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33rd International conference on artificial neural networks and Machine Learning (ICANN)

作者： Teichmann, Michael Larisch, Rene Hamker, Fred H. Tech Univ Chemnitz D-09107 Chemnitz Germany

ISBN: (纸本)9783031723582;9783031723599

The reliability of deep neural networks is critical for industrial applications as well as human safety and security. However, artificial deep neural networks have been found vulnerable to multiple kinds of natural, artificial, and adversarial image perturbations. In contrast, the human visual system has a remarkable robustness against a wide range of perturbations. At present, it is still unclear what mechanisms underlie this robustness. To better understand the robustness of biologically grounded neural networks, we evaluated two different biologically grounded neural networks of the primate visual system for their vulnerabilities to various image perturbations. We study a rate-based neural network, which utilizes Hebbian synaptic, intrinsic, and structural plasticity within a multi-layer neocortex-like architecture that includes feedforward excitation and inhibition, lateral inhibition, as well as feedback excitation and inhibition, and a spike-based neural network that focuses on a high degree of biologically plausible excitatory as well as inhibitory spike-timing-dependent plasticity. Both networks have been trained on natural scenes and have been earlier demonstrated to learn receptive fields and response properties of the visual cortex, and perform convincingly in object recognition in common computer vision benchmarks. We examine a subset of image perturbations from the corrupted MNIST dataset (MNIST-C) with the aim to test structural different perturbations. The investigated perturbations are namely Gaussian noise and blur, contrast, rotation, frost, and multi-line distractors. We applied them on the MNIST and EMNIST dataset. We report the degradation of recognition performance at different levels of perturbation intensity and indicate the improvement of the individual layers of both considered network types in comparison to the preprocessed input (LGN) as baseline.

关键词： robustness perturbations Hebbian learning STDP

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Efficient Hardware Acceleration of Spiking neural networks using FPGA: Towards Real-Time Edge Neuromorphic Computing 99

Efficient Hardware Acceleration of Spiking Neural Networks u...

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IEEE 99th Vehicular Technology conference (VTC-Spring)

作者： El Maachi, Soukaina Chehri, Abdellah Saadane, Rachid Hassania Sch Publ Works EHTP Intelligent Syst & Sensor Networks SIRC Casablanca Morocco Royal Mil Coll Canada Dept Math & Comp Sci Kingston ON Canada

ISBN: (纸本)9798350387414

This paper examines the critical function of Field-Programmable Gate Arrays (FPGAs) in speeding Spiking neural networks (SNNs) for real-time edge neuromorphic computing. Our work systematically evaluates the integration of FPGA technology for the optimization and speeding of SNN models. The analysis covers the power efficiency, low latency processing, and parallelism that are intrinsic benefits of FPGAs, emphasizing their relevance for edge computing applications. We discuss the smooth transfer of trained SNN models to FPGA platforms. Using an extensive analysis of state-of-the-art architectures, we demonstrate the efficiency benefits of using FPGA to accelerate SNNs. We derive more insights into the real-world applications of this FPGA-SNN integration in various fields. The analysis supports advances in edge computing and neuromorphic processing paradigms by adding to the collective knowledge of how FPGA enhances the real-time processing capabilities of Spiking neural networks.

关键词： Spiking neural networks Edge Computing FPGA artificial Intelligence Memory Optimization

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MLDC: multi-lung disease classification using quantum classifier and artificial neural networks

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neural COMPUTING & applications 2024年第7期36卷 3803-3816页

作者： Arora, Riya Rao, G. V. Eswara Banerjea, Shashwati Rajitha, B. Motilal Nehru Natl Inst Technol Dept Comp Sci & Engn Allahabad 211004 UP India

Lung diseases are one of the most common diseases around the world. The risk of these diseases are more in under-developed and developing countries, where millions of people are battling with poverty and living in polluted air. Chest X-Ray images are helpful screening tool for lung disease detection. However, disease diagnosis requires expert medical professionals. Furthermore, in developing and under-developed nations, the doctor-to-patient ratio is comparatively poor. Deep learning algorithms have recently demonstrated promise in the analysis of medical images and the discovery of patterns. In this current work, we have proposed a model MLDC (Multi-Lung Disease Classification) to detect common lung diseases. It introduces a MLDC feature extraction model with two different new classifiers, considering ANN (an artificial neural network) and QC (a quantum classifier). In this proposed model, tests are performed on the LDD (Lung Disease Dataset), which includes COVID-19, pneumonia, tuberculosis, and a healthy person's lung from chest X-ray images. Our proposed model achieves an accuracy of 95.6% for MLDC-ANN and 97.5% for MLDC-QC at a lower computational cost.

关键词： COVID-19 Pneumonia Tuberculosis image processing Deep learning Machine learning Quantum classifier

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Bionic model of information processing in the retina

Bionic model of information processing in the retina

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2024 International conference on artificial Intelligence, Computer, Data Sciences, and applications, ACDSA 2024

作者： Bohlmann, Sabine Clausthal University of Technology Institute of Computer Science Clausthal-Zellerfeld Germany

ISBN: (纸本)9798350394528

Today, bionic models for vision applications base on the general information pathways, structure and characteristics of the visual system implemented in intelligent algorithms, mostly based on AI, to improve the resolution, colour and contrast of images. These approaches often use machine learning techniques and neural networks, or simply mathematical pre-processing filters. The major drawback of all these methods is that they require a lot of computing power. But nature can't afford that kind of processing power just to improve contrast/resolution, detect contours or the true colours of an image and it doesn't use learning strategies or high-order mathematical equations to detect contours. Nature works very simply and very efficiently, it can only work with potentials and their differences. There is therefore a huge gap between AI-based approaches and image processing in the human visual system. This opens the way for a new bionic model of the general pathways of the retina - using only the simple principles of nature, potentials and their differences. © 2024 IEEE.

关键词： image enhancement

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image Denoising: A Comparative Study of Convolutional neural networks 4th

Image Denoising: A Comparative Study of Convolutional Neural...

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4th International conference on Digital Technologies and applications (ICDTA)

作者： Anzal, Oumaima Guessous, Najib Ouakrim, Youssef Univ Sidi Mohamed Ben Abdellah Dept Math & Comp Sci Lab M2PA Fes 30030 Morocco

ISBN: (纸本)9783031686528;9783031686535

image processing is a vigorous area of study that utilizes various algorithms to manipulate, analyze, and enhance digital images. image denoising is one of the crucial applications of image processing. Still, the occurrence of image noise is inevitable due to various sources, including low light conditions, high ISO settings, and transmission artifacts, necessitating the availability of denoising techniques to significantly improve visual image quality. This is particularly important in fields such as computer vision, medical imaging and remote sensing. Not only does it facilitate image analysis by retaining important details, but it also optimizes the performance of compression algorithms, improves storyteller detection. In this project, we propose an in-depth study of image denoising, focusing on the use of convolutional neural networks (CNNs). The problem of Gaussian noise will be treated by applying different levels of s (low sigma = 15, medium sigma = 25, and high sigma = 50). During this project, a full comparative analysis will be made with the three mainCNNarchitectures: DnCNN, RIDNet, and IRCNN, illustrative of the quantitative and qualitative experimental results obtained by these different approaches. In fact, these approaches have shown impressive performance in image processing tasks, including image denoising, since they used different techniques that can be adopted in CNN, such as regularization methods, batch normalization, and residual learning.

关键词： image Denoising Convolutional neural networks (CNNs) DnCNN RIDNET IRCNN Noise level PSNR SSIM

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Pixel-level clustering network for unsupervised image segmentation

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ENGINEERING applications OF artificial INTELLIGENCE 2024年第PartB期127卷

作者： Hoang, Cuong Manh Kang, Byeongkeun Seoul Natl Univ Sci & Technol Dept Elect Engn 232 Gongneung Ro Seoul 01811 South Korea

While image segmentation is crucial in various computer vision applications, such as autonomous driving, grasping, and robot navigation, annotating all objects at the pixel-level for training is nearly impossible. There-fore, the study of unsupervised image segmentation methods is essential. In this paper, we present a pixel-level clustering framework for segmenting images into regions without using ground truth annotations. The proposed framework includes feature embedding modules with an attention mechanism, a feature statistics computing module, image reconstruction, and superpixel segmentation to achieve accurate unsupervised segmentation. Additionally, we propose a training strategy that utilizes intra-consistency within each superpixel, inter-similarity/dissimilarity between neighboring superpixels, and structural similarity between images. To avoid potential over-segmentation caused by superpixel-based losses, we also propose a post-processing method. Furthermore, we present an extension of the proposed method for unsupervised semantic segmentation. We conducted experiments on three publicly available datasets (Berkeley segmentation dataset, PASCAL VOC 2012 dataset, and COCO-Stuff dataset) to demonstrate the effectiveness of the proposed framework. The experimental results show that the proposed framework outperforms previous state-of-the-art methods.

关键词： Unsupervised image segmentation Convolutional neural networks Clustering Unsupervised semantic segmentation

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CFENet: Cost-effective underwater image enhancement network via cascaded feature extraction

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ENGINEERING applications OF artificial INTELLIGENCE 2024年第PartF期133卷

作者： Ji, Xun Wang, Xu Hao, Li-Ying Cai, Cheng-Tao Dalian Maritime Univ Sch Marine Elect Engn Dalian 116026 Peoples R China Harbin Engn Univ Coll Intelligent Syst Sci & Engn Harbin 150001 Peoples R China Heilongjiang Prov Key Lab Environm Intelligent Per Harbin 150001 Peoples R China Harbin Engn Univ Key Lab Intelligent Technol & Applicat Marine Equi Minist Educ Harbin 150001 Peoples R China

Due to the inevitable light absorption and scattering, underwater images always suffer from severe quality degradation, leading to significant performance decline for various maritime engineering-related applications. In recent years, deep neural networks (DNNs) have been proven to achieve high-quality enhancement of underwater images, which aims to extract and learn abstract features to exhibit superior performance. However, existing solutions often stack multilayer processing units to enrich features, which not only significantly burdens computational load but also encounters difficulties in fully utilizing and interacting between features at different depths. To address the above problems, this paper presents a cost-effective underwater image enhancement network via cascaded feature extraction, termed CFENet. Specifically, we develop a cost-effective cascaded structure for sufficient feature extraction and multiscale establishment of pixel-level long-range dependencies. In addition, we construct a dual-branch structure for effective feature fusion, so that the detailed texture and semantic information in the image can be simultaneously enhanced. Extensive experiments reveal the superiority of our proposed CFENet in both underwater image enhancement effects and computational complexity. Sufficient ablation study is conducted to demonstrate the effectiveness of each component in the network.

关键词： Underwater image enhancement Deep learning Deep neural networks Contextual information Feature extraction Feature fusion

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Crop Disease and Pest Detection using Convolutional neural networks (CNN) 5

Crop Disease and Pest Detection using Convolutional Neural N...

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5th International conference on image processing and Capsule networks, ICIPCN 2024

作者： Kalaimanivel, S. France, K. Hindustan institute of technology and science Department of Computer Applications Chennai22295014 India Hindustan institute of technology and science Department of Computer Applications Chennai India

ISBN: (纸本)9798350367171

Agriculture is often known as the art and science of nurturing soil. It involves preparing plants and animals for use in products. Agriculture is the process of growing crops and rearing animals for human consumption, fiber production, and other reasons. It is one of the oldest and most important human activities, laying the groundwork for food production and billions of people's lives throughout the world. As technology advances, additional capabilities for crop protection and disease prevention become accessible. artificial Intelligence (AI) and Machine Learning (ML) algorithms capture features such as crop and soil monitoring, crop maturity detection, autonomous weeding, intelligent crop spraying, pest and disease detection, and more. This study suggests a novel technique for automated crop disease identification by utilizing Convolutional neural networks (CNNs) from the field of computer vision. By performing a thorough testing and validation on separate test sets, the proposed methodology outperforms other existing methods in terms of accuracy. The Plant Village collection, maintained by the Centers for Disease Control and Prevention (CDC), includes damaged plant leaf photos and labels. The proposed method has achieved an accuracy of about 99.6%. © 2024 IEEE.

关键词： Convolutional neural networks

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Diagnosing the spores of tomato fungal diseases using microscopic image processing and machine learning

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MULTIMEDIA TOOLS AND applications 2024年第26期83卷 67283-67301页

作者： Javidan, Seyed Mohamad Banakar, Ahmad Vakilian, Keyvan Asefpour Ampatzidis, Yiannis Rahnama, Kamran Tarbiat Modares Univ Dept Biosyst Engn Tehran Iran Gorgan Univ Agr Sci & Nat Resources Dept Biosyst Engn Gorgan Iran Univ Florida Southwest Florida Res & Educ Ctr Agr & Biol Engn Dept 2685 FL-29 Immokalee FL 34142 USA Gorgan Univ Agr Sci & Nat Resources Fac Plant Prod Dept Plant Protect Gorgan Iran

Accurate diagnosis of plant diseases by the assessment of pathogen presence to reduce disease-related production loss is one of the most fundamental issues for farmers and specialists. This will improve product quality, increase productivity, reduce the use of fungicides, and reduce the final cost of agricultural production. Today, new technologies such as image processing, artificial intelligence, and deep learning have provided reliable solutions in various fields of precision agriculture and smart farm management. In this research, microscopic image processing and machine learning have been used to identify the spores of four common tomato fungal diseases. A dataset including 100 microscopic images of spores for each disease was developed, followed by the extraction of the texture, color, and shape features from the images. The classification results using random forest revealed an accuracy higher than 98%. Besides, as a reliable feature selection algorithm, the butterfly optimization algorithm (BOA) was used to detect the effective image features to identify and classify diseases. This algorithm recognized image textural features as the most effective features in the diagnosis and classification of disease spores. Considering only the eight most effective features selected with BOA resulted in an accuracy of 95% in disease detection. To further investigate the performance of the proposed method, its accuracy was compared with the accuracies of convolutional neural networks and EfficientNet as two reliable deep learning algorithms. Not only the prediction accuracy of these methods was not favorable (65 and 83.55%, respectively), they were very time-consuming. According to the findings, the proposed framework has high reliability in disease diagnosis and can help in the management of tomato fungal diseases.

关键词： artificial intelligence Butterfly optimization algorithm Disease diagnosis Microscopic image processing Morphological features Tomato disease spores

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image generation method based on diversified feature learning attention mechanism

Image generation method based on diversified feature learnin...

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2024 International conference on image processing and artificial Intelligence, ICIPAl 2024

作者： Sun, Wenhao Liu, Gengchen Xue, Weiqi Zhou, Qingqi Wang, Jiaju Yan, Jiaqing Academy of Electronics & Control Engineering North China University of Technology Beijing China

ISBN: (纸本)9781510681514

Although attention mechanisms have been widely applied in natural language processing (NLP) tasks, there are still limitations in their utilization within the field of computer vision. To integrate the advantages of convolutional neural networks (CNNs) with attention mechanisms, this study proposes a multimodal image generation model based on the Stable Diffusion architecture. The model incorporates two types of convolutional modules, namely ICB and TCB. By replacing the linear networks with convolutional neural networks, the model enhances its capability to process complex features. Subsequently, the low-dimensional encoding reconstruction of images is achieved by maximizing the mutual information between the input of the new model and the output of the encoder. Finally, the proposed model is validated using a publicly available dataset. © 2024 SPIE.

关键词： Convolutional neural networks

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