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检索条件"任意字段=2023 IEEE International Conference on Image Processing and Computer Applications, ICIPCA 2023"

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Gaussian Label Distribution Learning for Spherical image Object Detection

Gaussian Label Distribution Learning for Spherical Image Obj...

引用

conference on computer Vision and Pattern Recognition (CVPR)

作者： Hang Xu Xinyuan Liu Qiang Zhao Yike Ma Chenggang Yan Feng Dai Hangzhou Dianzi University Hangzhou China Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences Institute of Computing Technology Chinese Academy of Sciences Beijing China

Spherical image object detection emerges in many applications from virtual reality to robotics and automatic driving, while many existing detectors use $l_{n}$ -norms loss for regression of spherical bounding boxes. There are two intrinsic flaws for $l_{n}$ -norms loss, i.e., independent optimization of parameters and inconsistency between metric (dominated by IoU) and loss. These problems are common in planar image detection but more significant in spherical image detection. Solution for these problems has been extensively discussed in planar image detection by using IoU loss and related variants. However, these solutions cannot be migrated to spherical image object detection due to the undifferentiable of the Spherical IoU (SphIoU). In this paper, we design a simple but effective regression loss based on Gaussian Label Distribution Learning (GLDL) for spherical image object detection. Besides, we observe that the scale of the object in a spherical image varies greatly. The huge differences among objects from different categories make the sample selection strategy based on SphIoU challenging. Therefore, we propose GLDL-ATSS as a better training sample selection strategy for objects of the spherical image, which can alleviate the drawback of IoU threshold-based strategy of scale-sample imbalance. Extensive results on various two datasets with different baseline detectors show the effectiveness of our approach.

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Iterative Refinement Network for Hyperspectral image Denoising

Iterative Refinement Network for Hyperspectral Image Denoisi...

引用

ieee international conference on Multimedia and Expo (ICME)

作者： Fengchao Xiong Jun Zhou Zhuang Zhao Yuntao Qian School of Computer Science and Engineering Nanjing University of Science and Technology China School of Information and Communication Technology Griffith University Australia School of Electronic Engineering and Optoelectronic Technology Nanjing University of Science and Technology China College of Computer Science Zhejiang University China

Hyperspectral image (HSI) denoising is an important pre-processing procedure for subsequent tasks. Learning a direct mapping from the observed noisy HSI to its clean counterpart is challenging, especially in the case of very severe noise. The learning difficulty can be greatly reduced with the iterative refinement, combining the denoising results with the noisy HSI to produce a cleaner HSI for further denoising. To this end, we introduce an iterative refinement denoising network (IRDNet) for HSIs. The network consists of three key components, i.e., a coarse estimation module, a multi-stage refinement module, and a λ(•) module. The coarse estimation module provides the initial estimate for the starting point of further refinement. The lightweight refinement module progressively performs noise reduction on the weighted combination of noisy inputs and denoising results from the previous layer. Instead of fixed weights, the λ(•) module adaptively provides the layer-wise weight for each band for the aforementioned combinations. Extensive experiments on synthetic and real-world datasets show that our IRDNet favorably outperforms alternative methods.

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7.7 CV-CIM: A 28nm XOR-Derived Similarity-Aware Computation-in-Memory for Cost-Volume Construction

7.7 CV-CIM: A 28nm XOR-Derived Similarity-Aware Computation-...

引用

ieee international conference on Solid-State Circuits (ISSCC)

作者： Zhiheng Yue Yang Wang Huizheng Wang Yabing Wang Ruiqi Guo Limei Tang Leibo Liu Shaojun Wei Yang Hu Shouyi Yin Tsinghua University Beijing China

Cost-volume construction, which accurately computes the similarities between pixels in paired images, is a fundamental kernel of stereo vision processing and has been directly used in robotic, autopilot, and AR/VR applications. However, the large parameter size and consecutive data accesses of real-time cost-volume construction (>30fps) exerts a high demand on the memory bandwidth (0.254Tb/s) and operation (391GOPs). A promising candidate to resolve the memory bottleneck is computation-in-memory (CIM), which provides computing parallelism based on a structured array [1–7]. However, to deploy CIM applications one needs to overcome three challenges: 1) Considering various image properties and frame rate scenarios, cost functions are pattern-dependent and could be, in essence, classified as distances between pixels. CIM only supports restrictedvector Boolean logic, but distance operations require combining variable pixel/vector arithmetic functions. 2) Cost functions are sensitive to the disturbance caused by the reduced CIM computing margin, which is even worse with higher output. Furthermore, distance computing cannot take advantage of input sparsity to reduce the output. 3) The sliding search plane alternates activating weights and incurs data loading overheads, which mismatches the rigid structured array and leads to low utilization.

关键词： image resolution Loading Parallel processing Cost function Common Information Model (computing) Real-time systems Stereo vision

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Semantic Preserving Learning for Task-Oriented Point Cloud Downsampling

Semantic Preserving Learning for Task-Oriented Point Cloud D...

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international conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Jianyu Xiong Tao Dai Yaohua Zha Xin Wang Shu-Tao Xia Tsinghua Shenzhen International Graduate School Tsinghua University College of Computer Science and Software Engineering Shenzhen University Research Center of Artificial Intelligence Peng Cheng Laboratory

Recent years have witnessed a tremendous growth in the scale and resolution of point clouds. To facilitate the applications of point cloud in downsampling tasks (e.g., point cloud classification), several task-oriented downsampling works have been developed by training with the task-specific loss with one-hot encoded label. However, these methods still suffer from performance degradation at high downsampling scales. In this paper, we propose a general semantic-preserved downsampling framework (SPDF) for point clouds by exploiting the rich knowledge inherent in the task network. Specifically, we firstly refine the previous pipeline to generate richer semantic supervised information. Then, the semantic feature learning is subdivided into label-level and feature-level to guide the training of downsampling network, which can better limit the semantic loss during downsampling. Extensive experiments on the benchmark dataset show that SPDF outperforms state-of-the-art downsampling methods.

关键词： Point cloud compression Training Representation learning Knowledge engineering Degradation Semantics Pipelines

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Medical image Fusion Solid Works Supporting Multiple Techniques with Feature-Level Transforms

Medical Image Fusion Solid Works Supporting Multiple Techniq...

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Sustainable Computing and Data Communication Systems (ICSCDS), international conference on

作者： Rakhi Mutha Devendra Nath Pathak Vandana Ahuja Prabhakar Ramesh Bhandari Pankaj Rahi Udit Mamodiya Department of Computer Application Lovely Professional University Punjab India Department of Computer Science and Engineering Poornima College of Engineering Jaipur Rajasthan India Department of Computer Science and Engineering Swami Vivekanand Institute of Engineering and Technology Punjab INDIA Department of Microbiology Sevadal Mahila Mahavidyalaya Umred Road Nagpur Maharashtra India Department of Artificial Intelligence & Data Science Poornima Institute of Engineering and Technology Jaipur Rajasthan India Department of Electrical Engineering Coordinator at PIET AICTE IDEA LAB Poornima Institute of Engineering and Technology Jaipur Rajasthan India

The use of group classifiers on similar features has resulted in computationally expensive and tedious, making them unsuitable for online applications. The clustering techniques are evaluated for both inter-subject and intra-subject variations, and it is discovered that intra-subject order execution is superior. Another approach to dealing with object recognition from medical images is considered. This is known as similarity matching, and it is a computationally simple process as compared to costly and complex characterization computations. The major challenges that occur in the image processing are high noise levels, limited image resolution, and geometric deformations throughout the image.

关键词： Technological innovation Three-dimensional displays image resolution Deformation Transforms Solids Object recognition

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Enhanced CNN Security based on Adversarial FGSM Attack Learning: Medical image Classification

Enhanced CNN Security based on Adversarial FGSM Attack Learn...

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ieee SSD international Multi-conference on Systems, Signals and Devices

作者： Lazhar Khriji Seifeddine Messaoud Soulef Bouaafia Ahmed Chiheb Ammari Mohsen Machhout Electrical & Computer Engineering Department Sultan Qaboos University Sultanate of Oman Laboratory of Electronics & Microelectronics Monastir University Monastir Tunisia

Convolutional Neural Networks (CNNs) have grown in popularity for clinical image processing applications like as Covid and cancer detection. A new study, however, shows that hostile attacks with modest, unnoticeable disruptions can damage deep healthcare learning systems. This creates safety issues about using these technologies in healthcare situations. In this study, we will look at the approaches used to fight adversarial attacks on medical imaging. Next, we intend to investigate the resilience of pre-trained CNN architectures, as well as LeNet5 and MobileNetV1 models against Fast Gradient Sign Method (FGSM) attacks in a medical healthcare application-based chest X-ray dataset. We discover that pre-trained CNN models are much more sensitive to antagonistic assaults than other models, due to key feature discrepancies between them and regular models. Finally, we propose to improve the CNN’ models security by investigating adversarial training. According to the numerical results, models with lower computational complexity and restricted layers are much more safe against malicious attacks than bigger models which are commonly utilized in medical healthcare systems.

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image Labeling Using Convolutional Neural Network

Image Labeling Using Convolutional Neural Network

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Network, Multimedia and Information Technology (NMITCON) international conference on

作者： Vasantha Kumari N Mounika Garimella Madhu Bhan Dept. of MCA Ramaiah Institute of Technology Bengaluru India Master of Computer Applications Ramaiah Institute of Technology Bengaluru India

Due to the rapid rise in the identification of digital materials, automatic image classification has emerged as the most difficult topic of computer vision. In comparison to human vision, automatic visual understanding and analysis by a system is challenging. The findings of many experiments that were done to fix problems of automatic picture classification with the current categorization method were simple picture primitives. However, those methods don't accurately classify photos. In this research work, a system has been created that makes use of deep learning to deliver the necessary results in the area of computer vision. Our technology automatically categorizes photographs using Convolutional Neural Network, a popular machine learning method. Our algorithm classifies grayscale photos using the Digit of MNIST dataset as a benchmark. The dataset that is employed for the training of the system contains grayscale images, which demand higher processing resources for image categorization. By employing Convolution Neural Network to train the photos, we were able to attain higher accuracy results in the experimental section, demonstrating that our model is highly accurate at classifying images.

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The Virtual Air Canvas Using image processing

The Virtual Air Canvas Using Image Processing

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New Frontiers in Communication, Automation, Management and Security (ICCAMS), international conference on

作者： K Pavithra A Geetha R Chinnaiyan Department of Computer Science and Engineering Alliance University Bengaluru Karnataka India

ISBN: (数字)9798350317060

ISBN: (纸本)9798350317077

One of the most interesting and challenging research focuses on pattern recognition and image processing has emerged in recent days is writing in the air. In many different applications, it can improve the interface between a machine and a human and offers a substantial contribution to the development of automated operations. In the field of computer vision, object tracking is considered a key challenge. The method of analyzing a video usually consists of three primary steps: recognizing the object, tracking its movement from frame to frame, and finally evaluating its behavior. Choosing an adequate object representation, selecting tracking features, identifying objects, and tracking them are the four problems taken into consideration for object tracking. Object tracking algorithms are widely used in many real-world applications, including autonomous surveillance, video indexing, and vehicle navigation. This work exploits this gap by developing a motion-to-text converter that may be used as software for wearable intelligent devices that allow writing in the air. The proposed work acts as a recorder of rare gestures. computer vision will be utilized to track the finger’s path. With the generated text, messages, emails, and other kinds of correspondence can all be sent. It will enable effective communication for the deaf. Keywords— object, emoji’s, image color, camera

关键词： YOLO Handwriting recognition computer vision Writing Software Object tracking Character recognition

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A Bolt Positioning Method Using Depth Map and Color image Fusion from a Depth Camera

A Bolt Positioning Method Using Depth Map and Color Image Fu...

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2023 7th international conference on Electrical, Mechanical and computer Engineering (ICEMCE)

作者： Haitao Jiang Bo Yuan Peng Wang Qingchao Sun Dalian University of Technology School of Mechanical Engineering Dalian China

With the increasing demand for automated detection of bolt connection parameters in the industrial field, there is an urgent need for the position information of bolts to guide the operation of automated equipment. Therefore, this paper proposes a bolt visual positioning method that combines depth maps and color images. Firstly, outlier points are eliminated from the depth map using histogram analysis. The points of the bolt bottom are projected onto a two-dimensional plane through depth slicing, and the coarse positioning of the bolt is achieved using the Density-Based Spatial Clustering of applications with Noise (DBSCAN) algorithm. Then, a circle detection method based on arc-support line segments (ASLS) with pre-processing using bicubic interpolation is applied to detect multiple candidate circles in the color image. Finally, the centroid position obtained from the depth map clustering is combined to obtain the final bolt positioning results. Experimental results show that the bolt spacing absolute error using the proposed method is less than 2.2 mm, validating the effectiveness of the proposed method.

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L-CoIns: Language-based Colorization With Instance Awareness

L-CoIns: Language-based Colorization With Instance Awareness

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conference on computer Vision and Pattern Recognition (CVPR)

作者： Zheng Chang Shuchen Weng Peixuan Zhang Yu Li Si Li Boxin Shi School of Artificial Intelligence Beijing University of Posts and Telecommunications National Key Laboratory for Multimedia Information Processing School of Computer Science Peking University National Engineering Research Center of Visual Technology School of Computer Science Peking University International Digital Economy Academy

Language-based colorization produces plausible colors consistent with the language description provided by the user. Recent studies introduce additional annotation to prevent color-object coupling and mismatch issues, but they still have difficulty in distinguishing instances corresponding to the same object words. In this paper, we propose a transformer-based framework to automatically aggregate similar image patches and achieve instance awareness without any additional knowledge. By applying our presented luminance augmentation and counter-color loss to break down the statistical correlation between luminance and color words, our model is driven to synthesize colors with better descriptive consistency. We further collect a dataset to provide distinctive visual characteristics and detailed language descriptions for multiple instances in the same image. Extensive experiments demonstrate our advantages of synthesizing visually pleasing and description-consistent results of instance-aware colorization.

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