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24th ISPRS Congress on Imaging Today, Foreseeing Tomorrow

作者： Zhang Hongping Du Mingyi Huang Wei Ding Lei Tang Dejin Jiang Jie Beijing Univ Civil Engn & Architecture Sch Geomat & Urban Spatial Informat Beijing Peoples R China Natl Geomat Ctr China Beijing Peoples R China

With the development of spatial information technology, the amount of geographic information data shows an explosive growth, which puts forward higher demands on the time efficiency and visualization effect of geographic information data release. vector tile maps have become the main map service mode in the fields of Internet maps and GIS industries because of the advantages of its powerful interactive capabilities, efficient data transmission and lower storage costs. In order to construct massive vector tiles quickly and enhance the scalability of vector tiles application, this paper researched the vector tiles construction technology based on the distributed computing framework. Firstly, we introduced the construction process of vector tiles such as pyramid model, data organization and data generalization, Specifically, the data generalization methods of different geometric types were discussed. Second, Apache Sedona, the distributed computing framework were researched and the advantages for vector data processing is introduced. Then, the Sedona based parallel construction technology is proposed, and pipeline optimization of Spark is applied in this process. Third, a comparative experiment to evaluate the performance were conducted, the result showed that the parallel construction technology had obvious performance advantages, the greater the volume and extent of vector data, the greater the advantage.

关键词： Web map Vector tile Pyramid model parallel processing Apache Sedona

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AMQGaussian: Efficient 3D Gaussian Representation with Asymmetric Mixed-precision Quantization

AMQGaussian: Efficient 3D Gaussian Representation with Asymm...

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International Symposium on parallel and distributed processing with Applications, ISPA

作者： Yong Kou Qing Ye Yanci Zhang Xia Yuan Jinlong He College of Computer Science Sichuan University Chengdu China

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

3D Gaussian Splatting (3DGS) has recently gained increasing attention in novel-view scene synthesis. However, it requires millions of 3D Gaussian spheres to achieve high-quality rendered images, leading to substantial GPU resource demands for training and rendering. This paper has developed an efficient framework to address the challenges faced by 3DGS. 1) We propose an asymmetric mixed-precision quantization strategy to efficiently quantize and dequantize the parameters of 3D Gaussian Spheres (excluding position and opacity) and utilized the Straight Through Estimator method to address the issue of non-backpropagatable gradients post-quantization. This approach significantly reduces GPU memory usage and model storage space. 2) Using statistical methods, we identify optimal gradient thresholds to enhance the densification algorithm of 3D Gaussian spheres, thereby further improving performance. 3) To address the speed bottlenecks in parallel processing of low-precision data using CUDA, we introduce a fast atomic operation method, increasing training speed tenfold. Overall, we validate the effectiveness of our framework across various datasets. It reduces GPU usage by 50% during training, decreases GPU usage threefold during rendering, halves the model storage space and maintains image quality comparable to 3DGS.

关键词： Training image quality Solid modeling Three-dimensional displays Quantization (signal) Statistical analysis Graphics processing units parallel processing Rendering (computer graphics) image reconstruction

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A Noise-Based Approach Augmented with Neural Cleanse and JPEG Compression to Counter Adversarial Attacks on image Classification Systems

A Noise-Based Approach Augmented with Neural Cleanse and JPE...

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Euromicro Conference on parallel, distributed and Network-Based processing

作者： Igor Kotenko Igor Saenko Oleg Lauta Nikita Vasiliev Vladimir Sadovnikov Laboratory of Computer Security Problems St. Petersburg Federal Research Center of the Russian Academy of Sciences (SPC RAS) Saint-Petersburg Russia Department of Telecommunications and Information Security Admiral Makarov State University of Maritime and Inland Shipping Saint-Petersburg Russia

ISBN: (数字)9798331524937

ISBN: (纸本)9798331524944

Adversarial attacks are now becoming quite a dangerous means of disrupting image processing systems that use machine learning methods for decision making. Therefore, developing effective countermeasures against adversarial attacks is becoming quite an important area of cybersecurity. The paper proposes a noise-based approach to countering adversarial attacks that is augmented with neural-cleanse and jpeg-compression technologies. The idea of the proposed approach is that adding noise distorts the effect of an adversarial attack, and neural cleaning and jpeg compression eliminate the consequences of such an effect. The paper examines the three most well-known types of adversarial attacks: Fast Gradient Sign Method, Zeroth Order Optimization and One Pixel Attack. These attacks manipulate input data, resulting in misclassification or incorrect predictions by exploiting high-frequency components that are undetectable to humans. The research was carried out on two datasets: MNIST-JPG and PC Parts images. Two types of noise were used: Gaussian and Poisson. During the experiments, optimal parameters for these types of noise were found, ensuring maximum accuracy of image recognition after exposure to adversarial attacks.

关键词： Resistance image recognition image coding image resolution Noise Transform coding Machine learning Distortion Optimization image classification

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Deep learning-based computer aided diagnosis model for skin cancer detection and classification

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distributed AND parallel DATABASES 2022年第4期40卷 717-736页

作者： Adla, Devakishan Reddy, G. Venkata Rami Nayak, Padmalaya Karuna, G. JNTUH Hyderabad India VJIT Dept IT Hyderabad India JNTUH Sch Informat Technol Hyderabad India GRIET Dept CSE Hyderabad India JNTUH Dept CSE GRIET Hyderabad India

Skin cancer is a commonly occurring disease, which affects people of all age groups. Automated detection of skin cancer is needed to decrease the death rate by identifying the diseases at the initial stage. The visual inspection during the medical examination of skin lesions is a tedious process as the resemblance among the lesions exists. Recently, imaging-based Computer Aided Diagnosis (CAD) model is widely used to screen and detect the skin cancer. This paper is designed with automated Deep Learning with a class attention layer based CAD model for skin lesion detection and classification known as DLCAL-SLDC. The goal of the DLCAL-SLDC model is to detect and classify the different types of skin cancer using dermoscopic images. During image pre-processing, Dull razor approach-based hair removal and average median filtering-based noise removal processes take place. Tsallis entropy based segmentation technique is applied to detect the affected lesion areas in the dermoscopic images. Also, a DLCAL based feature extractor is used for extracting the features from the segmented lesions using Capsule Network (CapsNet) along with CAL and Adagrad optimizer. The CAL layer incorporated into the CapsNet is intended to capture the discriminative class-specific features to cover the class dependencies and effectively bridge the CapsNet for further process. Finally, the classification is carried out by the Swallow Swarm Optimization (SSO) algorithm based Convolutional Sparse Autoencoder (CSAE) known as SSO-CSAE model. The proposed DLCAL-SLDC technique is validated using a benchmark ISIC dataset. The proposed framework has accomplished promising results with 98.50% accuracy, 94.5% sensitivity and 99.1% specificity over the other methods interms of different measures.

关键词： Skin lesion Dermoscopic images image classification ISIC dataset Deep learning image segmentation Hair removal Capsule Networks

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Spatial-Frequency Integration Network with Dual Prompt Learning for Few-shot image Classification

Spatial-Frequency Integration Network with Dual Prompt Learn...

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International Symposium on parallel and distributed processing with Applications, ISPA

作者： Yi Liu Jie He Shoukun Xu Guangqi Jiang Academy of Computing and Artificial Intelligence Changzhou University Changzhou 213164 Jiangsu China Changzhou China

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

Few-shot image classification is a challenging task that aims to recognize image classes based on only a few training images. However, existing methods face the following two main challenges: (1) Ignoring the frequency domain information during image feature extraction. (2) It does not take the semantic gap between multiple modalities into consideration, which limits the classification performance. To overcome these limitations, we propose a novel method named Spatial-Frequency Integration Network with Dual Prompt Learning for few-shot image classification. Firstly, we introduce a spatial-frequency integration module that combines spatial domain and low-frequency information to extract discriminative image features from the image modality. Secondly, we design a dual prompting module, which integrates learnable prompts and hand-crafted prompts to improve the generalization of applications to new classes. Thirdly, we propose an image-text interaction module to enhance inter-modal complementary and consistency. Both theoretical and experimental validations confirm the effectiveness of the proposed method in few-shot image classification.

关键词： Training image segmentation image recognition Frequency-domain analysis Face recognition Semantics Object detection Feature extraction Few shot learning image classification

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parallel Coregistration Algorithm For Sar images Based On Hadoop

Parallel Coregistration Algorithm For Sar Images Based On Ha...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Jiawei Li Guobing Zeng Huaping Xu School of Electronic and Information Engineering Beihang University Beijing China

As the availability of SAR images continues to grow, efficient coregistration of massive SAR images presents a greater challenge. Traditional serial coregistration methods impose an unbearable time overhead. To reduce this overhead and make full use of computing resources, a parallel coregistration strategy based on Hadoop is proposed for SAR images. The Hadoop distributed File System (HDFS) is used to store SAR image data in chunks, and Hadoop's distributed computing strategy MapReduce is used to realize distributed parallel processing of SAR images. Two distributed parallel coregistration methods are presented with the proposed parallel strategy: one based on the maximum correlation method and the other on the DEM-assisted coregistration method. These methods are evaluated through coregistration experiments on the same dataset, and they are verified by comparing the coregistration results and processing time.

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High-Precision Encrypted image Retrieval Scheme Using Dual-Stream Convolution Feature Fusion in Cloud Computing

High-Precision Encrypted Image Retrieval Scheme Using Dual-S...

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International Symposium on parallel and distributed processing with Applications, ISPA

作者： Mingyue Li Liudong Zheng Jiahui Liu Ruizhong Du Chunfu Jia Hebei University Baoding China Nankai University Tianjin China

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

The retrieval of encrypted images in cloud computing is a research hotspot at present. However, the existing schemes have the problem of low image retrieval accuracy since it is difficult to obtain accurate feature information through convolutional neural network from encrypted image. In this paper, a secure image retrieval method using dual-stream convolution structure for feature fusion is proposed. Specifically, the stream cryptographic image containing contour features and the Fourier transform image containing frequency domain features are used as the two inputs of the convolutional network stream, where the weighted average gate function helps to integrate the feature information in these streams to achieve a more comprehensive feature representation and improve the retrieval accuracy. Furthermore, to keep the contour features of the encrypted image, the fuzzy image and Arnold mapping algorithm are selected randomly to encrypt the encrypted image twice. Experimental evaluation on three datasets, MNIST, Fashion-MNIST and CIFAR-100, shows that the proposed encryption method has higher security than AES and stream cipher encryption. In addition, the retrieval average accuracy (mAP) of this scheme is superior to the existing methods.

关键词： Cloud computing Ciphers Accuracy Convolution image retrieval Streaming media Logic gates Encryption Convolutional neural networks Servers

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STA-VAD: Video Anomaly Detection Utilizing Spatio-Temporal Memory and Adaptive Deformation

STA-VAD: Video Anomaly Detection Utilizing Spatio-Temporal M...

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International Symposium on parallel and distributed processing with Applications, ISPA

作者： Huiyu Mu Jiashuai Su Luhui Wang Jiangwei Li Yadi Wang Xianyu Zuo Henan University Kaifeng China

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

Classical unsupervised video anomaly detection methods learn normal patterns from normal video frames and assume significant reconstruction deviations for unforeseen anomalous video frames. These methods often introduce additional constraints to distinguish anomalies, potentially leading to higher reconstruction errors for normal instances. While specific methods have alleviated this issue by estimating multiscale deformation fields, these rely on additional background information that is susceptible to changes in the real world. In this paper, we propose a spatio-temporal relationship-based video anomaly detection STA-VAD method. By emphasizing spatio-temporal relationships in videos, our model better captures contextual information, reducing the impact of irrelevant background information. Additionally, we enhance the multiscale deformation field by adaptively adjusting it to quantify the severity of anomalies in video frames, thereby improving discrimination between normal and anomalous samples. In the widely recognized video anomaly detection datasets UCSD Ped2 and CUHK Avenue, our method demonstrates significant enhancements in accuracy and reliability, achieving state-of-the-art performance overall.

关键词： Adaptation models image recognition Deformation Interference Feature extraction Reliability image reconstruction Anomaly detection Standards Optical flow

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Inverse Quantum Fourier Transform Inspired Algorithm for Unsupervised image Segmentation

Inverse Quantum Fourier Transform Inspired Algorithm for Uns...

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IEEE International Symposium on parallel and distributed processing Workshops and Phd Forum (IPDPSW)

作者： Taoreed Akinola Xiangfang Li Richard Wilkins Pamela Obiomon Lijun Qian Department of Electrical and Computer Engineering Prairie View A&M University Prairie View Texas USA

image segmentation is a very popular and important task in computer vision. In this paper, inverse quantum Fourier transform (IQFT) for image segmentation has been explored and a novel IQFT-inspired algorithm is proposed and implemented by leveraging the underlying mathematical structure of the IQFT. Specifically, the proposed method takes advantage of the phase information of the pixels in the image by encoding the pixels’ intensity into qubit relative phases and applying IQFT to classify the pixels into different segments automatically and efficiently. To the best of our knowledge, this is the first attempt of using IQFT for unsupervised image segmentation. The proposed method has low computational cost comparing to the deep learning based methods and more importantly it does not require training, thus make it suitable for real-time applications. The performance of the proposed method is compared with K-means and Otsu-thresholding. The proposed method outperforms both of them on the PASCAL VOC 2012 segmentation benchmark and the xVIEW2 challenge dataset by as much as 50% in terms of mean Intersection-Over-Union (mIOU).

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ESNet: An Efficient Real-time Semantic Segmentation Network

ESNet: An Efficient Real-time Semantic Segmentation Network

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International Symposium on parallel and distributed processing with Applications, ISPA

作者： Renping Xie Cong He Ming Tao Kai Ding School of Computer Science and Technology Dongguan University of Technology Dongguan China

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

Efficient image segmentation algorithms are critical in computer vision, as they maintain high processing speeds while handling large amounts of data and providing practical solutions in resource-limited environments. While existing classical segmentation methods achieve good results, their real-time performance can be further improved. To address this issue, we propose an efficient segmentation network (ESNet) to capture extensive contextual information and improve segmentation fineness, which are two main issues in semantic segmentation, while ensuring real-time capability. Then, we first introduce an efficient context module (ECM) that effectively enlarges the effective receptive field (ERF) of the model and improves its performance in integrating contextual information. Subsequently, a skip connection with a simple feature fusion module (SFFM) is designed to provide rich detail information, thereby improving segmentation fineness. The efficacy of ESNet was evaluated on the PASCAL VOC2012 semantic segmentation dataset against several state-of-the-art methods. ESNet achieves 71.4 FPS at a resolution of 1024×2048 on a single NVIDIA GeForce RTX 3090 GPU and achieves 62.42% mIoU at a resolution of 320×480 with an extremely simple training recipe, striking a fine balance between segmentation accuracy and inference speed.

关键词： Training distributed processing Computer vision Accuracy Semantic segmentation Computational modeling Graphics processing units Rendering (computer graphics) Real-time systems Context modeling

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