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检索条件"任意字段=2023 IEEE International Conference on Image Processing and Computer Applications, ICIPCA 2023"

共 4812 条记录，以下是401-410 订阅

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image Foreground Segmentation Based on Small Data Set for Visual Servo applications

Image Foreground Segmentation Based on Small Data Set for Vi...

引用

ieee/ASME international conference on Advanced Intelligent Mechatronics (AIM)

作者： Luo, Yan Chen, Gaoming Liu, Chao Xiong, Zhenhua Shanghai Jiao Tong Univ Sch Mech Engn Shanghai Peoples R China

ISBN: (纸本)9781665476331

Extraction of features is a key process in image-based visual servo. However, existing image processing methods are difficult to segment the target foreground and cannot overcome distracting factors, such as background and illumination, resulting in reduced accuracy of feature extraction. Therefore, target foreground segmentation is a critical problem in image-based visual servo tasks. In this paper, a method for image foreground segmentation and visual servo control based on small data training is proposed. Semantic segmentation is achieved by training a small number of images. Focusing on the target artefact region and blurring the background are also achieved to avoid its influence on feature recognition, especially for industry parts. It is shown that recognition and segmentation under different lighting conditions can be obtained, reducing the interference of lighting on visual servo. Experimental results show that the proposed method is effective in visual servo control applications.

关键词： Feature extraction foreground segmentation small data set visual servo

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Prototype-guided Unsupervised Domain Adaptation for Semantic Segmentation

Prototype-guided Unsupervised Domain Adaptation for Semantic...

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2023 ieee international conference on Control, Electronics and computer Technology, ICCECT 2023

作者： Xia, Shaoqi Huang, Hua Li, Qishen He, Yifan Nanchang Hangkong University Key Laboratory of Jiangxi Province for Image Processing and Pattern Recognition School of Information Engineering Jiangxi Nanchang China Nanchang Hangkong University School of Information Engineering Nanchang China Nanchang Hangkong University Key Laboratory of Jiangxi Province for Image Processing and Pattern Recognition School of Software Nanchang China

ISBN: (纸本)9798350399028

Semantic segmentation is one of the most important research directions in the field of computer vision, and has a wide range of applications for autonomous driving, medical imaging, intelligent security, etc. Unsupervised domain adaptation is the mainstream research topic in recent years, which can use a large number of labeled source samples to complete the segmentation task in target domain without labeled target samples. In this paper, we propose a prototype-guided unsupervised domain adaptation for semantic segmentation based on ProDA model. Due to lacking of labeled target samples and the prior probability, a prototype distance loss based on target domain is proposed to optimize the distribution of features by measuring the distance between features and the updated prototype and designing an adaptive threshold strategy. Meanwhile, a smoothing loss is proposed to alleviate the impact of source samples on our model and improve the prediction performance of the network. By conducting experiments on the GTA5 to Cityscapes scenarios, the results show that compared with the original model, the loss optimization improves mIoU by1.52. © 2023 ieee.

关键词： Semantic Segmentation

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An Efficient Anti-Tampering image Watermarking Algorithm with Low Distortion

An Efficient Anti-Tampering Image Watermarking Algorithm wit...

引用

2023 international conference on image processing and computer Vision, IPCV 2023

作者： Xie, Rongsheng Liu, Honghui School of Computer & Information Engineering Xiamen University of Technology Xiamen China

ISBN: (纸本)9781665473941

The existing anti-tampering image digital watermarking algorithms are difficult to balance the performance of watermarked image quality, tampering detection and localization. The paper proposes an efficient anti-tampering image watermarking algorithm with low distortion. The proposed algorithm dualizes the efficients of the DWT transformed image, and then modifies the DWT efficients corresponding to the sequence of embedding positions using a low-loss strategy, with the result that the values of the dualized DWT efficients are exactly equal to the corresponding watermark bits. To improve the quality of the watermarked image, the chaotic key segmentation adaptive watermarking algorithm is further proposed. The experimental results show that the proposed algorithm can not only calculate the tampering rate, but also locate the tampering location, and the quality of the watermarked image is improved. © 2023 ieee.

关键词： Discrete wavelet transforms

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Leveraging Classic Deconvolution and Feature Extraction in Zero-Shot image Restoration

Leveraging Classic Deconvolution and Feature Extraction in Z...

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ieee/CVF international conference on computer Vision (ICCV)

作者： Chobola, Tomas Mueller, Gesine Dausmann, Veit Theileis, Anton Taucher, Jan Huisken, Jan Peng, Tingying Tech Univ Munich Munich Germany Georg August Univ Gottingen Gottingen Germany GEOMAR Helmholtz Ctr Ocean Res Kiel Kiel Germany German Res Ctr Environm Hlth Helmholtz Munich Helmholtz AI Neuherberg Germany

ISBN: (纸本)9798350307443

Non-blind deconvolution aims to restore a sharp image from its blurred counterpart given an obtained kernel. Existing deep neural architectures are often built based on large datasets of sharp ground truth images and trained with supervision. Sharp, high quality ground truth images, however, are not always available, especially for biomedical applications. This severely hampers the applicability of current approaches in practice. In this paper, we propose a novel non-blind deconvolution method that leverages the power of deep learning and classic iterative deconvolution algorithms. Our approach combines a pre-trained network to extract deep features from the input image with iterative Richardson-Lucy deconvolution steps. Subsequently, a zero-shot optimisation process is employed to integrate the deconvolved features, resulting in a high-quality reconstructed image. By performing the preliminary reconstruction with the classic iterative deconvolution method, we can effectively utilise a smaller network to produce the final image, thus accelerating the reconstruction whilst reducing the demand for valuable computational resources. Our method demonstrates significant improvements in various real-world applications non-blind deconvolution tasks.

关键词： deconvolution microscopy self supervised zero shot

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Tiny Machine Learning for Underwater image Enhancement: Pruning and Quantization Approach 5

Tiny Machine Learning for Underwater Image Enhancement: Prun...

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5th international conference on computer and applications, ICCA 2023

作者： Rejal, Ayah Abo El Pester, Andreas Nagaty, Khaled The British University in Egypt Faculty of Informatics and Computer Science Department of Computer Science Cairo Egypt

ISBN: (纸本)9798350303254

Many people have expressed an interest in underwater image processing in a variety of fields, including underwater vehicle control, archaeology, marine biological studies, etc. Underwater exploration is becoming an increasingly important element of our lives, with applications ranging from underwater marine and creature research to pipeline and communication logistics, touristic and entertainment use. Underwater images suffer from poor visibility, distortion, and poor quality for a variety of causes, including light propagation. The major issue arises when these images must be captured at depths greater than 500 feet and artificial lighting needs to be provided. Efficient algorithms and models were proposed to enhance the images taken underwater. However, these networks challenge efficient real-time deployment and needs significant computational resources and energy costs. In this paper these challenges will be tackled by using different network compression techniques such as pruning and quantization. These techniques will be applied on a model named WGH-net and the resulting in a tiny machine learning model is named tiny WGH-net model. Moreover, we compare the results of both models along with other models proposed by other researchers. Sustainable Development Goals (SDG) will also be discussed along with our contribution to some of the goals. © 2023 ieee.

关键词： Machine learning

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Deep Video Demoireing via Compact Invertible Dyadic Decomposition

Deep Video Demoireing via Compact Invertible Dyadic Decompos...

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ieee/CVF international conference on computer Vision (ICCV)

作者： Quan, Yuhui Huang, Haoran He, Shengfeng Xu, Ruotao South China University of Technology School of Computer Science and Engineering Guangzhou 510006 China Pazhou Lab Guangzhou 510335 China Singapore Management University School of Computing and Information Systems 188065 Singapore

ISBN: (纸本)9798350307184

Removing moire patterns from videos recorded on screens or complex textures is known as video demoireing. It is a challenging task as both structures and textures of an image usually exhibit strong periodic patterns, which thus are easily confused with moire patterns and can be significantly erased in the removal process. By interpreting video demoireing as a multi-frame decomposition problem, we propose a compact invertible dyadic network called CIDNet that progressively decouples latent frames and the moire patterns from an input video sequence. Using a dyadic cross-scale coupling structure with coupling layers tailored for multi-scale processing, CIDNet aims at disentangling the features of image patterns from that of moire patterns at different scales, while retaining all latent image features to facilitate reconstruction. In addition, a compressed form for the networks output is introduced to reduce computational complexity and alleviate overfitting. The experiments show that CIDNet outperforms existing methods and enjoys the advantages in model size and computational efficiency.

关键词： Deep Video Computational Efficiency image Features Model Size Video Sequences image Pattern Latent Features Compressed Format Complex Texture Multiscale Process Latent image Deep Neural Network Attention Mechanism Video Frames Peak Signal-to-noise Ratio Multiscale Analysis Feature Alignment Adjacent Frames Supplement For Details Discrete Cosine Transform Frame Alignment U-shaped Structure Alignment Blocks Multi-scale Structure Reversible Loss Color Shift Information Fidelity Latent Code Spatial Pyramid Pooling Spatial Pyramid

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LeCA: In-Sensor Learned Compressive Acquisition for Efficient Machine Vision on the Edge 23

LeCA: In-Sensor Learned Compressive Acquisition for Efficien...

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50th Annual international Symposium on computer Architecture (ISCA)

作者： Ma, Tianrui Boloor, Adith Jagadish Yang, Xiangxing Cao, Weidong Williams, Patrick Sun, Nan Chakrabarti, Ayan Zhang, Xuan Washington Univ St Louis MO 63110 USA Univ Texas Austin Austin TX USA Tsinghua Univ Beijing Peoples R China

ISBN: (纸本)9798400700958

With the rapid advances of deep learning-based computer vision (CV) technology, digital images are increasingly consumed, not by humans, but by downstream CV algorithms. However, capturing high-fidelity and high-resolution images is energy-intensive. It not only dominates the energy consumption of the sensor itself (i.e. in low-power edge devices), but also contributes to significant memory burdens and performance bottlenecks in the later storage, processing, and communication stages. In this paper, we systematically explore a new paradigm of in-sensor processing, termed "learned compressive acquisition" (LeCA). Targeting machine vision applications on the edge, the LeCA framework exploits the joint learning of a sensor autoencoder structure with the downstream CV algorithms to effectively compress the original image into low-dimensional features with adaptive bit depth. We employ column-parallel analog-domain processing directly inside the image sensor to perform the compressive encoding of the raw image, resulting in meaningful hardware savings, and energy efficiency improvements. Evaluated within a modern machine vision processing pipeline, LeCA achieves 4x, 6x, and 8x compression ratios prior to any digital compression, with minimal accuracy loss of 0.97%, 0.98%, and 2.01% on imageNet, outperforming existing methods. Compared with the conventional full-resolution image sensor and the state-of-the-art compressive sensing sensor, our LeCA sensor is 6.3x and 2.2x more energy-efficient while reaching a 2x higher compression ratio.

关键词： CMOS image sensor image compression autoencoder

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INCREMENTAL image LABELING VIA ITERATIVE REFINEMENT

INCREMENTAL IMAGE LABELING VIA ITERATIVE REFINEMENT

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ieee international conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Giunchiglia, Fausto Diao, Xiaolei Bagchi, Mayukh Univ Trento DISI Trento Italy

ISBN: (纸本)9798350302615

Data quality is critical for multimedia tasks, while various types of systematic flaws are found in image benchmark datasets, as discussed in recent work. In particular, the existence of the semantic gap problem leads to a many-to-many mapping between the information extracted from an image and its linguistic description. This unavoidable bias further leads to poor performance on current computer vision tasks. To address this issue, we introduce a Knowledge Representation (KR)-based methodology to provide guidelines driving the labeling process, thereby indirectly introducing intended semantics in ML models. Specifically, an iterative refinement-based annotation method is proposed to optimize data labeling by organizing objects in a classification hierarchy according to their visual properties, ensuring that they are aligned with their linguistic descriptions. Preliminary results verify the effectiveness of the proposed method.

关键词： Data Quality Semantic Gap Problem image Datasets Visual Properties

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An Algorithm of Film image Coloring and image Restoration Based on DL

An Algorithm of Film Image Coloring and Image Restoration Ba...

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2023 international conference on Networking, Informatics and Computing, ICNETIC 2023

作者： Deng, Peng Zhujiang College of South China Agricultural University Guangdong Guangzhou510000 China

ISBN: (纸本)9798350313314

In image processing, color is usually the most basic visual element, and it is the element that people can obtain the most effective information in cognition. It plays an important role in various fields. Black-and-white image coloring is an important part of image processing system. As a computer-aided technology, image coloring has a wide range of applications in film and television processing and old photo restoration. Because the algorithm does not have the ability to understand the movie content, it extracts the feature information of the image and maps it to the similar parts, thus completing the coloring work. According to the research, the human eye can only recognize dozens of gray levels of images, but compared with color images, the human eye can recognize thousands of colors. It can be seen that the influence of color images on human eyes is far greater than that of gray images. In addition, the gray-scale image is obviously not conducive to the feature extraction of the image information by the algorithm, so coloring and pseudo-colorizing the gray-scale image will be beneficial to the feature extraction and expression of the image. And the colored image can better express the meaning of the image, so that the human eye can capture the meaning and information of the image itself faster, better and more accurately, and then give full play to and express the value contained in the image. © 2023 ieee.

关键词： Feature extraction

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Low-Light image Enhancement Based on Local Contrast Denoising and Fusion 5

Low-Light Image Enhancement Based on Local Contrast Denoisin...

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2023 5th international conference on Artificial Intelligence and computer applications, ICAICA 2023

作者： Zhang, Guiwen Kong, Qun Wang, Fuxiang Fang, Li Yu, Wenyue Xiao, Yingjie School of Computer and Information Engineering Qilu Institute of Technology Jinan China

ISBN: (纸本)9798350323313

The purpose of low-light image enhancement is to improve the clarity of objects in low-light environments to facilitate the recognition and detection of targets later. Local Contrast Denoising and Fusion Network (LCDFN) is constructed to address the problem of poor contrast and severe noise in low light images. images obtained in low-light environments have low contrast in the image as a whole, but usually some of the edge regions of the image have larger contrast due to the presence of the light source, and enhancement processing of low-light images often results in over-enhancement of the region, generating noise. In order to suppress the noise, firstly, determines the region with larger contrast by local inspection of low-light/bright-light image pairs and suppresses the region's weighting value;second, multiple denoising is performed on the low-light image by changing the coefficients of the weight map;then, the multiple denoising results are fused;finally, the color loss function is improved, and the luminance loss is added to the color loss function, so that the LCDFN network can better inhibit the generation of noise. The experimental results show that LCDFN has a better effect on low-light image enhancement. © 2023 ieee.

关键词： image enhancement

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