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Assessment of the Quality of Brick Products by the Method of Machine Vision

Assessment of the Quality of Brick Products by the Method of...

引用

Siberian conference on Control and communications (SIBCON)

作者： Mikhail Kataev Eugeny Kartashov Roman Karpov Faculty control systems Tomsk state university of control systems and radioelectronics (TUSUR) Tomsk Russia

ISBN: (纸本)9781665476294

At present, no one doubts that machine vision significantly increases the efficiency, quality and reliability of solving numerous problems, including the detection of various product defects. Machine vision acts as a replacement for the complex and time-consuming work of specialists who search for defects using visual inspection. However, for the application of machine vision methods, several conditions must be met, which are associated with lighting and a suitable photographic apparatus, in order to obtain high-quality images, as well as the method of their processing. image processing and analysis are important elements of a quality control system and obtaining information about defects. This study provides a problem statement for assessing the quality of the surface of bricks and describes the proposed approach to image processing and analysis. The results of processing real images obtained at a brick factory are presented.

关键词： visualization Semiconductor device measurement image color analysis Machine vision Lighting Quality control Reliability

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Spatial-Channel Context-Based Entropy Modeling for End-to-end Optimized image Compression

Spatial-Channel Context-Based Entropy Modeling for End-to-en...

引用

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

作者： Li, Chongxin Luo, Jixiang Dai, Wenrui Li, Chenglin Zou, Junni Xiong, Hongkai Shanghai Jiao Tong Univ Sch Elect Informat & Elect Engn Shanghai Peoples R China

ISBN: (纸本)9781728180687

End-to-end optimized image compression has emerged as a disruptive technique to reduce the spatial redundancies with an improved reconstruction quality. However, existing entropy model for latent representations cannot sufficiently exploit their spatial and channel-wise correlations. In this paper, we propose a novel entropy model based on spatial-channel contexts for end-to-end optimized image compression. The proposed model jointly leverages spatial structural dependencies and channel-wise correlations to improve the probabilistic estimation of latent representations. Instead of complex autoregressive hyperprior network, shallow artificial neural networks (ANNs) incorporating 3-D masks are developed to efficiently realize the entropy model with a guarantee of causality. Experimental results demonstrate that the proposed model achieves competitive rate-distortion performance and reduces model complexity in comparison to recent end-to-end optimized methods for image compression.

关键词： End-to-end optimized image compression context-based entropy coding artificial neural networks

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Locating people in Real-World for Assisting Crowd Behaviour Analysis Using SSD and Deep SORT Algorithm 6

Locating people in Real-World for Assisting Crowd Behaviour ...

引用

6th International conference on Wireless communications, Signal processing and Networking (IEEE WiSPNET)

作者： Juyal, Piyush Sharma, Sachin Graph Era Deemed Univ Dept Comp Sci & Engn Dehra Dun Uttarakhand India

ISBN: (纸本)9781665440868

In security-sensitive areas, tracking individuals can be of high importance or may even be used to understand crowd activity, how people change their movement within the crowd or most common shops. Crowd activity trends can help to determine the most common locations of public places, which can help attract a wider audience in advertising placement. A growing concern has been public safety;it can be a harrowing experience to track these mischievous elements inside the crowd. This paper provides an approach that can map people with a geographical coordinate, which denotes an individual's realtime position in the real world through a camera feed. This data helps us to track the road, the direction of motion and where a person has turned on a map. These insights include descriptions of in-depth crowd movement that can assist with better surveillance and evaluating crowd activity.

关键词： Monitoring Real-time systems image edge detection visual analytics Data visualization Deep learning

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Learned Lossless JPEG Transcoding via Joint Lossy and Residual Compression

Learned Lossless JPEG Transcoding via Joint Lossy and Residu...

引用

IEEE visual communications and image processing (VCIP)

作者： Xiaoshuai Fan Xin Li Zhibo Chen Unviersity of Science and Technology of China Hefei China

ISBN: (纸本)9781665475938

As a commonly-used image compression format, JPEG has been broadly applied in the transmission and storage of images. To further reduce the compression cost while maintaining the quality of JPEG images, lossless transcoding technology has been proposed to recompress the compressed JPEG image in the DCT domain. Previous works, on the other hand, typically reduce the redundancy of DCT coefficients and optimize the probability prediction of entropy coding in a hand-crafted manner that lacks generalization ability and flexibility. To tackle the above challenge, we propose the learned lossless JPEG transcoding framework via Joint Lossy and Residual Compression. Instead of directly optimizing the entropy estimation, we focus on the redundancy that exists in the DCT coefficients. To the best of our knowledge, we are the first to utilize the learned end-to-end lossy transform coding to reduce the redundancy of DCT coefficients in a compact representational domain. We also introduce residual compression for lossless transcoding, which adaptively learns the distribution of residual DCT coefficients before compressing them using context-based entropy coding. Our proposed transcoding architecture shows significant superiority in the compression of JPEG images thanks to the collaboration of learned lossy transform coding and residual entropy coding. Extensive experi-ments on multiple datasets have demonstrated that our proposed framework can achieve about 21.49% bits saving in average based on JPEG compression, which outperforms the typical lossless transcoding framework JPEG-XL by 3.51%.

关键词： image coding visual communication Redundancy Transform coding Termination of employment Transcoding Entropy coding

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Revelation of Geospatial Information using Augmented Reality 6

Revelation of Geospatial Information using Augmented Reality

引用

6th International conference on Wireless communications, Signal processing and Networking (IEEE WiSPNET)

作者： Bharath, G. Ch, Rupa Karthik, M. Bhavani, Leela A. Chowdary, Manish, V VR Siddhartha Engn Coll A Dept CSE Vijayawada India

ISBN: (纸本)9781665440868

Augmented Reality (AR) is the technology that enables us to combine digital knowledge into the user's real-world space. Augmented Reality uses the existing environment to overlay digital content and information onto the physical world. The Proposed method demonstrates AR's use to provide a lively and visual representation of information about the geography of different masses on the globe and how AR can be utilized to enhance research studies and education by visualizing data collected by various statistical sources. This work helps to visualize the globe in Augmented Reality where one can overlay any OGC (Open Geospatial Consortium) Web Map Service (WMS) layer onto the globe's surface and interact with it to understand the visualization of statistics around the world. Users can utilize any pre-defined marker, target or placeholders of the object, which is trained beforehand to be recognized later in the camera stream. When seeing the placeholder from the camera of phone, an augmented reality globe must appear over the placeholder. Moving the phone camera, it have to be possible to overlay any Web Outline on the globe. This application must provide an interface to choose the data such as Climate, Sea Floor Temperature, Social Progress, World Literacy information to be overlaid on the globe. Real-time information changes according to the observer's movement as if that information truly exists in the real world.

关键词： Augmented reality image target Rendering Vuforia Super imposition 3DModel

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EXTENDING CCSDS 123.0-B-1 FOR LOSSLESS 4D image COMPRESSION

EXTENDING CCSDS 123.0-B-1 FOR LOSSLESS 4D IMAGE COMPRESSION

引用

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

作者： Zhang, Panpan Wang, Xiuheng Gao, Tiande Feng, Zhenfu Chen, Jie Northwestern Polytech Univ Ctr Intelligent Acoust & Immers Commun Sch Marine Sci & Technol Xian Shaanxi Peoples R China Xian Univ Posts & Telecommun Sch Elect Engn Xian Shaanxi Peoples R China

ISBN: (纸本)9781728180687

A 4-dimensional (4D) image can be viewed as a stack of volumetric images over channels of observation depth or temporal frames. This data contains rich information at the cost of high demands for storage and transmission resources due to its large volume. In this paper, we present a lossless 4D image compression algorithm by extending CCSDS-123.0-B-1 standard. Instead of separately compressing the volumetric image at each channel of 4D images, the proposed algorithm efficiently exploits redundancy across the fourth dimension of data. Experiments conducted on two types of 4D images demonstrate the effectiveness of the proposed lossless compression method.

关键词： 4D images lossless compression CCSDS-123.0-B-1 channel redundancy

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T2CI-GAN: Text to Compressed image Generation Using Generative Adversarial Network 7th

T2CI-GAN: Text to Compressed Image Generation Using Generati...

引用

7th International conference on Computer Vision and image processing, CVIP 2022

作者： Rajesh, Bulla Dusa, Nandakishore Javed, Mohammed Dubey, Shiv Ram Nagabhushan, P. Department of IT IIIT Allahabad U.P Prayagraj211015 India Department of CSE Vignan University A.P Guntur522213 India

ISBN: (纸本)9783031314162

The problem of generating textual descriptions for the visual data has gained research attention in the recent years. In contrast to that the problem of generating visual data from textual descriptions is still very challenging, because it requires the combination of both Natural Language processing (NLP) and Computer Vision techniques. The existing methods utilize the Generative Adversarial Networks (GANs) and generate the uncompressed images from textual description. However, in practice, most of the visual data are processed and transmitted in the compressed representation. Hence, the proposed work attempts to generate the visual data directly in the compressed representation form using Deep Convolutional GANs (DCGANs) to achieve the storage and computational efficiency. We propose GAN models for compressed image generation from text. The first model is directly trained with JPEG compressed DCT images (compressed domain) to generate the compressed images from text descriptions. The second model is trained with RGB images (pixel domain) to generate JPEG compressed DCT representation from text descriptions. The proposed models are tested on an open source benchmark dataset Oxford-102 Flower images using both RGB and JPEG compressed versions, and accomplished the state-of-the-art performance in the JPEG compressed domain. The code will be publicly released at GitHub after acceptance of paper. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Generative adversarial networks

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image Compressed Sensing Reconstruction via Deep image Prior with Feature Space and Texture Information 8th

Image Compressed Sensing Reconstruction via Deep Image Prior...

引用

8th International conference on Cognitive Systems and Information processing, ICCSIP 2023

作者： Peng, Zhao Jinchan, Wang Huanqing, Peng Fei, Xiang Liwen, Zhang College of Information Engineering Henan University of Science and Technology Luoyang471023 China

ISBN: (纸本)9789819980178

Deep learning has yielded remarkable achievements in compressed sensing image reconstruction in recent years. However, when the sampling rate is low, the reconstruction image is blurred and lacks texture details. In this paper, we design a dual-path compressed sensing reconstruction network based on image feature space information flow and texture information, which can solve the above problems to a certain extent. The feature space information flow path employs proximal gradient descent algorithms to map essential structural information from the pixel space to the feature space, effectively reducing data dimensionality and redundancy. On the other hand, after combining the attention mechanism, the texture information path restores the texture details of the image in the frequency domain, which can effectively enhance the quality and visual effects of the reconstruction image. During training, a unified loss function guides the alternating optimization of both paths. Evaluation of prominent benchmark datasets, including Set5, Set11, and BSD68, reveals that our proposed method outperforms traditional iterative approaches and existing deep learning-based methodologies in terms of both reconstructed image quality and network robustness under low sampling rates. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： image reconstruction

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Drone-Based Car Counting via Density Map Learning

Drone-Based Car Counting via Density Map Learning

引用

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

作者： Huang, Jingxian Ding, Guanchen Guo, Yujia Yang, Daiqin Wang, Sihan Wang, Tao Zhang, Yunfei Wuhan Univ Sch Remote Sensing & Informat Engn Wuhan Peoples R China Tencent Inc Shenzhen 518052 Peoples R China

ISBN: (纸本)9781728180687

Car counting on drone-based images is a challenging task in computer vision. Most advanced methods for counting are based on density maps. Usually, density maps are first generated by convolving ground truth point maps with a Gaussian kernel for later model learning (generation). Then, the counting network learns to predict density maps from input images (estimation). Most studies focus on the estimation problem while overlooking the generation problem. In this paper, a training framework is proposed to generate density maps by learning and train generation and estimation subnetworks jointly. Experiments demonstrate that our method outperforms other density map-based methods and shows the best performance on drone-based car counting.

关键词： Car counting density map learning drone-based image

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Mining Regional Relation from Pixel-wise Annotation for Scene Parsing

Mining Regional Relation from Pixel-wise Annotation for Scen...

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IEEE visual communications and image processing (VCIP)

作者： Zichen Song Hongliang Li Heqian Qiu Xiaoliang Zhang University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9781665475938

Scene parsing is an important and challenging task in computer vision, which assigns semantic labels to each pixel in the entire scene. Existing scene parsing methods only utilize pixel-wise annotation as the supervision of neural network, thus, some similar categories are easy to be misclassified in the complex scenes without the utilization of regional relation. To tackle these above challenging problems, a Regional Relation Network (RRNet) is proposed in this paper, which aims to boost the scene parsing performance by mining regional relation from pixel-wise annotation. Specifically, the pixel-wise annotation is divided into a lot of fixed regions, so that intra- and inter-regional relation are able to be extracted as the supervision of network. We firstly design an intra-regional relation module to predict category distribution in each fixed region, which is helpful for reducing the misclassification phenomenon in regions. Secondly, an inter-regional relation module is proposed to learn the relationships among each region in scene images. With the guideline of relation information extracted from the ground truth, the network is able to learn more discriminative relation representations. To validate our proposed model, we conduct experiments on three typical datasets, including NYU-depth-v2, PASCAL-Context and ADE20k. The achieved competitive results on all three datasets demonstrate the effectiveness of our method.

关键词： image segmentation Computer vision Annotations visual communication Semantics Neural networks Data mining

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