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Better image dehazing Networks Based on Structural Priors 4

Better Image dehazing Networks Based on Structural Priors

引用

4th international conference on Signal processing and computer Science, SPCS 2023

作者： An, Da Guo, Zhan Shi, Huajun The 32nd Research Institute of China Electronics Technology Group ShangHai China

ISBN: (纸本)9781510672505

Convolutional neural networks are more and more widely used in our lives. computer vision tasks dominated by traditional algorithms are also trying to introduce deep learning networks. image dehazing is a good field. At present, the most widely used image dehazing field is the unsupervised dehazing algorithm. By decomposing low-quality images, image dehazing (separation into clear image and haze map), it can be performed expansion operations. At present, due to the lack of paired high-quality images and hazy images in the field of image dehazing, this further intensifies the need for unsupervised dehazing algorithms. In order to further improve this problem, this article proposes an extractor DIPV1 based on a new U-Net-like structure, and based on this, combined with the biased fitting problem of the network, the existing Double-DIP structure is improved. we call it Better Double-DIP(BDouble-DIP). this network is an end-to-end network and is jointly trained by 2 *** stopping early, we can get better results. By applying the physical dehazing model, we can finally get a good dehazing result. We conduct experiments on the open source dataset O-Haze dataset, and compare BDouble-DIP with existing traditional algorithms: SIHR, EIDBC, supervised neural networks like DehazeNet: NTDF, NLD, URIE, and BOPBL, as well as unsupervised neural networks: DoubleDIP. © 2023 SPIE.

关键词： image reconstruction

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recognition Performance Validation of Weather Map images by ChatGPT 28th

Recognition Performance Validation of Weather Map Images by ...

引用

28th international conference on Knowledge Based and Intelligent information and Engineering Systems, KES 2024

作者： Takasuka, Takumi Takano, Yuki H. Watanabe, Shotaro Kumoi, Gendo Nagaoka University of Technology 1603-1 Kamitomioka Nagaoka Niigata940-2188 Japan Weather Map Co. Ltd. The HEXAGON 5F 5-4-7 Akasaka Minato-ku Tokyo107-0052 Japan

Weather map recognition is a complex image recognition task that requires two cognitive processes: the interpretation of symbols and future predictions. Recent advancements in multimodal AI have shown the potential to solve such complex tasks. this research uses ChatGPT, a type of multimodal AI, to validate the recognition performance of weather map images and proposes prompt engineering for more accurate recognition. Weather map recognition is a problem that is also featured in the Common University Entrance Test. If weather maps can be recognized, automatic grading and question generation become possible, leading to learning support. Furthermore, it is expected that weather commentary text can be automatically generated from weather maps. therefore, weather map image recognition is an important task, but it requires not only recognizing time-series changes in local weather data but also recognizing information on large-scale weather patterns such as pressure pattern and making future predictions. In this research, we use ChatGPT4-vision, a multimodal AI, to validate the recognition performance of weather map images. We investigate whether it can answer questions about weather maps from university entrance exams and generate weather commentary text from weather map images. By conducting multiple experiments with varying tasks and information in the prompts, and evaluating the accuracy of the generated commentaries, we propose and validate improvements in reading performance through prompt engineering. © 2024 the Authors.

关键词： Weather forecasting

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Multi-frequency Fine-Grained Matching for Audio-Visual Segmentation 27th

Multi-frequency Fine-Grained Matching for Audio-Visual Segm...

引用

27th international conference on pattern recognition, ICPR 2024

作者： Zhang, Yinhao Xu, Tianyang Wu, Xiao-Jun Zhao, Shaochuan Kittler, Josef School of Artificial Intelligence and Computer Science Jiangnan University Wuxi China Centre for Vision Speech and Signal Processing University of Surrey GuildfordGU2 7XH United Kingdom

ISBN: (纸本)9783031781094

Audio-visual segmentation is a recently proposed task, whose main goal is to locate the target of the sound in the image at the pixel level. In practical scenarios, multiple types of audio can coexist, with different objects emitting sounds at different frequencies. However, existing methods only use single-frequency audio information when fusing audio and visual modalities. Moreover, the process of combining images and audio can be quite rough. therefore, we propose a multi-frequency fine-grained matching method for multiple sound sources scenario. Firstly, we use short-time Fourier transform (STFT) to extract different frequency spectrograms and input them into the audio encoder to extract multi-frequency audio features. Secondly, multi-frequency audio information serves as a prompt in the pixel decoder stage to guide model segmentation. To obtain high-quality prompts, we use an attention method in the Audio-Visual Matching Module (AVMM) to match visual and audio information. the experiments show that our method has a significant improvement over the baseline and achieves state-of-the-art results on the MS3 benchmark (64.1 mIoU on MS3). © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： image segmentation

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Optimizing Rice Variety recognition through Transfer Learning with Pretrained ResNet Models 4

Optimizing Rice Variety Recognition Through Transfer Learnin...

引用

4th international conference of Science and Information Technology in Smart Administration, ICSINTESA 2024

作者： Prasetyo, Simeon Yuda Bina Nusantara University School of Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350376111

the identification of rice varieties holds paramount importance in addressing global food challenges, given that rice serves as a staple food source for more than half of the world's population. Accurate identification is crucial for ensuring food security and meeting the dietary needs of diverse populations. However, accurately distinguishing between rice varieties remains a complex task due to genetic mixing and hybridization, which can lead to variations in morphological and genetic traits among different varieties. Recent advancements in deep learning, particularly convolutional neural networks (CNNs), offer promising solutions for improving rice variety classification by leveraging large datasets and sophisticated image processing techniques. this study conducts a comprehensive comparative analysis of ResNet50 and ResNet101 models in identifying rice varieties using transfer learning and fine-tuning approaches. Leveraging a dataset containing 75,000 high-resolution rice images representing various varieties, the models undergo thorough evaluation based on various performance metrics, including accuracy, precision, recall, and F1-score. Results highlight the effectiveness of pretrained ResNet models in accurately classifying rice varieties, with ResNet50 achieving the highest accuracy of 98.813% post-fine-tuning. these findings underscore the significance of fine-tuning pretrained models to enhance classification accuracy, contributing to the advancement of precision agriculture and sustainable food production practices. the insights gained from this study have implications for improving crop management practices, optimizing resource allocation, and ultimately, enhancing global food security. © 2024 IEEE.

关键词： Convolutional neural networks

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Research on image recognition based on Reinforcement Learning

Research on Image Recognition based on Reinforcement Learnin...

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computer vision, image and Deep Learning (CVIDL), international conference on

作者： Jiabin Luo Rongzhen Luo School of Mathematics and Statistics Northeastern University at Qinhuangdao Hebei China Department of Mechanical Engineering University Baoding Campus of North China Electric Power University Hebei China

Nowadays, the images recognition has become a fundamental component in computer vision and assist to identify the objectives in the complex images. With the development of images processing methods, the image-level objectives recognition accuracy has been greatly enhanced by utilizing the machine learning models or neural networks. However, existing methods are mainly concentrated on the primary features of input images and concentrate on some certain areas, which ignore the environment features and the deep investigation of the image data-set. In this paper, we propose a novel image recognition method to identify the objectives and obtain the policy gradients for decreasing orders. Furthermore, we compare our proposed models with existing traditional machine learning methods to evaluate the performance of recognition accuracy. From our extensive experimental results, we can conclude that our proposed methods can achieve the subjective detection from numerous images data-set with reasonable computation costs.

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Handwritten Digit recognition Using Encryption Methods 4

Handwritten Digit Recognition Using Encryption Methods

引用

4th international conference on pattern Analysis and Intelligent Systems, PAIS 2022

作者： Abdelmoumene, Zebdi Lakhdar, Laimeche Abdeldjamil, Gattal Larbi Tebessi Univesity Mathematics and Computer Science Tebessa12002 Algeria Tebessi Tebessa12002 Algeria

ISBN: (数字)9781665461610

ISBN: (纸本)9781665461610

Over the past years, deep learning techniques has had a strong impact on many areas of technical intelligence, including handwriting recognition. Handwriting recognition it is defined as the domain which allow a computer to interpret intelligible handwritten inputs from different sources. It is plays an important role in many areas, such as authenticating bank checking, exchanging remote computer files, and document duration. In this paper, we present a novel handwritten digit recognition method based on deep learning. In order to improve the performance of recognizing handwritten digits, we have proposed a new data augmentation method by using different encrypted digit images and image fusion techniques. the experimental results based on the CVL database shown that the proposed method achieved higher accuracy rates that were compared to the state of the art. © 2022 IEEE.

关键词： Cryptography

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Classroom Learning Situation Analysis System Based on computer vision 4th

Classroom Learning Situation Analysis System Based on Comput...

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4th international conference on Machine Learning, image processing, Network Security and Data Sciences (MIND)

作者： Chen, Yixin Hu, Xi Jianghan Univ Sch Artificial Intelligence Wuhan Hubei Peoples R China

ISBN: (纸本)9783031243660;9783031243677

With the continuous progress of society and the change of learning methods and teaching environment, research on the analysis of College Students' learning situation in the classroom is emerging in endlessly. the real-time learning situation analysis (LSA) system of college students mainly analyzes college students' learning status and behavior in the classroom, which can provide college teachers with the basis and means to effectively manage the classroom. Based on computer vision (CV) technology, this paper takes the students' listening behavior in college theory class as the research object, and uses CAMSHIFT tracking algorithm to analyze the behavior of college students in class. On this basis, the system of classroom learning situation analysis (CLSA) based on CV is designed and implemented. through the simulation experiment, the traditional algorithm is compared with the CV CAMSHIFT tracking algorithm;the test results show that the recognition rate of this algorithm is about 10% higher in the case of facial interference, which proves the accuracy, availability and effectiveness of the classroom real-time LSA system designed and studied in this paper, and provides a new way of LSA for college teachers and students.

关键词： computer vision Classroom CAMSHIFT tracking algorithm Learning situation analysis

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Proceedings - 2022 4th international conference on Natural Language processing, ICNLP 2022

Proceedings - 2022 4th International Conference on Natural L...

引用

4th international conference on Natural Language processing, ICNLP 2022

ISBN: (纸本)9781665495448

the proceedings contain 108 papers. the topics discussed include: a two-stage algorithm for automatic diagnosis of keratitis;fabric defect detection method based on projection location and superpixel segmentation;global multi-scale fog salient object detection based on local attention feature;fire smoke detection based on vision transformer;a survey of text detection algorithms in images based on deep learning;gait feature extraction and recognition based on video compression;gait feature extraction and recognition based on video compression;a herd effect detection method based on text features;face inpainting algorithm combining face sketch and gate convolution;and compact fuzzy BLS driven semi-supervised multi-objective evolutionary adaptive multiple-kernel fuzzy clustering for color image segmentation.

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Research on image recognition System Based on computer Artificial Intelligence Technology

Research on Image Recognition System Based on Computer Artif...

引用

Power Electronics, computer Applications (ICPECA), IEEE international conference on

作者： Zhihua Xu School of Design and Art Jingdezhen Ceramic University Jingdezhen China

this paper uses mobile network mode to develop and model Python crawler based on Ubuntu16.04 and CUDA9.0. the visualization of this pattern was achieved using Tensor Board software. the hardware design of the system consists of three modules: linear array CCD sensor, DSP processor and signal processing circuit. through the reconstruction and learning of such models, the trained models are stored, so as to obtain higher precision image recognition. A method of classifying objects using Fourier description operators is proposed. the experimental results show that the accuracy of the image is 96.8% and the recognition time is 0.55 seconds. Motion tracking is based on the positioning and capture of the specific position of the human hand in each screen, and the captured frame rate is 28 frames per second. the system can quickly acquire images of 1024*1500 pixels at a rate of 5 photos/second. through the static identification of posture and tracking of motion trajectory, the interaction between human and machine is better.

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Research on Virtual College English Learning Aid System of computer Intelligent Campus 4

Research on Virtual College English Learning Aid System of C...

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4th international conference on computers, Information processing and Advanced Education, CIPAE 2023

作者： Jin, Xiang Zhao, Zhijian Nanjing Engineering Branch Jiangsu Union Technical Institute Nanjing China

ISBN: (纸本)9798350342710

this paper proposes a virtual system for college English vocabulary assisted learning based on B/S computer. then an image recognition method based on word context vector similarity matching and CNN+BLSTM is proposed to improve the efficiency of word retrieval algorithm. the functional elements and technical requirements of communication AIDS such as recording, storage and playback are realized by using the microcontroller ATmega16L, MMC memory card and VS1003 audio chip. the system includes word learning module, listening learning module, reading and writing module, examination guide and excellent students' secret book module and edutainment module. the application results show that the system can realize the interaction between teachers and students before class, during class and after class, and close the connection between teachers and students. this system improves students' interest in foreign language learning and effectively promotes the improvement of teaching quality. © 2023 IEEE.

关键词： Learning systems

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