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Automatic crack detection and calculation based on deep learning and digital image processing 3

Automatic crack detection and calculation based on deep lear...

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3rd International conference on Computer Vision and pattern analysis, ICCPA 2023

作者： Kong, Siyu Liu, Yufei Fan, Jiansheng Department of Civil Engineering Tsinghua University Beijing10084 China

ISBN: (纸本)9781510667563

Structural health monitoring is an important way to ensure the safe and reliable work of the structures during the service period. Surface crack is one of the important indicators for monitoring and assessing the damage of structures. However, crack detection still mainly relies on human visual observation and recording. Besides, people could only record the crack width of a few points instead of all points. To solve this problem, this paper aims to propose a method which can automatically detect crack and calculate the width of every point on the crack but not several points. Thus, this paper built a large database containing more than 100,000 pictures which were taken in different scenarios, and used this database and self-designed deep convolutional neural network to detect cracks. And the prediction accuracy of CNN reached 95%. In addition, combined with image processing technology, this paper further carried out crack extraction and width calculation. Thus, the whole process of crack detection, extraction and width calculation is fully automated, and the calculation results were proved to be close to the actual results, which meets the engineering requirements and perform better than traditional OTSU algorithm. © 2023 SPIE.

关键词： Crack detection

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1st International Virtual conference on Visual pattern Extraction and recognition for Cultural Heritage Understanding (VIPERC 2022) 1

1st International Virtual Conference on Visual Pattern Extra...

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1st International Virtual conference on Visual pattern Extraction and recognition for Cultural Heritage Understanding, VIPERC 2022

作者： Amelio, Alessia Montelpare, Sergio Ursino, Domenico InGeo University "G. d’Annunzio" Chieti-Pescara Viale Pindaro 42 Pescara65127 Italy DII Marche Polytechnic University Via Brecce Bianche 12 Ancona60131 Italy

This document introduces the 1st International Virtual conference on Visual pattern Extraction and recognition for Cultural Heritage Understanding (VIPERC 2022), a premier forum for presenting the state-of-the-art, new research, ongoing work, academic and project reports in advanced statistics and machine learning, 3D modelling and simulation, knowledge representation, intelligent systems, information retrieval and software engineering, for visual pattern extraction, analysis and recognition to preserve the cultural heritage. © 2022 Copyright for this paper by its authors.

关键词： 3D modeling

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Film Shot Type Classification Based on Camera Movement Styles 10th

Film Shot Type Classification Based on Camera Movement Style...

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10th iberian conference on pattern recognition and image analysis (IbPRIA)

作者： Petrogianni, Antonia Koromilas, Panagiotis Giannakopoulos, Theodoros Univ Thessaly Volos Greece Natl Ctr Sci Res Demokritos Athens Greece

ISBN: (纸本)9783031048814;9783031048807

Visual information contains the most important characteristics of a movie regarding the related content and filming techniques. Especially the way the camera moves to capture the scene is vital to define the director's aesthetics. However, most of the machine learning tasks existing in the literature treat the movie as shallow content, rather than as an artistic work, and therefore focus on detecting objects and faces, recognizing activities and extracting plot-related topics. On the other hand, cinematography is closely connected to the choice of different ways to handle the camera, and thus camera movements include information that is useful in order to analyse the artistic style of a movie. In this work we present an original, publicly available (https://***/magcil/moyie_shot_classification_dataset) dataset for film shot type classification that is associated with the distinction across 10 types of camera movements that cover the vast majority of types of shots in real movies. In addition, two different methods are evaluated on the new dataset, one static that is based on feature statistics across frames, and one sequential that tries to predict the target class based on the input frame sequence using LSTMs. Based on the evaluation process it is inferred that the sequential method is more suited for modeling the camera movements.

关键词： Shot classification Camera movement classification Movie analysis

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Contribution of Low, Mid and High-Level image Features of Indoor Scenes in Predicting Human Similarity Judgements 10th

Contribution of Low, Mid and High-Level Image Features of In...

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10th iberian conference on pattern recognition and image analysis (IbPRIA)

作者： Mikhailova, Anastasiia Santos-Victor, Jose Coco, Moreno, I Univ Lisbon Inst Super Tecn Lisbon Portugal Sapienza Univ Rome Rome Italy

ISBN: (纸本)9783031048814;9783031048807

Human judgments can still be considered the gold standard in the assessment of image similarity, but they are too expensive and time-consuming to acquire. Even though most existing computational models make almost exclusive use of low-level information to evaluate the similarity between images, human similarity judgements are known to rely on both high-level semantic and low-level visual image information. The current study aims to evaluate the impact of different types of image features on predicting human similarity judgements. We investigated how low-level (colour differences), mid-level (spatial envelope) and high-level (distributional semantics) information predict within-category human judgements of 400 indoor scenes across 4 categories in a Four-Alternative Forced Choice task in which participants had to select the most distinctive scene among four scenes presented on the screen. Linear regression analysis showed that low-level (t = 4.14, p < 0.001), mid-level (t = 3.22, p< 0.01) and high-level (t = 2.07, p < 0.04) scene information significantly predicted the probability of a scene to be selected. Additionally, the SVM model that incorporates low-mid-high level properties had 56% accuracy in predicting human similarity judgments. Our results point out: 1) the importance of including mid and high-level image properties into computational models of similarity to better characterise the cognitive mechanisms underlying human judgements, and 2) the necessity of further research in understanding how human similarity judgements are done as there is a sizeable variability in our data that it is not accounted for by the metrics we investigated.

关键词： image similarity Scene semantics Spatial envelope SVM Hierarchical regression

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Research on Efficient image Semantic Segmentation Algorithm for Glass Transparent Containers Based on Improved DeepLabV3+ 3

Research on Efficient Image Semantic Segmentation Algorithm ...

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3rd International conference on Computer Vision and pattern analysis, ICCPA 2023

作者： Wei, Yujie Xu, Zhengguang School of Automation University of Science and Technology Beijing China

ISBN: (纸本)9781510667563

Aiming at the problems of slow segmentation speed and excessive computational complexity in practical engineering applications when using existing models to segment glass transparent containers, an efficient image semantic segmentation algorithm based on improved DeepLabV3+for glass transparent containers is proposed. The proposed algorithm uses the MobileNetV3 network to replace the backbone feature extraction network Xception of the original model, effectively reduces the number of parameters of the semantic segmentation model, improves the ASPP module, introduces the strip pooling module (SPM) and depthwise separable atrous convolution (DCAC), and uses the densely connected multiple receptive field cascade (MRFC) to obtain multi-scale feature information. In the decoding structure, multi-level feature fusion (MLFF) is used to recover the details and levels of features lost during the down sampling process. Adding the spatial attention mechanism module SAM helps refine the target boundaries. The proposed model reasoning speed is 1.5 times faster than DeepLabV3+, and FLOPs are reduced by 71.4%. In addition to a 0.14% decrease in the cuvette's Miou, the beaker and reagent bottle's Miou increased by 0.19% and 0.18%, respectively. From the experimental results, we have improved the reasoning speed without completely losing accuracy, achieved the segmentation effect that is comparable to the original model, and well considered the accuracy, computational complexity, and reasoning speed, achieving a good balance between various aspects. © 2023 SPIE.

关键词： Semantic Segmentation

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3rd International Workshop on Multiscale Multimodal Medical Imaging, MMMI 2022, held in conjunction with the 25th International conference on Medical image Computing and Computer Assisted Intervention, MICCAI 2022

3rd International Workshop on Multiscale Multimodal Medical ...

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3rd International Workshop on Multiscale Multimodal Medical Imaging, MMMI 2022, held in conjunction with the 25th International conference on Medical image Computing and Computer Assisted Intervention, MICCAI 2022

ISBN: (纸本)9783031188138

The proceedings contain 12 papers. The special focus in this conference is on Multiscale Multimodal Medical Imaging. The topics include: Towards Optimal Patch Size in Vision Transformers for Tumor Segmentation;Improved Multi-modal Patch Based Lymphoma Segmentation with Negative Sample Augmentation and Label Guidance on PET/CT Scans;visual Modalities Based Multimodal Fusion for Surgical Phase recognition;cross-Scale Attention Guided Multi-instance Learning for Crohn’s Disease Diagnosis with Pathological images;vessel Segmentation via Link Prediction of Graph Neural Networks;A Bagging Strategy-Based Multi-scale Texture GLCM-CNN Model for Differentiating Malignant from Benign Lesions Using Small Pathologically Proven Dataset;Liver Segmentation Quality Control in Multi-sequence MR Studies;pattern analysis of Substantia Nigra in Parkinson Disease by Fifth-Order Tensor Decomposition and Multi-sequence MRI;gabor Filter-Embedded U-Net with Transformer-Based Encoding for Biomedical image Segmentation;Learning-Based Detection of MYCN Amplification in Clinical Neuroblastoma Patients: A Pilot Study.

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An End-to-End Approach for Seam Carving Detection Using Deep Neural Networks 10th

An End-to-End Approach for Seam Carving Detection Using Deep...

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10th iberian conference on pattern recognition and image analysis (IbPRIA)

作者： Moreira, Thierry P. Santana, Marcos Cleison S. Passos, Leandro A. Papa, Joao Paulo da Costa, Kelton Augusto P. Sao Paulo State Univ Dept Comp Av Eng Luiz Edmundo Carrijo Coube 14-01 BR-17033360 Bauru SP Brazil Univ Wolverhampton Sch Engn & Informat CMI Lab Wolverhampton England

ISBN: (纸本)9783031048814;9783031048807

Seam carving is a computational method capable of resizing images for both reduction and expansion based on its content, instead of the image geometry. Although the technique is mostly employed to deal with redundant information, i.e., regions composed of pixels with similar intensity, it can also be used for tampering images by inserting or removing relevant objects. Therefore, detecting such a process is of extreme importance regarding the image security domain. However, recognizing seam-carved images does not represent a straightforward task even for human eyes, and robust computation tools capable of identifying such alterations are very desirable. In this paper, we propose an end-to-end approach to cope with the problem of automatic seam carving detection that can obtain state-of-the-art results. Experiments conducted over public and private datasets with several tampering configurations evidence the suitability of the proposed model.

关键词： Seam carving Convolutional neural networks image security

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Research on Adaptive Segmentation Algorithm of Sports image based on Support Vector Machine 22

Research on Adaptive Segmentation Algorithm of Sports Image ...

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3rd Asia-Pacific conference on image Processing, Electronics and Computers, IPEC 2022

作者： Fan, Wenjie Ministry of Public Education Department of Physical Education Jiangxi Vocational Technical College of Industry & Trade Nanchang330038 China

ISBN: (纸本)9781450395786

image segmentation is an important problem in image analysis and pattern recognition. The so-called image segmentation refers to distinguishing different regions with special significance in the image, so that these regions do not intersect with each other and satisfy certain similarity criteria. At present, many different types of image segmentation methods have been proposed, such as those based on thresholds, regions, and edges. The threshold method is simple to calculate and thus the segmentation speed is fast, the area method is ideal for processing relatively uniform images, and the edge method is suitable for processing images with less noise. With the development of image segmentation theory, image segmentation methods combined with specific theories have emerged, such as fuzzy C-means theory and support vector machine theory. Some scholars have tried to improve the SVM algorithm and reduce the influence of noise by using the ideas of image neighborhood information or relaxing segmentation boundaries. The support vector machine method (SVM) is a common method to solve the problem of image segmentation. In this paper, support vector machine is used to perform adaptive image segmentation, which has strong generalization ability and can handle image segmentation problems with fewer sample points. © 2022 ACM.

关键词： Support vector machines

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Robust Fault Diagnostics of Industrial Motors: The Signal-to-image Approaches for Sensor Data Using Multi-Task Transformers with Diverse Attention Mechanism

Robust Fault Diagnostics of Industrial Motors: The Signal-to...

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International conference on Control, Instrumentation, Energy and Communication (CIEC)

作者： Sudipta Mukhopadhyay Indrajit Kar Zonunfeli Ralte Zensar Technologies Bangalore India

Vibration analysis serves as a valuable method to identify defects within mechanical systems. This process involves monitoring the auditory signals generated by the system and discerning patterns that signify potential issues. Nevertheless, due to the intricate nature of motors, relying solely on one modality (such as acoustic signals) might prove inadequate for comprehensively detecting all faults. Our research introduces a novel approach to enhance fault detection in mechanical systems, specifically focusing on motors. A more comprehensive system health assessment is achieved by utilizing multi-modal vibration analysis, combining acoustic signals and heat signatures. Effective information fusion is facilitated by integrating attention-based transformers, capable of capturing cross-modal correlations. The proposed neural network employs classification and regression loss functions for fault identification and severity assessment respectively. Evaluation on a motor fault dataset demonstrates significant classification accuracy improvements compared to conventional deep and machine learning methods. Incorporating a signal-to-image transformation further aids in fault pattern recognition. This method offers a promising avenue for accurate and comprehensive fault diagnosis in mechanical systems, showcasing the potential of multi-modal analysis and attention mechanisms for industrial equipment maintenance.

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Parallel Architecture Based on Transformers and CNNs for Renal Cell Carcinoma image Segmentation 3

Parallel Architecture Based on Transformers and CNNs for Ren...

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3rd International conference on Computer Vision and pattern analysis, ICCPA 2023

作者： Ao, Bin Liu, Rong Wen, Qing Wu, Xin Li, Kuan Yin, Jianping School of Cyberspace Security Dongguan University of Technology Dongguan China

ISBN: (纸本)9781510667563

image segmentation of Renal Cell Carcinoma is a crucial prerequisite for pathologists to diagnose the disease and implement treatment. In various previous medical image segmentation tasks, convolutional neural networks(CNNs) have been widely used and have become a practical benchmark with significant success. However, the inherently local nature of the convolution operation leads to its inability to balance long-range relations and local dependencies in the modeling process, resulting in redundancy-deepening networks and the loss of local details. With its innate global self-attention mechanism, transformers were designed for sequence-to-sequence prediction to better extract global information. However, on account of the lack of low-level details, it may cause limited localization abilities. In this paper, we propose a parallel architecture based on Transformers and CNNs, combining Transformers and CNNs in a parallel manner to obtain global and local information by utilizing location-encoded Transformers model and local convolution neural networks, respectively. So that global contextual information can be captured more efficiently while maintaining a strong grasp of low-level spatial detailed information for Renal Cell Carcinoma image segmentation. Through a series of comparative experiments and extension experiments, the effectiveness and progressiveness of our method were validated, demonstrating remarkable potential in the image segmentation task of Renal Cell Carcinoma. © 2023 SPIE.

关键词： Semantic Segmentation

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