3888页

作者： Tsakanikas, Panagiotis Manolakos, Elias S. Univ Athens Dept Informat & Telecommun Athens 15784 Greece

One of the most commonly used methods for protein separation is 2-DE. After 2-DE gel scanning, images with a plethora of spot features emerge that are usually contaminated by inherent noise. The objective of the denoising process is to remove noise to the extent that the true spots are recovered correctly and accurately i.e. without introducing distortions leading to the detection of false-spot features. In this paper we propose and justify the use of the contourlet transform as a tool for 2-DE gel images denoising. We compare its effectiveness with state-of-the-art methods such as wavelets-based multiresolution image analysis and spatial filtering. We show that contourlets not only achieve better average SIN performance than wavelets and spatial filters, but also preserve better spot boundaries and faint spots and alter less the intensities of informative spot features, leading to more accurate spot volume estimation and more reliable spot detection, operations that are essential to differential expression proteomics for biomarkers discovery.

关键词： 2-DE gel images Contourlets Denoising multiresolution image analysis Wavelets

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A wavelet-based change-detection technique for multitemporal SAR images

A wavelet-based change-detection technique for multitemporal...

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3rd International Workshop on the analysis of Multi-Temporal Remote Sensing images

作者： Bovolo, F Bruzzone, L Univ Trent Dept Informat & Commun Technol I-38050 Trento Italy

ISBN: (纸本)0780391187

This paper presents a novel wavelet-based multiscale technique for unsupervised change detection in multitemporal synthetic aperture radar (SAR) images. The proposed approach is based on the analysis of a set of scale-dependent images characterized by a different trade-off between speckle reduction and preservation of geometrical details. The different scales are obtained by means of a multiresolution decomposition of the log-ratio image (obtained by a comparison of a pair of co-registered images acquired at different times on the same area). The final change-detection map is derived according to an adaptive scale-driven fusion algorithm, which properly exploits information at different resolution levels. According to an automatic local analysis of the statistic of the data, for each pixel only a sub-set of reliable scales is selected and exploited in the decision process thus producing an accurate and reliable change-detection map in both homogeneous and border areas. Experimental results confirm the effectiveness of the proposed technique.

关键词： change detection synthetic aperture radar images multiresolution image analysis

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A cognitive pyramid for contextual classification remote sensing images

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2003年第12期41卷 2906-2922页

作者： Binaghi, E Gallo, I Pepe, M Univ Insubria Dept Informat & Commun Sci I-21100 Varese Italy CNR ITIM I-20131 Milan Italy

Many cases of remote sensing classification present complicated patterns that "cannot be identified on the basis of spectral data alone, but require contextual methods that base class discrimination on the spatial relationships between the individual pixel and local and global configurations of neighboring pixels. However, the use of contextual classification is still limited by critical issues, such as complexity and problem dependency. We propose here a contextual classification strategy for object recognition in remote sensing images in an attempt to solve recognition tasks operatively. The salient characteristics of the strategy are the definition of a multiresolution feature extraction procedure exploiting human perception and the use of soft neural classification based on the multilayer perceptron model. Three experiments were conducted to evaluate the performance of the methodology one in an easily controlled domain using synthetic images, the other two in real domains involving builtup pattern recognition in panchromatic aerial photographs and high-resolution satellite images.

关键词： contextual classification featuie extraction multiresolution image analysis neural networks object recognition

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multiresolution-based watersheds for efficient image segmentation

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PATTERN RECOGNITION LETTERS 2003年第1-3期24卷 473-488页

作者： Kim, JB Kim, HJ Kyungpook Natl Univ Artificial Intelligence Lab Dept Comp Engn Puk Gu Taegu 702701 South Korea

This paper presents an efficient method for image segmentation based on a multiresolution application of a wavelet transform and watershed segmentation algorithm. The procedure toward complete segmentation consists of four steps: pyramid representation, image segmentation, region merging and region projection. First, pyramid representation creates multiresolution images using a wavelet transform. Second, image segmentation segments the lowest-resolution image of the pyramid using a watershed segmentation algorithm. Third, region merging merges the segmented regions using the third-order moment values of the wavelet coefficients. Finally, the segmented low-resolution image with label is projected into a full-resolution image (original image) by inverse wavelet transform. Experimental results of the presented method can be applied to the segmentation of noise or degraded images as well as reduce over-segmentation. In addition, we applied our method to human face detection with accurate and closed boundaries. (C) 2002 Elsevier Science B.V. All rights reserved.

关键词： image segmentation watershed image segmentation wavelet transform multiresolution image analysis

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Unsupervised multiresolution image segmentation based on color moments

Unsupervised multiresolution image segmentation based on col...

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6th International Conference on Signal Processing

作者： Xing, Q Hu, MX Yuan, BZ No Jiaotong Univ Inst Informat Sci Beijing 100044 Peoples R China

ISBN: (纸本)0780374886

This paper describes a novel multiresolution image segmentation algorithm which is designed to separate a sharply focused object of interest from background. According to the principle of human vision system, the algorithm first searches salient blocks in global image domain. Then a multiscale approach based on color moments is used to perforin context-dependent classification of these blocks. The algorithm is fully automatic in that all parameter are image independent. Unlike other color-based approaches which use global optimization methods, our algorithm does perform a multiresolution process and achieves better segmentation results at higher speed.

关键词： content-based image retrieval image segmentation multiresolution image analysis

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Unsupervised multiresolution segmentation for images with low depth of field

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IEEE TRANSACTIONS ON PATTERN analysis AND MACHINE INTELLIGENCE 2001年第1期23卷 85-90页

作者： Wang, JZ Li, J Gray, RM Wiederhold, G Penn State Univ Sch Informat Sci & Technol University Pk PA 16801 USA Penn State Univ Dept Stat University Pk PA 16802 USA Stanford Univ Dept Elect Engn Stanford CA 94305 USA Stanford Univ Dept Comp Sci Stanford CA 94305 USA

Unsupervised segmentation of images with law depth of field (DOF) is highly useful in various applications including image enhancement for digital cameras, target recognition, image indexing for content-based retrieval, and 3D microscopic image analysis. This paper describes a novel multiresolution image segmentation algorithm for low DOF images. The algorithm is designed to separate a sharply focused object-of-interest from other foreground or background objects. The algorithm is fully automatic in that all parameters are image independent. A multiscale approach based on high frequency wavelet coefficients and their statistics is used to perform context-dependent classification of individual blocks of the image. Unlike other edge-based approaches, our algorithm does not rely on the process of connecting object boundaries. The algorithm has achieved high accuracy when tested on more than 100 low DOF images. many with inhomogeneous foreground or background distractions. Compared with the state of the art algorithms, this new algorithm provides better accuracy at higher speed.

关键词： content-based image retrieval image region segmentation low depth-of-field wavelet multiresolution image analysis

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Visualization of a Turbulent Jet Using Wavelets

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Journal of Thermal Science 2001年第3期10卷 211-217页

作者： Hui LI Department of Mechanical Engineering, Kagoshima University 1-21-40, Korimoto, Kagoshima City 890-0065, Japan Department of Mechanical Engineering Kagoshima University Kagoshima City Japan

An application of multiresolution image analysis to turbulence was investigated in this paper, in order to visualize the coherent structure and the most essential scales governing turbulence. The digital imaging photograph of jet slice was decomposed by two-dimensional discrete wavelet transform based on Daubechies, Coifman and Baylkin bases. The best choice of orthogonal wavelet basis for analyzing the image of the turbulent structures was first discussed. It is found that these orthonormal wavelet families with index N<10 were inappropriate for multiresolution image analysis of turbulent flow. The multiresolution images of turbulent structures were very similar when using the wavelet basis with the higher index number, even though wavelet bases are different functions. From the image components in orthogonal wavelet spaces with different scales, the further evident of the multi-scale structures in jet can be observed, and the edges of the vortices at different resolutions or scales and the coherent structure can be easily extracted.

关键词： digital image processing multiresolution image analysis orthogonal wavelet transform turbulent jet vortex.

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