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Identification of External Defects on Fruits Using Deep Learning 10th

Identification of External Defects on Fruits Using Deep Lear...

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

作者： Aguiar, Henrique Tavares Vasconcelos, Raimundo C. S. Fed Inst Brasilia BR-72146050 Taguatinga DF Brazil

ISBN: (纸本)9783031048814;9783031048807

The quality of fruit plays a fundamental role in their marketing and is mainly defined by its shape, color, and size. The classification process is traditionally done manually and takes time. The use of image processing techniques can help this task. Some methodologies for image classification are presented, using deep neural networks. A set of combinations between Convolution Neural Networks (CNN), deep neural networks (DNN) using Gabor filter, over RGB and grayscale images, extracting texture properties of a GLCM (Gray Level Co-occurrence Matrices) is used in this project. Background segmentation, contrast enhancement, and data augmentation are also used to improve generalization and minimize overfitting. Applying it to a set of tropical fruits resulted in an excellent set of results, above 95% on average.

关键词： Defect detection Feature extraction image processing

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Architectural Decorative image pattern recognition in the Context of Iconography for Efficient Information Mining

Architectural Decorative Image Pattern Recognition in the Co...

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Sentiment analysis and Deep Learning (ICSADL), International conference on

作者： Yu Qin College of Art and Design Guangzhou Panyu Polytechnic Guangzhou City China

ISBN: (数字)9798350396157

ISBN: (纸本)9798350396164

This study addresses the crucial task of architectural decorative image pattern recognition in the context of iconography, with an emphasis on efficient information mining. The proposed research work presents a novel algorithm that combines edge detection, ResNet50, and the triplet attention mechanism for image style analysis. The proposed methodology is organized into comprehensive sections, including a literature review that explores various studies in image pattern recognition. The proposed algorithm focuses on overcoming the challenges faced by existing deep learning methods in capturing aesthetic style features. It introduces a multifaceted self-supervised task and incorporates edge detection using the Canny method, followed by the application of ResNet50 with a triplet attention mechanism. The paper then focuses on the optimization of the network structure for the targeted task of architectural decorative image recognition, introducing a feature embedding encoder (FEE) to effectively handle multi-level structures. In the experimental phase, the proposed model is tested against traditional CNN and FCM models, demonstrating superior performance with recognition accuracy consistently ranging from 97% to 99%. The comparative analysis highlights the effectiveness of the proposed algorithm in achieving high accuracy, positioning it as a promising solution for image recognition tasks.

关键词： Deep learning Surveys Visualization Sentiment analysis Accuracy Attention mechanisms image edge detection

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Research on Smoke Removal of Industrial Scene image Based on an Improved Dark Channel Prior 3

Research on Smoke Removal of Industrial Scene Image Based on...

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

作者： Liang, Tian Hunan University of Technology Zhuzhou412008 China

ISBN: (纸本)9781510667563

In response to the challenge of obtaining clear monitoring images in non-ferrous metal smelters, particularly under complex atmospheric conditions and varying levels of dust and fog concentrations, we have designed an indoor dust and fog image clarification algorithm in this paper. This algorithm utilizes a concentration evaluation to obtain fog weights and construct a refined transmittance in dense fog regions. Transmittance values for non-dense fog regions are calculated using the a priori dark channel and ambient light matrix of the region. The Alpha blending technique is used to fuse ambient light and transmittance matrices of different regions, while bootstrap filtering is utilized to suppress generated noise and preserve image edge information. Finally, the global ambient light and transmittance values are obtained and substituted into the atmospheric scattering model to recover low-illumination dust and fog images. Experimental results demonstrate that the proposed algorithm achieves excellent performance in non-ferrous metal smelting environments by effectively reducing fog concentration in images, improving image illumination, and preserving image edge information. © 2023 SPIE.

关键词： Smoke

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An image Classification Method Based On Few-shot Learning 3

An Image Classification Method Based On Few-shot Learning

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

作者： Wang, Jian Zhang, Mingjun Dept. Network Technology Software Engineering Institute Of GuangZhou GuangZhou China

ISBN: (纸本)9781510667563

image classification technology is at the heart of many complex computer vision applications, including object tracking, video categorization, and action identification. In the area of picture classification, data-driven supervised learning has had considerable success, but these model's capacity to generalize is highly reliant on a huge amount of labeled data. Not all job contexts, though, may quickly acquire a sizable number of labeled dataset samples. This article builds a deep learning approach appropriate for few-sample image classification tasks utilizing Meta-Learning in order to address the issue of image classification tasks in scenarios with few samples. Finally, to confirm the success of the approach, comparative experiments are run on the handwritten letter data set from Omniglot. © 2023 SPIE.

关键词： image classification

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English Handwriting image Edge Detection Based on Fuzzy Logic 3

English Handwriting Image Edge Detection Based on Fuzzy Logi...

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

作者： Wang, Xueying Liu, Hongwei Wang, Yuanshen School of Mathematics and Big Data Chongqing University of Arts and Sciences Chongqing China

ISBN: (纸本)9781510667563

This paper mainly applies fuzzy logic to the edge detection of handwritten images in English. image edge detection is an important part of image processing. Through image edge detection, the amount of data can be greatly reduced, and the information that can be considered irrelevant can be eliminated, and the important structural attributes of the image are retained. It uses six traditional operators to detect the edge of English handwritten images: canny, log, sobel, sprewitt, zerocross, and roberts. By comparing the edge detection results of the two kinds of experiments, we can find that the canny operator has the best edge detection effect among the traditional operators. In the fuzzy logic experiment, with the growth of Sx and Sy, the edge detection effect gradually weakened. In this paper, the experiment with Sx = 0. 05 and Sy = 0. 05 has the best edge detection results. In conclusion, the edge detection effect of English handwritten images based on fuzzy logic is better than that of English handwritten images based on traditional methods. © 2023 SPIE.

关键词： Fuzzy logic

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Transformer-based multi-scale gradient feature fusion for low-light image enhancement 3

Transformer-based multi-scale gradient feature fusion for lo...

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

作者： Li, Canlin Song, Shun Zhang, Wenjiao Wang, Xinyue Zhengzhou University of Light Industry HeNan Zhengzhou China

ISBN: (纸本)9781510667563

In deep learning-based low-light image enhancement (LLIE) methods, the more effective use of image features plays a crucial role in enhancing the quality of images. In the paper, a Transformer-based multi-scale gradient feature fusion network (TMGFFN) is proposed to improve the overall image quality by making full use of the multi-scale, non-local gradient features of an image. Specifically, to exploit the non-local features of an image, an asymmetric Transformer structure is designed to enhance the image as a whole by learning the non-local mapping of the image in different directions. In addition, to extract richer features, a multi-scale structure with an asymmetric Transformer as a benchmark is designed to accomplish accurate information injection by progressive fusion of features from different sensory fields and by exploiting the contextual features of the image. Finally, a comparison with several classical LLIE methods is made. Experimental results show that our method can significantly improve the quality and sharpness of low-light images while retaining more detailed information. © 2023 SPIE.

关键词： image enhancement

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A Remote Sensing image Change Detection Method Incorporating Variable Convolution and Attention Mechanism 3

A Remote Sensing Image Change Detection Method Incorporating...

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

作者： Feng, Yunfei Li, Jingwen Lu, Yanling Guilin University of Technology Guangxi Guilin541006 China Ecological Spatiotemporal Big Data Perception Service Laboratory Guangxi Guilin541006 China

ISBN: (纸本)9781510667563

In order to solve the problem that the existing remote sensing image change detection methods based on deep learning mostly focus on designing complex convolutional neural network models to enhance the detection capability, ignoring the counteracting elimination of background interference information of image features and the attention and characterization of target information of image features in the detection process, a VCA-Net remote sensing image change detection method incorporating variable convolution and attention mechanism is proposed based on the U-Net model, which introduces deformable convolution and attention mechanism modules in the encoding and jumping stages of the model, respectively, to adaptively acquire the feature information of the before and after changes of remote sensing images, filter and suppress the background noise interference in the detection process, and enhance the learning and characterization ability of the network model on the key feature information of the ground *** the experiments conducted on LEVIR-CD and CDD datasets, VCA-Net obtained 91.31% and 94.12% accuracy results, respectively, and the experimental comparison and analysis with existing model methods showed that the method has better accuracy and stability in remote sensing image building change detection. © 2023 SPIE.

关键词： Convolution

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Increasing Accuracy of Crop Production and Yield Forecasting Using Hyper Spectral image analysis 3

Increasing Accuracy of Crop Production and Yield Forecasting...

引用

3rd International conference for Innovation in Technology, INOCON 2024

作者： Singh, Devendra Uttaranchal University Uttaranchal Institute of Technology Dehradun India

ISBN: (纸本)9798350381931

Hyper Spectral up-to-date evaluation has up to date be an increasing number of vital up-to-date in precision farming for predicting crop manufacturing and yield forecasting. The era offers an innovative and green way of reliably measuring the fitness of vegetation and crop production ability by providing high-resolution imaging information that allows estimates of crop biomass, vigor, and yield. This technology is mainly helpful in fast crop health assessment, with on-hand visuals that permit fast evaluation within the field. A significant gain of this generation is its capacity to date capture the spectral signature of flora appropriately and precisely, with date consideration advances in precision farming and precision agriculture. Using the technology, researchers have been up-to-date in creating specific spatial assessment maps of crop health, imparting local and nearby statistics approximately crop productiveness capacity. This information can be used up to date optimize management practices up to date increase productivity and ***, the generation has recently become even more accurate by improving synthetic Intelligence (AI) algorithms, such as up-to-date gadgets and hyperspectral up-to-date, enhancing the era's accuracy and usual reliability in modeling crop fitness. Hyperspectral picture analysis is an increasing number of unique techniques for forecasting crop manufacturing and yield. It involves the spectral measurements of a pattern of plants throughout the visible and up-to-date infrared (NIR) spectrum, which is then utilized to date to create a hyperspectral profile of the sample. analysis of such profiles can reveal the updated presence of yield-figuring components, including updated water, nutrients, and up-to-date biomass. By comparing these components, predictive models of increase estimates may be created. The combination of high-resolution hyperspectral up-to-date analyses with airborne far-off sensing offers a remarkable level of temp

关键词： Crops

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DBA: Downsampling-Based Adversarial Attack in Medical image analysis 3

DBA: Downsampling-Based Adversarial Attack in Medical Image ...

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

作者： Wang, Zhaoxuan Zhang, Shiyu Li, Yang Pan, Quan School of Cybersecurity Northwestern Polytechnical University Xi’an710072 China School of Automation Northwestern Polytechnical University Xi’an710072 China Institute of Modern Physics Chinese Academy of Sciences Lanzhou730000 China

ISBN: (纸本)9781510667563

The next-generation of artificial intelligence technology has contributed significantly to the development of medical intelligence. However, the widespread use of deep neural networks (DNNs) has also brought about serious security threats. In this paper, we present an adversarial attack approach for deep learning-based image segmentation models in the field of medical image analysis. In our solutions, we propose a novel adversarial attack method, which is designed to exploit the DNNs’ generic down-sampling operation to ensure the effectiveness, stealthiness, and transferability of the attack. We perform the attack on two State-Of-The-Art (SOTA) models, DDANet and CaraNet in a general medical image dataset Kvasir-SEG, and a comprehensive evaluation shows that our attack is effective stealthy, and transferrable. © 2023 SPIE.

关键词： Deep neural networks

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A First Approach to image Transformation Sequence Retrieval 10th

A First Approach to Image Transformation Sequence Retrieval

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

作者： Mas-Candela, Enrique Rios-Vila, Antonio Calvo-Zaragoza, Jorge Univ Alicante UI Comp Res Alicante Spain

ISBN: (纸本)9783031048814;9783031048807

Detecting the corresponding editions from just a pair of input-output images represents an interesting task for artificial intelligence. If the possible image transformations are known, the task can be easily solved by enumeration with brute force, yet this becomes an unfeasible solution for long sequences. There are several state-of-the-art approaches, mostly in the field of image forensics, which aim to detect those transformations;however, all related research is focused on detecting single transformations instead of a sequence of them. In this work, we present the image Transformation Sequence Retrieval (ITSR) problem and describe a first attempt to solve it by considering existing technology. Our results demonstrate the huge difficulty of obtaining a good performance-being even worse than a random guess in some cases and the necessity of developing specific solutions for ITSR.

关键词： Computer vision image transformations Deep learning

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