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检索条件"任意字段=Conference on Statistical and Stochastic Methods for Image Processing"

共 4715 条记录，以下是101-110 订阅

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Pseudo Pairs Based Unsupervised Deep Hashing for image Retrieval 7

Pseudo Pairs Based Unsupervised Deep Hashing for Image Retri...

引用

7th International conference on Vision, image and Signal processing, ICVISP 2023

作者： Zhang, Hao School of Management Tianjin University of Technology Tianjin China

ISBN: (纸本)9781839539848

The research presents a new deep learning framework, the pseudo-pair-based unsupervised deep hashing (PPUDH), designed to enhance image retrieval systems. PPUDH employs a soft clustering approach that iteratively trains clusters with strong discriminative capabilities and creates binary codes (BCs) with heightened correlation sensitivity. These clusters are then amalgamated to form an additional distribution for deriving hash codes (HCs). The model undergoes optimization via standard stochastic gradient descent (SGD). This optimization process marries the reconstruction loss from the encoder tasked with auto-reconstruction with the loss incurred from meeting binary code requirements. The efficacy of PPUDH has been validated through comprehensive evaluations of three renowned datasets. The outcomes of these tests demonstrate that PPUDH offers a considerable advancement over existing top-tier methods in the field. © The Institution of Engineering & Technology 2023.

关键词： Binary codes

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LEARNING GRAPH FEATURES FOR COLORED MESH VISUAL QUALITY ASSESSMENT 29

LEARNING GRAPH FEATURES FOR COLORED MESH VISUAL QUALITY ASSE...

引用

IEEE International conference on image processing (ICIP)

作者： El Hassouni, Mohammed Cherifi, Hocine Mohammed V Univ Rabat FLSH Rabat Morocco Univ Burgundy LIB EA 7534 Dijon France

ISBN: (数字)9781665496209

ISBN: (纸本)9781665496209

This paper proposes a novel method for colored mesh visual quality assessment based on graph features learning. We first extract color features from each distorted colored mesh data. Then, we map them into a weighted graph with weights derived from the color features. One then computes various local topological properties of the network nodes (Degree, Strength, Clustering coefficient). Estimates of their statistical properties (Mean, Variance, Skewness, Kurtosis, Entropy) form a signature vector are used by a machine learning algorithm. The random forest regression is used to predict the quality score. Experiments are conducted on the publicly available CMDM database specifically constructed for the colored mesh quality assessment task. The proposed method is compared to the most influential and effective full and no-reference methods (CMDM and NR-NSS). The excellent correlations with subjective decisions prove its good performance.

关键词： Weighted graph topological properties statistical measures quality assessment colored mesh

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Correction of uneven illumination in microscopic image through robust brightness distribution estimation and deviation rectification 1

Correction of uneven illumination in microscopic image throu...

引用

1st International conference on Optical Communication and Optical Information processing, OCOIP 2023

作者： Ding, Tao Wu, Kaijie Cheng, Hao Gu, Chaocheng Department of Automation Shanghai Jiao Tong University Shanghai200240 China

For diagnostic purposes, the automatic microscopic scanning system can scan and stitch multiple slide images together to produce a Whole Slide image. This process provides a clear, high-resolution picture of the slide sample under the high-power objective lens of the microscope, enabling accurate diagnosis and analysis. However, uneven illumination affects every image acquired by a microscope, resulting in the existence of artifacts. In this paper, a novel retrospective approach based on robust brightness distribution estimation and deviation rectification is proposed. Least squares estimation and Cauchy estimation with a smooth regularization term are used to obtain the accurate estimation of background illumination distribution. Then the deviation map is given based on the deviation function of the two directions for illumination correction. This method leverages the statistical properties of image sequences to enhance stability and robustness against biological image artifacts. Through experimental validation, this approach successfully eliminates visible edge artifacts that commonly appear between neighboring tiles and exhibits superior performance when compared to other methods. © 2024 Institute of Physics Publishing. All rights reserved.

关键词： image enhancement

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Automatic summarization of Instagram social network posts by combining semantic and statistical approaches 6

Automatic summarization of Instagram social network posts by...

引用

6th International conference on Pattern Recognition and image Analysis (IPRIA)

作者： Tabanmehr, Zainab Akhtarkavan, Ehsan Khatam Univ Dept Comp Engn Tehran Iran

ISBN: (纸本)9798350314557

The increasing spread of data and text documents such as articles, web pages, books, posts on social networks, etc. on the Internet, creates a fundamental challenge in various fields of text processing under the title of "automatic text summarization". Manual processing and summarization of large volumes of textual data is a very difficult, expensive, time-consuming, and impossible process for human users. Text summarization systems are divided into extractive and abstract categories. In the extractive summarization method, the final summary of a text document is extracted from the important sentences of the same document without any kind of change. In this method, it is possible to repeat a series of sentences repeatedly and interfere with pronouns. But in the abstract summarization method, the final summary of a textual document is extracted from the meaning of the sentences and words of the same document or other documents. Many of the performed works have used extraction methods or abstracts to summarize the collection of web documents, each of which has advantages and disadvantages in the results obtained in terms of similarity or size. In this research, by developing a crawler, extracting the popular text posts from the Instagram social network, suitable pre-processing, and combining the set of extractive and abstract algorithms, the researcher showed how to use each of the abstract algorithms. and used extraction as a supplement to increase the accuracy and accuracy of another algorithm. Observations made on 820 popular text posts on the Instagram social network show the accuracy (80%) of the proposed system.

关键词： text summarization extractive approach abstract approach natural language processing social networks

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Noise-robust registration of microscopic height data using convolutional neural networks

Noise-robust registration of microscopic height data using c...

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SPIE conference on Future Sensing Technologies

作者： Siemens, Stefan Kaestner, Markus Reithmeier, Eduard Leibniz Univ Hannover Inst Measurement & Automat Control Univ 1 D-30823 Hannover Germany

ISBN: (纸本)9781510657229

In this work, a deep convolutional neural network is proposed to improve the registration of microtopographic data. For this purpose, different mechanical surfaces were optically measured using a confocal laser scanning microscope. A wide range of surfaces with different materials, processing methods, and topographic properties, such as isotropy and anisotropy or stochastic and deterministic features, are included. Training and testing datasets with known homographies are generated from these measurements by cropping a fixed and moving image patch from each topography and then randomly perturbing the latter. A pseudo-siamese network architecture based on the VGG Net is then used to predict these homographies. The network is trained with a supervised learning approach where the Euclidean distance between the predicted and the ground truth gives the loss function. The 4-point homography parameterization is used to improve the loss convergence. Furthermore, different amounts of image noise are added to enhance the prediction's robustness and prevent overfitting. The effectiveness of the proposed method is evaluated through different experiments. First, the network performance is compared to intensity-based and feature-based conventional registration algorithms regarding the resulting error, the noise-robustness, and the processing speed. In addition, images from the Microsoft Common Objects in Context (COCO) dataset are used to verify the network's generalization capability to new image types and contents. The results show that the learning-based approach offers much higher robustness regarding image noise and a much lower processing time. In contrast, conventional algorithms have a smaller registration error without image noise.

关键词： image registration Homography estimation Surface metrology Machine learning Convolutional Neural Network Confocal Laser Scanning Microscopy

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image Encryption Design Based on Henon Mapping 5

Image Encryption Design Based on Henon Mapping

引用

5th Asia-Pacific conference on image processing, Electronics and Computers, IPEC 2024

作者： Su, Wenyi Zhou, Mingjie Bao, Jiaqing Yang, Lanjing Zhu, Liping Li, Guodong Guilin University of Electronic Technology School of Mathematics and Computing Science Guilin China Changji University School of Mathematics and Data Science Changji China

ISBN: (纸本)9798350374407

image data, compared to textual information, possesses visual characteristics that require novel encryption methods to ensure its security. Therefore, it is necessary to explore new encryption techniques tailored specifically for image data. By applying the Henon chaotic map, two random sequences are generated. These sequences, along with a given key, are used to produce four equally-sized random matrices through specific formulas. Two of these matrices are employed in the forward and backward diffusion modules, while the remaining two are utilized in the permutation module. Through two rounds of diffusion operations and one permutation operation, the four random matrices are utilized to encrypt the image, resulting in a secure, encrypted image. The security performance of this encryption algorithm is analyzed theoretically, considering encryption and decryption speeds, histogram statistical characteristics, correlations between adjacent pixels, and information entropy. Additionally, simulation experiments demonstrate the effectiveness of this digital image encryption scheme, showcasing excellent encryption results and security performance. © 2024 IEEE.

关键词： Chaos theory

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FUB-Clustering: Fully Unsupervised Batch Clustering

FUB-Clustering: Fully Unsupervised Batch Clustering

引用

3rd International conference on image processing and Vision Engineering, IMPROVE 2023

作者： Giurato, Salvatore Ortis, Alessandro Battiato, Sebastiano Image Processing Laboratory Dipartimento di Matematica e Informatica Universita’ degli Studi di Catania Viale A. Doria 6 Catania95125 Italy

ISBN: (纸本)9789897586422

Traditional methods for unsupervised image clustering such as K-means, Gaussian Mixture Models (GMM), and Spectral Clustering (SC) have been proposed. However, these strategies may be time-consuming and labor-intensive, particularly when dealing with a vast quantity of unlabeled images. Recent studies have proposed incorporating deep learning techniques to improve upon these classic models. In this paper, we propose an approach that addresses the limitations of these prior methods by allowing for the association of multiple images at a time to each group and by considering images that are extremely close to the images that are already associated to the correct cluster. Additionally, we propose a method for reducing and unifying clusters when the number of clusters is deemed too high by the user, utilizing four different heuristics while considering the clustering as a single element. Our proposed method is able to analyze and group images in real-time without any prior training. Experiments confirm the effectiveness of the proposed strategy in various setting and scenarios. © 2023 by SCITEPRESS – Science and Technology Publications, Lda.

关键词： stochastic systems

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Exact Bayesian Inference on Discrete Models via Probability Generating Functions: A Probabilistic Programming Approach 37

Exact Bayesian Inference on Discrete Models via Probability ...

引用

37th conference on Neural Information processing Systems (NeurIPS)

作者： Zaiser, Fabian Murawski, Andrzej S. Ong, C. -H. Luke Univ Oxford Dept Comp Sci Oxford England Nanyang Technol Univ Sch Comp Sci & Engn Singapore Singapore

ISBN: (纸本)9781713899921

We present an exact Bayesian inference method for discrete statistical models, which can find exact solutions to a large class of discrete inference problems, even with infinite support and continuous priors. To express such models, we introduce a probabilistic programming language that supports discrete and continuous sampling, discrete observations, affine functions, (stochastic) branching, and conditioning on discrete events. Our key tool is probability generating functions: they provide a compact closed-form representation of distributions that are definable by programs, thus enabling the exact computation of posterior probabilities, expectation, variance, and higher moments. Our inference method is provably correct and fully automated in a tool called Genfer, which uses automatic differentiation (specifically, Taylor polynomials), but does not require computer algebra. Our experiments show that Genfer is often faster than the existing exact inference tools PSI, Dice, and Prodigy. On a range of real-world inference problems that none of these exact tools can solve, Genfer's performance is competitive with approximate Monte Carlo methods, while avoiding approximation errors.

关键词： Monte Carlo methods

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High-Perceptual Quality JPEG Decoding via Posterior Sampling

High-Perceptual Quality JPEG Decoding via Posterior Sampling

引用

2023 IEEE/CVF conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2023

作者： Man, Sean Ohayon, Guy Adrai, Theo Elad, Michael Technion - Israel Institute of Technology Haifa Israel

ISBN: (纸本)9798350302493

JPEG is arguably the most popular image coding format, achieving high compression ratios via lossy quantization that may create visual artifacts degradation. Numerous attempts to remove these artifacts were conceived over the years, and common to most of these is the use of deterministic post-processing algorithms that optimize some distortion measure (e.g., PSNR, SSIM). In this paper we propose a different paradigm for JPEG artifact correction: Our method is stochastic, and the objective we target is high perceptual quality - striving to obtain sharp, detailed and visually pleasing reconstructed images, while being consistent with the compressed input. These goals are achieved by training a stochastic conditional generator (conditioned on the compressed input), accompanied by a theoretically well-founded loss term, resulting in a sampler from the posterior distribution. Our solution offers a diverse set of plausible and fast reconstructions for a given input with perfect consistency. We demonstrate our scheme's unique properties and its superiority to a variety of alternative methods on the FFHQ and imageNet datasets. © 2023 IEEE.

关键词： image coding

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SelfDeblur with Sparsity Enforced Bregman Learning

SelfDeblur with Sparsity Enforced Bregman Learning

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2025 IEEE International conference on Acoustics, Speech, and Signal processing, ICASSP 2025

作者： Zeng, Yaoyun Chen, Beier Wang, Hongxia Department of Mathematics National University of Defense Technology Changsha China

ISBN: (纸本)9798350368741

Learning-driven methods have revolutionized the field of blind deconvolution, with SelfDeblur as a pioneering method. It uses deep image priors to jointly estimate blur kernels and latent clear images, employing deep neural networks to automatically learn these priors and effectively capture their statistical properties. Building on the success of SelfDeblur, this paper proposes a novel sparse optimization method, named B-SelfDeblur, which employs linearized Bregman iterations to train neural networks. Our approach addresses the challenges of processing large-scale blurry images by starting with minimal parameters and incrementally adding necessary ones in an inverse scale space manner. Additionally, we incorporate a smoothing technique for the total variation (TV) regularization term, which ensures our loss function is continuously differentiable. Experimental results demonstrate that B-SelfDeblur can significantly optimize network weight storage while maintaining deblurring effectiveness. Furthermore, we provide a partial theoretical analysis and ablation studies, which confirm that our loss function is monotonically decreasing and that our method is well-suited for alternating iterative optimization. © 2025 IEEE.

关键词： Blind deconvolution Bregman learning deep image priors SelfDeblur

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