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Visual Place Recognition Datasets for Indoor Spaces

Visual Place Recognition Datasets for Indoor Spaces

引用

ieee international conference on Multimedia and Expo (ICME)

作者： Guo, Zemian Zhu, Yingying Shenzhen Univ Coll Comp Sci & Software Engn Shenzhen Peoples R China

ISBN: (纸本)9781665468916

Visual place recognition (VPR) is widely cast as a challenging image retrieval problem. Recently, many studies in this area have achieved superior results. However, most existing works consider outdoor spaces rather than indoor spaces. To fill this gap, this paper for the first time constructs a benchmark based on realistic environments for indoor VPR, involving three new indoor datasets which contains 25, 233 RGB images in total. These datasets cover typical indoor environments with over 250 places and provide a wide range of challenging cases. Moreover, this paper introduces a patch relation module based on spatial coordinate position of image patches and a global average pooling pyramid to get discriminative and robust features. Extensive experiments are conducted on our datasets to validate the effectiveness of the proposed method. The results indicate that indoor VPR in realistic setting is still challenging, fostering new research in this direction. Our dataset and code will be released at https://***/Dauntless-Wind/indoor-visual-place-recognition.

关键词： Visual place recognition Indoor spaces image retrieval

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Energy-Efficient Joint Communication and Trajectory Design for UAV-Assisted internet of Things With RSMA 15

Energy-Efficient Joint Communication and Trajectory Design f...

引用

15th ieee international conference on Wireless Communications and signal Processing, WCSP 2023

作者： Liu, Lizhou Qiu, Ling University of Science and Technology of China Key Laboratory of Wireless-Optical Communications Chinese Academy of Sciences School of Information Science and Technology Hefei China

ISBN: (纸本)9798350324662

This paper studies a rate splitting multiple access (RSMA) based unmanned aerial vehicle (UAV)-assisted internet of things (IoT) system, in which a UAV is dispatched to serve multiple ground nodes (GNs). Under this setup, we maximize the energy efficiency of the system by jointly optimizing the GN scheduling, the UAV trajectory, and the RSMA precoding, subject to the rate requirements at the GNs, the maximum transmit power at the UAV, and the UAV mobility constraints. The optimization problem is a mixed integer nonlinear fractional programming problem that is hard to solve. In order to handle it, we propose an efficient algorithm based on block coordinate descent and successive convex approximation. Finally, numerical results show that the proposed joint communication and trajectory design based on RSMA effectively improves the energy efficiency, and outperforms the benchmark schemes based on non-orthogonal multiple access (NOMA) and spatial division multiple access (SDMA). © 2023 ieee.

关键词： energy efficiency internet of things (IoT) Rate splitting multiple access (RSMA) Unmanned aerial vehicle (UAV)

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Low-quality image binarization method based on threshold array system 4

Low-quality image binarization method based on threshold arr...

引用

4th international conference on Computer Vision, image and Deep Learning, CVIDL 2023

作者： Mei, Xinguo Mu, Dazhong Cao, Peng Beijing Institution of Graphic Communication Beijing Key Laboratory of Signal and Information Processing for High-end Printing Equipment Beijing China

ISBN: (纸本)9798350326444

In order to improve the anti-noise performance of traditional image binarization methods, this paper proposes a novel binarization method for low-quality images based on threshold array system. The proposed method involves converting the image signal into a one-dimensional time sequence signal via Hilbert scanning, followed by normalization of the time sequence signal. The normalized signal is then input to the threshold array system for stochastic resonance processing. The output signal from the threshold array system undergoes demodulation and reverse scanning to generate the final binary image. Experimental results demonstrate that this approach outperforms traditional binarization methods such as Niblack, Otsu, and Sauvola under condition of strong noise interference. © 2023 ieee.

关键词： Binary images

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Comparative Study of Implementation of Very Deep Super Resolution Neural Network and Bicubic Interpolation for Single image Super Resolution Quality Enhancement 3

Comparative Study of Implementation of Very Deep Super Resol...

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3rd ieee international conference on Applied Electromagnetics, signal Processing, and Communication, AESPC 2023

作者： Ganesan, P. Ravichandran, M. Sathish, B.S. Joseph, L.M.I.Leo Sajiv, G. Murugesan, R. Vidya Jyothi Institute of Technology Dept. of Electronics and Communication Engineering Hyderabad India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Dept. of Computer Science Engineering Chennai India Sri Venkateshwara College of Engineering Dept. of Computer Science Engineering Ai Bengaluru India Sr University Dept. of Electronics and Communication Engineering Telangana Warangal India Saveetha School of Engineering Simats Chennai India Narshima Reddy Engineering College Dept. of Electronics and Communication Engineering Secunderabad India

ISBN: (纸本)9798350358742

To improve the resolution and quality of a low level image, single image super resolution (SISR) is a challenging endeavor in the turf of computer vision. It is essential to many applications, including image editing, security systems, medical imaging, and others. In order to increase the spatial quality of a low level image, SISR attempts to approximate the high frequency details that are missing from the image. The field was formerly based on manually created features and interpolation techniques, but current developments in deep learning have completely changed it. The suggested work compares the effectiveness of bicubic interpolation and neural network based very deep super resolution for single image super resolution image quality improvement. Utilizing blind and complete reference picture quality measures, the effectiveness of the two approaches is evaluated. The effectiveness of the very deep super resolution for single image is clearly demonstrated by the experimental results. © 2023 ieee.

关键词： Medical imaging

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Improving Clinical Outcomes in Fracture Detection with Hybrid CNN Models and Data Augmentation 2

Improving Clinical Outcomes in Fracture Detection with Hybri...

引用

2nd international conference on Intelligent Cyber Physical systems and internet of Things (ICoICI)

作者： Kant, Vishnu Chitkara Univ Inst Engn & Technol Rajpura 140401 Punjab India

ISBN: (纸本)9798331540661;9798331540678

Common medical conditions stemming from trauma, accidents, or osteoporosis-bone fractures-demand quick and accurate diagnosis to guarantee appropriate treatment and reduce consequences. Under duress or with tiny fractures especially, radiologists have traditionally depended on visual assessment of X-ray images, a method prone to mistakes and time-consuming. The study presents a Convolutional Neural Network (CNN) model meant to greatly improve X-ray image bone fracture detection accuracy. Overcoming its constraints, our model offers a fast and dependable substitute for hand diagnosis by using advanced deep learning approaches. Carefully created and trained on a large dataset from Kaggle, the CNN shows an amazing accuracy of 97% in separating fractured from non-fractured bones. Dense layers for final classification, Max Pooling 2D layers for dimensionality reduction, and several Conv2D layers for hierarchical feature extraction define the model. Especially, our method uses a modified Canny edge technique to emphasize fracture sites, therefore enhancing the performance of the model. Our CNN shows better computational accuracy and efficiency than present state-of- the-art methods. This work greatly adds to the increasing corpus of research in artificial intelligence-assisted medical diagnostics by highlighting the possibilities of CNNs to improve patient care and maximize fracture diagnosis. Our work corresponds with worldwide objectives to improve health outcomes and support sustainable medical practices by improving medical technology, supporting effective healthcare solutions, and raising diagnosis accuracy. By means of methodical data collecting, pre-processing, model creation, and thorough evaluation, it provides a strong instrument for exact and efficient bone fracture diagnosis, thereby redefining the field's benchmark.

关键词： bone fracture detection Convolutional Neural Networks (CNN) X-ray image analysis deep learning medical image classification fracture diagnosis artificial intelligence in healthcare image pre-processing model accuracy Kaggle dataset

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A U-Shaped Architecture based on Attention Mechanism 23

A U-Shaped Architecture Based on Attention Mechanism

引用

23rd ieee international conference on Communication technology, ICCT 2023

作者： Lu, Chang Lin, Lan Xu, Minyang Tongji University Dept. of Electronic Science and Technology Shanghai China School of Computer Science Fudan University Shanghai China

ISBN: (纸本)9798350325959

With the rapid development of computer and artificial intelligence, image segmentation technology based on deep learning plays an increasingly important role in the medical field. In this paper, we propose the attention mechanism-based U-Net++, a new and more powerful medical image segmentation architecture. Our architecture is essentially an attention-based encoder-decoder network that uses dense skip connection to capture features at different levels, by introducing channel attention and spatial attention, the neural network pays more attention to the pixel regions that are decisive for classification and ignores the insignificant regions, so as to handle the distribution relationship of the feature map channels. In addition, we design a hybrid loss function that integrates the cross-entropy loss, Dice loss, and Focal loss to focus more on the part which is difficult to divide of the medical image dataset. We evaluate the attention-based U-Net++ compared to U-Net and UNet++ architectures in a medical image segmentation task for skin lesion datasets. Our experiments show that the average IoU gain of the U-Net++ networks based on the attention mechanism exceeds that of general U-Net and UNet++. © 2023 ieee.

关键词： attention mechanism dense skip connection hybrid loss medical image segmentation

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Implementation and Comparative Analysis of CNN and Discrete Haar Wavelet Transform in image Steganography 16

Implementation and Comparative Analysis of CNN and Discrete ...

引用

16th ieee international conference on Computational Intelligence and Communication Networks, CICN 2024

作者： Srusti, R. Shruthi, M.L.J. Pes University Department of ECE Bengaluru India

ISBN: (数字)9798331505264

ISBN: (纸本)9798331505264

In the developing field of Steganography, there is a constant search for more secure and efficient methods of hiding secret images within digital images. This paper presents the implementation and comparative analysis of the traditional Discrete Wavelet Transform (DWT) steganography technique using Haar wavelets with the modern Convolutional Neural Network (CNN)-based method. Both the models are compared using metrics such as Structural Similarity Index (SSI), Mean Squared Error (MSE), and Peak signal-to-Noise Ratio (PSNR) to determine the efficiency and robustness in effectively hiding the secret image. The results show an improvement in SSI by 8.42%, improvement in PSNR by 58.46%, and decrement in MSE by 85.05%, implying that CNN based steganography method significantly outstrips the traditional Discrete Haar wavelet based steganography technique. The CNN approach not only enhances the undetectability and security of the hidden information but also maintains higher accuracy of the steganographic images, thus marking an important advancement in the domain of digital steganography. © 2024 ieee.

关键词： Convolutional neural networks

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Design and implementation of a scenario classification system for the internet of things application 3

Design and implementation of a scenario classification syste...

引用

3rd international conference on Advanced Algorithms and signal image Processing, AASIP 2023

作者： Zheng, Hao Li, Hong Xu, Bo Heilongjiang Provincial Research Institute of Spatial Planning China

ISBN: (纸本)9781510668522

With the rapid development of the internet of Things industry today, it is imperative to classify the internet of Things scenarios to adapt to the needs of the internet of Things in different scenarios. In recent years, Convolutional Neural Network (CNN) has succeeded in scene parsing. The methods based on CNN generally extract the visual features of the input images through the step-by-step abstraction of multiple convolutional layers and then parse the corresponding segmentation results of the output images through the step-by-step restoration of multiple deconvolutions. Screen the scene pictures of the internet of Things from the large data set, use the image recognition algorithm to extract the features of the photographs, and use the extracted features to cluster the image set (for example, it can be divided into a family environment, car environment, etc.), divide the pictures with similar features into the same set, and finally measure the clustering accuracy to realize the internet of Things scene classification. Finally, scene recognition accuracy is about 87%, and the clustering of feature vectors is also satisfactory. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： internet of things

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Fhelo-MTH: Floret Heliotropic Optimized Multilevel Thresholding-based image Segmentation 2

Fhelo-MTH: Floret Heliotropic Optimized Multilevel Threshold...

引用

2nd international conference on Self Sustainable Artificial Intelligence systems, ICSSAS 2024

作者： Sushir, R.D. Bhutada, Sarita S. Prpcem Maharashtra Amravati444604 India Sgbau Maharashtra Amravati444604 India

ISBN: (纸本)9798350368413

The process of segmenting one image into several segments is called image segmentation, which is widely used in several visual understanding applications. Multilevel thresholding-based segmentation enhances the ability to discern subtle changes in the image, making it suitable for detailed image analysis tasks. However, the traditional multilevel segmentation algorithms often pose several challenges including poor segmentation accuracy, stuck in local optimum issues, and minimized convergence speed. Therefore, to tackle the aforementioned challenges, this research proposes a Floret Heliotropic optimized Multilevel Thresholding (FHelO-MTH) algorithm. The integration of the heliotropic movement of sunflowers with the natural pollination process of flowers enhances the FHelO-MTH algorithm to attain optimal threshold values for effective segmentation. Furthermore, the FHelO-MTH algorithm reduces the error rate, improving the proposed algorithm's overall performance and dependability. When the FHelO-MTH algorithm's performance is compared to other image segmentation algorithms, the outcomes show a superior Peak signal-to-noise ratio (PSNR) of 45.61 dB. © 2024 ieee.

关键词： image segmentation

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FPGA-based Implementation of the Quantized Neural Network for image Super-Resolution 8

FPGA-Based Implementation of the Quantized Neural Network fo...

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8th international conference on image, Vision and Computing, ICIVC 2023

作者： Ruan, Zhihao Xiao, Shanzhu Deng, Qiuqun Tao, Huamin Wu, Jiadong Zhang, Huan College of Electronic Science and Technology National University of Defense Technology Chang Sha China

ISBN: (纸本)9798350335231

The convolutional neural network(CNN)-based method for image super-resolution(SR) reconstruction has been becoming an important part in the fields of image processing such as security monitoring, object tracking. However, the huge computational complexity of neural networks make it shackled to satisfy the requirements for power consumption and complexity of reconstruction systems in specific application circumstances. In this paper, we propose a structure-unsophisticated and parameter-slight CNN model, and design a quantization scheme that allows inference to be performed by using integer-only arithmetic to alleviate parameter storage and communication pressure in hardware. Furthermore, convolution loop unroll and parallel acceleration analysis are performed for the model under the premise of board resources to optimize hardware inference. The results show that when the quantization precision is 8 bits, memory requirement of parameters is reduced by 75%, and the inference peak signal-to-noise ratio(PSNR) value loss is less than 1%. Quantization model was performed on the Xilinx Zynq7035 platform with inference losses not exceeding 2%, while hardware resources consumption is greatly reduced. © 2023 ieee.

关键词： Acceleration

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