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Colorization of Thermal Facial images into Visible Facial image Using RGB-GAN 1

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8th International conference on Computer Vision and image processing (CVIP)

作者： Bhadane, Dnyaneshwar Chakraborty, Soumendu Indian Inst Informat Technol Lucknow Dept Comp Sci Lucknow 226002 Uttar Pradesh India

ISBN: (数字)9783031585357

ISBN: (纸本)9783031585340;9783031585357

images captured by thermal cameras are independent of lighting conditions. However, it can be challenging for human examiners to identify thermal face photos. Facial recognition technology enables automatic identification or verification of individuals in digital images or video frames extracted from video sequences. There are multiple methods employed by facial recognition systems, but they typically involve comparing the features extracted from a specific image with those stored in a database. This technology finds application in various areas, including access control and identification systems. It is worth noting that facial features can exhibit unique characteristics specific to an individual throughout their lifetime. In this paper, the process of colorizing thermal facial images into the visible spectrum based on Cycle GAN is undertaken. There are many variations of the GAN but to translate or map from the one domain image into another domain image the cycle GAN fits with its application. CycleGAN aims to acquire knowledge of the relationship between two distinct image collections originating from separate domains, each possessing unique styles, textures, or visual attributes. The RGB-GAN which is proposed in this paper refers to the red, green, and blue channel generative adversarial network that individually takes the independent images in the thermal format to colorize in the independent domain channel network-merging three generated channel results combined to make one RGB-colored image. One more generator network involves identifying and comparing the result with the original visible colored image to give feedback to the network. At last after training the final model, the classification task involves generating and classifying the correct person out group of persons when the thermal image is given as input. The output includes face recognition accuracy of generated images, comparative analysis with protocols and state-of-the-art techniques.

关键词： image translation Cycle GAN Colorization Thermal images Facial data

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Machine Vision-based Tool Location and Wear Monitoring in Micro turning: Optical Setup and image processing Approaches 1

Machine Vision-based Tool Location and Wear Monitoring in Mi...

引用

1st International conference on Innovations in communications, Electrical and Computer Engineering, ICICEC 2024

作者： Shalupriya, M. Indira, K.P. Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Electronics and Communication Engineering Chennai India Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Nanobiomaterials Chennai India

ISBN: (纸本)9798350376517

In micro turning, tool placement, wear, and breakage are crucial as they affect precision and surface finish. Flank wear is problematic since it impacts dimensional accuracy, and current methods that rely on CNC encoder inputs and radius compensation are insufficient for micro turning due to the small tool sizes involved. Detecting flank wear in micro turning is difficult as the tools are often too small for visual inspection, making monitoring at micron levels a challenge. This research investigates using machine vision to monitor tool position and flank wear in micro turning. The optical system, comprising a 12X super zoom lens and 3MP CMOS image sensor, is assessed alongside three lighting techniques: direct, indirect, and backlighting. Preliminary image analysis, using Gaussian filters and histogram equalization, indicates lighting's importance. Early results, comparing new and worn tools using exclusive operators, are promising, but further image enhancements are necessary for accurate classification and wear detection. © 2024 IEEE.

关键词： Equalizers

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Integrating image processing and Convolution Neural Networks for Water Quality Detection 6th

Integrating Image Processing and Convolution Neural Networks...

引用

6th International conference on communications and Cyber-Physical Engineering, ICCCE 2023

作者： Patil, Jayashree S. Mailaram, Akhila Basa, Pavani Naga Kumari Sai Sravya, A. Yadam, Banvita Shaikpet Telangana Hyderabad India

ISBN: (纸本)9789819971367

This study proposes a water quality detection system that combines image processing and Convolutional Neural Network (CNN) models to accurately identify and classify water quality based on visual features. The quality of water has significant impacts on a variety of areas, such as the survival of aquatic creatures, agricultural irrigation, and human health. It is also a crucial factor in global economic development. Therefore, there is a great importance to develop a monitoring system that is simple, quick, low-cost, and reliable. There are certain drawbacks associated with using automatic water quality sensors, including their high cost and the challenges involved in maintaining them. Water colour is a crucial indicator of water quality in lakes or ponds, as it reflects the unique characteristics of the water. The proposed system captures images of water bodies and extracts features such as color, texture, and turbidity, using image processing techniques. These features are used as input to the trained CNN models for water quality prediction and classification. The proposed system is evaluated using a real-world water quality dataset, and the results demonstrate that the proposed system achieves great precision in water quality detection. The system's ability to detect visual anomalies in water quality can provide early warning signals for potential health and environmental hazards. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Water quality

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AG-Mono: A Monocular Dataset for Unmanned Air Vehicles 30

AG-Mono: A Monocular Dataset for Unmanned Air Vehicles

引用

30th IEEE Signal processing and communications Applications conference (SIU)

作者： Simsek, Bugra Giden, Ibrahim Halil Bilge, Hasan Sakir ASELSAN Girisimcilik Merkezi Ankara Turkey Gazi Univ Ankara Turkey

ISBN: (数字)9781665450928

ISBN: (纸本)9781665450928

One of the most important information needed while performing unmanned aerial vehicles (UAV) operations is about the platform location and the environment. Such platforms mostly use GNSS signals outdoors. However, in indoor areas where GNSS signals cannot be received or in situations where signals are jammed, it is not possible to obtain location information using these signals. For that reason, alternative navigation systems have become so crucial. One of the most preferred systems among navigation technologies is the visual simultaneous localization and mapping (vSLAM) method performed using RGB cameras on the UAVs. In this study, an open monocular image dataset called AG-Mono was created and published online to test the performance of vSLAM algorithms. This dataset was created at three different exposure times using a handheld platform, and it includes video sequences at 640x480 image resolution. The experimental area where the images were created is a closed corridor with 16.5 x 4.5 meters and four sharp corners.

关键词： Unmanned Aerial Vehicles (UAVs) visual SLAM (vSLAM) Monocular image Dataset

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Learned Progressive image Compression with Spatial Autoregression

Learned Progressive Image Compression with Spatial Autoregre...

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2023 IEEE International conference on visual communications and image processing, VCIP 2023

作者： Tian, Wenxin Li, Shaohui Dai, Wenrui Lu, Cewu Hu, Weisheng Zhang, Lin Du, Junfeng Xiong, Hongkai Shanghai Jiao Tong University China Tsinghua Shenzhen International Graduate School China Shanghai Cogent Technologies Co. Ltd. China Limited China

ISBN: (纸本)9798350359855

Entropy modeling plays an important role in estimating the rates of latent representations and optimizing the rate-distortion performance for learned image compression. Autoregression modules are demonstrated to eliminate spatial/channel-wise redundancy of latent representations in fixed-rate learned image compression. However, it cannot be efficiently achieved in progressive coding due to the high computational complexity raised by element-wise probability prediction. In this paper, we propose a learned progressive image compression method that enables spatial autoregression for entropy modeling. Specifically, we develop a novel codeword alignment scheme to prevent coding redundancy and achieve efficient autoregression of latent representations in different quality layers. Consequently, conditional probability estimation for the latent prediction can be achieved based on spatial autoregression in a layer-wise manner. We further extend the proposed method by dead-zone quantizers to obtain promoted rate-distortion performance. The proposed method is a successful attempt to enable spatial autoregression in learned progressive coding and further bridge the performance gap with fixed-rate models. Experimental results show that it outperforms traditional methods such as JPEG and BPG, as well as recent fine-grained learned progressive coding models DPICT and PLONQ in terms of rate-distortion performance. © 2023 IEEE.

关键词： image compression

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image Super Resolution Reconstruction Algorithm Based on Multiple Prior Constraints

Image Super Resolution Reconstruction Algorithm Based on Mul...

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International conference on communications, Signal processing, and Systems, CSPS 2023

作者： Liu, Ting Wang, Kun College of Marine Electrical Engineering Dalian Maritime University Dalian116026 China

ISBN: (纸本)9789819975396

High spatial resolution is necessary for several applications such as visual inspection. However, the conflict between resolution and image distance limits the applications of image devices. In this paper, a super-resolution framework with multiple priors is proposed. Firstly, the directional generalized total variational and the non-local self-similar constraint are incorporated to enhance image texture details and smooth edge effects. Especially, an adaptive Gaussian kernel is used to better descript the non-local prior. Secondly, the proposed multi-constraint problem is solved by the alternate direction multiplier method. Generally, a large number of qualitative and quantitative results demonstrated the effectiveness and superiority of our method over traditional methods. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： image texture

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Complex-valued SAR image Compression: A Novel Approach for Amplitude and Phase Recovery

Complex-valued SAR Image Compression: A Novel Approach for A...

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2023 IEEE International conference on visual communications and image processing, VCIP 2023

作者： Maharjan, Paras Li, Zhu University of Missouri Kansas City United States

ISBN: (纸本)9798350359855

Synthetic Aperture Radar (SAR) is an active and coherent imaging system that utilizes radio waves to illuminate the Earth's surface, generating complex-valued images of the ground. It is widely employed in environmental monitoring and surveillance due to its ability to function effectively in diverse weather and lighting conditions, including day and night. SAR achieves this through the emission of coherent radio frequency signals and the capture of slant range and azimuth information. These details are stored as a complex signal in the form of Inphase (I) and Quadrature (Q) components, known as complex-valued SAR. The complex values (I/Q) can be transformed into amplitude and phase information. SAR images have a substantial size and a wide dynamic range, which pose challenges for storage and transmission. Thus, the adoption of compression techniques becomes essential to manage the large size of SAR images. In contrast to existing methods that primarily compress amplitude information, our study presents a novel pipeline to compress complex-valued SAR images using a prediction-recovery framework that utilizes a conventional image compression algorithm, High-Efficiency Video Codec (HEVC), to encode the original image to a bitstream. Subsequently, the framework decodes a prediction of the I/Q channels and then recovers the phase and amplitude via SARRecoveryNet, a deep-learning-based network, to effectively remove compression artifacts and recover information that suits different applications. By directly compressing the I/Q values as a predictor, and recovery via a deep learning module, our method preserves both the amplitude and phase information of the SAR image as part of the loss function in the training. Experimental results demonstrate that our proposed approach significantly enhances complex-valued SAR image compression in terms of rate-distortion (RD) performance and visual perception. The code will be made available on GitHub after the paper is accepted. © 20

关键词： Synthetic aperture radar

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Efficient Lightweight Attention Based Learned image Compression

Efficient Lightweight Attention Based Learned Image Compress...

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2023 IEEE International conference on visual communications and image processing, VCIP 2023

作者： He, Ziyang Luo, Lei Zhang, Le Guo, Hongwei Zhu, Ce Chongqing University of Posts and Telecommunications School of Communications and Information Engineering Chongqing400065 China University of Electronic Science and Technology of China School of Information and Communication Engineering Chengdu611731 China Honghe University School of Engineering Yunnan661199 China

ISBN: (纸本)9798350359855

The CNN-based end-To-end learned image compression methods have already achieved a significant improvement in terms of coding efficiency. Moreover, with the capability of modeling long-range global correlation, the transformer-based image compression has further elevated the coding efficiency to outperform the latest Versatile Video Coding (VVC) standard. Nonetheless, the high computational burden of the self-Attention mechanism in the transformer design present a significant obstacle for practical applications. To address this concern, we propose a relatively low-complexity end-To-end learned image compression approach by integrating an efficient lightweight attention module, which effectively mitigates the computational overhead associated with self-Attention in the transformer design. The experimental results demonstrate that the proposed method achieves better RD performance than VVC. Furthermore, as compared to the prevailing state-of-The-Art transformer-based approach, our method accelerates the coding speed by over 6 times while maintaining comparable coding efficiency. © 2023 IEEE.

关键词： image compression

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Knowledge-Enhanced Medical visual Question Answering: A Survey 6th

Knowledge-Enhanced Medical Visual Question Answering: A Surv...

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6th Asia Pacific Web (APWeb) and Web-Age Information Management (WAIM) Joint International conference on Web and Big Data (APWeb-WAIM)

作者： Wang, Haofen Du, Huifang Tongji Univ Shanghai Peoples R China

ISBN: (纸本)9789819913534;9789819913541

Medical visual Question Answering (Med-VQA) is a task in the field of Artificial Intelligence where a medical image is given with a related question, and the task is to provide an accurate answer to the question. It involves the integration of computer vision, natural language processing, and medical domain knowledge. Furthermore, incorporating medical knowledge in Med-VQA can improve the reasoning ability and accuracy of the answers. While knowledge-enhanced visual Question Answering (VQA) in the general domain has been widely researched, medical VQA requires further examination due to its unique features. In the paper, we gather information on and analyze the current publicly accessible Med-VQA datasets with external knowledge. We also critically review the key technologies combined with knowledge in Med-VQA tasks in terms of the advancements and limitations. Finally, we discuss the existing challenges and future directions for Med-VQA.

关键词： Medical visual question answering Medical knowledge Computer vision Natural language processing

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Transformer-based Deep Frame Rate up Conversion for Hybrid High-Efficiency Video Coding

Transformer-based Deep Frame Rate up Conversion for Hybrid H...

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2023 IEEE International conference on visual communications and image processing, VCIP 2023

作者： Nguyen, Van Thang Hung Lu, Manh VinUniversity Viettel High Technology Industries Corporation College of Engineering and Computer Science Hanoi Viet Nam Viettel High Technology Industries Corporation Camera Center Hanoi Viet Nam

ISBN: (纸本)9798350359855

Mainstream state-of-The-Art video compression standards such as High-Efficiency Video Coding (HEVC/H.265) and Versatile Video Coding (VVC/H.266) are block-based coding. On the other hand, neural video compression networks have obtained impressive results recently. However, neural video compression is not practical for use due to its high computations. In this paper, we propose a hybrid approach that exploits a deep frame rate up-conversion network to improve the coding efficiency of existing video codecs such as HEVC. We analyze a coding scenario of a combination between the Frame Rate Up-Conversion (FRUC) network and the random access coding configuration. In addition, we show that the FRUC network trained on compressed images outperforms the model trained on non-compressed images for the video frame interpolation task. The proposed framework skips encoding odd frames in the display order of original video sequences at the encoder side and reconstructs them at the decoder side via the FRUC network. Experimental results show the proposed framework improves the coding efficiency of HEVC 4.2 % for random access configuration. © 2023 IEEE.

关键词： image compression

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