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An Efficient Vision Transformer Model for PCB Component Classification 31

An Efficient Vision Transformer Model for PCB Component Clas...

引用

31st IEEE conference on Signal processing and communications Applications (SIU)

作者： Surmeli, Cagri Can Ekenel, Hazim Kemal Istanbul Tech Univ ASELSAN Inc Istanbul Turkiye Istanbul Tech Univ Istanbul Turkiye

ISBN: (纸本)9798350343557

Printed circuit board (PCB) assemblies in everyday electronic devices are mass-produced. As a result of this production volume, a fast way of visual inspection is necessary. An integral part of visual inspection systems is PCB component classification. In this paper, we have explored use of the Vision Transformer (ViT), which is a recent state-of-the-art image classification approach, for PCB component classification. We have employed several ViT models that are available in the literature and also proposed a new compact, efficient, and high performing ViT model, named as ViT-Mini. We have conducted extensive experiments on the FICS-PCB dataset in order to comparatively evaluate the ViT models' performance. The highest achieved accuracy is 99.46% for capacitor and resistor classification and 96.52% for classification of capacitor, resistor, inductor, transistor, diode, and IC. The proposed compact model's performance is comparable with the ones obtained with larger models, which indicates its suitability for real-time applications.

关键词： PCB Components image Classification Vision Transformers

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Low Light RAW image Enhancement Using Paired Fast Fourier Convolution and Transformer

Low Light RAW Image Enhancement Using Paired Fast Fourier Co...

引用

IEEE International conference on visual communications and image processing (VCIP)

作者： Zhang, Yichi Liu, Hengyu Ding, Dandan Ma, Zhan Hangzhou Normal Univ Sch Informat Sci & Technol Hangzhou Peoples R China Nanjing Univ Sch Elect Sci & Engn Nanjing Peoples R China

ISBN: (纸本)9781665475921

Compared to RGB images non-linearly mapped from RAW data through the image Signal Processor (ISP), RAW data are linear to scene radiance and contain more native information, which is better to be modeled in many vision tasks. This work proposes to enhance low-light images in the RAW domain via a cross-scale framework using paired Fast Fourier Convolution (FFC) and Transformer, driving the network to characterize images effectively. The entire framework has three scales to abstract low-level, mid-level, and high-level representations of input images. We embed paired FFC and Transformer in each scale to attain spatial-spectral information extraction and aggregation. Specifically, by transforming features from the spatial domain into the spectral domain with FFC, pixel correlations can be effectively exploited locally and globally, generating representative features for the input image. Immediately, the Transformer using multi-head self-attention mechanism is applied to aggregate and embed important features. Experimental results demonstrate that our method significantly outperforms state-of-the-art low-light enhancement works in both full reference assessment metrics, including PSNR, MPSNR, and SSIM, and no-reference metrics, such as NIMA. Meanwhile, the perceptual quality of the proposed method is more visually pleasing than that of other methods.

关键词： Computational photography low-light transformer fast Fourier convolution

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DISTINGUISHING COMPUTER-GENERATED imageS FROM PHOTOGRAPHIC imageS: A TEXTURE-AWARE DEEP LEARNING-BASED METHOD

DISTINGUISHING COMPUTER-GENERATED IMAGES FROM PHOTOGRAPHIC I...

引用

IEEE International conference on visual communications and image processing (VCIP)

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)9781665475921

With the rapid development of computer graphics and generative models, computers are capable of generating images containing non-existent objects and scenes. Moreover, the computer-generated (CG) images may be indistinguishable from photographic (PG) images due to the strong representation ability of neural network and huge advancement of 3D rendering technologies. The abuse of such CG images may bring potential risks for personal property and social stability. Therefore, in this paper, we propose a dual-stream neural network to extract features enhanced by texture information to deal with the CG and PG image classification task. First, the input images are first converted to texture maps using the rotation-invariant uniform local binary patterns. Then we employ an attention-based texture-aware feature enhancement module to fuse the features extracted from each stage of the dual-stream neural network. Finally, the features are pooled and regressed into the predicted results by fully connected layers. The experimental results show that the proposed method achieves the best performance among all three popular CG and PG classification databases. The ablation study and cross-database validation experiments further confirm the effectiveness and generalization ability of the proposed algorithm.

关键词： Computer-generated photographic image classification dual-stream neural network texture map

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Seq-Masks: Bridging the gap between appearance and gait modeling for video-based person re-identification

Seq-Masks: Bridging the gap between appearance and gait mode...

引用

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

作者： Chang, Zhigang Yang, Zhao Chen, Yongbiao Zhou, Qin Zheng, Shibao Shanghai Jiao Tong Univ Inst Image Proc & Network Engn Shanghai Peoples R China Shanghai Jiao Tong Univ Sch Biomed Engn Shanghai Peoples R China Shanghai Jiao Tong Univ Shanghai Key Lab Scalable Comp & Syst Shanghai Peoples R China

ISBN: (纸本)9781728185514

Video-based person re-identification (Re-ID) aims to match person images in video sequences captured by disjoint surveillance cameras. Traditional video-based person Re-ID methods focus on exploring appearance information, thus, vulnerable against illumination changes, scene noises, camera parameters, and especially clothes/carrying variations. Gait recognition provides an implicit biometric solution to alleviate the above headache. Nonetheless, it experiences severe performance degeneration as camera view varies. In an attempt to address these problems, in this paper, we propose a framework that utilizes the sequence masks (SeqMasks) in the video to integrate appearance information and gait modeling in a close fashion. Specifically, to sufficiently validate the effectiveness of our method, we build a novel dataset named MaskMARS based on MARS. Comprehensive experiments on our proposed large wild video Re-ID dataset MaskMARS evidenced our extraordinary performance and generalization capability. Validations on the gait recognition metric CASIA-B dataset further demonstrated the capability of our hybrid model. Our codes and dataset MaskMARS will be open-sourced as a strong baseline.

关键词： Multi-modal Fusion Video-based Person Re-ID Gait Recognition appearance model

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AN IMPROVED image COMPRESSION SCHEME WITH AN ADAPTIVE PARAMETERS SET IN ENCRYPTED DOMAIN

AN IMPROVED IMAGE COMPRESSION SCHEME WITH AN ADAPTIVE PARAME...

引用

IEEE International conference on visual communications and image processing (VCIP)

作者： Zhang, Guochao Liu, Shaohui Jiang, Feng Zhao, Debin Gao, Wen Harbin Inst Technol Dept Comp Sci & Technol Harbin 150006 Peoples R China

ISBN: (纸本)9781479902880

A growing societal awareness about privacy and security push the development of signal processing techniques in the encrypted domain. Data compression in encrypted domain attracts much attention recently years due to its avoiding the leakage of data source during compression. This paper proposes an improved block-by-block compression scheme of encrypted image with flexible compression ratio. The original image is encrypted by permuting the blocks of the image and then permuting the pixels in the blocks. In the compression, pixels chosen randomly used as reference information, and remaining pixels are compressed by coset code. At the decoder side, side information (SI) which is generated by combining correlation among blocks and image restoration from partial random samples (IRPRS) is utilized to assist the decompression. Moreover, an adaptive system parameters selection method is also given in this paper. The experimental results show that the proposed method can achieve a better reconstructed result compared with the earlier method.

关键词： image compression image encryption coset code image reconstruction

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MGTN: Multi-scale Graph Transformer Network for 3D Point Cloud Semantic Segmentation

MGTN: Multi-scale Graph Transformer Network for 3D Point Clo...

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2024 conference on visual communications and image processing

作者： Ai, Da Qin, Siyu Nie, Zihe Yuan, Hui Liu, Ying Xian Univ Posts & Telecommun Xian Key Lab Image Proc Technol & Applicat Publ S Xian Shaanxi Peoples R China Xian Univ Posts & Telecommun Sch Commun & Informat Engn Xian Shaanxi Peoples R China Shandong Univ Sch Control Sci & Engn Jinan Shandong Peoples R China

ISBN: (纸本)9798331529543;9798331529550

The structural similarity of point clouds presents challenges in accurately recognizing and segmenting semantic information at the demarcation points of complex scenes or objects. In this study, we propose a multi-scale graph transformer network (MGTN) for 3D point cloud semantic segmentation. First, a multi-scale graph convolution (MSG-Conv) is devised to address the limitations faced by existing methods when extracting local and global features of point cloud data with varying densities simultaneously. Subsequently, we employ a graph-transformer (G-T) module to enhance edge details and spatial position information in the point cloud, thereby improving recognition accuracy for small objects and confusing elements such as columns and beams. Extensive testing on ShapeNet parts and S3DIS datasets was conducted to demonstrate the effectiveness of MGTN. Compared to the baseline network DGCNN, our proposed MGTN achieves substantial performance improvements, as evidenced by notable increases in mIoU of 1.5% and 18.5% on the ShapeNet parts and S3DIS datasets respectively. Additionally, MGTN outperforms the recent CFSA-Net by 2.3% and 3.4% on OA and mIoU respectively.

关键词： point cloud semantic segmentation multi-scale graph convolution transformer feature extraction

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Ultra High Definition Video Saliency Database

Ultra High Definition Video Saliency Database

引用

IEEE visual communications and image processing conference

作者： Li, Duo Zhai, Guangtao Yang, Xiaokang Shanghai Jiao Tong Univ Inst Image Commun & Informat Proc Shanghai Peoples R China

ISBN: (纸本)9781479961399

visual attention plays an important role in image and video processing. Nowadays, high definition (HD) techniques have been widely used. And ultra high definition (UHD) is becoming more and more popular. However, existing researches in visual attention mainly focus on relatively low resolution videos or images. There is very limited studies in visual attention of UHD videos. In this paper, we built a Ultra High Definition (4k) Video Saliency Database. Using this database, we explored the characteristics of visual attention related to ultra high definition videos. A concept of aggregation maps (AGM) for videos is put forward to better analyse the characteristics of visual attention of videos. Through the experiment, we find that there exist fairly strong correlations between the video resolution and visual attention behaviors. We also find that people tend to focus on the center of videos of relatively low resolution. The database will be make publicly available at *** soon.

关键词： visual attention human visual system (HVS) saliency video database eye-tracker UHD

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Healed Photomontage via Poisson Equation

Healed Photomontage via Poisson Equation

引用

21st Signal processing and communications Applications conference (SIU)

作者： Dizdaroglu, Bekir Karadeniz Tech Univ Bilgisayar Muhendisligi Bolumu Trabzon Turkey

ISBN: (纸本)9781467355636;9781467355629

In this study, a method is proposed for pasting a user selected and copied part of a region from the source image to the target image. Since the selected areas in the source and target images are not homogenous which means they contain texture information, most of the previous methods in the literature depending on the Poisson equation cause occurrence of adverse effects such as blur or color leakage in the processed region. The proposed method does not cause those artifacts in most cases but it makes an improvement and minimizes the artifacts. The visual results also prove that the method is promising.

关键词： Poisson equation photomontage image gradient vector field guided vector field

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Optical, analog and digital domain architectural considerations for visual communications

Optical, analog and digital domain architectural considerati...

引用

conference on visual communications and image processing 2008

作者： Metz, W. A. Texas Instruments Inc Dallas TX 75243 USA

ISBN: (纸本)9780819469946

The end of the performance entitlement historically achieved by classic scaling of CMOS devices is within sight, driven ultimately by fundamental limits. Performance entitlements predicted by classic CMOS scaling have progressively failed to be realized in recent process generations due to excessive leakage, increasing interconnect delays and scaling of gate dielectrics. Prior to reaching fundamental limits, trends in technology, architecture and economics will pressure the industry to adopt new paradigms. A likely response is to repartition system functions away from digital implementations and into new architectures. Future architectures for visual communications will require extending the implementation into the optical and analog processing domains. The fundamental properties of these domains will in turn give rise to new architectural concepts. The limits of CMOS scaling and impact on architectures will be briefly reviewed. Alternative approaches in the optical, electronic and analog domains will then be examined for advantages, architectural impact and drawbacks.

关键词： image systems architecture optical computing analog computing image processing CMOS limits

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Hybrid Representation for 4D Medical image Compression

Hybrid Representation for 4D Medical Image Compression

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2024 conference on visual communications and image processing

作者： Zheng, Wuyang Meng, Jiarui Zhang, Jiaqi Zhang, Jian Ma, Siwei Peking Univ Sch Elect & Comp Engn Beijing Peoples R China Peking Univ Sch Comp Sci Beijing Peoples R China

ISBN: (纸本)9798331529543;9798331529550

Due to the substantial storage requirements of the 4D medical images, achieving efficient compression of such images is a crucial topic. Existing traditional image/video coding methods have achieved remarkable results in most compression tasks, but their performance in encoding 4D medical images remain poor. This is because these methods cannot fully exploit the spatio-temporal correlations in 4D images. Recently, implicit neural representation (INR) based image/video compression methods have made significant progress, with coding performance comparable to traditional methods. However, they also suffer from significant performance losses in 4D medical image compression like traditional methods. In this paper, we propose an efficient hybrid representation framework, which includes six learnable feature planes and a tiny MLP decoder. This framework alleviates the issue of previous methods lacking the ability to utilize the spatio-temporal correlations in 4D medical images, enabling it to capture these information more effectively. We also introduce a novel adaptive plane scaling strategy that allocates the numbers of parameter in each plane based on the resolution of the image. This design allows the model to further enhance the reconstruction quality at the same compression ratio. Extensive experiments show that our model achieves better RD performance compared to traditional and INR-based methods, and it also offers faster encoding speeds than INR-based methods.

关键词： Medical Data Compression Hybrid Representation Feature Planes

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