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检索条件"任意字段=Conference on Image Processing - Algorithms and Systems III"

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Comprehensive Weather Recognition Using ResNet-50 and a Novel Weather image Dataset 4

Comprehensive Weather Recognition Using ResNet-50 and a Nove...

引用

4th International Signal processing, Communications and Engineering Management conference, ISPCEM 2024

作者： Wang, Jun Zhou, Yunhan Fu, Min School of Electrical and Electronics Engineering UESTC Chengdu China Cyrinn Science & Technology Ltd. Guangzhou China

ISBN: (纸本)9798331528676

Weather conditions play a crucial role in both daily human activities and industrial operations. For example, recognizing different weather patterns is critical for outdoor automation systems. With the development of AI technology, scientific researchers have trained many models which receive weather images as inputs and output the weather in these images fast and accurately. However, many existing weather recognition models are limited in scope, typically identifying only four types of weather with varying degrees of accuracy. In this study, the two key contributions of this research are: first, the creation of a custom dataset designed specifically for recognizing seven distinct weather conditions, and second, the exploration of multiple machine learning techniques to identify the most effective model for this task. By developing our own dataset, we ensure a broader representation of weather types, allowing the models to train and be tested on more diverse and realistic scenarios. We evaluate five different algorithms: Support Vector Machine (SVM), Random Forest, Multilayer Perceptron (MLP), Convolutional Neural Network (CNN), and ResNet50. To enhance model generalization and reduce overfitting, we employ data augmentation techniques and add dropout layers in the CNN and ResNet50 architectures. Through extensive experimentation, ResNet50 emerges as the best-performing model, achieving an accuracy of approximately 87% on the custom dataset. This work demonstrates the importance of creating domain-specific datasets and using a range of machine learning methods to develop accurate and reliable weather recognition models. Our approach offers a robust solution for outdoor vision systems, with potential applications in autonomous vehicles, surveillance systems, and other environments where accurate weather detection is critical for safety and performance. ©2024 IEEE.

关键词： Convolutional neural networks

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Automatic Classification and Recognition of Tourist Attraction images using k-Means Algorithm and image processing Technology

Automatic Classification and Recognition of Tourist Attracti...

引用

Distributed systems, Computer Networks and Cybersecurity (ICDSCNC), International conference on

作者： Qian Jiang School of Public Administration Chongqing Vocational College of Transportation China

ISBN: (数字)9798350375442

ISBN: (纸本)9798350375459

This study investigates the automatic classification and recognition of tourist attraction images through an enhanced K-means algorithm paired with image processing techniques. Given the growing interest in efficient tourist image management and promotion, our research presents a system that significantly advances the processing and categorization of scenic spot images. We implemented a variety of image preprocessing steps—such as grayscale conversion, normalization, Gaussian filtering, and edge detection — to improve the accuracy of feature extraction. Our method involved an optimized K-means algorithm, modified to select initial centroids more effectively and adjust cluster numbers dynamically. Experimental results, based on a dataset of 150 images, demonstrated a classification accuracy of 92%, with the system maintaining robust performance even as dataset sizes increased, confirming the algorithm's efficiency and stability. The processing time for 5000 images was recorded at 12.3 seconds, showcasing a linear relationship between data size and processing time. These findings highlight the potential of our proposed system in enhancing tourism management and marketing strategies by providing a reliable and scalable image classification solution.

关键词： Accuracy Filtering image processing image edge detection Heuristic algorithms Clustering algorithms Feature extraction Classification algorithms image preprocessing Reliability

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Comparison of algorithms for contrast enhancement based on TOD assessments by convolutional neural networks 19

Comparison of algorithms for contrast enhancement based on T...

引用

conference on Electro-Optical and Infrared systems - Technology and Applications XIX

作者： Wegner, Daniel Kessler, Stefan Fraunhofer IOSB Gutleuthausstr 1 D-76275 Ettlingen Germany

ISBN: (纸本)9781510655461

A current approach for performance assessment of imagers is triangle orientation discrimination (TOD). This approach requires observers or human visual system (HVS) models to recognize equilateral triangles pointing in four different directions. imagers may apply embedded advanced digital signal processing (ADSP) for contrast enhancement, noise reduction, edge sharpening, etc. Unfortunately, applied methods are in general not documented and hence unknown. Within the last decades a vast amount of techniques for constrast enhancement has been proposed. There are some comparisons of such algorithms for few images and figures of merit. However, many of these figures of merit cannot assess usability of altered image content for specific tasks such as object recognition In this work different algorithms for contrast enhancement are compared in terms of TOD assessments by convolutional neural networks (CNN) as models. These models are trained by artificial images with single triangles. Many methods for contrast enhancement highly depend on the content of the entire image. Therefore, the images are superimposed by natural backgrounds with varying standard deviations to provide different signal-to-background ratios. Then these images are degraded by Gaussian blur and noise representing degradational camera effects and sensor noise. Different algorithms are applied, such as the constrast-limited adaptive histogram equalization or local range modification. Then accuracies of the trained models on these images are compared for different ADSP algorithms. Accuracy gains for low signal-to-background ratio and sufficiently large triangles are found, while impairments are found for high signal-to-background ratio and small triangles. Finally, implications of replacing triangles by real target signatures when using such ADSP algorithms are discussed. The results can be a step towards the assessment of those algorithms for generic target recognition.

关键词： CNN TOD image contrast enhancement advanced digital signal processing imager assessment method comparison

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Synthetic image Generation With a Fine-Tuned Latent Diffusion Model for Organ on Chip Cell image Classification 26

Synthetic Image Generation With a Fine-Tuned Latent Diffusio...

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26th IEEE Signal processing: algorithms, Architectures, Arrangements, and Applications, SPA 2023

作者： Ivanovs, Maksims Leja, Laura Zviedris, Karlis Rimsa, Roberts Narbute, Karina Movcana, Valerija Rumnieks, Felikss Strods, Arnis Gillois, Kevin Mozolevskis, Gatis Abols, Arturs Kadikis, Roberts Institute of Electronics and Computer Science Riga Latvia Cellbox Labs Riga Latvia Latvian Biomedical Research and Study Centre Riga Latvia

ISBN: (纸本)9798350304985

Augmentation of the datasets of authentic microscopy images with synthetic images is a promising solution to the problem of the limited availability of biomedical data for training deep neural network (DNN) based classifiers. In the present study, we use a text-to-image latent stable diffusion model fine-tuned by means of low-rank adaptation (LoRA) to augment a small dataset of the images of organ on chip cells. While the resulting synthetic images appear quite similar to the authentic images on which the low-rank adaptation was performed, we find that neither training the EfficientNetB7 DNN model solely on the synthetic data nor augmentation of the real-world dataset with different proportions (10, 25, 50, and 75 percent) of these data leads to the improvement of the accuracy of the model. The findings of our study suggest that a further exploration of the low-rank adaptation options is needed to fully use the capacity of latent diffusion models for the synthesis of biomedical images. © 2023 Division of Signal processing and Electronic systems, Poznan University of Technology (DSPES PUT).

关键词： Neural network models

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DeTrack: In-model Latent Denoising Learning for Visual Object Tracking 38

DeTrack: In-model Latent Denoising Learning for Visual Objec...

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38th conference on Neural Information processing systems, NeurIPS 2024

作者： Zhou, Xinyu Li, Jinglun Hong, Lingyi Jiang, Kaixun Guo, Pinxue Ge, Weifeng Zhang, Wenqiang Shanghai Key Lab of Intelligent Information Processing School of Computer Science Fudan University Shanghai China Shanghai Engineering Research Center of AI & Robotics Academy for Engineering and Technology Fudan University Shanghai China

Previous visual object tracking methods employ image-feature regression models or coordinate autoregression models for bounding box prediction. image-feature regression methods heavily depend on matching results and do not utilize positional prior, while the autoregressive approach can only be trained using bounding boxes available in the training set, potentially resulting in suboptimal performance during testing with unseen data. Inspired by the diffusion model, denoising learning enhances the model's robustness to unseen data. Therefore, We introduce noise to bounding boxes, generating noisy boxes for training, thus enhancing model robustness on testing data. We propose a new paradigm to formulate the visual object tracking problem as a denoising learning process. However, tracking algorithms are usually asked to run in real-time, directly applying the diffusion model to object tracking would severely impair tracking speed. Therefore, we decompose the denoising learning process into every denoising block within a model, not by running the model multiple times, and thus we summarize the proposed paradigm as an in-model latent denoising learning process. Specifically, we propose a denoising Vision Transformer (ViT), which is composed of multiple denoising blocks. In the denoising block, template and search embeddings are projected into every denoising block as conditions. A denoising block is responsible for removing the noise in a predicted bounding box, and multiple stacked denoising blocks cooperate to accomplish the whole denoising process. Subsequently, we utilize image features and trajectory information to refine the denoised bounding box. Besides, we also utilize trajectory memory and visual memory to improve tracking stability. Experimental results validate the effectiveness of our approach, achieving competitive performance on several challenging datasets. The proposed in-model latent denoising tracker achieve real-time speed, rendering denoising learning

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image Captioning with Reinforcement Learning 2

Image Captioning with Reinforcement Learning

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2nd IEEE International conference on Computer Vision and Machine Intelligence, CVMI 2023

作者： Verma, Anand Agarwal, Saurabh Arya, K.V. Petrlik, Ivan Esparza, Roberto Rodriguez, Ciro ABV-IIITM Gwalior Department of Information Technology Madhya Pradesh 474015 India ABV-Indian Institute of Information Technology and Management Multimedia and Information Security Research Group Department of Computer Science and Engineering Gwalior474015 India National University Federico Villarreal Faculty of Industrial and Systems Engineering Lima Peru National University Mayor de San Marcos Faculty of Systems Engineering and Informatics Peru

ISBN: (纸本)9798350305142

image captioning involves generating a natural language description that accurately represents the content and context of an image. To achieve this, image captioning utilises various machine learning techniques and fields, such as computer vision and natural language processing. In the field of image captioning, a lot of advances have been made with encoder-decoder models and reinforcement learning algorithms. However, there are still problems of imbalance between testing and training, as reinforcement learning only handles single comparator metrics such as CIDEr, SPICE, and BLEU and could not perform better in multiple metrics at once. Which is why a lack of diversity can be seen in generated captions. This idea proposes a general technique for collaborative updating that can bridge the gap between evaluation measures and test metrics to produce captions that are more human-like. To increase the precision of image captions, the approach involves using a compiled reward system that considers multiple evaluation metrics to compare the generated sentence with the provided sentences. We will evaluate the model's performance and the reward updating process on standard datasets like MS COCO. © 2023 IEEE.

关键词： Reinforcement learning

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Single image Depth Estimation Based on Improved U-NET 3

Single Image Depth Estimation Based on Improved U-NET

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3rd International conference on Digital Society and Intelligent systems, DSInS 2023

作者： Wang, Yingkang Zhang, Yicheng Zhao, Wei Liu, Congbiao Tianjin Vocational and Technical Normal University National and Local Joint Engineering Laboratory of Automotive Mold Intelligent Manufacturing Technology Tianjin300350 China National-Local Joint Engineering Laboratory of Intelligent Manufacturing Oriented Automobile Die & Mould Tianjin300350 China

ISBN: (纸本)9798350331387

para>In response to the problems of low accuracy and complex network structure in existing deep learning based monocular image depth estimation algorithms, an improved U-NET [1] monocular image depth estimation method based on dense skip connections is proposed. The traditional U-NET network has the problem of depth distortion when processing depth estimation tasks. In response to this problem, this algorithm proposes skip connections to achieve multi-scale feature fusion from encoder to decoder;And introduce attention mechanism into the decoder to further improve the accuracy of depth estimation. The training and verification were carried out on the indoor data set KITTI [2] and the outdoor data set NYU Depth V2 respectively. The experimental results show that compared with the advanced algorithm DORN, all indicators have been improved to varying degrees, in which the root-mean-square error has been reduced by 24.1% and the square relative error has been reduced by 15.2%. © 2023 IEEE.

关键词： Complex networks

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Real-time Low Vision Simulation in Mixed Reality 16

Real-time Low Vision Simulation in Mixed Reality

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16th International conference on Signal-image Technology and Internet-Based systems (SITIS)

作者： Acevedo, Valeria Colantoni, Philippe Dinet, Eric Tremeau, Alain Univ Jean Monnet Univ Lyon Lab Hubert Curien UMR 5516 St Etienne France

ISBN: (纸本)9781665464956

Visual impairments are a global health issue with profound socioeconomic ramifications in both the developing and the developed world. There exist ongoing research projects, that aim to investigate the influence of light in the perception of low vision individuals. But as of today, there is neither clear knowledge nor extensive data regarding the influence of light in low vision situations. This research will address these issues by introducing a methodology and a system to simulate visual impairments. A pipeline based on eye anatomy coupled with real-time image processing algorithms allows to dynamically simulate low vision specific characteristics of selected impairments in mixed reality. An original new approach based on massively parallelized processing combined with an efficient modeling of eye refractive errors aims to improve the accuracy of the low vision simulation.

关键词： mixed reality low-vision simulation parallel programming image processing

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Bacterial colony detection method for microbiological photographic images

Bacterial colony detection method for microbiological photog...

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IEEE International conference on Fuzzy systems (FUZZ-IEEE) / IEEE World Congress on Computational Intelligence (IEEE WCCI) / International Joint conference on Neural Networks (IJCNN) / IEEE Congress on Evolutionary Computation (IEEE CEC)

作者： Gorokhov, Oleg Fazylov, Ramazan Kazachuk, Maria Lazukhin, Ivan Mashechkin, Igor Pankratyeva, Liudmila Popov, Ivan Lomonosov Moscow State Univ MSU Comp Sci Dept Moscow Russia Dmitry Rogachev Natl Med Res Ctr Pediat Hematol Moscow Russia

ISBN: (数字)9781728186719

ISBN: (纸本)9781728186719

This paper is dedicated to the research and development of automated methods for detecting bacterial colonies on photographic images of Petri dishes. We propose a segmentation algorithm for microbiological photographic images which is based on the convolutional neural network architecture of the encoder-decoder class, trained on artificially generated image data. Our method consists of two stages: sliding window image processing by the proposed neural network to find areas containing isolated objects potentially suitable for further research, and further segmentation of the resulting binary area map into regions containing colonies by classical computer vision algorithms. According to the experimental research, the developed algorithm shows the best results in comparison with the existing neural network algorithms we adapt in this work as well as watershed segmentation algorithms. The work also proposes algorithms for microbiological photographic images artifact searching based on heuristic and neural network approaches (including flare, magnetic balls, barcodes and condensate), the use of which allows to increase the quality of the image segmentation algorithm, as well as to find errors caused by the human factor. The results of this work might be further used for classification algorithms of the bacterial colonies in the segments found, which will allow in some cases fully automated process of visual bacteria recognition on Petri dishes photographic images.

关键词： microbiological analysis bacterial colony detection artifact detection object detection computer vision machine learning data mining image segmentation heuristic algorithms neural networks Petri dish

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Musa Base - Mobile Application for Banana Farmers to Minimize the Challenges in Banana Production during the Pre-Harvest and Post-Harvest Period

Musa Base - Mobile Application for Banana Farmers to Minimiz...

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2023 International conference on Innovative Computing, Intelligent Communication and Smart Electrical systems, ICSES 2023

作者： Kavinda, G.H.M. Dissanayake, D.D. Nanayakkara, K.A.K.K. Swrnakantha, N.H.P. Ravi Supunya Liyanagolla, Mudiyanselage. N. V. K. Kumari, P.K.S. Sri Lanka Institute of Information Technology Department of Information Technology Malabe Sri Lanka

ISBN: (纸本)9798350319200

This research paper presents an innovative solution to address the multifaceted challenges encountered by banana farmers in Sri Lanka, encompassing the entire spectrum of banana production from pre-harvest to post-harvest stages. The proposed system, titled "Musa-Base,"is a user-friendly mobile application that seamlessly integrates cutting-edge technologies, including machine learning, IoT-based approaches, natural language processing, and image processing methodologies. This holistic approach aims to elevate the quality and productivity of banana cultivation. The Musa-Base system comprises several interlinked modules: crop yield prediction, disease identification through advanced image processing and interactive chatbot interactions, personalized recommendations for optimal watering and fertilizer strategies, and comprehensive guidance on best agronomic practices. The research rigorously employs a diverse range of algorithms, including deep learning models, for accurate crop yield prediction, rapid disease diagnosis, and finely tuned recommendation systems. The system empowers farmers with informed decision-making capabilities, enabling them to mitigate losses, efficiently allocate resources, and ultimately enhance banana production and product quality. The integration of images of banana plants, sensor data, and user input data forms the backbone of the data-driven research methodology that underpins the development of the Banana Mobile Application. The pivotal outcomes of this research include precise disease detection, reliable harvest forecasting, and the dynamic integration of a chatbot for timely assistance, all of which collectively contribute to superior banana crop management, heightened agricultural efficiency, and enhanced sustainability. This comprehensive solution caters to the needs of both smallholder farmers and commercial producers, representing a significant advancement in modernizing banana farming practices and ensuring food security. © 2023 IE

关键词： Banana Farming Banana quality Banana symptoms Disease detection Fertilization technique Harvest prediction image processing IoT-based technologies Machine learning Mobile application post-harvest practices Pre-harvest practices Watering plans

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