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检索条件"任意字段=Third International Conference on Image Processing and its Applications"

共 10021 条记录，以下是11-20 订阅

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piQture: A QML Library for image processing 5

piQture: A QML Library for Image Processing

引用

2024 international conference on Quantum Computing and Engineering

作者： Joshi, Saasha Justice, Daniel Stege, Ulrike Univ Victoria Victoria BC Canada Carnegie Mellon Univ Pittsburgh PA 15213 USA

ISBN: (纸本)9798331541378

Quantum Machine Learning (QML) leverages quantum mechanical properties to enhance computational capabilities. With its emergence, there is a need to integrate QML models into machine learning pipelines for real-life applications such as image processing. While standalone programs exist to demonstrate the performance of QML models, a well-defined model workflow is noticeably absent. We present piQture, an open-source Python and Qiskit-based library that streamlines the implementation and training of QML models tailored for image processing. its design and structure prioritize usability among users familiar with classical machine learning without prior QML experience.

关键词： image processing Quantum Machine Learning Software Development

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A Novel Color image Encryption based on Chaos and DNA Mutation

A Novel Color Image Encryption based on Chaos and DNA Mutati...

引用

9th international conference on Signal and image processing (ICSIP)

作者： Sun, Mingliang Yuan, Jie Li, Xiaoyong Liu, Dongxiao Wei, Xinghai Beijing Univ Posts & Telecommun Key Lab Trustworthy Distributed Comp & Serv BUPT Beijing Peoples R China

ISBN: (纸本)9798350350920

The security of the color images is challenged by the illegal copying and distribution over various communication networks. This work proposed a novel color image encryption scheme based on the chaos and Deoxyribonucleic acid (DNA). Firstly, A novel four dimensions chaotic system is constructed and analyzed. Secondly, the DNA coding rules are controlled by the chaos-based pseudorandom sequence to substitute the image pixels. The column and row rotation are designed to disturb the image pixels. Finally, The DNA matrix coded from the color image is divided into blocks to implement the XOR and addition operation with the pseudorandom sequence. its security is simulated and compared with other schemes. The comparison show that the proposed scheme has a higher key sensitivity, lower correlation of adjacent pixels, and higher NPCR and information entropy. Therefore, the proposed image encryption scheme is more capable against statical attacks and efficiency while keeping high robustness.

关键词： chaos DNA coding image encryption pseudorandom sequence

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L-MFFN: Lightweight Multiscale Feature Fusion Network with Limited Samples for HSI Classification

L-MFFN: Lightweight Multiscale Feature Fusion Network with L...

引用

9th international conference on Signal and image processing (ICSIP)

作者： Ali, Aamir Mu, Caihong Wang, Yafeng Liu, Yi Xidian Univ Sch Artificial Intelligence Xian 710071 Peoples R China Xidian Univ Sch Elect Engn Xian 710071 Peoples R China

ISBN: (纸本)9798350350920

Hyperspectral image (HSI) classification is valuable in remote sensing due to its rich spectral and spatial information. In the last decade, deep learning methods, especially Convolutional Neural Networks (CNNs), have revolutionized HSI classification by extracting intangible semantic features and maintaining the spatial structure during feature extraction. However, the efficacy of these techniques can be constrained by the limited availability of labeled samples in HSI data. To address the issue of small-sample HSI classification, a Lightweight Multiscale Feature Fusion Network (L-MFFN) is introduced. The Multiscale Feature Extraction Module (MFEM) and the enhanced Spectral-Spatial Attention Module (SSAM) are designed and combined in L-MFFN, optimizing the use of deep and shallow features. This integration improves the extraction and fusion of multiscale spectral-spatial features, enhancing classification performance. The proposed model demonstrates state-of-the-art performance across two HSI datasets and stands out in situations with limited labeled samples, highlighting its capability to effectively tackle the challenge of small-sample HSI classification.

关键词： hyperspectral image classification small sample multiscale feature fusion spectral-spatial attention

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A Robust Coverless Steganography Based on Controllable Semantic Camouflage

A Robust Coverless Steganography Based on Controllable Seman...

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9th international conference on Signal and image processing (ICSIP)

作者： Guo, Yifan Chen, Liquan Li, Xiao Chen, Jiuhao Liu, Yinqiang Southeast Univ Nanjing Peoples R China Purple Mt Labs Nanjing Peoples R China Hohai Univ Nanjing Peoples R China Nanjing Tech Univ Nanjing Peoples R China

ISBN: (纸本)9798350350920

Recently, most coverless steganography (CIS) methods are based on robust mapping rules. However, due to the limited mapping expression relationship between secret information and hash sequence, it is a challenge to further improve the hiding ability of coverless information hiding. Hence we propose a robust coverless steganography based on controllable semantics camouflage. In this scheme, sender transform stego images into camouflage images through generative hiding process by changing images semantics. Receiver perform revealing process to restore stego images. During hiding and revealing, secret information is not involved, thus this scheme can resist steganalysis effectively. Experiment shows the remarkable performance of this scheme, particularly in terms of its resilience to noise, surpassing other methods significantly and capable of achieving a successful extraction rate up to 100%.

关键词： coverless image steganography semantic camouflage image features diffusion model

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Classification Of Defect And Non-Defect Durian Using image processing Technique 14

Classification Of Defect And Non-Defect Durian Using Image P...

引用

14th Symposium on Computer applications & Industrial Electronics (ISCAIE)

作者： Khazri, Faris Ilyasa Ahmad Kutty, Suhaili Beeran Sani, Maizura Mohd Kassim, Murizah Saaidin, Shuria Univ Teknol MARA Sch Elect Engn Coll Engn Shah Alam 40450 Selangor Malaysia

ISBN: (纸本)9798350348798;9798350348804

Durian, a renowned fruit in Southeast Asia, particularly in countries like Malaysia, Thailand, and Indonesia, poses a challenge during its season as the manual classification of a large stock of durians based on grade and quality becomes a laborious task for sellers. This project aims to streamline the process by employing image processing techniques for the classification of durians into defect and non-defect categories. The methodology involves image collection and image filtering analysis using Gaussian and Median filters, followed by applying Canny edge detection techniques to identify the durian region. Subsequently, classification algorithms based on pixel connection are deployed to distinguish between defect and non-defect durians. The obtained results reveal comparable accuracy and precision rates for both defect and non-defect durian images, standing at 87% and 75%, respectively. This project successfully demonstrates the feasibility of automating durian classification through image processing methods.

关键词： durian classification image processing technique defect detection edge detection image filtering

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Vision Language Models for Oil Palm Fresh Fruit Bunch Ripeness Classification 8

Vision Language Models for Oil Palm Fresh Fruit Bunch Ripene...

引用

IEEE 8th international conference on Signal and image processing applications (IEEE ICSIPA)

作者： Goh, Jin Yu Yunos, Yusri Md Sheikh, Usman Ullah Ali, Mohamed Sultan Mohamed Univ Teknol Malaysia Fac Elect Engn Johor Baharu 81310 Johor Malaysia Qatar Univ Dept Elect Engn Coll Engn Doha Qatar

ISBN: (纸本)9798350352368

The accurate classification of fresh fruit bunch ripeness is crucial for optimizing oil quality and yield in the palm oil industry. Traditional manual inspection methods are labor-intensive, subjective, and prone to errors, motivating the exploration of automated solutions. This paper examined the potential of vision language models, including LLaVA 1.5, YiVL, and PaliGemma, to automate and enhance FFB ripeness assessment. The models were evaluated on their ability to classify ripeness stages and the accuracy of generated descriptive text using metrics like BLEU and ROUGE scores. Yi-VL achieved the highest descriptive accuracy with a ROUGE-L score of 93.14. However, it processes 0.18 samples per second, which is slower than PaliGemma (0.53 samples/second). PaliGemma is 194.44% more efficient in samples/second than Yi-VL, making it better suited for realtime applications despite its lower accuracy (ROUGE-L: 26.15). LLaVA 1.5 offers a balance between accuracy (ROUGE-L: 82.16) and efficiency (0.22 samples/second). This research highlighted the trade-offs between different VLMs for FFB ripeness assessment, demonstrating their potential to revolutionize the agriculture industry. Future work may focus on optimizing model performance and deploying these technologies in real-world scenarios.

关键词： FFB Large Language Models Visual Question Answering Deep Learning image processing

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LIDAR DEPTH MAP GUIDED image COMPRESSION MODEL 31

LIDAR DEPTH MAP GUIDED IMAGE COMPRESSION MODEL

引用

2024 international conference on image processing

作者： Gnutti, Alessandro Della Fiore, Stefano Savardi, Mattia Chen, Yi-Hsin Leonardi, Riccardo Peng, Wen-Hsiao Univ Brescia CNIT Dept Informat Engn Brescia Italy Univ Salerno Dept Informat Salerno Italy Univ Brescia DSMC Brescia Italy Natl Yang Ming Chiao Tung Univ Dept Comp Sci Hsinchu Taiwan

ISBN: (纸本)9798350349405;9798350349399

The incorporation of LiDAR technology into some high-end smartphones has unlocked numerous possibilities across various applications, including photography, image restoration, augmented reality, and more. In this paper, we introduce a novel direction that harnesses LiDAR depth maps to enhance the compression of the corresponding RGB camera images. To the best of our knowledge, this represents the initial exploration in this particular research direction. Specifically, we propose a Transformer-based learned image compression system capable of achieving variable-rate compression using a single model while utilizing the LiDAR depth map as supplementary information for both the encoding and decoding processes. Experimental results demonstrate that integrating LiDAR yields an average PSNR gain of 0.83 dB and an average bitrate reduction of 16% as compared to its absence.

关键词： Learned image compression LiDAR depth map prompts transformer

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Algorithm for finding reference brightness correction coefficients 3

Algorithm for finding reference brightness correction coeffi...

引用

3rd international conference on Optics, Computer applications, and Materials Science, CMSD 2023

作者： Abdullayeva, Ozoda Beknazarova, Saida Namangan Engeneering-Construction Institute Namangan Uzbekistan Tashkent University of Information Technologies Named after Muhammad Al-Khwarizmi Tashkent Uzbekistan

ISBN: (纸本)9781510674486

This article presents an algorithm for determining reference brightness correction coefficients to improve image quality. The algorithm utilizes a combination of statistical analysis and image processing techniques to identify and correct brightness discrepancies in digital images. By establishing a robust and efficient method for calculating these coefficients, the algorithm aims to enhance the overall visual fidelity of images across various applications, such as photography, medical imaging, and remote sensing. The proposed algorithm demonstrates its effectiveness through experimental results and comparisons with existing methods, highlighting its potential for practical implementation in image processing workflows. In the world, the filtering of digital images by the convolution method with a pulse characteristic in the spectral region scientific research is being conducted to improve the quality level of digital television images, methods for modeling filtration processes and highly efficient control systems in a number of priority areas, including: on the formation of mathematical models of filtration processes, improving the methods of wavelet, Fourier, Haar, Walsh-Hadamard, Karhunen-Loev in increasing the clarity and brightness of images based on linear and nonlinear differential equations;creation of methods for eliminating additive, pulsed and adaptive-Gaussian types of noise in images using additive and adaptive filtering;methods of algorithms and software for introducing intra-frame and interframe image transformations;methods of adaptive brightness system control using the Chebyshev matrix series;methods of gradient, static and Laplace methods for image segmentation and dividing it into contours;formation of criteria and conditions for evaluating image quality. Conducting scientific research in the above research areas confirms the relevance of the topic of this article. © 2024 SPIE. All rights reserved.

关键词： image enhancement

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THROUGH-WALL IMAGING BASED ON WIFI CHANNEL STATE INFORMATION 31

THROUGH-WALL IMAGING BASED ON WIFI CHANNEL STATE INFORMATION

引用

2024 international conference on image processing

作者： Strohmayer, Julian Sterzinger, Rafael Stippel, Christian Kampel, Martin TU Wien Comp Vis Lab Favoritenstr 9-193-1 A-1040 Vienna Austria

ISBN: (纸本)9798350349405;9798350349399

This work presents a seminal approach for synthesizing images from WiFi Channel State Information (CSI) in through-wall scenarios. Leveraging the strengths of WiFi, such as cost-effectiveness, illumination invariance, and wall-penetrating capabilities, our approach enables visual monitoring of indoor environments beyond room boundaries and without the need for cameras. More generally, it improves the interpretability of WiFi CSI by unlocking the option to perform image-based downstream tasks, e.g., visual activity recognition. In order to achieve this crossmodal translation from WiFi CSI to images, we rely on a multimodal Variational Autoencoder (VAE) adapted to our problem specifics. We extensively evaluate our proposed methodology through an ablation study on architecture configuration and a quantitative/qualitative assessment of reconstructed images. Our results demonstrate the viability of our method and highlight its potential for practical applications.

关键词： WiFi Channel State Information Through-Wall Imaging image Synthesis Multimodality

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RL-LOGO: DEEP REINFORCEMENT LEARNING LOCALIZATION FOR LOGO RECOGNITION 49

RL-LOGO: DEEP REINFORCEMENT LEARNING LOCALIZATION FOR LOGO R...

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49th IEEE international conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Fujitake, Masato Fast Accounting Co Ltd FA Res Tokyo Japan

ISBN: (纸本)9798350344868;9798350344851

This paper proposes a novel logo image recognition approach incorporating a localization technique based on reinforcement learning. Logo recognition is an image classification task identifying a brand in an image. As the size and position of a logo vary widely from image to image, it is necessary to determine its position for accurate recognition. However, because there is no annotation for the position coordinates, it is impossible to train and infer the location of the logo in the image. Therefore, we propose a deep reinforcement learning localization method for logo recognition (RL-LOGO). It utilizes deep reinforcement learning to identify a logo region in images without annotations of the positions, thereby improving classification accuracy. We demonstrated a significant improvement in accuracy compared with existing methods in several published benchmarks. Specifically, we achieved an 18-point accuracy improvement over competitive methods on the complex dataset Logo-2K+. This demonstrates that the proposed method is a promising approach to logo recognition in real-world applications.

关键词： Logo Recognition image Classification Deep Reinforcement Learning

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