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Hand Gesture Recognition based on Millimeter-Wave Radar using iFormer

Hand Gesture Recognition based on Millimeter-Wave Radar usin...

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ieee International conference on Signal and image processing (ICSIP)

作者： Jiaxin Chen Pengwei Wen Gao Chen Yu Wang Yifan Wang Jianpeng Zheng Zhongyuan University of Technology Zhengzhou China Dongguan University of Technology Dongguan China National Key Laboratory of Security Communication Chengdu China

ISBN: (数字)9798350350920

ISBN: (纸本)9798350350937

Gesture recognition is an important way of human-computer interaction, and gesture recognition using millimetre wave radar has a wider range of application scenarios. Deep learning is widely used in millimetre wave radar gesture recognition, but the features extracted by existing deep learning methods are not sufficient. To address this problem, this paper applies Inception Transformer(iFormer) network to gesture recognition to extract more comprehensive features and improve the accuracy of gesture recognition. Firstly, the radar data of six gestures, namely left wave, right wave, left and right wave, push, pull, push and pull, are processed into micro-Doppler spectrograms using short-time Fourier transform, and then the high-dimensional and low-dimensional features of the spectrograms are extracted and classified using the iFormer network, and the recognition accuracy of this mothod can be up to 98.87%. In addition, a comparison is made with five methods based on AlexNet, visual Geometry Group11(VGG11), GoogLeNet, Residual Network34(ResNet34) and Vision Transformer(VIT), and the experimental results show that the method proposed in this paper performs excellently in terms of classification accuracy and stability.

关键词： Deep learning visualization Accuracy image recognition Gesture recognition Millimeter wave radar Feature extraction

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Enhancing Secret image Authentication Through Encryption-Based Steganography with Reversible Watermarking

Enhancing Secret Image Authentication Through Encryption-Bas...

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Mobile Networks and Wireless communications (ICMNWC), ieee International conference on

作者： P.V. Lakshmi G Sunil Ali Ashoor Issa Kassem Al-Attabi Ghazi Mohamad Ramadan Dept CSE GST GITAM(Deemed to be University) Visakhapatnam India School of Computer Science & Artificial Intelligence SR University Warangal Telangana India National University of Science and Technology Dhi Qar Iraq The Islamic University Najaf Iraq College of MLT Ahl Al Bayt University Karbala Iraq

Reversible data hiding is a special technique primarily used for embedding a secret image that can be later restored from the watermarked image. The image encryption algorithms have been developed based on the Chaotic Logistic-Sine Coupling map (CLSM) While these algorithms perform well in the steganography process there is still room for further development to enhance the level of security they offer. The implementation of the algorithm and its security have been partially analysed using statistical measures like sensitivity to the key space and pixel correlation. Depending on the specific application the CLSM can be computationally intensive making it less suitable for real-time image processing on low-resource devices. To ensure authentication, the MATLAB standard image is exclusively utilized for converting the secret image and embedding it using the Multi-Region Watermark Mechanism (MRWM) designed for color images. In this paper, the Histogram Equalization is utilized feature region is selected and processed and watermarking is performed multiple times due to overlapping feature regions. Comparatively, the results obtained using CLSM show a PSNR of 65.99, MSE of 0.0120, and SSIM of 0.9998 on the MATLAB standard image, surpassing other existing models like Pixel Value Ordering (PVO) and Discrete Cosine Transform (DCT) demonstrating superior image performance.

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Cleaning Highly Unbalanced Multisource image Dataset for Quality Control in Cervical Precancer Screening 4th

Cleaning Highly Unbalanced Multisource Image Dataset for Qua...

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4th International conference on Recent Trends in image processing and Pattern Recognition, RTIP2R 2021

作者： Xue, Zhiyun Guo, Peng Angara, Sandeep Pal, Anabik Jeronimo, Jose Desai, Kanan T. Ajenifuja, Olusegun K. Adepiti, Clement A. Sanjose, Silvia D. Schiffman, Mark Antani, Sameer National Library of Medicine BethesdaMD20894 United States National Cancer Institute RockvilleMD20850 United States Obafemi Awolowo University Ile-Ife Nigeria

ISBN: (纸本)9783031070044

Automated visual evaluation (AVE) of uterine cervix images is a deep learning algorithm that aims to improve cervical pre-cancer screening in low or medium resource regions (LMRR). image quality control is an important pre-step in the development and use of AVE. In our work, we use data retrospectively collected from different sources/providers for analysis. In addition to good images, the datasets include low-quality images, green-filter images, and post Lugol’s iodine images. The latter two are uncommon in VIA (visual inspection with acetic acid) and should be removed along with low-quality images. In this paper, we apply and compare two state-of-the-art deep learning networks to filter out those two types of cervix images after cervix detection. One of the deep learning networks is DeepSAD, a semi-supervised anomaly detection network, while the other is ResNeSt, an improved variant of the ResNet classification network. Specifically, we study and evaluate the algorithms on a highly unbalanced large dataset consisting of four subsets from different geographic regions acquired with different imaging device types. We also examine the cross-dataset performance of the algorithms. Both networks can achieve high performance (accuracy above 97% and F1 score above 94%) on the test set. © Springer Nature Switzerland AG 2022.

关键词： Iodine

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Underwater image enhancement by using color correction and contrast techniques 6

Underwater image enhancement by using color correction and c...

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6th International conference on image Information processing, ICIIP 2021

作者： Gowda B N, Vijay Kumar Gauni, Sabitha Maik, Vivek SRM Institute of Science and Technology ECE Department Chennai India

ISBN: (纸本)9781665433617

An image in the underwater ocean depth of 10 meters by capturing a low-resolution camera. An image is characterized by low contrast and blurriness. Despite water medium has considering the propagation of light, it degrades the underwater image due to refraction, scatters, and color absorption. This underwater image needs to improve its color contrast and quality image by using Simple Histogram Equalization for color correction and DSNMF (Deep Sparse Non-Negative Matrix Factorization) for color contrast improvement. Finally, the proposed method results describe based on the observations of the qualitative and quantitative parameters of PSNR (Peak Signal to Noise Ratio), RMSE (Root Mean Square Error), and SSIM (System Similarity Index Matrix). The output of This proposed method is shown a qualitative state-of-The-Art underwater enhanced image with better brightness and contrast and developed technique simulation produces a quantitative output of enhancing the image of PSNR, RMSE, and SSIM are 25.256, 13.235, and 8.232, with these parameters increasing better quality visual perception. © 2021 ieee.

关键词： image enhancement

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Intelligent Surveillance and Protection System for Farmlands from Animals

Intelligent Surveillance and Protection System for Farmlands...

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Contemporary Computing and communications (InC4), ieee International conference on

作者： Shashi Raj K Basavaraj S. Sannakashappanavar Mayur Kumar Guruprasada S Hegde Megha M Department of Electronics and Communication Engineering Dayananda Sagar College of Engineering Bangalore India

ISBN: (数字)9798350383652

ISBN: (纸本)9798350383669

Agriculture is an important area for growing crops, as food is the main source of sustainability. India, with its vast agricultural area, has a wide variety of crops, from food grains to vegetables. However, few crops are destroyed by animal attacks, so a proper monitoring system is needed to protect the crops from animals. Crops are mainly damaged by animals such as buffalo, cattle, goats, birds, and wild elephants. This causes huge losses to farmers. The farmer cannot protect the crops if animals stay in the field for more than 24 hours. To address this issue, an animal detection system that detects the presence of animals has been developed. It provides the warning and redirects animals without harming them by using Machine learning and image processing technique. The proposed system is deployed in the fields with a camera which continuously monitors. When an animal enters the field, it receives a short urge, which only threatens the animal to leave the field but doesn't harm the animal by using a laser beam instead of a fence that can harm the animal. In addition, the animal is redirected by playing sound of certain frequency and the same is intimated through SMS for quick notification to farmer. Light-dependent resistance sensors (LDRs) are often used for distance measurement. This system is also excellent at detection of various obstacles (wood, plastic, mirrors, plywood, concrete, etc.). The minimum obstacle size that the system can detect is about 5cmx5cm. The proposed developed model is a lab scale and can be scaled for further use. The overall safety of the crop was ensured from the animals by this system and thus protects the losses that occurred to farmers. To detect and monitor the movements of animals the developed system uses laser beam. Further, this model ensures the complete safety of the crop from the animals and prevents the loss of the farmer.

关键词： Resistance Animals Surveillance image processing Crops Measurement by laser beam Machine learning

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Eye Movement Analysis using Virtual Reality in Early Detection of Alzheimer’s Disease

Eye Movement Analysis using Virtual Reality in Early Detecti...

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image processing, Computer Vision and Machine Learning (ICICML), International conference on

作者： Haiwei Zuo King Hann Lim Eswaran Sivaraman Jonathan Then Sien Phang Shenzhen City Polytechnic School of Information and Communications Technology Curtin Malaysia Research Institute Curtin University Malaysia

ISBN: (数字)9798350355413

ISBN: (纸本)9798350355420

Early detection of Alzheimer’s disease (AD) is important at the initial stage to provide prior treatment and control. In traditional diagnostic methods, medical doctors perform manual AD checking by asking patients to repeat several round of text reading and game exercises. The quantification of this manual assessment can be subjective due to the profession experience and it requires a further verification using the brain imaging. In this paper, an automated digital AD assessment method using Virtual Reality (VR) console is proposed to evaluate patient’s condition by tracking their eye movement during text reading. First, the eye movement pattern is captured using VR console during text reading. The recorded eye movement is then converted into frequency domain to study its frequency components during reading process. The simulation of AD symptom is set up by prompting subjects to read their familiar native language and unfamiliar language. As a result, the experiment shows that there are significant differences in eye movement patterns between native and unfamiliar language reading. The group of subjects reading unfamiliar language have random eye movement in less efficient visual processing. This pattern is similar to the patients who suffer in the AD symptom. In addition, the frequency features are extracted to study its correlations with reading comprehension performance in order to detect the early AD condition.

关键词： visualization Correlation Tracking Frequency-domain analysis Virtual reality Manuals Frequency conversion Feature extraction Alzheimer's disease Medical diagnostic imaging

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Self-supervised Continual Learning for Object Recognition in image Sequences 28th

Self-supervised Continual Learning for Object Recognition in...

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28th International conference on Neural Information processing

作者： Dai, Ruiqi Lefort, Mathieu Armetta, Frederic Guillermin, Mathieu Difiner, Stefan Univ Lyon INSA Lyon Lyon France Univ Claude Bernard Lyon 1 Univ Lyon Lyon France CNRS UMR5205 LIRIS Villeurbanne France Univ Catholique Lyon Lyon France

ISBN: (纸本)9783030923068;9783030923075

The autonomous learning of different objects in images, with a continual and unsupervised context, relies on detecting unknown objects and recognizing known ones based on the learned visual representation. Novelty detection is challenging because of the internal representation drifts of known objects not been seen for a long time. Most existing approaches either perform offline unsupervised learning on a large dataset, or continual supervised learning. Nevertheless, very few existing approaches propose unsupervised continual learning for object recognition. In this paper, we propose a new neural network-based approach for continually learning representations of objects from image sequences, that is able to autonomously detect novel objects and to recognize previously learned ones during training. It is based on a statistical test, performed on internal representations, adapted to counterbalance the concept drift, without storing any image. Experimental results show that our approach outperforms the state of the art on MNIST and Fashion-MNIST datasets. In particular, our approach avoids to over-segment the distribution of clusters, which artificially increases traditional indicators such as clustering accuracy.

关键词： Object recognition

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PicoVO: A Lightweight RGB-D visual Odometry Targeting Resource-Constrained IoT Devices

PicoVO: A Lightweight RGB-D Visual Odometry Targeting Resour...

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ieee International conference on Robotics and Automation (ICRA)

作者： He, Yuquan Wang, Ying Liu, Cheng Zhang, Lei Chinese Acad Sci Inst Comp Technol Beijing Peoples R China Univ Chinese Acad Sci Beijing Peoples R China Jeejio Ningbo Technol Co Ltd Ningbo Peoples R China

ISBN: (纸本)9781728190778

Ego motion estimation with 3D perception using visual odometry (VO) is known to be robust and economical among the existing odometry techniques. However, existing VO solutions are typically both computation intensive and memory intensive, which dramatically inhibits their deployment in IoT platforms such as robotic vehicles and handheld devices mostly equipped with resource-constrained MCU-level processors. To enable real-time and high-quality VO on these scenarios with thrifty resource budgets, we investigate state-of-the-art edge-based VO (EBVO) and propose an optimization framework called PicoVO that can greatly reduce the amount of computation as well as the memory footprint from the perspectives of both algorithm and implementation. First of all, we revisit the key processing stages of EBVO and propose an EBVO-oriented lightweight edge detector in the pre-processing stage, a sparse-to-dense processing scheme in the tracking stage, and a lightweight key-frame management in the post-processing stage. In addition to the algorithmic optimization, we further develop a dedicated quantization scheme particularly for the 3D feature calculation and Levenberg-Marquardt (LM) solver that are critical to the memory footprint and computation requirements of PicoVO. Evaluation on realistic RGB-D benchmark datasets is conducted on NUCLEO-F767ZI equipped with a 216MHz Cortex-M7 MCU and 512KB RAM. It reveals that PicoVO achieves 33fps@320x240 with high tracking precision comparable to state-of-the-art VOs on PC.

关键词： Three-dimensional displays Target tracking Simultaneous localization and mapping image edge detection Memory management Random access memory Detectors

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BEV-CV: Birds-Eye-View Transform for Cross-View Geo-Localisation

BEV-CV: Birds-Eye-View Transform for Cross-View Geo-Localisa...

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ieee/RSJ International conference on Intelligent Robots and Systems (IROS)

作者： Tavis Shore Simon Hadfield Oscar Mendez Centre for Vision Speech and Signal Processing (CVSSP) University of Surrey Guildford United Kingdom

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Cross-view image matching for geo-localisation is a challenging problem due to the significant visual difference between aerial and ground-level viewpoints. The method provides localisation capabilities from geo-referenced images, eliminating the need for external devices or costly equipment. This enhances the capacity of agents to autonomously determine their position, navigate, and operate effectively in GNSS-denied environments. Current research employs a variety of techniques to reduce the domain gap such as applying polar transforms to aerial images or synthesising between perspectives. However, these approaches generally rely on having a 360° field of view, limiting real-world feasibility. We propose BEV-CV, an approach introducing two key novelties with a focus on improving the real-world viability of cross-view geo-localisation. Firstly bringing ground-level images into a semantic Birds-Eye-View before matching embeddings, allowing for direct comparison with aerial image representations. Secondly, we adapt datasets into application realistic format - limited Field-of-View images aligned to vehicle direction. BEV-CV achieves state-of-the-art recall accuracies, improving Top-1 rates of 70° crops of CVUSA and CVACT by 23% and 24% respectively. Also decreasing computational requirements by reducing floating point operations to below previous works, and decreasing embedding dimensionality by 33% - together allowing for faster localisation capabilities.

关键词： visualization Limiting Accuracy Navigation image matching Semantics Crops Transforms image representation Intelligent robots

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Fast Object Detection Algorithm using Edge-based Operation Skip Scheme with Viola-Jones Method

Fast Object Detection Algorithm using Edge-based Operation S...

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ieee International conference on Artificial Intelligence Circuits and Systems (AICAS)

作者： Cheol-Ho Choi Joonhwan Han Jeongwoo Cha Jungho Shin Hyun Woo Oh Pangyo Research and Development (R&D) Center Hanwha Systems Co. Ltd. Seongnam Republic of Korea

ISBN: (数字)9798350383638

ISBN: (纸本)9798350383645

Machine learning approaches are preferred over deep learning in embedded systems due to their resource efficiency. The widely adopted Viola-Jones method and related algorithms are selected for their high detection accuracy and reasonable processing speed. However, a limitation arises as processing time increases with additional classification iterations based on sub-window operations. To address this issue, we propose an enhanced object detection algorithm that incorporates the Viola-Jones method with edge component calibration and an edge-based operation skip scheme. The introduction of edge component calibration ensures detection performance comparable to conventional methods. This scheme, relying on edge values, significantly reduces unnecessary computations in the background, leading to a marked decrease in classification operations compared to conventional methods. visual comparisons in experimental results demonstrate that our method increases the detection precision factor while maintaining recall. In terms of classification operations, our approach reduces their number by 31.38% to 85.78% compared to conventional methods. In simpler terms, our method improves processing speed by minimizing classification operations, making it well-suited for embedded systems with limited resource utilization.

关键词： visualization Embedded systems Machine learning algorithms image edge detection Object detection Transforms Classification algorithms

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