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

共 14657 条记录，以下是201-210 订阅

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Refining U-Net Architecture Through Genetic algorithms for Improved Skin Lesion image Segmentation 4th

Refining U-Net Architecture Through Genetic Algorithms for I...

引用

4th International conference on Intelligent systems and Pattern Recognition-ISPR

作者： El Abassi, Fouzia Darouichi, Aziz Ouaarab, Aziz Cadi Ayyad Univ Comp & Syst Engn Lab L2IS FSTG Marrakech 40000 Morocco

ISBN: (纸本)9783031821554;9783031821561

Recently, various approaches using manually designed convolutional neural networks (CNNs) and U-shaped (encoder-decoder) models have yielded encouraging results in the automatic segmentation of medical images. Nevertheless, the manual construction of these models is laborious, requires a high level of expertise, is time-consuming, error-prone and can result in the loss of crucial details. To improve segmentation performance, these models often incorporate numerous parameters, which can lead to problems such as overfitting, vanishing and exploding gradient and increased computational complexity. In this study, we implement a genetic algorithm (GA) specifically designed for a U-Net architecture to segment images of skin lesions, using two datasets ISIC-SMALL1 and ISIC-SMALL2 constructed from ISIC-2017 dataset. The main objective is to optimize internal block topologies within the U-Net, in order to identify a high-performance deep neural network with a minimum of parameters. Compared to the U-Net baseline, our proposed methodology demonstrates favorable outcomes in terms of both the number of trainable parameters and Intersection over Union (IoU). For the ISIC-SMALL1 dataset, the leading model surpasses U-Net with a reduced parameter count (0.352001M) and an elevated IoU score (0.826052 vs. 0.736352). Similarly, the second-best model outperforms U-Net with fewer parameters (0.258781M vs. 31.043586M) and a higher IoU score (0.821956 vs. 0.736352). In the ISIC-SMALL2 dataset, both top models outshine U-Net in IoU (0.753122 and 0.722079 vs. 0.725363, respectively) while maintaining lower parameter counts.

关键词： Medical image Segmentation Convolutional Neural Networks (CNNs) U-Net architecture Genetic algorithms (GA) Skin Lesion

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Analysis of Decision Tree and Random Forest algorithms for Nutrient Deficiency Classification in Citrus Leaves through image processing 17

Analysis of Decision Tree and Random Forest Algorithms for N...

引用

17th International conference on Telecommunication systems, Services, and Applications, TSSA 2023

作者： Kamelia, Lia Nurmalasari, Rin Rin UIN Sunan Gunung Djati Bandung Electrical Engineering Department Bandung Indonesia

ISBN: (纸本)9798350309164

The research presents a hybrid approach to identify and categorise nutritional deficiency syndrome in citrus leaves using image processing and machine learning. The method includes processing images, segmenting images and extracting color-Texture information. The Decision Tree and the Random Forest algorithms use various statistical measures for classification, including mean, skewness, variance, entropy, maximum probability, angular second moment, contrast, correlation, dissimilarity, energy, and homogeneity. The test results show a range of performance levels, with an accuracy of 80.28% being attained by utilising an 80:20 data ratio in the Decision Tree approach. The Random Forest method's highest accuracy, 91.25%, was reached using an 80:20 ratio. In conclusion, the accuracy of the Random Forest approach regularly beats that of the Decision Tree method. © 2023 IEEE.

关键词： Decision trees

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HVS-based AR Display image Enhancement With Delta Look-Up Table Considering Ambient Light 20

HVS-based AR Display Image Enhancement With Delta Look-Up Ta...

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20th International SoC Design conference (ISOCC)

作者： Cho, Donghun Lee, Hyeseong Lee, Jeonghun You, Jaehee Hongik Univ Integrated Syst Chip Seoul South Korea

ISBN: (纸本)9798350327038

Based on human visual systems, image processing algorithms, and efficient hardware implementation methodologies are proposed to optimize the image qualities of AR displays according to the changes in ambient lights. To this end, methods are described to improve the image qualities perceived by humans. In addition, the delta look-up table is presented to minimize the number of additional circuits without significant changes in existing hardware. HOSA, an image quality assessment based on the human visual system is used to verify the image qualities for the extreme ambient light conditions.

关键词： image quality enhancement AR display Ambient light Human visual system Delta LUT

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Enhancing Palm Recognition Accuracy Through image processing Deep Learning Techniques 2

Enhancing Palm Recognition Accuracy Through Image Processing...

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2nd IEEE International conference on Data Science and Network Security, ICDSNS 2024

作者： Mercy, J. Sheela Priscila, S. Silvia Bharath Institute of Higher Education and Research Department of Computer Applications Chennai India Bharath Institute of Higher Education and Research Department of Computer Science Chennai India

ISBN: (纸本)9798350373110

Palm recognition systems play an important role in biometric authentication;however, existing systems frequently have low accuracy and resiliency due to problems such as changing lighting conditions, occlusions, and hand orientations. The paper describes a novel way to improve palm recognition accuracy by combining modern image processing and deep learning approaches. Unlike traditional systems that utilize handcrafted features and shallow learning algorithms, the proposed system uses convolutional neural networks (CNNs) for feature extraction and classification, as well as advanced image preprocessing techniques. The technology preprocesses palm images to increase quality and consistency, thereby reducing the impact of environmental factors. Cross-validation trials yielded consistent results, with an average accuracy of 95%, precision of 92%, recall of 96%, and Fl-score of 94%. Furthermore, computational efficiency comparisons reveal that the proposed system beats existing ones in terms of training time, inference time, and model size, indicating that it is a promising option for accurate and efficient palm recognition in a variety of real-world applications. © 2024 IEEE.

关键词： Convolutional neural networks

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Deep Learning Approaches In Automatic Number Plate Recognition: Result and Analysis 4

Deep Learning Approaches In Automatic Number Plate Recogniti...

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4th IEEE International conference on ICT in Business Industry and Government, ICTBIG 2024

作者： Sharma, Rajeshwari Soni, Nandani Chaturvedi, Payashwani Malleri, Nimisha Bahety, Priyal Kukade, Jyoti Mishra, Trapti Medicaps University Indore India

ISBN: (纸本)9798331518981

systems known as Automatic Number Plate Recognition (ANPR), license plate recognition or LPR are now widely used in many sectors such as law enforcement, traffic control, vehicle access etc. This is a technology that uses object detection and optical character recognition to identify the numbers on number plates from input images or video. These systems use high-level hardware and software components together to automatically capture, recognize and process license plate information. Key elements of any ANPR system include high-resolution cameras, image processing algorithms, and optical character recognition (OCR) methods. The operational workflow of an ANPR system comprises several steps like image acquisition, preprocessing, license plate localization, character segmentation, OCR, and post-processing stages. Different applications of ANPR technology include traffic flow monitoring;helping police track stolen vehicles;congestion detection for traffic management purposes;and automated entry/exit processes based on authorized number plate recognition among others for supporting vehicle access control. A accuracy of 90% was achieved by proposed, ANPR systems represent a significant technological advance that improves accuracy;efficiency as well as automation in various critical areas of our lives. © 2024 IEEE.

关键词： image acquisition

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Infrared small target detection method based on full-direction dilation Gaussian filtering and local background estimation

Infrared small target detection method based on full-directi...

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2023 International conference on Computer Graphics, Artificial Intelligence, and Data processing, ICCAID 2023

作者： Sun, Xiyu Pan, Shengda College of Information Engineering Shanghai Maritime University Shanghai China

ISBN: (纸本)9781510677685

Addressing the limitations of currently rare small target detection algorithms based on Human Visual systems (HVS) that struggle with achieving satisfactory performance in complex backgrounds and lack high real-time capabilities, this paper introduces an innovative small target detection algorithm. This approach is founded on the integration of the Full-Direction Dilation Difference of Gaussians (FDD-DOG) and background estimation based on local contrast (LCBE). First, the high-frequency signal in the target area is enhanced by using the FDD-DOG operation, based on the difference between the real target and the background. Then, the background noise is further suppressed by assigning appropriate weights to the enhanced image by using local contrast-based background estimation. After that, the target to be detected is extracted by the adaptive threshold segmentation method. Experiments comparing the algorithm proposed in this paper with other HVS-based detection algorithms show that the method in this paper exhibits better detection performance in different infrared image sequences, where the background suppression factor is improved by an average of about 25 times and the signal-to-clutter ratio gain is improved by an average of about 15 times. © 2024 SPIE.

关键词： image enhancement

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Does progress on imageNet transfer to real-world datasets? 37

Does progress on ImageNet transfer to real-world datasets?

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37th conference on Neural Information processing systems (NeurIPS)

作者： Fang, Alex Kornblith, Simon Schmidt, Ludwig Univ Washington Seattle WA 98195 USA Google Res Brain Team Mountain View CA USA Univ Washington AI2 Seattle WA 98195 USA

ISBN: (纸本)9781713899921

Does progress on imageNet transfer to real-world datasets? We investigate this question by evaluating imageNet pre-trained models with varying accuracy (57% -83%) on six practical image classification datasets. In particular, we study datasets collected with the goal of solving real-world tasks (e.g., classifying images from camera traps or satellites), as opposed to web-scraped benchmarks collected for comparing models. On multiple datasets, models with higher imageNet accuracy do not consistently yield performance improvements. For certain tasks, interventions such as data augmentation improve performance even when architectures do not. We hope that future benchmarks will include more diverse datasets to encourage a more comprehensive approach to improving learning algorithms.

关键词： image classification

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Emerging Versatile Context Representation on Modulation Recognition with Vision Transformer 2

Emerging Versatile Context Representation on Modulation Reco...

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2nd IEEE International conference on Signal, Information and Data processing, ICSIDP 2024

作者： Chen, Baihong Rao, Bin Zou, Xiaohai Wang, Wei Sun Yat-Sen University School of Electronics and Communication Engineering Shenzhen China

ISBN: (纸本)9798331515669

Accurate recognition of intra-pulse modulation patterns is essential for enhancing radar system performance. Tranditional recognition algorithms are typically designed under ideal conditions and handcrafted features, the presence of substantial noise interference in complex electromagnetic conditions can significantly degrade the performance of these algorithms. Although Transformers have achieved great success with computer vision tasks, existing vision transformers are lack of capturing long-range dependencies. To remedy these flaws, this paper proposes an accurate automatic modulation recognition algorithm based on vision transformer. First, we utilize traditional image processing techniques to provide position of signal in time-frequency image and introduce a radius basis function to generate the positional embeddings. This procedure enhances the spatial awareness of our model. Second, dynamic scales of 2D patches are embedded as the input to the encoders. Third, inspired by dilated convolutions, vanilla attention is reformed in the cascade encoders. Experiments and ablation studies exhibit the proposed algorithm performs well under conditions of various signal-to-noise ratio conditions. It is worth mentioning that the classification accuracy can be up to 93.4% when SNR is -10 dB, and reach to 94.4% when SNR is 0 dB, which indicates the proposed algorithm demonstrates superior robustness against noise perturbations compared to other methods. The present work provides a sound experimental basis for further studying automatic modulation classification for the sake of future field application in electronic warfare systems. © 2024 IEEE.

关键词： image enhancement

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Deconvolution-based image enhancement for optical coherence tomography 3

Deconvolution-based image enhancement for optical coherence ...

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conference on Biomedical Spectroscopy, Microscopy, and Imaging iii

作者： Mendroch, Damian Bauer, Niklas Harings, David Heisterkamp, Alexander Cologne Univ Appl Sci Inst Appl Opt & Elect Betzdorfer Str 2 D-50679 Cologne Germany Leibniz Univ Hannover Inst Quantum Opt Welfengarten 1 D-30167 Hannover Germany

ISBN: (纸本)9781510673311;9781510673304

This work focuses on enhancing the quality of A- and B-scans of a novel linear optical coherence tomography system (LOCT), addressing the image degradation caused by noise and the blurring characteristics of the system's three-dimensional point spread function. The enhancement procedure includes an initial spatial and frequency-based pre-filtering that is applied to the measured interference pattern. Subsequently, a more robust envelope detection technique based on the Hilbert transform is employed. Lastly, image structures are reconstructed using a deconvolution algorithm based on maximum likelihood estimation, tailored to meet our unique requirements by adapting it to Rician distributed intensity values and employing a sparseness regularization term. For the deconvolution, both the lateral and axial blur of the system are considered. Emphasis is placed on the optimization of signal detection in high-noise regions, while simultaneously preventing image boundary artifacts. The efficacy of this approach is demonstrated across multiple types of measurement objects, including both artificial and biological samples. All results show a significant reduction in noise as well as enhanced resolution. Structure distinguishability is also increased, which plays a crucial role in tomography applications. In summary, the proposed enhancement method substantially improves image quality. This is achieved by still using the same initial measurement data, but incorporating prior knowledge and maximizing the amount of extracted information. Although initially designed for LOCT systems, the processing steps have potential for broader application in other types of optical coherence tomography and imaging systems.

关键词： Optical coherence tomography deconvolution filtering biomedical imaging signal processing

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Generalized Single-image-Based Morphing Attack Detection Using Deep Representations from Vision Transformer

Generalized Single-Image-Based Morphing Attack Detection Usi...

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IEEE/CVF conference on Computer Vision and Pattern Recognition (CVPR)

作者： Zhang, Haoyu Ramachandra, Raghavendra Raja, Kiran Busch, Christoph Norwegian Univ Sci & Technol Trondheim Norway Darmstadt Univ Appl Sci Darmstadt Germany

ISBN: (纸本)9798350365474

Face morphing attacks have posed severe threats to Face Recognition systems (FRS), which are operated in border control and passport issuance use cases. Correspondingly, morphing attack detection algorithms (MAD) are needed to defend against such attacks. MAD approaches must be robust enough to handle unknown attacks in an open-set scenario where attacks can originate from various morphing generation algorithms, post-processing and the diversity of printers/scanners. The problem of generalization is further pronounced when the detection has to be made on a single suspected image. In this paper, we propose a generalized single-image-based MAD (S-MAD) algorithm by learning the encoding from Vision Transformer (ViT) architecture. Compared to CNN-based architectures, ViT model has the advantage on integrating local and global information and hence can be suitable to detect the morphing traces widely distributed among the face region. Extensive experiments are carried out on face morphing datasets generated using publicly available FRGC face datasets. Several state-of-the-art (SOTA) MAD algorithms, including representative ones that have been publicly evaluated, have been selected and benchmarked with our ViT-based approach. Obtained results demonstrate the improved detection performance of the proposed S-MAD method on inter-dataset testing (when different data is used for training and testing) and comparable performance on intra-dataset testing (when the same data is used for training and testing) experimental protocol.

关键词： Face Recognition Morphing Attack Detection

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