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Pixel-level clustering network for unsupervised image segmentation

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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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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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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HyCMAx: Power-Efficient Hybrid CMOS-Memristor Based Approximate Dividers for Error-Resilient applications 38

HyCMAx: Power-Efficient Hybrid CMOS-Memristor Based Approxim...

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38th International conference on VLSI Design and International conference on Embedded Systems

作者： Pokharia, Monika Trivedi, Het Doshi, Siddharth Hegde, Ravi S. Mekie, Joycee Indian Inst Technol Ahmadabad Gujarat India

ISBN: (纸本)9798331522452;9798331522445

Approximate computing is a promising paradigm for improving the performance parameters of electronic systems at the expense of accuracy in error-resilient tasks such as multimedia processing, image multiplication, and neural networks. While approximate circuits utilizing CMOS technology have been extensively studied, integrating approximate computing with emerging technologies like memristors offers further performance enhancements. HyCMAx investigates a hybrid CMOS-memristor approach for designing approximate circuits. In this paper, an approximate subtractor has been proposed, which was subsequently used to implement a restoring divider using the hybrid CMOS-memristor approach. HyCMAx dividers implemented in 28nm CMOS technology node gave up to similar to 43.8% dynamic power reduction and similar to 31.3% transistor count reduction as compared to only-CMOS implementation. Different levels of approximation were introduced in the divider to study the limits of approximation, which would give acceptable results. The proposed designs were then evaluated in the context of neural networks and image processing applications. This study highlights the potential of combining CMOS and memristor technologies to create high-performance, power-efficient approximate circuits suitable for various error-resilient computational tasks.

关键词： hybrid-CMOS memristor approximate subtractor restoring divider image processing neural networks

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Epilepsy Detection using Time-Frequency Domain and Entropy Based EEG Analysis 31

Epilepsy Detection using Time-Frequency Domain and Entropy B...

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31st IEEE conference on Signal processing and Communications applications (SIU)

作者： Ficici, Cansel Telatar, Ziya Kocak, Onur Baskent Univ Ankara Univ Elekt Elekt Muhendisligi Bolumu Biyomed Muhendisligi Bolumu Ankara Turkiye

ISBN: (纸本)9798350343557

Abnormal electrical activities due to brain tumor, developmental anomaly, neural-atrophy in cortical/sub-cortical brain regions cause an epileptic seizure. Electroencephalography (EEG) is an important diagnostic test used for observing waveforms such as epileptic brain activities. In this study, a new method which detects epileptic seizure from EEG signals automatically is proposed. Discrete wavelet transform and time dependent entropy based statistical features of the EEG signal are used to train artificial neural networks. The proposed method has been applied on EEG signals obtained from healthy individuals and epileptic patients for epileptic seizure detection, and accuracy of 100% has been achieved. This method has also been applied on EEG signals containing normal, interictal and ictal states, and accuracy, sensitivity and specificity of 98.6%, 96.0% and 99.3% have been achieved, respectively.

关键词： epilepsy discrete wavelet transform time dependent entropy artificial neural networks

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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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Comparison of Vision Transformer with Convolutional neural networks for Brain Cancer Classification 17

Comparison of Vision Transformer with Convolutional Neural N...

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17th IEEE International conference on Computer Research and Development, ICCRD 2025

作者： Manali, Dogu Demirel, Hasan Eastern Mediterranean University Electrical and Electronic Engineering Famagusta Cyprus

ISBN: (纸本)9798331531881

Brain cancer is one of the most deadly illnesses. It causes abnormal cells to grow in the brain. Planning for treatment and the prognosis of patients with brain tumors depend greatly on early diagnosis. Brain tumors can have different characteristics, treatments, and forms. Consequently, the process of manually detecting brain tumors is difficult, labor-intensive, and error-prone. Doctors use magnetic resonance imaging to detect those abnormal cells in the brain. With the growth of artificial intelligence, it is possible to diagnose the brain tumor from MIR images. For instance, convolutional neural networks and transformers could be used. The self-Attention mechanism is implemented by transformers, which are models that give each input data component a distinct weight. Transformers have limited applications in image classification tasks because they were originally designed for use in natural language processing applications. Thus far, the majority of image classification research has employed convolutional neural networks. In this paper, six different pretrained convolutional neural networks and a vision transformer are used to classify four distinct brain tumor classes. The models include ResNet50, AlexNet, VGG16, InceptionV3, MobileNetV2, FractalNet, and the Vision Transformer. The goal of this study is to compare the performance of these pretrained convolutional neural network models with that of the vision transformer, demonstrating that transformers can also be effectively applied to image classification tasks. The performance of a vision transformer model shows 84.39% accuracy in the classification problem, which is better than the other six architectures. © 2025 IEEE.

关键词： Convolutional neural networks

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A Comparative Study on Pruning Deep Convolutional neural networks Using Clustering Methods: K-Means, CLIQUE, DENCLUE, and OptiGrid 24

A Comparative Study on Pruning Deep Convolutional Neural Net...

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9th International conference on Multimedia and image processing (ICMIP)

作者： Alqemlas, Danah Saud Jeragh, Mohammad Esmaeel Kuwait Univ Kuwait Kuwait Kuwait Oil Co Kuwait Kuwait

ISBN: (纸本)9798400716164

In the past years, machine learning (ML) and deep learning (DL) have led to the advancement of several applications, including computer vision, natural language processing, and audio processing. These complex tasks require large models, which is a challenge to deploy in devices with limited resources. These resource-constrained devices have limited computation power and memory. Hence, the neural networks must be optimized through network acceleration and compression techniques. This paper proposes a novel method to compress and accelerate neural networks from a small set of spatial convolution kernels. Firstly, a novel pruning algorithm is proposed based on the density-based clustering method that identifies and removes redundancy in CNNs while maintaining the accuracy and throughput tradeoff. Secondly, a novel pruning algorithm based on the grid-based clustering method is proposed to identify and remove redundancy in CNNs. The performance of the three pruning algorithms (density-based, grid-based, and partitional-based clustering algorithms) is evaluated against each other. The experiments were conducted using the deep CNN compression technique on the VGG-16 and ResNet models to achieve higher accuracy on image classification than the original model at a higher compression ratio and speedup.

关键词： neural Network Pruning Clustering Methods image processing

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Correction of Banding Errors in Satellite images With Generative Adversarial networks (GAN)

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IEEE ACCESS 2023年 11卷 51960-51970页

作者： Paola, Zarate L. Jesus, Lopez S. Christian, Arroyo H. Sonia, Rincon U. Colombian AF Res Ctr Aerosp Technol CITAE Cali CO Colombia Univ Autonoma Occidente Sch Engn Cali CO760001 CO Colombia

This research proposes an innovative method for correcting banding errors in satellite images based on Generative Adversarial networks (GAN). Small satellites are frequently launched into space to obtain images that can be used in scientific or military research, commercial activities, and urban planning, among other applications. However, its small cameras are more susceptible to radiometric, geometric errors, and other distortions caused by atmospheric interference. The proposed method was compared to the conventional correction technique using experimental data, showing the similar performance (92.64% and 90.05% accuracy, respectively). These experimental results suggest that generative models utilizing artificial Intelligence (AI) techniques, specifically Deep Learning, are getting closer to achieving automatic correction close to conventional methods. Advantages of the GAN models include automating the task of correcting banding in satellite images, reducing the required time, and facilitating the processing without requiring prior technical knowledge in handling Geographic Information Systems (GIS). Potentially, this technique could represent a valuable tool for satellite image processing, improving the accuracy of the results and making the process more efficient. The research is particularly relevant to the field of remote sensing and can have practical applications in various industries.

关键词： Satellite broadcasting Generative adversarial networks Generators Training Radiometry Remote sensing image coding artificial neural network deep learning generative adversarial network satellite images radiometric error banding

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