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检索条件"任意字段=Image Processing: Machine Vision Applications II"

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Self-Adaptive Logit Balancing for Deep Learning Robustness in Computer vision 1

引用

21st International Conference on image Analysis and processing (ICIAP)

作者： Wei, Jiefei Meng, Qinggang Yao, Luyan Loughborough Univ Epinal Way Loughborough LE11 3TU Leics England Univ Nottingham Univ Pk Nottingham NG7 2RD England

ISBN: (数字)9783031064272

ISBN: (纸本)9783031064272;9783031064265

With wide applications of machine learning algorithms, machine learning security has become a significant issue. The vulnerability to adversarial perturbations exists in most machine learning algorithms, including cutting-edge deep neural networks. The standard adversarial perturbation defence techniques with adversarial training need to generate adversarial examples during the training process, which require high computational costs. This paper proposed a novel defence method using self-adaptive logit balancing and Gaussian noise boost training. This method can improve the robustness of deep neural networks without high computational cost and achieve competitive results compared with the adversarial training methods. Meanwhile, this defence method enables deep learning systems to have proactive and reactive defence during the operation. A sub-classifier is trained to determine whether the system is under attack and detect attack algorithms via the patterns of the Log-Softmax values. It can achieve high accuracy for detecting clean inputs and adversarial examples created by seven attack methods.

关键词： machine learning security Adversarial robustness Adversarial examples Deep neural networks

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Capturing Prior Knowledge in Soft Labels for Classification with Limited or Imbalanced Data 5th

Capturing Prior Knowledge in Soft Labels for Classification ...

引用

5th Chinese Conference on Pattern Recognition and Computer vision (PRCV)

作者： Zhong, Zhehao Zhao, Shen Wang, Ruixuan Sun Yat Sen Univ Sch Comp Sci & Engn Guangzhou Peoples R China Sun Yat Sen Univ Sch Intelligent Syst Engn Shenzhen Peoples R China Key Lab Machine Intelligence & Adv Comp MOE Guangzhou Peoples R China

ISBN: (纸本)9783031189098;9783031189104

Successful applications of deep learning often depend on large amount of training data. However, in practical image recognition tasks, available training data are often limited or imbalanced across classes, causing the over-fitting issue or the prediction bias issue during model training. In this paper, based on word embedding models from studies in natural language processing, the prior knowledge about the relationships between image classes is utilized to help train more generalizable classifiers under the condition of limited or class-imbalanced training data. Such inter-class relational knowledge is captured in the word embedding vectors for the textual names of image classes. Using these word embedding vectors as soft labels for corresponding image classes, the feature extractor part of a deep learning model can be guided to learn to extract visual features which contain both class-specific and class-shared information. Experiments on multiple image classification datasets confirm that the proposed learning framework helps improve model performance when training data is limited or class-imbalanced.

关键词： Prototype learning image classification Limited data Imbalance data Soft label

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11th EAI International Conference on Context-Aware Systems and applications, ICCASA 2022

11th EAI International Conference on Context-Aware Systems a...

引用

11th EAI International Conference on Context-Aware Systems and applications, ICCASA 2022

ISBN: (纸本)9783031288159

The proceedings contain 14 papers. The special focus in this conference is on Context-Aware Systems and applications. The topics include: Prediction of Chaotic Time Series Based on LSTM, Autoencoder and Chaos Theory;an Approach to Selecting Students Taking Provincial and National Excellent Student Exams;safe Interaction Between Human and Robot Using vision Technique;application of the image processing Technique for Powerline Robot;collaborative Recommendation with Energy Distance Correlation;blockchain Model in Industrial Pangasius Farming;multiple-Criteria Rating Recommendation with Ordered Weighted Averaging Aggregation Operators;a Survey of On-Chip Hybrid Interconnect for Multicore Architectures;a Framework for Brain-Computer Interfaces Closed-Loop Communication Systems;identification of Abnormal Cucumber Leaves image Based on Recurrent Residual U-Net and Support Vector machine Techniques;lung Lesion images Classification Based on Deep Learning Model and Adaboost Techniques;balltree Similarity: A Novel Space Partition Approach for Collaborative Recommender Systems.

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Enhancing Human-Computer Interaction: Hand Detection for Air Writing Utilizing NumPy and OpenCV 3

Enhancing Human-Computer Interaction: Hand Detection for Air...

引用

3rd International Conference on Technological Advancements in Computational Sciences, ICTACS 2023

作者： Shukla, Pranjal Das, Prasenjit Chandigarh University Department of Cse Punjab Mohali India

ISBN: (纸本)9798350342338

In recent years, there has been a remarkable increase in interest and challenges in image processing and pattern recognition, specifically in the context of air writing. This exciting research area has significant potential to advance automation processes and improve human-machine interfaces in various applications. The emergence of faster computers, affordable high-performance video cameras, and the need for automated analysis of videos has led to an increase in the popularity of object tracking, a critical task in computer vision. The process of video analysis typically encompasses object detection, tracking, and behavior analysis. Object tracking involves four main aspects choosing the suitable object representation, selecting features for tracking, detecting the object, and tracking the object. Object tracking algorithms find applications in different domains, including vehicle navigation, video indexing, and surveillance that are automated. The objective of the paper is to create a software application for smart wearable devices that utilizes computer vision to track finger gestures in the air, functioning as a motion-to-text converter for air-writing. The technology will facilitate communication for people by enabling them to generate text for multiple purposes, like sending emails and messages, through intermittent gestures. This is a productive means of communication that curbs the usage of laptops and mobiles, making it particularly beneficial for individuals who are deaf. © 2023 IEEE.

关键词： Air Writing Computer vision Digital sketching Gesture Recognition Hand Detection Hand Tracking Interactive whiteboard

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Lane Detection and Distance Estimation Using Computer vision Techniques 4th

Lane Detection and Distance Estimation Using Computer Vision...

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4th International Conference on machine Learning, image processing, Network Security and Data Sciences (MIND)

作者： Henry, Alan Rahesh, R. Das Barman, Kuntal Sujee, R. Amrita Vishwa Vidyapeetham Amrita Sch Engn Dept Comp Sci & Engn Coimbatore Tamil Nadu India Amrita Vishwa Vidyapeetham Amrita Sch Engn Ctr Computat Engn & Networking Coimbatore Tamil Nadu India Volvo Grp India Private Ltd Bangalore Karnataka India

ISBN: (纸本)9783031243660;9783031243677

In the realm of computer vision, the term "autonomous driving" has become a buzzword. The main goal of the autonomous driving is to reduce human efforts while driving. However, dealing with measurements of distance raises numerous obstacles, both in terms of equipment and approach. The use of cameras to measure the distance of an object is practical and popular for obstacle avoidance and navigation.. This work focuses on vehicle distance measuring of traffic signs and cars, which is a critical task in the image processing domain. In this research, the suggested system employs two cameras installed in the hosting vehicle in front, to obtain the data and estimate distance. The proposed pipeline starts with YOLO v3 and YOLOv2 algorithms for detecting traffic signs and cars in the video frames. The distances of the detected objects are measured using triangle similarity approach. In final phase, lane segmentation and grid marking are added along with these results. As a result, it will assist drivers inmaking decisions prior to reaching signs, potentially resulting in improved safety decisions.

关键词： Object detection Lane segmentation Collision avoidance YOLO Self-driving

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Feature Extraction from image Sensing (Remote) : image Segmentation 5

Feature Extraction from Image Sensing (Remote) : Image Segme...

引用

5th IEEE International Conference on Advances in Computing, Communication Control and Networking, ICAC3N 2023

作者： Chauhan, Kaushal Tomar, Harshit Kamal, Kushagra Goel, Pallavi Galgotias College Of Engineering And Technology Department Of Computer Science And Engineering Greater Noida India

ISBN: (纸本)9798350330861

In this research paper, an overview of computer methods for segmenting continuous-tone images into meaningful parts and characterizing these parts with 'features' is presented. image segmentation is an essential pre-processing step in computer vision and image processing softwares. This paper discusses various techniques and algorithms used for image segmentation, including both supervised and unsupervised methods. The performance of these techniques is evaluated on different test images, and future research directions are proposed. Feature extraction from image sensing is a critical step in multimedia processing. This paper explores the process of feature extraction from remotely sensed images. With the increasing use of remote sensing technology, it is crucial to have effective and accurate methods for extracting features from remotely sensed data, which are utilized in various applications such as land-use mapping, environmental proctoring, and in natural-disaster management. This paper discusses several techniques for feature extraction, including pixel-based, object-based, and machine learning-based approaches. Additionally, it highlights the difficulties associated with feature extraction from remote sensing data, such as atmospheric effects, radiometric and geometric distortions, and the presence of noise. The paper concludes by discussing future directions in remote sensing feature extraction, including the integration of deep learning methods and the development of more robust methods has the ability of handling large-scale datasets. With the help of deep learning methods and algorithms, especially CNN's, has tremendously improved the accuracy of image scene classification. In this study, a CNN model which is already trained was utilized to fetch-out deep features from images at various network layers, including the average pooling layer. © 2023 IEEE.

关键词： image segmentation

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Investigating Bidimensional Downsampling in vision Transformer Models 21st

Investigating Bidimensional Downsampling in Vision Transform...

引用

21st International Conference on image Analysis and processing (ICIAP)

作者： Bruno, Paolo Amoroso, Roberto Cornia, Marcella Cascianelli, Silvia Baraldi, Lorenzo Cucchiara, Rita Univ Modena & Reggio Emilia Modena Italy

ISBN: (纸本)9783031064302;9783031064296

vision Transformers (ViT) and other Transformer-based architectures for image classification have achieved promising performances in the last two years. However, ViT-based models require large datasets, memory, and computational power to obtain state-of-the-art results compared to more traditional architectures. The generic ViT model, indeed, maintains a full-length patch sequence during inference, which is redundant and lacks hierarchical representation. With the goal of increasing the efficiency of Transformer-based models, we explore the application of a 2D max-pooling operator on the outputs of Transformer encoders. We conduct extensive experiments on the CIFAR-100 dataset and the large imageNet dataset and consider both accuracy and efficiency metrics, with the final goal of reducing the token sequence length without affecting the classification performance. Experimental results show that bidimensional downsampling can outperform previous classification approaches while requiring relatively limited computation resources.

关键词： vision Transformer ViT Bidimensional downsampling

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Recognizing Indian Classical Dance Forms Using Transfer Learning 4th

Recognizing Indian Classical Dance Forms Using Transfer Lear...

引用

4th International Conference on machine Intelligence and Signal processing, MISP 2022

作者： Reshma, M.R. Kannan, B. Jagathyraj, V.P. Sabu, M.K. Department of Computer Applications CUSAT Kerala Kochi India School of Management Studies CUSAT Kerala Kochi India

ISBN: (纸本)9789819900466

The rise of cultural tourism in India and massive digitization over the last decade has necessitated preserving Indian art forms. Recent advances in artificial intelligence (AI) have provided the tools and techniques to make these efforts more efficient. The potential research areas involved are digital image processing, computer vision, and machine intelligence. Since researchers have not much explored the scope of the digital preservation of Indian classical dance, this paper contributes a method to classify different Indian classical dance forms. Computerized dance modeling offers a wide range of uses, including cultural conservation, online learning, entertainment, and even remedial treatment. Because the dataset is small, the paper uses transfer learning models for feature extraction and deep neural networks for classification. The proposed method showed a remarkable accuracy of 97.05 percent. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Deep neural networks

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Advancements in Road Surface Classification: A Comprehensive Review of Computer vision and IoT Approaches

Advancements in Road Surface Classification: A Comprehensive...

引用

International Conference on Computer vision and Internet of Things 2023, ICCVIoT 2023

作者： Kovilpillai, Judeson Antony J. Dhar, Soumi Singh, Uday Kumar Prakash, S. Saidala, Ravi Kumar Jayanthy, S. Department of CSE Alliance University Anekal Bangalore India Department of Research and Innovation Alliance University Anekal Bangalore India Department of ECE Sri Ramakrishna Engineering College Coimbatore India

ISBN: (纸本)9781839539848

The rapid expansion of autonomous driving technologies necessitates the development of robust systems for accurate road surface identification and classification to ensure safe and reliable driving. This review article addresses the imperative requirement for effective road surface classification by investigating diverse methodologies within the realms of computer vision and the Internet of Things (IoT). Through an extensive investigation, various techniques encompassing image processing, machine Learning(ML), Deep Learning(DL), and IoT are examined for their effectiveness in classifying road surfaces at different terrains. Moreover, this article also reviews datasets, signals, sensors, communication protocols, IoT implementation strategies, pre-processing methodologies, and feature extraction techniques. This investigation delves into novel approaches aimed at resolving road surface classification challenges, meticulously examining their respective strengths and limitations. Furthermore, this article includes a comparative analysis of these advanced methodologies, facilitating the identification of the most suitable model for the task. The assessment takes into account intricate methodological aspects, types of sensors/cameras, dataset variations, and performance metrics, thereby providing valuable insights into the landscape of road surface classification for autonomous driving applications. © The Institution of Engineering & Technology 2023.

关键词： Internet of things

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Development of methods for parallel processing of series of images obtained by a machine vision system in various electromagnetic ranges 2

Development of methods for parallel processing of series of ...

引用

Conference on Optical Instrument Science, Technology, and applications ii Held as Part of SPIE Optical System Design Conference

作者： Semenishchev, Evgenii Voronin, Viacheslav Alepko, Andrey Zelensky, Aleksandr Agaian, Sos Moscow State Tech Univ STANKIN Moscow Russia CUNY Dept Comp Sci New York NY 10314 USA

ISBN: (数字)9781510645974

ISBN: (纸本)9781510645974;9781510645967

machine vision systems used in modern industrial complexes, based on the analysis of multi and hyperspectral imaging. The transition to implementing the "Industry 4.0" program is not possible when using one type of data. The first control system used only the visible range image. They made it possible to analyze the trajectories of movement of objects, control product quality, carry out security functions (control of perimeter crossing), etc. The development of new industrial robotic cells and processing complexes using cognitive functions implying the receipt, analysis, and processing of heterogeneous data. The construction of a unified information field, which allows performing multidimensional operations with data, allows increasing the speed of decision-making and the implementation of automated robot-human systems at the level of an assistant working in a unified workspace. The use of machine vision systems analyzing information received in: visible (shape, the trajectory of movement, position of objects, etc.);near-infrared range (data is similar to visible, allows operation in dusty, foggy, low light conditions);far-infrared range - thermal (plotting temperature gradients, identifying areas of overheating);ultraviolet range (analysis of ionization sources, corona discharges, static charges, tags);X-ray and microwave ranges (analysis of the surface and internal structure of objects, allow the identification of defects);range and 3D sensors (construction of volumetric figures, analysis of the relative position of objects and their interaction), etc. Data analysis is often performed not by a single camera but by a group of sensors located not in a single housing. Primary data integration reduces the number of information channels while maintaining the functionality and accuracy of the analysis. The article discusses creating fusion images obtained by industrial sensors into a combined image containing joint data. Combining multi and hyperspectral imaging makes i

关键词： preprocessing IR image local features denoising combining image fusion image

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