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

共 4812 条记录，以下是4641-4650 订阅

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Hand Gesture Detection using Transfer Learning with Deep Neural Networks

Hand Gesture Detection using Transfer Learning with Deep Neu...

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international conference for Convergence of Technology (I2CT)

作者： Thanushree M P Manjula R Bharamagoudra Syeda Zoya Noorain Sharon Maria SP Electronics and Computer Engineering School of Electronics and Communication Engineering REVA University Bangalore India School of Electronics and Communication Engineering REVA University Bangalore India

Large segments of the population value sign language as one of the many languages used for communication. Each sign in each sign language differs in terms of hand shape, motion profile, and positioning of the hand. These differences will have a substantial influence on the accuracy and reliability of the identification process. Therefore, one of the key techniques for detecting gestures is sign language identification utilizing deep learning. In this paper, we have considered four major algorithms of image classification EfficientNet- B0, GoogleNet, ResNext 50 32x4d and MobileNet-V2 which were tested on GPU system. To determine the optimum model that uses the fewest GPU cores, many factors have been considered, including training time, training accuracy, total loss and inference. GoogleNet performance is better as compared to other models.

关键词： Training Deep learning Neural networks Memory management Transfer learning Graphics processing units Gesture recognition

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Deep Learning-Based Techniques for Precise Vehicle Detection and Distance Estimation in Autonomous Systems

Deep Learning-Based Techniques for Precise Vehicle Detection...

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ieee international conference on Cloud Computing in Emerging Markets (CCEM)

作者： Srinivasa C Suresha R Manohar N Dharun G K Sheela T Tian Jipeng Department of Computer Science School of Computing Mysuru Amrita Vishwa Vidyapeetham India Department of Computer Science Maharani's Science College for Women (Autonomous) Mysuru India Computer Science College Zhongyuan University of Technology Zhengzhou Henan Province china

ISBN: (数字)9798350360059

ISBN: (纸本)9798350360066

Vehicle detection, recognition, and distance estimation play a crucial role in computer vision, particularly in applications such as intelligent traffic systems. However, existing methods need help in achieving accurate vehicle detection, recognition, and distance estimation, often resulting in low detection accuracy and limited distance estimation capability. This paper proposes an integrated approach combining Faster R-CNN architecture with scale-based distance estimation to address these issues. Through experimental evaluations, the results validate the effectiveness of the proposed method in achieving 93.5%, 95.6%, and high detection accuracy, recognition accuracy, and accurate distance estimation. The proposed framework shows the potential to enhance the performance and robustness of vehicle detection and distance estimation systems, offering promising advancements in this field.

关键词： Accuracy Vehicle detection Estimation Deep architecture Robustness Real-time systems Safety Indexes image enhancement Vehicles

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Moiré Vision: A Signal processing Technology Beyond Pixels Using the Moiré Coordinate

Moiré Vision: A Signal Processing Technology Beyond Pixels ...

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ieee international conference on Computational Photography (ICCP)

作者： Kensei Jo Masahiko Inami University of Tokyo Japan

Nowadays, many computer vision (CV) techniques require high-precision feature point detection in calibration targets. Checkerboards or circle grids are common patterns used to infer the locations of the points directly from the image. However, such approaches can lead to degradation in sub-pixel accuracy for low-resolution or sub-sampled sensor data (e.g., Bayer pattern). This paper proposes a target-based calibration technique applicable to various conventional CV methods, leveraging the moiré phenomena. We use a moiré pattern that is created between the digital camera’s pixels and the high-frequency texture on the calibration target. Even a tiny movement of the target or camera leads to a significant motion of the moiré, acting as a magnifier of the target texture for estimating accurate location of targets or camera. In contrast to previous moiré approaches that employ frequency domain analyses, we introduce the moiré coordinates and derive them from the difference between the sensor and target texture position. In the moiré coordinate, each moiré pattern is undistorted. Unlike moiré in standard images, each moiré pattern has almost the same scale and shape in these coordinates. Utilizing this property, we propose a straightforward algorithm to detect and handle correspondence in the moiré peaks in image and texture location, treating them as feature points of calibration target. In addition, the moiré vision can magnify pixels, which means the pattern in the moiré coordinate visualizes the pixel sensitivity with high spatial resolution. To validate the proposed approach, we present an empirical evaluation using simulated and real Bayer sensor patterns. We show the advantages of the proposed method in terms of accuracy and identify promising future directions.

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Res-ViT: Residual Vision Transformers for image Recognition Tasks.

Res-ViT: Residual Vision Transformers for Image Recognition ...

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international conference on Tools for Artificial Intelligence (ICTAI)

作者： Sayda Elmi Bell Morris Yale University University of New Haven New Haven USA School of Medicine Yale University New Haven USA

Transformers have recently dominated a wide range of tasks in natural language processing. To obtain competitive image classification performance, the Vision Transformer (ViT) is the first computer vision model to rely exclusively on the Transformer architecture. Despite the success of vision Transformers at large scale, the performance is still below similarly sized convolutional neural network (CNN) counterparts (e.g., ResNets). We present in this paper a new architecture, named Residual Vision Transformer (Res-Vit), that improves Vision Transformer (ViT) in performance and efficiency by introducing Residual networks into ViT to yield the best of both designs. The classic architecture of ViT is mainly modified by: (i) a hierarchy of Transformers containing a new residual token embedding, and (ii) a residual Transformer block leveraging a residual projection. Moreover, the positional encoding, a crucial component in existing vision transformers, can be safely removed in the ResVit model, simplifying the design for higher resolution vision tasks. We validate Res-Vit by conducting extensive experiments, showing that this approach achieves state-of-the-art performance over other Vision Transformers and ResNets on imageNet-1k. In addition, performance gains are maintained when pretrained on larger datasets (e.g. imageNet-22k) and fine-tuned to downstream tasks. Pretrained on imageNet-22k, our Res-Vit obtains a top-1 accuracy on the imageNet-1k proving that the Res-ViT model is a promising approach for image classification offering improved performance, efficiency, and robustness.

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Vision Transformers(ViT) Pretraining on 3D ABUS image and Dual-CapsViT: Enhancing ViT Decoding via Dual-Channel Dynamic Routing

Vision Transformers(ViT) Pretraining on 3D ABUS Image and Du...

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ieee international conference on Bioinformatics and Biomedicine (BIBM)

作者： Mingwang Xu Wei Wang Kuanquan Wang Suyu Dong Pengzhong Sun Jinwei Sun Gongning Luo Faculty of Computing Harbin Institute of Technology Harbin China School of Computer Science and Technology Harbin Institute of Technology Shenzhen China College of Computer and Control Engineering Northeast Forestry University Harbin China School of Instrumentation Science and Engineering Harbin Institute of Technology Harbin China

Breast cancer continues to be a pressing global health concern, emphasizing the essential need for effective diagnostic techniques. Automated Breast Ultrasound Systems (ABUS) provide a promising advance in breast tumor detection, yet they require significant expertise in interpreting 3D ABUS images, a task fraught with distinctive challenges. Although Vision Transformers (ViT) display remarkable potential for image processing, their low inductive bias and significant data requirements pose obstacles, particularly in the data-constrained medical field. To mitigate these issues, we introduce a Mask-Recover strategy for pretraining Transformer models on 3D ABUS images, enhancing model adaptability and reducing the data demands of the ViT model. Moreover, recognizing the risk that ViTs’ average pooling approach may unintentionally mask small but vital features, we propose Dual-CapsViT, an inventive model combining Transformers and Capsule Networks. This integration affords efficient token routing while preserving fine-grained details. To reconcile potential inconsistencies between capsules and tokens, we engineer a novel dual-channel routing algorithm, strengthening the decoder’s performance. We benchmarked our models against well-known standards such as ResNet and ViT for classifying breast tumors in ABUS images. Our models exhibited superior performance, as evidenced by improved accuracy, specificity, and Area Under the Receiver Operating Characteristic Curve (AUC) metrics, thereby affirming Dual-CapsViT’s potential to enhance breast cancer diagnostics.

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Edge-Preserving Multi-Exposure Fusion with Guided Filtering for Structural Patch Decomposition

Edge-Preserving Multi-Exposure Fusion with Guided Filtering ...

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ieee international conference on Electronic Information and Communication Technology (ICEICT)

作者： Yang Zhen Junwei Qi Yingsong Li College of Information and Communication Engineering Harbin Engineering University Harbin China College of Computer Science and Technology Harbin Engineering University Harbin China Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University Hefei China

Multi-exposure image fusion (MEF) is one of the effective ways to obtain high dynamic range (HDR) images. Multi-scale structural patch decomposition based MEF(MSPD) and edge-preserving structural patch decomposition (MESPD) have achieved state-of-the-art fusion quality. MESPD incorporates edge-preserving factors into MSPD to preserve details in a single-scale setting. However, we find that the edge-preserving factors put too much emphasis on the edge, resulting in artifacts on some edges and loss of details. To tackle this issue, guided filter is introduced into MSPD, which is directly used as an edge-preserving filter instead of mean filter to implicitly decompose images. In addition, we have designed a flexible adaptive weight, which can further maintain details of the light and dark areas. Likewise, we inherit MESPD and MSPD to extend our method to multi-scale framework. Extensive experimental results demonstrate our method can produce satisfactory results in both static and dynamic scenes with fewer artifacts and low computational cost.

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Duck Egg Setter System with Sorting Process Using image processing

Duck Egg Setter System with Sorting Process Using Image Proc...

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international conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management (HNICEM)

作者： Ryan C. Reyes Roel M. Mendoza Rovenson V. Sevilla John Gelvic C. Calagday Renielle Carl C. General Samantha Joynna J. Leyco Jose Noel C. Lozano Kristine R. Monteser Edmon O. Fernandez Jennifer C. Dela Cruz Electronics Engineering Department Technological University of the Philippines Manila Electronics and Computer Engineering School of Electrical Mapua University Manila Philippines

ISBN: (数字)9798350381177

ISBN: (纸本)9798350381184

The project focused on the development of a machine that automates the duck egg incubation and sorting process with the utilization of image processing for the balut and penoy production industries in the Philippine Agricultural Sector. The study attempted to mitigate the challenges and problems faced in the traditional setup of duck egg-incubated production systems. The project involved the fabrication of a machine that is capable of moving eggs through three different processes, specifically incubation, candling, and sorting, by means of motor mechanisms, computer programming, and image processing. The incubation stage utilized a ceramic bulb setup as a source of heat, and a pre-programmed microcontroller allowed a consistent temperature in the egg incubator cabin by activating an exhaust fan, while the system was able to maintain a stable humidity ranging from 50% to 80% and a temperature varying from 36°C to 38°C. The candling stage was equipped with a vertically moving camera surface linked with an image processing threshold software program to detect the maturity of the embryo inside the eggs. After several trials, test results showed 66.67% to 91.67% accuracy in correctly determining the correct egg maturity stage in the candling stage, and 91.67% of all processed eggs have not incurred any damage caused by movement from the conveyors and sorting mechanisms.

关键词： Temperature distribution Technological innovation image processing Prototypes Process control Programming Software

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Text Classification on Imbalanced Data using Graph Neural Networks and Adversarial Weight Balancer

Text Classification on Imbalanced Data using Graph Neural Ne...

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computer Science and Data Engineering (CSDE), ieee Asia-Pacific conference on

作者： Fatemeh Badiei Makan Kananian S. AmirAli Gh. Ghahramani Department of Computer Engineering Sharif University of Technology International Campus Kish Island Iran

Text classification in imbalanced datasets poses a significant challenge in natural language processing (NLP) applications. In this paper, we address this issue by treating text data as graphs, with words as nodes, and employ two strategies for establishing connections between words within these graphs. The first strategy employs a fixed-size context window to connect words that lie within the same window, while the second strategy leverages word embedding vectors and a k-nearest neighbors (KNN) approach to connect each word to its k most similar neighbors. To further enhance the classification performance, we combine Graph Convolutional Neural Networks (GCNs) and Long Short-Term Memory (LSTM) networks to process the textual information. Importantly, we address the challenge of class imbalance using an adversarial loss framework. We introduce separate weight generator networks for each class within the dataset, responsible for dynamically assigning weights to individual samples during training. While the classifier aims to minimize its weighted cross-entropy loss, the weight-generating networks strategically assign higher weights to misclassified samples and reduce the weights assigned to correctly classified ones, thus increasing the classifier's loss in subsequent epochs. This adversarial loss, in conjunction with the LSTM-GCN architecture, results in finely tuned sample weights, leading to significantly improved accuracy and F1-Score compared to conventional methods for handling imbalanced text classification problems. Our experimental results demonstrate the effectiveness of our approach in addressing the challenges of imbalanced text classification, offering a solution for a wide range of NLP applications where imbalanced datasets are prevalent.

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Feature Level Medical image Fusion with Deep Learning

Feature Level Medical Image Fusion with Deep Learning

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Evolutionary Algorithms and Soft Computing Techniques (EASCT), international conference on

作者： R. Prathipa R. Ramadevi Sathyabama Institute of Science and Technology Chennai India Department of Biomedical Engineering Saveetha School of Engineering Chennai India

In recent years, medical image fusion has emerged as a comprehensive analytical method that frequently employs various modification techniques. The objective of multi-modal medical image fusion is to enhance image quality while preserving specific characteristics by combining images from different imag- ing modalities. This field encompasses cutting-edge domains such as image processing, computer vision, pattern recognition, ma- chine learning, and artificial intelligence. Furthermore, medical image fusion has found extensive clinical applications, enabling healthcare professionals to gain a deeper understanding of lesions by integrating medical images from multiple modalities. In this study, we propose the utilization of a Grey Wolf algorithm, which comprises various learning models, to minimize squared error and optimize the process through weighted entropy during image fusion. The successful results are attributed to a hybridization of the Discrete Wavelet Transform (DWT) and the Discrete Curvelet Transform (DCT). By employing Principal Component Analysis, Grey Wolf Optimization, and a Recurrent Neural Network, we have developed a novel fusion model for unsupervised categorization known as GWO-RNN. This model is designed to work with multi-modality images that incorporate structural and sparse constraints. The flexibility and precision of image fusion are further enhanced through correlation and covariance analysis using eigenvalues and eigenvectors. Matlab simulations of the proposed workbench are conducted, comparing it against state-of-the-art methods to assess performance metrics, including peak signal-to-noise ratio, mean square error, correlation, and accuracy. The proposed workbench not only meets but surpasses all performance benchmarks while also reducing computational overhead.

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Detection and Location of Litchi Fruit based on Object Detector and Depth image

Detection and Location of Litchi Fruit based on Object Detec...

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Advances in Electrical Engineering and computer applications( AEECA), 2020 ieee international conference on

作者： Liang Mao Zhishang Liang Yinqiao Peng Ji Wang Linlin Wang Institute of Applied Artificial Intelligence of the Guangdong-Hong Kong-Macao Greater Bay Area Shenzhen Polytechnic University Shenzhen China College of Electronics and Information Engineering Guangdong Ocean University Zhanjiang China Research Center of Guangdong Smart Oceans Sensor Networks and Equipment Engineering Zhanjiang China School of Artificial Intelligence Shenzhen Polytechnic University Shenzhen China

computer vision is the core of harvesting robots, and computer vision-based litchi fruit detection is an important direction for research on automated harvesting. However, the current detection methods of litchi fruit cannot meet the detection requirements in the complex orchard environment and cannot simultaneously detect a single litchi fruit and calculate the spatial location of the fruit. In this paper, a detection and location method based on object detector and depth images was proposed in this paper for litchi fruit in an orchard environment. After comparing the performance of multiple object detectors, YOLOv5 was chosen as the base model for litchi detection. An attention function and an improved loss function are added to YOLOv5. Meanwhile, to transmit more shallow features to deep layers, the transmission path of the neck was adjusted. The experimental results show that the mAP and F1 scores of the improved YOLOv5 are 0.9534 and 0.9428, which are higher than the corresponding performance values of YOLOv5, and the improved YOLOv5 can overcome the influence of various factors on the detection performance in practical detection scenarios. The method was tested in the orchard environment Intel RealSense D435 was used to collect RGB images and depth images of litchi during the test. The improved YOLOv5 detected litchi fruits in RGB images, the values of their corresponding positions in depth images were read. The experimental results show that the proposed method has high accuracy and strong robustness.

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