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Adaptive Kernelized Evidence C-Means Clustering Combining Spatial Information for Noisy image Segmentation 5

Adaptive Kernelized Evidence C-Means Clustering Combining Sp...

引用

5th International conference on Natural Language processing, ICNLP 2023

作者： Rong, Lan Haowen, Mi Na, Qu Feng, Zhao Haiyan, Yu Lu, Zhang Xi'an University of Posts and Telecommunications School of Communications and Information Engineering Xi'an710121 China

ISBN: (纸本)9798350302219

Although the evidence c-means clustering (ECM) has the capability to process uncertain information, it is not suitable for noisy image segmentation, because the spatial information of pixels is not considered. To solve the problem, an adaptive kernelized evidence c-means clustering combining spatial information for noisy image segmentation algorithm is proposed. Firstly, an adaptive noise distance that can be iteratively updated is constructed using the local information of the pixels. Secondly, to improve the classification performance, an adaptive kernel function is proposed to measure the distance between the pixel and the cluster center. Simultaneously, the original, local and non-local information of pixels are introduced adaptively into the objective function to enhance the robustness to noise. In the iteration, the noise cluster is automatically recovered using the recovery factor constructed by the gray and spatial information of neighborhood pixels. Finally, the credal partition is transformed into a fuzzy partition by pignistic transformation, the classification of pixel be determined by the maximum membership principle. Experiments on synthetic images and real images demonstrate that the proposed algorithm has strong noise suppression ability. visual effects and evaluation indexes verify the effectiveness of the proposed algorithm for noisy image segmentation. © 2023 IEEE.

关键词： image segmentation

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Generative DNA: Representation Learning for DNA-based Approximate image Storage

Generative DNA: Representation Learning for DNA-based Approx...

引用

IEEE International conference on visual communications and image processing (VCIP) - visual communications in the Era of AI and Limited Resources

作者： Franzese, Giulio Yan, Yiqing Serra, Giuseppe D'Onofrio, Ivan Appuswamy, Raja Michiardi, Pietro EURECOM Dept Data Sci Biot France

ISBN: (纸本)9781728185514

Synthetic DNA has received much attention recently as a long-term archival medium alternative due to its high density and durability characteristics. However, most current work has primarily focused on using DNA as a precise storage medium. In this work, we take an alternate view of DNA. Using neural-network-based compression techniques, we transform images into a latent-space representation, which we then store on DNA. By doing so, we transform DNA into an approximate image storage medium, as images generated back from DNA are only approximate representations of the original images. Using several datasets, we investigate the storage benefits of approximation, and study the impact of DNA storage errors (substitutions, indels, bias) on the quality of approximation. In doing so, we demonstrate the feasibility and potential of viewing DNA as an approximate storage medium.

关键词： DNA storage representation learning approximate storage compression

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Spatio-spectral image Reconstruction Using Non-local Filtering

Spatio-spectral Image Reconstruction Using Non-local Filteri...

引用

IEEE International conference on visual communications and image processing (VCIP) - visual communications in the Era of AI and Limited Resources

作者： Sippel, Frank Seiler, Juergen Kaup, Andre Friedrich Alexander Univ Erlangen Nurnberg FAU Multimedia Commun & Signal Proc Cauerstr 7 D-91058 Erlangen Germany

ISBN: (纸本)9781728185514

In many image processing tasks it occurs that pixels or blocks of pixels are missing or lost in only some channels. For example during defective transmissions of RGB images, it may happen that one or more blocks in one color channel are lost. Nearly all modern applications in image processing and transmission use at least three color channels, some of the applications employ even more bands, for example in the infrared and ultraviolet area of the light spectrum. Typically, only some pixels and blocks in a subset of color channels are distorted. Thus, other channels can be used to reconstruct the missing pixels, which is called spatio-spectral reconstruction. Current state-of-the-art methods purely rely on the local neighborhood, which works well for homogeneous regions. However, in high-frequency regions like edges or textures, these methods fail to properly model the relationship between color bands. Hence, this paper introduces non-local filtering for building a linear regression model that describes the inter-band relationship and is used to reconstruct the missing pixels. Our novel method is able to increase the PSNR on average by 2 dB and yields visually much more appealing images in high-frequency regions.

关键词： Spatio-spectral Reconstruction Error Concealment Linear Regression Block Matching Non-Local Filtering

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Combining image Enhancement Techniques and Deep Learning for Shallow Water Benthic Marine Litter Detection

Combining Image Enhancement Techniques and Deep Learning for...

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European conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD)

作者： Emmanuel Bancud, Gil John Labanon, Alex Angelo Abreo, Neil Kobayashi, Vladimer Univ Philippines Mindanao AIR ES Lab Davao Philippines

ISBN: (纸本)9783031236174;9783031236181

The scarcity of information about benthic marine litter especially in developing countries hampers the implementation of targeted actions to minimize the extent of its impacts. This study developed a system using image processing and deep learning methods for detecting/tracking marine macro litter that can efficiently identify and quantify its amount in benthic environments in shallow coastal areas. Shallow underwater litter detection poses several challenges. First is the low quality of images. Second is the difficulty in recognizing litter brought by their varying visual characteristics. Third is the lack of available data for training. Underwater images of litter were collected from marine litter hotspots in coastal areas in southern Philippines. This study experimented with various object detection algorithms. The best object detection model is then paired with various image enhancement techniques to determine the optimal combination. Among the combinations that were tested, YOLOv5n combined with CLAHE gave the best performance for simple binary task (litter or not litter) with a mAP@0.5 of 0.704. Furthermore, the results showed that applying underwater image enhancement techniques provides noticeable improvement for object detection models on detecting marine litter.

关键词： Yolov5 image enhancement Marine litter Object detection

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Two-stage Parallax Correction and Multi-stage Cross-view Fusion Network Based Stereo image Super-Resolution

Two-stage Parallax Correction and Multi-stage Cross-view Fus...

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IEEE International conference on visual communications and image processing (VCIP) - visual communications in the Era of AI and Limited Resources

作者： Zheng, Yijian Li, Sumei Tianjin Univ Sch Elect & Informat Engn Tianjin Peoples R China

ISBN: (纸本)9781728185514

Stereo image super-resolution (SR) has achieved great progress in recent years. However, the two major problems of the existing methods are that the parallax correction is insufficient and the cross-view information fusion only occurs in the beginning of the network. To address these problems, we propose a two-stage parallax correction and a multi-stage cross-view fusion network for better stereo image SR results. Specially, the two-stage parallax correction module consists of horizontal parallax correction and refined parallax correction. The first stage corrects horizontal parallax by parallax attention. The second stage is based on deformable convolution to refine horizontal parallax and correct vertical parallax simultaneously. Then, multiple cascaded enhanced residual spatial feature transform blocks are developed to fuse cross-view information at multiple stages. Extensive experiments show that our method achieves state-of-the-art performance on the KITTI2012, KITTI2015, Middlebury and Flickr1024 datasets.

关键词： Stereo image super-resolution Parallax correction Multi-stage cross-view fusion

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Bibliometric Analysis of Road Distress Detection Based on Citespace 24

Bibliometric Analysis of Road Distress Detection Based on Ci...

引用

5th International conference on Computer Science and Management Technology, ICCSMT 2024

作者： Li, Yaoye Zhang, Yinbo He, Rong Ma, Lianxia Chen, Fang Xinjiang communications Construction Group Co. Ltd. Xinjiang Urumqi China Xinjiang Key Laboratory of Transportation Prefabricated Construction and Maintenance Xinjiang Urumqi China Xinjiang University Xinjiang Urumqi China

ISBN: (纸本)9798400709999

To analyze the research progress in the field of road disease detection, this article searched the Web of Science Core Collection database for papers related to road disease detection from 2000 to *** CiteSpace software, a visual analysis was conducted on the 247 retrieved documents, focusing on annual publication numbers, publishing countries, core institutions and authors, keywords, burst words, and timeline mapping to explore the current status and trends in this *** findings indicate that since 2004, the overall research trend in road disease detection has been on an upward trajectory, with a notable increase in the number of research papers published in 2021, reflecting a significant surge in research ***, China, the United States, and Italy have been the most prolific in research *** leading research institutions are primarily scientific and technological academies, with Chang'an University taking a dominant position in the field of road disease *** keyword co-occurrence analysis, burst detection, and keyword clustering analysis, it was determined that disease detection technology, deep learning and machine learning, image processing and intelligent analysis, and system integration and development have been the research hotspots in the road disease detection field from 2000 to *** is anticipated that future research will evolve towards the directions of performance evaluation, multimodal data fusion, and real-time monitoring and analysis. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Data fusion

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Combined RNN Approach for Addressing Morphological, Syntactical, and Semantic Challenges in image Captioning 5th

Combined RNN Approach for Addressing Morphological, Syntacti...

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5th International conference on Computing, communications, and Cyber Security, IC4S 2023

作者： Gaware, Ishaan Rajendra Khandelwal, Rishabh Rajpal, Dev Tyagi, Arnav Das, Sanchali School of Computer Science Engineering and Technology Bennett University Uttar Pradesh Greater Noida India

ISBN: (纸本)9789819725496

image caption generation is an emerging field that combines deep learning and natural language processing (NLP) techniques to generate descriptive and accurate captions for images automatically. This not only facilitates the efficient organization of digital files but also aids visually impaired individuals in understanding the context of images. This research investigates the application of state-of-the-art deep learning and NLP methods for image caption generation, specifically focusing on the InceptionV3 and GloVe (global vectors for word representation) models. The InceptionV3 model, a convolutional neural network (CNN) architecture designed for image classification tasks, has demonstrated a high level of accuracy in the imageNet Large Scale visual Recognition Challenge (78.3%). The Keras sequential model was utilized to create four dense layers for the image and caption models, employing the InceptionV3 architecture for image classification. This modular design allows for stacking multiple layers to create a highly effective model for classifying images. In parallel, this research explores the GloVe model, an unsupervised approach for generating word embeddings that capture semantic and syntactic meaning. GloVe achieved an accuracy of 75.0%, surpassing the scores of competing methods such as Word2Vec (72.3%) and FastText (69.3%). Through our investigation, the research analyzes the effectiveness of these methods in generating descriptive and accurate captions for images. Additionally, the paper also identifies the challenges and limitations of current approaches, such as handling rare words and improving the diversity and coherence of generated captions. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： image classification

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A Novel Optimized Context-Based Deep Architecture for Scene Parsing 29th

A Novel Optimized Context-Based Deep Architecture for Scene ...

引用

29th International conference on Neural Information processing

作者： Mandal, Ranju Verma, Brijesh Azam, Basim Selvaraj, Henry Cent Queensland Univ Sch Engn & Technol Ctr Intelligent Syst Brisbane Qld Australia Univ Nevada Dept Elect & Comp Engn Las Vegas NV USA

ISBN: (纸本)9789819916443;9789819916450

Determining the optimal parameter values for a scene parsing network architecture is an important task as a network with optimal parameters produces the best performance. A manual selection of parameter values requires expert domain knowledge of the intrinsic structure and extensive trial and error, which does not guarantee optimal results. An automatic search of the optimal parameters is desirable to harness the full potential of a scene parsing framework. The network architecture needs to be evaluated with various combinations for several parameters to achieve optimum performance. We propose a stacked three-level deep context-adaptive end-to-end network. The end-to-end network architecture extracts visual features, and contextual features, and optimally integrates both features in three logical levels for scene parsing. Particle Swarm Optimization (PSO) algorithm has been used to efficiently search for optimal solutions. Using PSO, we set an optimal set of parameters in all three levels to obtain optimum performance. The PSO aims to optimize the network globally by considering all hyperparameters in the network to achieve the best performance.

关键词： image Parsing Semantic Segmentation Scene Parsing PSO Deep Learning

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A Full-Reference Quality Assessment Metric for Fine-Grained Compressed images

A Full-Reference Quality Assessment Metric for Fine-Grained ...

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IEEE International conference on visual communications and image processing (VCIP) - visual communications in the Era of AI and Limited Resources

作者： Zhang, Zicheng Sun, Wei Min, Xiongkuo Wang, Tao Lu, Wei Zhai, Guangtao Shanghai Jiao Tong Univ Inst Image Commun & Network Engn Shanghai Peoples R China

ISBN: (纸本)9781728185514

Compressed image quality assessment (IQA) has been a crucial part of a wide range of image services such as storage and transmission. Due to the effect of different bit rates and compression methods, the compressed images usually have different levels of quality. Nowadays, the mainstream full-reference (FR) metrics are effective to predict the quality of compressed images at coarse-grained levels, however, they may perform poorly when quality differences of the compressed images are quite subtle. To better improve the Quality of Experience (QoE) and provide useful guidance for compression algorithms, we propose an FR-IQA metric for fine-grained compressed images, which estimates the image quality by analyzing the difference of structure and texture. Our metric is mainly validated on the fine-grained compression IQA (FGIQA) database and is tested on other commonly used compression IQA databases as well. The experimental results show that our metric outperforms mainstream FR-IQA metrics on the fine-grained compression IQA database and also obtains competitive performance on the coarse-grained compression IQA databases.

关键词： compressed image full-reference image quality assessment fine-grained

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AutoDerain: Memory-efficient Neural Architecture Search for image Deraining

AutoDerain: Memory-efficient Neural Architecture Search for ...

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IEEE International conference on visual communications and image processing (VCIP) - visual communications in the Era of AI and Limited Resources

作者： Fu, Jun Hou, Chen Chen, Zhibo Univ Sci & Technol China CAS Key Lab Technol Geospatial Informat Proc & Ap Hefei 230027 Peoples R China

ISBN: (纸本)9781728185514

Learning-based image deraining methods have achieved remarkable success in the past few decades. Currently, most deraining architectures are developed by human experts, which is a laborious and error-prone process. In this paper, we present a study on employing neural architecture search (NAS) to automatically design deraining architectures, dubbed AutoDerain. Specifically, we first propose an U-shaped deraining architecture, which mainly consists of residual squeeze-andexcitation blocks (RSEBs). Then, we define a search space, where we search for the convolutional types and the use of the squeeze-and-excitation block. Considering that the differentiable architecture search is memory-intensive, we propose a memory-efficient differentiable architecture search scheme (MDARTS). In light of the success of training binary neural networks, MDARTS optimizes architecture parameters through the proximal gradient, which only consumes the same GPU memory as training a single deraining model. Experimental results demonstrate that the architecture designed by MDARTS is superior to manually designed derainers.

关键词： memory-efficient differentiable architecture search image deraining

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