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Abundant Semantic Information Module for Object Detection 2

Abundant Semantic Information Module for Object Detection

引用

2nd international conference on Algorithms, High Performance Computing and Artificial Intelligence, AHPCAI 2022

作者： Wu, Taida Li, Qishen Li, Qiufeng Gao, Jun School of Information Engineering Nanchang Hangkong University Jiangxi Nanchang China School of Software Nanchang Hangkong University Jiangxi Nanchang China Key Laboratory of Jiangxi Province for Image Processing and Pattern Recognition Jiangxi Nanchang China Jiangxi Nanchang China

ISBN: (纸本)9798350334784

Nowadays, with the high-speed iteration of convolution neural network, the efficient object detector emerges one after another. As an important branch of computer vision, object detection aims to detect where and what the object is. However, nowadays, many detector cannot extract abundant semantic information to discriminate the location and size of the objects, resulting in poor performance of the network. In this paper, a new module is proposed, named Abundant Semantic Information Module (ASIM), to enrich and expand the semantic information of the object with more and larger receptive fields. In ASIM, we blend the extracted feature maps to different degrees with different blending factors and fuse them so that all object information is given full attention. Compared to the baseline method, a wealth of experiments show that our module has achieved a significant performance improvement. © 2022 IEEE.

关键词： Object detection

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Biological Random Dynamic Focusing Disparity Estimation Within Time-Sequence images

Biological Random Dynamic Focusing Disparity Estimation With...

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image processing and computer Applications (ICIPCA), IEEE international conference on

作者： Shijun Chen Juan Du Jie Lin Yongchao Tang Dawei Chen Qiang Chen Zhijun Zhang Zhongxing Telecom Equipment Shenzhen China School of Automation Science and Engineering South China University of Technology Guangzhou China

ISBN: (数字)9798350360240

ISBN: (纸本)9798350384161

In traditional computer stereo vision systems, a single focus is employed for capturing and processing stereo image pairs to estimate depth. However, the inherent variability of object distances in real world stereo images imposes limitations on this method, rendering it less reliable. To tackle this issue, a novel biological random dynamic focusing theory grounded in time-sequence images is proposed in this paper. In the proposed framework, the images of different focal points are captured according to the time sequence, the target is extracted and manipulated, ultimately yielding either a single focus or multi-focus image processing output depending on the application scenario. Moreover, a biological focusing disparity estimation algorithm is proposed and this theory is subsequently extended to the domain of disparity estimation, with its foundation rooted in the Semi Global Matching (SGM) algorithm. The disparity map obtained by this algorithm exhibits a more accurate disparity values and achieves a good balance between speed and accuracy. Experimental results on real world images demonstrate that our method enhanced robustness and flexibility compared to traditional methodologies.

关键词： Accuracy Heuristic algorithms image processing Focusing Estimation Reliability theory Aerodynamics Biology Stereo vision Biomedical imaging

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LMSA: Low-Relation Multi-head Self-attention Mechanism in Visual Transformer 2nd

LMSA: Low-Relation Multi-head Self-attention Mechanism in Vi...

引用

2nd international conference on image, vision and Intelligent Systems, ICIVIS 2022

作者： Wang, Jingjie Wei, Xiang Liu, Xiaoyu Lu, Siyang School of Software Engineering Beijing Jiaotong University Beijing100044 China School of Computer and Information Technology Beijing Jiaotong University Beijing100044 China

ISBN: (纸本)9789819909223

The Transformer backbone network with self-attention mechanism as the core has achieved great success in the field of natural language processing and computer vision. However, compared with classical visual feature extraction methods, the self-attention mechanism needs more training data to capture the relationship between tokens, which makes it challenging to train the transformer effectively on small datasets. We design a novel lightweight self-attention mechanism: Low-relation Multi-head Self-Attention (LMSA), which is superior to the recent self-attention and can fully explore the relationship between rare tokens. Specifically, the proposed self-attention mechanism breaks the barrier of the dimensional consistency of the traditional self-attention mechanism, making the feature relationship focus on a small number of dimensions, thereby reducing computational complexity and occupying less storage space. Experimental results show that the dimensional consistency inside the traditional self-attention mechanism is unnecessary. In particular, using Swin as the backbone model for training, the accuracy of the CIFAR-10 image classification task is improved by 0.43%, in the meanwhile, the consumption of a single self-attention resource is reduced by 64.58%, and the number of model parameters and model size is reduced by more than 15%. By appropriately condensing the self-attention relationship variables, the Transformer network can be more efficient and even perform better. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： image enhancement

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FG-PIH: A Fusion of Fresnelet Transform and Gradient Directional Pattern for Perceptual image Hashing

FG-PIH: A Fusion of Fresnelet Transform and Gradient Directi...

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Recent Trends in Microelectronics, Automation, Computing and Communications Systems (ICMACC), international conference on

作者： Pavani Meesala Dalton Meitei Thounaojam Computer Vision Laboratory Computer Science and Engineering National Institute of Technology Silchar Silchar India

ISBN: (数字)9798350366570

ISBN: (纸本)9798350366587

Perceptual image hashing is pivotal in various image processing applications, including image authentication, content-based image retrieval, tampered image detection, and copyright protection. This paper proposes a novel approach for perceptual image hashing by combining the Fresnelet Transform with Gradient Directional Patterns. Using the FG-PIH technique, the proposed method achieves superior robustness against common image processing attacks while maintaining perceptual similarity for near-duplicate images. Experimental results on standard benchmark datasets demonstrate the effectiveness and efficiency of the proposed Fresnelet Transform-based perceptual image hashing scheme. Furthermore, comparative analysis against state-of-the-art methods underscores the competitiveness of our approach in terms of hash quality and computational complexity.

关键词： Salt Translation Noise Transform coding Transforms Speckle Feature extraction Robustness Microelectronics Standards

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An Improved Reversible Data Hiding Scheme Based on Referred Frequencies for a VQ Compressed Index

An Improved Reversible Data Hiding Scheme Based on Referred ...

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Machine vision, image processing and Imaging Technology (MVIPIT), international conference on

作者： Min-Shiang Hwang Kanza Fatima Yu-Lun Wang Chi-Shiang Chan Chia-Chun Wu Department of Computer Science & Information Engineering Fintech and Blockchain Research Center Asia University Taichung Taiwan Department of Computer Science & Information Engineering Asia University Taichung Taiwan Department of Management Information Systems National Chung Hsing University Taichung Taiwan Department of M-Commerce and Multimedia Applications Asia University Taichung Taiwan Department of Computer Science and Information Engineering National Quemoy University Kinmen Taiwan

ISBN: (数字)9798331543037

ISBN: (纸本)9798331543044

Nowadays, we usually compress images before uploading them to social media. However, images on social media can easily be copied, so embedding secret messages in compressed images has become increasingly popular. There are many compression methods, such as Huffman, VQ, ZIP, AMBTC, RAR, JPEG, etc. In this article, we propose an improved data hiding in VQ compression method to achieve better capacity and high quality. Experimental results show that our data-hiding approach is practical.

关键词： Steganography image coding Social networking (online) Vector quantization Bit rate Transform coding Imaging Indexes

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Visual communication design in tourism products based on computer vision and complex feature mining algorithm 2

Visual communication design in tourism products based on com...

引用

2nd international conference on Smart Electronics and Communication, ICOSEC 2021

作者： Zu, Ruizhi Taiyuan Normal University Taiyuan030619 China

ISBN: (纸本)9781665433686

Visual communication design in tourism products based on computer vision and complex feature mining algorithm is studied in this paper. In essential aspect, the visual communication should be considered. A core typical industrial robot vision application system includes light source, optical imaging system, and image capture system. Hence, this paper considers the complex feature information analysis as the basis for processing the related data. Then, the designed framework is applied to the visual analysis of the tourism products. Furthermore, the proposed model is simulated from different perspectives. © 2021 IEEE.

关键词： Data mining

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Dual Adversarial Contrastive Learning for Underwater image Enhancement

Dual Adversarial Contrastive Learning for Underwater Image E...

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image processing and Media Computing (ICIPMC), international conference on

作者： Mengyi Yan Xiaoyue Jiang Yuanbo Ren Jiayi Li Sihang Dang Xiaoyi Feng Zhaoqiang Xia Northwestern Polytechnical University Xi’an China

Underwater images are highly distorted, which makes high-level computer vision tasks difficult. Existing underwater image enhancement algorithms mainly focus on restoring the appearance of images. As a result, enhanced images may not be useful for high-level computer vision tasks. The lack of label images also makes most supervised learning networks impractical. In this paper, a dual adversarial contrastive learning enhancement network is proposed, which is both visually friendly and task-oriented. A circular network is proposed to achieve self-supervised learning between unpaired images. We also introduce a contrastive prior between the enhanced and degraded results in feature space to ensure the good visual appearance of the enhanced results. Furthermore, the high-level detection task is also used to constrain the enhanced results. The experiments were carried out on a popular underwater dataset, the enhanced images of the proposed method showed better visual quality and improve tracking performance as well.

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Oracle Bone Script Similiar Character Screening Approach Based on Simsiam Contrastive Learning and Supervised Learning

Oracle Bone Script Similiar Character Screening Approach Bas...

引用

Algorithm, image processing and Machine vision (AIPMV), international conference on

作者： Xinying Weng Yifan Li Shuaidong Hao Jialiang Hou College of Computer and Information Engineering Henan Normal University Xinxiang Henan China

ISBN: (数字)9798350390254

ISBN: (纸本)9798350390261

This project proposes a new method that uses fuzzy comprehensive evaluation method to integrate ResN et-50 self- supervised and Rep Vggsupervised learning. The source image dataset HWOBC oracle is taken as input, the target image is selected, and finally the most similar image is output in turn without any manual intervention. The same feature encoding method is not used for images of different modalities. Before the model training, the image data is preprocessed, and the image is enhanced by random rotation processing, self-square graph equalization theory algorithm, and gamma transform, which effectively enhances the key feature learning. Finally, the fuzzy comprehensive evaluation method is used to combine the results of supervised training and unsupervised training, which can better solve the “most similar” problem that is difficult to quantify. At present, there are many unknown oracle-bone inscriptions waiting for us to crack. Contacting with the glyphs can provide new ideas for cracking.

关键词： Training Representation learning image coding Shape Machine vision Supervised learning Transforms Manuals Writing Bones

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Elevating Perception: Unified Recognition Framework and vision-Language Pre-Training Using Three-Dimensional image Reconstruction

Elevating Perception: Unified Recognition Framework and Visi...

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Artificial Intelligence, Human-computer Interaction and Robotics (AIHCIR), international conference on

作者： ZhiQiang Wang Aryan Joshi GuiYing Zhang WenJia Ren Feng Jia XiaoHang Sun Dept. of Information Tech & Engineering Tianjin University of Technology and Education Tianjin China Dept. of Information Tech & Engineering Tianjin Ai Shen De Si Technology Co. Ltd Tianjin China

This research project explores a paradigm shift in perceptual enhancement by integrating a Unified Recognition Framework and vision-Language Pre-Training in three-dimensional image reconstruction. Through the synergy of advanced algorithms from computer vision & language processing, the project tries to enhance the precision and depth of perception in reconstructed images. This innovative approach holds the potential to revolutionize fields such as medical imaging, virtual reality, and computer-aided design, providing a comprehensive perspective on the intersection of multimodal data processing and perceptual advancement. The anticipated research outcomes are expected to significantly contribute to the evolution of technologies that rely on accurate and contextually rich three-dimensional reconstructions. Moreover, the research aims to reduce the constant need for new datasets by improving pattern recognition through 3D image patterning on backpropagation. This continuous improvement of vectors is envisioned to enhance the efficiency and accuracy of pattern recognition, contributing to the optimization of perceptual systems over time.

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ResViT: An Integrated Approach Using ResNet50v2 and vision Transformer for Enhanced Bangla Handwritten Character Recognition

ResViT: An Integrated Approach Using ResNet50v2 and Vision T...

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Information and Communication Technology (ICICT), international conference on

作者： Kazi Tanvir Md Sadi Al Huda Md. Sayem Kabir Rezwan Obayed Md. Fahim Attef Md. Asraf Ali Department of Mathematics Vellore Institute of Technology Vellore Tamil Nadu India Department of Computer Science American International University-Bangladesh (AIUB) Dhaka Bangladesh Electrical & Computer Engineering ( ECE ) North South University Dhaka Bangladesh

ISBN: (数字)9798331508227

ISBN: (纸本)9798331508234

Bangla, also known as Bengali, serves as the official language of Bangladesh and is the second most widely spoken language in India, flourishing in the voices of 300 million people as a language that bridges hearts and cultures. This linguistic richness is accompanied by a diverse character system and a vibrant literary heritage. However, the transition to digital storage and the conversion of handwritten documents pose notable challenges. ResNet-50 v2, a more advanced version of the ResNet-50 design, was released to improve deep network training by adding identity mappings and residual blocks. The vision Transformer (ViT) extends the Transformer model from natural language processing to computer vision, employing patch-based image division and self-attention mechanisms for recognizing the global context. This study utilizes a model that combines features from the ViT and ResNet50V2 architectures, incorporating backbones for image processing, global average pooling, feature concatenation, and custom top layers with SoftMax activation, which achieved an impressive 97.21% accuracy on the extensive BanglaLekha- Isolated Dataset containing 166,000 images.

关键词： Training computer vision Computational modeling computer architecture Linguistics Transformers Natural language processing Information and communication technology Character recognition Resilience

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