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The Optimal MLP-based Model for Displacement Field Measurement in 2D images and Its Application Perspective 24

The Optimal MLP-based Model for Displacement Field Measureme...

引用

2nd asia conference on computer vision, image processing and pattern recognition (cvippr)

作者： Mangileva, Daria Ural Fed Univ Ekaterinburg Russia

ISBN: (纸本)9798400716607

Popularity of using deep network architectures consisting of multi-layer perceptrons (MLPs) has increased recently. The main reason for the phenomenon is the emergence of state-of-the-art technologies that overcome the problem of MLP underfitting in generating graphical information: Gaussian Fourier Function Transformation (GFFT) of MLP input and SineLayer included. Specifically, fully unsupervised MLP-based schemes combining the work of two MLP, grid generator and image generator, have been developed for measuring displacement fields and optical flow in images. However, because of the novelty of these technologies, there is little research dedicated to studying the optimal architecture for such schemes, as well as comparing the methods of SineLayer and GFFT specifically for the task of measuring displacement fields in 2D images. Therefore, the main advantage of this research is fitting the most optimal architecture of MLP in the scheme grid generator - image generator. The results showed that the best variant is an architecture with grid generator and image generator with 4 hidden layers including SineLayers. The testing on synthetic images revealed that the final model has advantages in computing displacement fields in images with gradient-changing pixel values compared to the state-of-the-art method (Unsupervised DICNet and ALDIC). Furthermore, it showed promising results in realistic images of the heart's surface with additional elements, providing displacement fields without any sharp jumps in displacement MSE. Thus, it can be concluded that the use of MLP-based models for measuring displacement fields in 2D images has potential for the further research and development of novel methods for similar tasks.

关键词： unsupervised learning multilayer perceptron SineLayer displacement field measurement

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EDO-SANet: Shape-Aware Network with Edge Detection Operator for Polyp Segmentation 24

EDO-SANet: Shape-Aware Network with Edge Detection Operator ...

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2nd asia conference on computer vision, image processing and pattern recognition (cvippr)

作者： Zhao, Yifei Wang, Xiaoying Li, Yang Yin, Junping North China Elect Power Univ Beijing Peoples R China Northeast Normal Univ Changchun Peoples R China Inst Appl Phys & Computat Math Beijing Peoples R China

ISBN: (纸本)9798400716607

Automatic and accurate segmentation of colonic polyps can effectively help physicians quickly identify the size and location of polyps during endoscopy. However, due to the variable intestinal environment, polyps are highly similar to the surrounding tissue leading to over-segmentation or under-segmentation. To tackle these concerns, we propose a novel network with shape-awareness. Specifically, the network employs a dual-stream encoder, including the mainstream Transoformer-based encoder and an auxiliary encoder to extract global and local representations. The auxiliary encoder is built on deformable convolutional v3 for sensing the shape of polyps. In order to better identify the edges of polyps, the Edge Detection Guided Module (EDGM) is proposed to utilize edge detection operators to highlight the importance of edge information in low-level features. Furthermore, we introduce a Semantic Interaction Module (SIM) to better localize and calibrate targets by integrating global and local semantics in high-level features. Compared to other state-of-the-art methods, our model achieves better performance both in learning and generalization ability on five polyp segmentation datasets. The potential applicability of our model to other biomedical fields is demonstrated through its outstanding performance. The code is available at https://***/xff12138/EDOSANet

关键词： Polyp segmentation Medical image processing Deep learning Deformable convolution Edge detection operator

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Research on Model-Free 6D Object Pose Estimation Based on vision 3D Matching 2

Research on Model-Free 6D Object Pose Estimation Based on Vi...

引用

2nd asia conference on computer vision, image processing and pattern recognition (cvippr)

作者： Chen, Yan Yi, Pengfei Guo, Yujie Liu, Rui Dong, Jing Zhou, Dongsheng Dalian Univ Sch Software Engn Key Lab Adv Design & Intelligent Comp Minist Educ Dalian Liao Ning Peoples R China Dalian Univ Technol Sch Comp Sci & Technol Dalian Liao Ning Peoples R China

ISBN: (纸本)9798400716607

6D object pose estimation is a crucial and fundamental task in the field of human-robot interaction. Existing visual target localization methods either need to rely on an additional depth camera to obtain spatial information of the target object or need to resort to a specific CAD model, which leads to high cost and poor adaptability of pose estimation. In this paper, we propose a 6D pose estimation method for objects based on RGB images and without CAD models, starting from the 3D reconstruction of objects. First, this paper constructs a sparse model of the target object by simple RGB scanning, then, 2D key points in the image are matched with 3D points in the object model using a feature-matching network, and finally, to alleviate the poor matching of such methods on low-resolution images, we introduce a 2D-3D-3D key points matching method, which achieves efficient and robust object 6D pose estimation results. Experimental results on the Onepose dataset demonstrate the accuracy and robustness of the method.

关键词： 6D Object Pose Estimation Key Points Matching RGB Modeling

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Self-Attention-based Multi-Scale Feature Fusion Network for Road Ponding Segmentation 2

Self-Attention-based Multi-Scale Feature Fusion Network for ...

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2nd asia conference on computer vision, image processing and pattern recognition (cvippr)

作者： Yang, Shangyu Zhang, Ronghui Sun, Wencai Chen, Shengru Ye, Cong Wu, Hao Li, Mengran Sun Yat Sen Univ Sch Intelligent Syst Engn Guangdong Prov Key Lab Intelligent Transport Syst Guangzhou 510275 Peoples R China Jilin Univ Coll Transportat Changchun 130025 Peoples R China

ISBN: (纸本)9798400716607

Road ponding, an omnipresent anomaly in traffic scenarios, poses a substantial hazard to road safety, manifesting as potential vehicular loss of control, minor abrasions, or severe collisions. Its transparency renders road ponding susceptible to environmental influences, presenting a formidable challenge for precise edge detection. Existing methodologies exhibit shortcomings in accurately discerning road ponding. In response, we propose an innovative approach called Fusformer (Self-Attention-based Multi-Scale Feature Fusion Network). Representing the pioneering application of Transformer architecture in road ponding detection, Fusformer integrates an enhanced self-attention mechanism derived from Segformer. This mechanism adeptly captures highly non-local contextual attention, thereby facilitating improved identification of ponding edges. Augmenting this approach, we incorporate a multi-scale feature fusion module to mitigate semantic disparities between high-resolution coarse features and low-resolution fine features. This strategy enhances feature interactions, preventing the loss of intricate ponding details and yielding more precise segmentation results. This work contributes valuable insights to proactive warning research in the realm of road traffic safety.

关键词： vision-based Self-attention Feature Fusion Proactive traffic safety

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Hybrid Cross-View Attention Network for Lightweight Stereo image Super-Resolution

Hybrid Cross-View Attention Network for Lightweight Stereo I...

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IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Yang, Yuqiang Zhang, Zhiming Du, Yao Yang, Jingjing Bao, Long Sun, Heng Xiaomi Inc Beijing Peoples R China

ISBN: (纸本)9798350365474

The goal of stereo image super-resolution is to enhance the quality of low-resolution stereo image pairs by utilizing complementary information across views. Although transformer-based methods have shown high efficiency in single-image super-resolution tasks, they have not been fully used in stereo super-resolution tasks. Therefore, it is crucial to incorporate the complementary information of stereo images into the transformer method to improve image details. To address this challenge, we propose a lightweight Hybrid Cross-view Attention Stereo Super-Resolution network (HCASSR), which uses a Transformer-based network for intra-view feature extraction and a cross-view attention module to aggregate stereo image information. We also employ multi-stage training strategies and data ensemble in test-time to improve image quality. Our method has been extensively tested on the KITTI 2012, KITTI 2015, Middlebury, and Flickr1024 datasets, and the experimental results demonstrate that the proposed method outperforms existing works with smaller model size. Additionally, we won 3rd and 2nd place respectively in Track 1 and Track 2 of the NTIRE 2024 Stereo image Super-Resolution Challenge. Codes and models will be released at https://***/YuqiangY/HCASSR.

关键词： Stereo image processing

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image Enhancement for Iris Analysis Using Deep Learning Algorithms - A Brief Survey 2

Image Enhancement for Iris Analysis Using Deep Learning Algo...

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2nd International conference on Emerging Trends in Information Technology and Engineering, ic-ETITE 2024

作者： Poovayar Priya, M. Ezhilarasan, M. Pondicherry Engineering College Dept. of Computer Science and Engineering Puducherry India Pondicherry Engineering College Dept. of Information Technology Puducherry India

ISBN: (纸本)9798350328202

With applications including medical image analysis, picture sharpening and restoration, robot vision, pattern recognition, and video processing, among many others, image enhancement is the main topic in image processing and computer vision. There have been many algorithms for improving images established in the past. More extensive work on image-enhancing techniques has been done utilizing deep learning models in recent years, as these algorithms have achieved their peak in vision applications. In this study, we present a thorough analysis of machine learning, deep learning, and literature. The literature on image enhancement is then summarised, and future approaches for iris analysis are discussed. © 2024 IEEE.

关键词： image enhancement

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Applying Computational Topology for Enhanced image recognition and computer vision 2

Applying Computational Topology for Enhanced Image Recogniti...

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2nd IEEE International conference on Advances in Information Technology, ICAIT 2024

作者： Siddhan, Saravanan Moorthy, A. Christy, S. Sivasankar, C. Bhaskar, K. Vijaya Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Tamilnadu Chennai India Saveetha School of Engineering SIMATS Saveetha University Thandalam Tamil Nadu Chennai India Tamilnadu Chennai India

ISBN: (纸本)9798350383867

Computational topology has consequently shorten the time taken for image recognition with good accuracy and therefore has boosted the performance of computer vision. This paper uses computational topology in different domains like society, livestock, medicine, and remote-sensing. Two primary outcomes have been revealed in the experiment: tightly-spaced pictures, thanks to topology, have increased accuracy by up to 5.0 percent compared to conventional facilitation. Considering the topology-enhanced method edge processing time, the calculation time will increase slightly, but it will have a significant performance advantage. Additionally, the edge, texture, shape, and pattern detection are contributed to the overall classifier output, and the performed method reaches the rate of accuracy of entities detection up to the prior methods. Primarily, we develop and compare the two methods with each other on the basis of accuracy, computational cost, scalability, noise resistance. The grading results show that the improved technique influences picture identification and computer vision system, making the technologies more mature and precise to be used on the real-world tasks. © 2024 IEEE.

关键词： Machine vision

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Automatic Multiple Choice Question Evaluation Using Tesseract OCR and YOLOv8 2

Automatic Multiple Choice Question Evaluation Using Tesserac...

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2nd IEEE conference on Artificial Intelligence (CAI)

作者： Mahmud, Saikat Biswas, Kawshik Alain, Api Rudro, Rifat Al Mamun Anannya, Nusrat Jahan Mouri, Israt Jahan Nur, Kamruddin Amer Int Univ Bangladesh AIUB Dept Comp Sci Dhaka Bangladesh

ISBN: (纸本)9798350354102;9798350354096

This paper presents a novel approach for automating the grading of multiple-choice question (MCQ) answer sheets using computer vision and pattern recognition techniques. The system examines student's marked answer sheet images by comparing with the question sheet image and answer keys. The computer vision and pattern recognition helps extracting pertinent data such as question number detection, MCQ option detection and the answer markings. The proposed approach reliably produces the output report that displays the students' correct answers with an accuracy of 0.98 F1 score and 0.99 mAP from any form or unstructured question script. This approach can provide a dependable and effective grading system, reducing manual work and offering prompt feedback to students without any constraints on the answer sheets.

关键词： Optical character recognition (OCR) image processing object detection YOLOv8

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Single image Dehazing Using CNN 2nd

Single Image Dehazing Using CNN

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2nd International conference on Computational Intelligence in Machine Learning, ICCIML 2022

作者： Bhadane, Samarth Bidwe, Ranjeet Vasant Zope, Bhushan Pune Institute of Computer Technology Maharashtra Pune India Maharashtra Lavale Pune India

ISBN: (纸本)9789819979530

Particulate matter in the atmosphere obscures the visibility of the atmosphere, causing a condition known as haze. Other natural phenomena like mist, fog, and dust also obscure the vision;this is because of scattering of light which attenuates the light intensity. All these instances are responsible for the degradation of image quality. Hazy images are problematic because these images cannot be used for computer vision and image processing applications like pattern and object recognition. Dehazing images improve the clarity and contrast of the images making them more suitable for computer vision and image processing. This paper presents a method of dehazing images using CNN. The proposed model is trained on D-HAZY (Ancuti et al. in 2016 IEEE international conference on image processing (ICIP), 2016) and SOTS (Li et al. in IEEE Trans image Process 28:492–505, 2019) datasets which contain a mix of natural and synthesized hazy images. To assess the model’s performance, we employ PSNR and SSIM metrics. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： image enhancement

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Detecting Brain Tumor Stages using Convolutional AutoEncoder (CAE) with Hybrid Deep Learning Method 2

Detecting Brain Tumor Stages using Convolutional AutoEncoder...

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2nd International conference on Sustainable Computing and Smart Systems (ICSCSS)

作者： Agalya, D. Kamalakkannan, S. Vels Inst Sci Technol & Adv Studies Sch Comp Sci Dept Comp Sci Chennai Tamil Nadu India

ISBN: (纸本)9798350391558;9798350379990

The Brain Tumor (BT) is the formation of abnormal brain cells, few of which can develop into cancer. Early and prompt detection of disease and development of treatment strategies improve patients' wellbeing and life span. Models are created using Deep Learning (DL) and Magnetic Resonance Imaging (MRI) to classify and diagnose brain tumours. This facilitates the effortless detection of brain tumours. The BT is usually caused through abnormal brain cell proliferation that damage the brain structure as well as eventually resulted in various stages of BT. Early BT discovery as well as timely treatment have reduced the mortality rate. Consideration of deep structure for analysis that have been used in both non-linear feature extraction and unsupervised learning, which relies significantly on the Autoencoder (AE). This transfer learning is utilized to obtain better accuracy. Applications for the AE and its variations have been effective in many domains, including recommender systems, data production, pattern recognition, and computer vision. Moreover, the Convolutional AE (CAE) toolkits are addressed for better performance in detection of the brain tumor. The goal of this research is determining whether DL methods can be utilized to automate the detection process. This work uses Convolutional Neural Network (CNN) with VGG19 as the hybrid model to provide the better or maximum accuracy of 92.39% than the traditional Convolutional Neural Network (CNN) with VGG19. This study objective is to use TL with hybrid CNN techniques through this assessment and analysis to direct researchers and medical experts towards effective BT detection systems.

关键词： Deep Learning Autoencoder brain tumor Convolutional Neural Network image processing CNN with VGG19 Convolutional Autoencoder

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