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Identification of various food residuals on denim based on hyperspectral imaging system and combination optimal strategy

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Artificial Intelligence in Agriculture 2021年第1期5卷 125-132页

作者： Yuzhen Chen Ziyi Xu Wencheng Tang Menghan Hu Douning Tang Guangtao Zhai Qingli Li Shanghai Key Laboratory of Multidimensional Information Processing School of Communication&Electronic EngineeringEast China Normal UniversityShanghai 200062China School of Statistics East China Normal UniversityShanghai 200062China Institute of Image Communication and Information Processing Shanghai Jiao Tong UniversityShanghai 200240China McCormick School of Engineering Northwestern UniversityEvanstonILUnited States

As the science and technology develop,crime methods and scenes have become increasingly complex and *** evidence analysis has become amore and more important criminal investigation technology and liquid is the main form of trace *** can provide not only energy,but clues to solve *** this study,we build a hyperspectral imaging system to detect liquid residue traces,including apple juice,coffee,cola,milk and tea,on denims with light,middle and dark *** obtained hyperspectral images are first subjected to spectral calibration and hyperspectral data ***,Partial Least Squares(PLS)is applied to select the informative wavelengths from the preprocessed *** modeling phase,the combination optimal strategy,support vector machine(SVM)combined with random forest(RF),is developed to establish classification *** experimental results demonstrate that the combination optimal model can achieve TPR,FPR,Precision,Recall,F1,and AUC of 83.5%,2.30%,79.7%,83.5%,81.6%,and 94.7%for classifying fabrics contaminated by various food *** respect to the classification of liquid and fabric types,the combination optimalmodel also yields satisfactory classification *** future work,wewill expand the types of liquid,and make appropriate adjustment to algorithms for improving the robustness of classification *** research may play a positive role in the construction of a harmonious society.

关键词： Hyperspectral imaging Food residual on denim Combination optimal strategy Variable selection Forensic application

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A Framework for Generating an ICGA from a Fundus image using GAN 19

A Framework for Generating an ICGA from a Fundus Image using...

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19th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and information Technology, ECTI-CON 2022

作者： Tanachotnarangkun, Pumipach Marukatat, Sanparith Kumazawa, Itsuo Chanvarasuth, Pisit Ruamviboonsuk, Paisan Amornpetchsathaporn, Anyarak Chainakul, Methaphon Kaothanthong, Natsuda Thammasat University Sirindhorn International Institute of Technology Pathum Thani Thailand AI Research Group NECTEC Image Processing and Understanding Team Pathum Thani Thailand Tokyo Institute of Technology Department of Information and Communication Engineering Tokyo Japan Rajavithi Hospital Department of Ophthalmology Bangkok Thailand

ISBN: (纸本)9781665485845

Fundus image is an initial method for examining eye diseases. Due to its visualization limitation, diseases such as wet macular degeneration (wet amd) and polypoidal choroidal vasculopathy (PCV) looks similar. An indocyanine green angiography (ICGA) was introduced to cope with this limitation, but the obtaining process is invasive and complicated. To avoid unnecessary painful processes for non-PCV patients, this work presents a framework to generate an ICGA from an input fundus image for a PCV screening. Given pairs of high-resolution fundus and ICGA images, they are pre-processed to be trained using a generative adversarial network (GAN). The generated patches are combined and post-processed to obtained the generated ICGA image. Several experiments were conducted to acquire suitable pre-processing, augmentation, post-processing function of the proposed framework. The image quality assessment (IQA) called PSNR was employed to assess the generated images. The average PSNR of max and mean function of the generated ICGA when comparing with the original colored fundus is 16.35, while the one with the original ICGA is 16.25. Although the PSNR score is less than the acceptable range, the visual output shows the sign of the polyp that can be applied in further classification tasks. © 2022 IEEE.

关键词： Generative adversarial networks

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Multi-Scale Local and Global Feature Fusion for Blind Quality Assessment of Enhanced images

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IEEE Transactions on Circuits and Systems for Video Technology 2025年

作者： Cao, Jingchao Zhang, Shuai Liu, Yutao Gao, Feng Gu, Ke Zhai, Guangtao Dong, Junyu Kwong, Sam Ocean University of China School of Computer Science and Technology Qingdao266100 China Beijing University of Technology Faculty of Information Technology Beijing100124 China Shanghai Jiao Tong University Institute of Image Communication and Information Processing Shanghai200240 China Lingnan University Department of Computing and Decision Science Hong Kong

image enhancement plays a crucial role in computer vision by improving visual quality while minimizing distortion. Traditional methods enhance images through pixel value transformations, yet they often introduce new distortions. Recent advancements in deep learning-based techniques promise better results but challenge the preservation of image fidelity. Therefore, it is essential to evaluate the visual quality of enhanced images. However, existing quality assessment methods frequently encounter difficulties due to the unique distortions introduced by these enhancements, thereby restricting their effectiveness. To address these challenges, this paper proposes a novel blind image quality assessment (BIQA) method for enhanced natural images, termed multi-scale local feature fusion and global feature representation-based quality assessment (MLGQA). This model integrates three key components: a multi-scale Feature Attention Mechanism (FAM) for local feature extraction, a Local Feature Fusion (LFF) module for cross-scale feature synthesis, and a Global Feature Representation (GFR) module using Vision Transformers to capture global perceptual attributes. This synergistic framework effectively captures both fine-grained local distortions and broader global features that collectively define the visual quality of enhanced images. Furthermore, in the absence of a dedicated benchmark for enhanced natural images, we design the Natural image Enhancement Database (NIED), a largescale dataset consisting of 8,581 original images and 102,972 enhanced natural images generated through a wide array of traditional and deep learning-based enhancement techniques. Extensive experiments on NIED demonstrate that the proposed MLGQA model significantly outperforms current state-of-the-art BIQA methods in terms of both prediction accuracy and robustness. © 1991-2012 IEEE.

关键词： image quality

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A Multi-input Deep Neural Network Framework for Non-invasive Detection of Anemia using Finger Nail images

A Multi-input Deep Neural Network Framework for Non-invasive...

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International Conference on Biosignals, images and Instrumentation (ICBSII)

作者： Krithika S Ajay Kumar Reddy Poreddy Thunakala Balakrishna Priyanka Kokil Department of Computer Science and Engineering Amrita Vishwa Vidyapeetham Chennai India Department of Electronics and Communication Engineering Advanced Signal and Image Processing (ASIP) Lab Indian Institute of Information Technology Design and Manufacturing Kancheepuram Chennai

ISBN: (数字)9798350350951

ISBN: (纸本)9798350350968

Anemia, characterized by a deficiency in red blood corpuscles or hemoglobin, poses a significant global health challenge, particularly affecting vulnerable populations. Traditional diagnostic methods often involve invasive procedures, posing challenges in resource-limited settings. This study aims to explore non-invasive anemia detection using fingernail images and convolutional neural networks (CNNs) as a promising alternative to conventional diagnostic approaches. The study utilizes a dataset of fingernail images collected from hospitals in Ghana, comprising both anemic and non-anemic cases. The dataset undergoes preprocessing, including selective enhancement of red components, conversion to the CIELAB color space, and feature extraction. A multi-input Deep Neural Network (DNN) framework employing pre-trained CNNs is proposed for anemia classification. The pre-trained CNN architectures include EfficientNet B1, EfficientNet B4, and MobileNet V3. The framework’s performance was assessed using two methodologies: The first involved random shuffling of the dataset, followed by division into training, testing, and validation sets, with evaluation metrics including Accuracy, Precision, F1 scores, and a Confusion Matrix. The second employed five-fold cross-validation, measured using accuracy. The evaluation of the proposed DNN framework using both of the methodologies indicates that EfficientNet B4 achieved the highest testing accuracy (97.87%), precision (97.88%), recall (97.87%), and F1 score (97.88%) and a cross-validation accuracy of 97.37% for the first and second methodologies respectively making it best fit for the proposed DNN framework. The findings demonstrate that the proposed framework yields promising results, especially under the second approach, and opens avenues for further exploration in transfer learning, fine-tuning of deep neural networks for multi-input feature integration, and cross-validation.

关键词： Training Accuracy Transfer learning Sociology Nails Quality assessment Convolutional neural networks

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Dataset and Metric for Quality Assessment of HDR Tone Mapping: Detail Visibility, Color Naturalness, and Overall Quality

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IEEE Transactions on Multimedia 2025年

作者： Jiang, Qiuping Li, Xiwen Wang, Xinyi Wang, Zhihua Zhai, Guangtao Ningbo University School of Information Science and Engineering Ningbo315211 China Hangzhou DtDream Science and Technology Ltd. Hangzhou310024 China City University of Hong Kong Department of Computer Science Kowloon999077 Hong Kong Shanghai Jiao Tong University Institute of Image Communication and Information Processing Shanghai200240 China

Tone-Mapping Operators (TMOs) aim at converting high dynamic range (HDR) images into standard dynamic range (SDR) ones that are suitable for being displayed on standard screens. As the visual quality of tone-mapped image (TMI) is paramount, conducting quality assessment of TMIs becomes crucial. Despite the growing body of research on TMI quality assessment, the existing metrics are often limited to a narrow selection of hand-picked examples generated by a restricted range of TMOs. Consequently, their ability of generalizing to the wide array of TMIs encountered in practical scenarios remains unclear. Moreover, the quality degradation in practical TMIs can be intricate, diverse, and complex. To overcome these limitations, we construct so far the largest subjective-annotated TMI quality assessment dataset which comprises a total number of 14,000 TMIs generated by applying 20 representative TMOs to 700 HDR images. The dataset is accompanied by subjective scores that encompass multiple quality dimensions, i.e., TMI quality dataset in terms of Detail visibility, Color naturalness, and overall Quality (TDCQ). In addition, we also design a multi-branch deep neural network tailored to characterize the multi-dimensional quality perception of TMIs, i.e., Color naturalness-, Detail visibility-aware TMI Quality (CDTIQ) metric, allowing for a comprehensive and multifaceted quality assessment of TMIs. Through extensive experiments, we demonstrate the superiority of our proposed metric, showcasing a higher correlation with subjective rating results compared to other relevant no-reference image quality metrics. Our newly constructed TDCQ database and the implementation of our CDTIQ model are available at https://***/zzeu001/CDTIQ. © 1999-2012 IEEE.

关键词： Mapping

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Joint Luminance-Chrominance Learning for image Debanding

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IEEE Transactions on Circuits and Systems for Video Technology 2025年

作者： Chen, Zijian Sun, Wei Jia, Jun Huang, Ru Lu, Fangfang Chen, Ying Min, Xiongkuo Zhai, Guangtao Zhang, Wenjun Shanghai Jiao Tong University Institute of Image Communication and Information Processing Shanghai200240 China East China University of Science and Technology School of Information Science and Engineering Shanghai200237 China Shanghai University of Electric Power College of Computer Science and Technology Shanghai200290 China Alibaba Group Hangzhou311121 China

2=0.58ex Banding is a visually annoying artifact that frequently occurs along the chain of video acquisition, production, distribution, and display, showing a significant need for improvement in many fields. Thus far, efforts on banding removal are mainly knowledge-driven or merely learning on RGB space, which is either limited by domain knowledge or lacks the consideration for banding in chrominance channels. In this work, we propose a unified deep neural network that explicitly disentangles the luminance and chrominance channels, and simultaneously recovers intensity gradients and color discontinuity from detection-free measurement in an end-to-end manner. Our debanding model is comprised of a luminance restoration network (LR-Net) and a chrominance restoration network (CR-Net). Each of them follows an encoder-decoder architecture, where a cascade of residual blocks is employed to exploit hierarchical non-local features in spatial dimensions for more powerful feature representation. Moreover, we investigate the characteristics of banding artifacts and apply specific loss functions to guide the debanding in different channels, thus boosting the restoration performance. Both qualitative and quantitative experiments show that our model significantly surpasses the existing method in terms of all 7 metrics. Ultimately, our network trained on simulated data exhibits good adaptiveness under various compression scenarios, which further demonstrates the effectiveness of the proposed model. © 2025 IEEE.

关键词： Restoration

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LMM-VQA: Advancing Video Quality Assessment with Large Multimodal Models

arXiv

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arXiv 2024年

作者： Ge, Qihang Sun, Wei Zhang, Yu Li, Yunhao Ji, Zhongpeng Sun, Fengyu Jui, Shangling Min, Xiongkuo Zhai, Guangtao The Institute of Image Communication and Information Processing Shanghai Jiao Tong University Shanghai200240 China The MoE Key Laboratory of Artificial Intelligence AI Institute Shanghai Jiao Tong University Shanghai200240 China The Huawei Technologies Shanghai200240 China

The explosive growth of videos on streaming media platforms has underscored the urgent need for effective video quality assessment (VQA) algorithms to monitor and perceptually optimize the quality of streaming videos. However, VQA remains an extremely challenging task due to the diverse video content and the complex spatial and temporal distortions, thus necessitating more advanced methods to address these issues. Nowadays, large multimodal models (LMMs), such as GPT-4V, have exhibited strong capabilities for various visual understanding tasks, motivating us to leverage the powerful multimodal representation ability of LMMs to solve the VQA task. Therefore, we propose the first Large Multi-Modal based Video Quality Assessment (LMM-VQA) model, which introduces a novel spatiotemporal visual modeling strategy for quality-aware feature extraction. Specifically, we first reformulate the quality regression problem into a question and answering (Q&A) task and construct Q&A prompts for VQA instruction tuning. Then, we design a spatiotemporal vision encoder to extract spatial and temporal features to represent the quality characteristics of videos, which are subsequently mapped into the language space by the spatiotemporal projector for modality alignment. Finally, the aligned visual tokens and the quality-inquired text tokens are aggregated as inputs for the large language model (LLM) to generate the quality score as well as the quality level. Extensive experiments demonstrate that LMM-VQA achieves state-of-the-art performance across five VQA benchmarks, exhibiting an average improvement of 5% in generalization ability over existing methods. Furthermore, due to the advanced design of the spatiotemporal encoder and projector, LMM-VQA also performs exceptionally well on general video understanding tasks, further validating its effectiveness. Our code will be released at https://***/Sueqk/LMM-VQA. © 2024, CC BY.

关键词： Video streaming

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No-Reference Point Cloud Quality Assessment via Graph Convolutional Network

arXiv

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arXiv 2024年

作者： Chen, Wu Jiang, Qiuping Zhou, Wei Shao, Feng Zhai, Guangtao Lin, Weisi School of Information Science and Engineering Ningbo University Ningbo315211 China School of Computer Science and Informatics Cardiff University Cardiff United Kingdom Institute of Image Communication and Information Processing Shanghai Jiao Tong University Shanghai200240 China School of Computer Science and Engineering Nanyang Technological University Singapore

Three-dimensional (3D) point cloud, as an emerging visual media format, is increasingly favored by consumers as it can provide more realistic visual information than two-dimensional (2D) data. Similar to 2D plane images and videos, point clouds inevitably suffer from quality degradation and information loss through multimedia communication systems. Therefore, automatic point cloud quality assessment (PCQA) is of critical importance. In this work, we propose a novel no-reference PCQA method by using a graph convolutional network (GCN) to characterize the mutual dependencies of multi-view 2D projected image contents. The proposed GCN-based PCQA (GC-PCQA) method contains three modules, i.e., multi-view projection, graph construction, and GCN-based quality prediction. First, multi-view projection is performed on the test point cloud to obtain a set of horizontally and vertically projected images. Then, a perception-consistent graph is constructed based on the spatial relations among different projected images. Finally, reasoning on the constructed graph is performed by GCN to characterize the mutual dependencies and interactions between different projected images, and aggregate feature information of multi-view projected images for final quality prediction. Experimental results on two publicly available benchmark databases show that our proposed GC-PCQA can achieve superior performance than state-of-the-art quality assessment metrics. The code will be available at: https://***/chenwuwq/GC-PCQA. Copyright © 2024, The Authors. All rights reserved.

关键词： Convolution

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Analysis of Video Quality Datasets via Design of Minimalistic Video Quality Models

arXiv

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arXiv 2023年

作者： Sun, Wei Wen, Wen Min, Xiongkuo Lan, Long Zhai, Guangtao Ma, Kede The Institute of Image Communication and Information Processing Shanghai Jiao Tong University Shanghai200240 China The Department of Computer Science City University of Hong Kong Kowloon Hong Kong The Institute for Quantum Information State Key Laboratory of High Performance Computing College of Computer Science and Technology National University of Defense Technology Changsha410073 China The Institute of Image Communication and Information Processing The MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University Shanghai200240 China The Department of Computer Science The Shenzhen Research Institute City University of Hong Kong Kowloon Hong Kong

Blind video quality assessment (BVQA) plays an indispensable role in monitoring and improving the end-users’ viewing experience in various real-world video-enabled media applications. As an experimental field, the improvements of BVQA models have been measured primarily on a few human-rated VQA datasets. Thus, it is crucial to gain a better understanding of existing VQA datasets in order to properly evaluate the current progress in BVQA. Towards this goal, we conduct a first-of-its-kind computational analysis of VQA datasets via designing minimalistic BVQA models. By minimalistic, we restrict our family of BVQA models to build only upon basic blocks: a video preprocessor (for aggressive spatiotemporal downsampling), a spatial quality analyzer, an optional temporal quality analyzer, and a quality regressor, all with the simplest possible instantiations. By comparing the quality prediction performance of different model variants on eight VQA datasets with realistic distortions, we find that nearly all datasets suffer from the easy dataset problem of varying severity, some of which even admit blind image quality assessment (BIQA) solutions. We additionally justify our claims by comparing our model generalization capabilities on these VQA datasets, and by ablating a dizzying set of BVQA design choices related to the basic building blocks. Our results cast doubt on the current progress in BVQA, and meanwhile shed light on good practices of constructing next-generation VQA datasets and models. Code is available at https://***/sunwei925/***. Copyright © 2023, The Authors. All rights reserved.

关键词： Deep neural networks

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Understanding the Robustness of 3D Object Detection with Bird'View Representations in Autonomous Driving

Understanding the Robustness of 3D Object Detection with Bir...

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2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2023

作者： Zhu, Zijian Zhang, Yichi Chen, Hai Dong, Yinpeng Zhao, Shu Ding, Wenbo Zhong, Jiachen Zheng, Shibao Institute of Image Communication and Network Engineering Shanghai Jiao Tong University China Institute for Ai Tsinghua University BNRist Center Thbi Lab Dept. of Comp. Sci. and Tech. China School of Computer Science and Technology Anhui University Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Information Materials and Intelligent Sensing Laboratory of Anhui Province China Saic Motor Ai Lab Zhongguancun Laboratory China

ISBN: (纸本)9798350301298

3D object detection is an essential perception task in autonomous driving to understand the environments. The Bird's-Eye-View (BEV) representations have significantly improved the performance of 3D detectors with camera inputs on popular benchmarks. However, there still lacks a systematic understanding of the robustness of these vision-dependent BEV models, which is closely related to the safety of autonomous driving systems. In this paper, we evaluate the natural and adversarial robustness of various representative models under extensive settings, to fully understand their behaviors influenced by explicit BEV features compared with those without BEV. In addition to the classic settings, we propose a 3D consistent patch attack by applying adversarial patches in the 3D space to guarantee the spatiotemporal consistency, which is more realistic for the scenario of autonomous driving. With substantial experiments, we draw several findings: 1) BEV models tend to be more stable than previous methods under different natural conditions and common corruptions due to the expressive spatial representations;2) BEV models are more vulnerable to adversarial noises, mainly caused by the redundant BEV features;3) Camera-LiDARfusion models have superior performance under different settings with multi-modal inputs, but BEV fusion model is still vulnerable to adversarial noises of both point cloud and image. These findings alert the safety issue in the applications of BEV detectors and could facilitate the development of more robust models. © 2023 IEEE.

关键词： Autonomous driving

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