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A Learning-based Approach for Martian image Compression

A Learning-based Approach for Martian Image Compression

引用

IEEE visual communications and image processing (VCIP)

作者： Qing Ding Mai Xu Shengxi Li Xin Deng Qiu Shen Xin Zou Beihang University Beijing China Nanjing University Nanjing China Beijing Institute of Spacecraft System Engineering Beijing China

ISBN: (纸本)9781665475938

For the scientific exploration and research on Mars, it is an indispensable step to transmit high-quality Martian images from distant Mars to Earth. image compression is the key technique given the extremely limited Mars-Earth bandwidth. Recently, deep learning has demonstrated remarkable performance in natural image compression, which provides a possibility for efficient Martian image compression. However, deep learning usually requires large training data. In this paper, we establish the first large-scale high-resolution Martian image compression (MIC) dataset. Through analyzing this dataset, we observe an important non-local self-similarity prior for Marian images. Benefiting from this prior, we propose a deep Martian image compression network with the non-local block to explore both local and non-local dependencies among Martian image patches. Experimental results verify the effectiveness of the proposed network in Martian image compression, which outperforms both the deep learning based compression methods and HEVC codec.

关键词： Deep learning Earth Microwave integrated circuits Mars image coding Codecs visual communication

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Relevant Tag Extraction Based on image visual Content 1st

Relevant Tag Extraction Based on Image Visual Content

引用

1st International conference on Applied Intelligence, ICAI 2023

作者： Fazal, Nancy Fränti, Pasi School of Computing University of Eastern Finland Joensuu Finland

ISBN: (纸本)9789819708260

Social media web services like Flickr allow users to share and freely annotate images with textual tags. These tags play a crucial part for text-based social image retrieval and browsing tasks. However, these tags are usually irrelevant and incomplete which limits their effectiveness and use. One fundamental problem is to interpret the relevance of tags with respect to the image visual content. Existing solutions have targeted either image visual content or user contributed tags separately to address the issue. Our proposed method Tag-tag, however, combines both aspects and exploit their semantic relationship. We use state of the art pretrained machine learning models in Tensorflow for object detection and NLP (Natural Language processing) for semantic analysis. Our experiments on a dataset of 219 randomly collected Flickr images demonstrates the applicability of our method. images with missing tags were excluded from the experiments. We identified two reasons where relevant tag was not found as: (1) relevant tag itself was missing from user contributed tag list, (2) relevant tag got skipped because it was either not found in the Wordnet dictionary or has no pretrained vector in word2vec-google-news-300 model. We identify this as one limitation of the proposed method. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Object detection

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SEPL-Net: A Semantics-Enhanced Pseudo Labeling Network for Semi-Supervised image Analysis

SEPL-Net: A Semantics-Enhanced Pseudo Labeling Network for S...

引用

2022 IEEE International conference on Multimedia and Expo, ICME 2022

作者： Xiao, Wenjing Hwang, Kai Chen, Min Li, Xianzhi School of Computer Science and Technology Huazhong University of Science and Technology China School of Data Science Chinese University of Hong Kong Hong Kong The Shenzhen Institute of Artificial Intelligence and Robotics for Society China

ISBN: (数字)9781665485630

ISBN: (纸本)9781665485630

As the mainstream solution for semi-supervised learning (SSL), pseudo-labeling-based approaches have achieved re-markable success. However, an obvious drawback of existing methods is that the valuable semantic relationships among categories are often ignored, thus leading to suboptimal encoded embeddings. To address this, we present a novel Semantics-Enhanced Pseudo Labeling Network, called SEPL-Net, for image analysis in a semi-supervised manner. SEPL-Net explores the prior knowledge of visual similarity between different classes to improve the quality of pseudo label decision making. Particularly, we encode semantic labels combined with the one-hot label to jointly train our network by exploiting their disagreement. To alleviate the difficulty of labeling unlabeled images due to the introduction of semantic labels, we further design different classifiers with differentiated strong augmentation modes to enable cooperative pseudo labeling. Extensive experimental results show that our SEPL-Net outperforms existing SSL methods with the averaged 1.84% accuracy improvement on image classification task. Code is available at https://***/sweetvicky/***. © 2022 IEEE.

关键词： Embeddings

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A Completed Gaussian Extended Binary Pattern for Texture image Classification 23

A Completed Gaussian Extended Binary Pattern for Texture Ima...

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Proceedings of the 2nd International conference on Signal processing, Computer Networks and communications

作者： Xiaochun Xu Weizheng Lin Dingrong Chen Nanling Su Leinuo Tang Changxu Cai College of Computer and Control Engineering Minjiang University China International Digital Economy College Minjiang University China

ISBN: (纸本)9798400716430

Texture image classification is a fundamental and challenging visual task and has wide range of applications. Binary pattern methods play an important role in texture feature extraction due to its ease of implementation and promising performance. To extract completed texture feature representation and improve the classification performance, this paper proposes a completed gaussian extend binary pattern for texture image classification. First, instead of the original pixel value, this paper uses the mean of local range to encode the binary pattern. Second, this paper introduces a novel gaussian sign pattern to fully represent the macro texture structure. Third, to achieve completed texture feature description, this paper presents a completed gaussian extended binary pattern, which combines the novel gaussian sign pattern, the local sign and magnitude pattern extracted from mean-processing texture image. To validate the effectiveness of the proposed completed gaussian extend binary pattern, experimental evaluations are conducted on three test subsets from Outex database. The evaluation results show that the proposed completed gaussian extended binary pattern achieves the state-pf-the-art classification performance.

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image Enhancement and Corrosion Detection for UAV visual Inspection of Pressure Vessels 7th

Image Enhancement and Corrosion Detection for UAV Visual Ins...

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7th International conference on Life System Modeling and Simulation, LSMS 2021, and 7th International conference on Intelligent Computing for Sustainable Energy and Environment, ICSEE 2021

作者： Fei, Zixiang Yang, Erfu Yang, Beiya Yu, Leijian Department of Design Manufacturing and Engineering Management University of Strathclyde GlasgowG1 1XJ United Kingdom

ISBN: (纸本)9789811672064

The condition of a pressure vessel is normally checked by human operators, which have health and safety risks as well as low working efficiency and high inspection cost. visual inspection for pressure vessels can be done by an Unmanned Aerial Vehicle (UAV) with the sensing module. image enhancement techniques and image processing techniques are vital in the UAV inspection of pressure vessels. However, there are several issues to be overcome in the UAV visual inspection of pressure vessels. The images captured by the UAV are of low quality under the cluttered environment due to poor lighting, noises and vibrations caused by the UAV. In this research, a system is developed for UAV visual inspection of pressure vessels using image processing and image enhancement techniques. In the developed system, the input image is captured by the UAV first. Next, efficient image enhancement techniques are applied to the images in order to enhance image qualities. After that, the corrosion part is detected and the percentage of the corrosion area in the entire image is measured. The proposed system has the potential to be implemented for the autonomous correction detection with the image enhancement techniques in UAV visual inspection for the pressure vessels. © 2021, Springer Nature Singapore Pte Ltd.

关键词： image enhancement

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Fast Inter Prediction Mode Decision Method Based On Random Forest For H.266/VVC

Fast Inter Prediction Mode Decision Method Based On Random F...

引用

IEEE visual communications and image processing (VCIP)

作者： Kundan Xie Jianquan Zhou Saiping Zhang Fuzheng Yang School of Telecommunications Engineering Xidian University Xi'an China

ISBN: (纸本)9781665475938

In H.266/VVC, many new tools are introduced in the inter prediction process. These new techniques enable the encoder in H.266/VVC to predict motion vectors more accurately, but inevitably increase the coding complexity. To solve this problem, in this paper, we propose an early termination algorithm for inter prediction based on random forest which is characterized by the information provided by the temporal co-located block and the spatial adjacent block of the current Coding Unit (CU). Specifically, the random forest is used to predict whether the inter prediction process of the current CU will be terminated in advance. Our proposed algorithm is implemented on Fraunhofer Versatile Video Encoder (VVenC). Experimental results have shown that, in the Random Access (RA) mode, the encoding time of VVenC is reduced by 7.71 % on average while Bjontegaard Delta Bit Rate (BDBR) increases by 1.48 %.

关键词： image coding Correlation visual communication Video sequences Bit rate Forestry Prediction algorithms

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Rate Controllable Learned image Compression Based on RFL Model

Rate Controllable Learned Image Compression Based on RFL Mod...

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IEEE visual communications and image processing (VCIP)

作者： Saiping Zhang Luge Wang Xionghui Mao Fuzheng Yang Shuai Wan School of Telecommunications Engineering Xidian University Xi'an China School of Electronics and Information Northwestern Polytechnical University Xi'an China

ISBN: (纸本)9781665475938

In this paper, we propose a rate controllable image compression framework, Rate Controllable Variational Autoencoder (RC-VAE), based on the Rate-Feature-Level (RFL) model established through our exploration on the correlation among target rates, image features and quantization levels. Considering that, when meeting the same target rate, different images should be quantized in different levels, we focus on jointly utilizing the target rate and the extracted features of the image to predict the corresponding quantization level and propose the RFL model. Combining the proposed RFL model with a Hyperprior Continuously Variable Rate (HCVR) image compression network, we further propose the RC-VAE. By controlling information loss in quantization process, the RC-VAE can work at the target rate. Experimental results have demonstrated that one single RC-VAE model can adapt to multiple target rates with higher rate control accuracy and better R-D performance compared with the state-of-the-art rate controllable image compression networks.

关键词： Adaptation models image coding Quantization (signal) Costs Correlation visual communication Process control

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A Lightweight Tire Tread image Classification Network

A Lightweight Tire Tread Image Classification Network

引用

IEEE visual communications and image processing (VCIP)

作者： Fenglei Zhang Da Li Shenghua Li Weili Guan Meng Liu Shandong Jianzhu University Jinan China Monash University elbourne Australia

ISBN: (纸本)9781665475938

VCIP 2022 “Tire pattern image classification based on lightweight network challenge” aims to design lightweight networks that correctly classify tire surface tread patterns and indentation images using less overhead. To this end, we present a novel lightweight tire tread classification network. Concretely, we adopt the ShuffleNet-V2-x0.5 network as our backbone. To reduce the computation complexity, we introduce the Space-To-Depth and Anti-Alias Downsampling modules to pre-process the input image. Moreover, to enhance the classification ability of our model, we adopt the knowledge distillation strategy by considering Vision Transformer as the teacher network. To ensure the robustness of our model, we pre-train it on imageNet and fine-tune the training set of the challenge. Experiments on the challenge dataset demonstrate that our model achieves supe-rior performance, with 99.00% classification accuracy, 25.51M FLOPs, and 0.20M parameters.

关键词： Knowledge engineering Training visual communication Computational modeling Transfer learning Transformers Tires

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Learning from the NN-based Compressed Domain with Deep Feature Reconstruction Loss

Learning from the NN-based Compressed Domain with Deep Featu...

引用

IEEE visual communications and image processing (VCIP)

作者： Liuhong Chen Heming Sun Xiaoyang Zeng Yibo Fan Fudan University Shanghai P.R. China Waseda University Tokyo Japan JST PRESTO Saitama Japan

ISBN: (纸本)9781665475938

To speedup the image classification process which conventionally takes the reconstructed images as input, compressed domain methods choose to use the compressed images without decompression as input. Correspondingly, there will be a certain decline about the accuracy. Our goal in this paper is to raise the accuracy of compressed domain classification method using compressed images output by the NN-based image compression networks. Firstly, we design a hybrid objective loss function which contains the reconstruction loss of deep feature map. Secondly, one image reconstruction layer is inte-grated into the image classification network for up-sampling the compressed representation. These methods greatly help increase the compressed domain image classification accuracy and need no extra computational complexity. Experimental results on the benchmark imageNet prove that our design outperforms the latest work ResNet-41 with a large accuracy gain, about 4.49% on the top-1 classification accuracy. Besides, the accuracy lagging behinds the method using reconstructed images is also reduced to 0.47 %. Moreover, our designed classification network has the lowest computational complexity and model complexity.

关键词： image coding image recognition visual communication Artificial neural networks Linear programming Proposals Computational complexity

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A Deep Learning Based System for Automatic Sorting and Quality Grading of Citrus Fruits

A Deep Learning Based System for Automatic Sorting and Quali...

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International conference on Emerging Trends in Networks and Computer communications (ETNCC)

作者： Nidhi Kundu Sonam Geeta Rani Vijaypal Singh Dhaka Computer Science Sri Karan Narendra Agriculture University Jobner Jaipur India Computer Science Engg. Manipal University Jaipur Jaipur India Computer and Communication Engg. Manipal University Jaipur Jaipur India

ISBN: (数字)9798350353266

ISBN: (纸本)9798350353273

The citrus industry plays a pivotal role in the global agricultural sector, contributing significantly to the economy. Approximately 161 million tonnes of citrus fruits were produced worldwide. One third of the total production is used in processing industry. The price of citrus fruits is highly dependent on its quality parameters such as size, citrus color index, maturity index, diseases etc. Therefore, grading and sorting of citrus fruits based on the quality parameters is demand of the market to maximize the profit. Manual grading and sorting are labour-intensive, time-consuming, and often prone to human error. Automatic grading and sorting of citrus fruits can resolve the above-stated issues. In this paper, we utilize the pattern recognition and image processing potential of deep learning techniques and propose a deep learning based automated system to revolutionize the citrus fruits grading process. The system utilizes Convolutional Neural Networks (CNNs) to extract high-level features from lemon, orange images, allowing for accurate classification based on quality parameters such as size, color, and visual defects. We curated a dataset comprising diverse lemon, orange samples from Kaggle to evaluate the performance of the developed model. Additionally, our system incorporates real-time processing capabilities, ensuring a swift and continuous flow of fruits through the sorting process. Thus, it offers a robust and scalable solution for the citrus industry to enhance profit in the global citrus market.

关键词： Industries Deep learning visualization Accuracy image color analysis Real-time systems Convolutional neural networks Reliability Indexes Sorting

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