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检索条件"任意字段=3rd International Conference on Image Processing and Vision Engineering, IMPROVE 2023"

共 1699 条记录，以下是101-110 订阅

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Early Stage Skin Cancer Detection Using image processing 3

Early Stage Skin Cancer Detection Using Image Processing

引用

3rd international conference on Pervasive Computing and Social Networking, ICPCSN 2023

作者： Rajulu, Lanke Govinda Madan, Parul Varshney, Neeraj Yadav, Dinesh Set, Ashok Kumar Thakur, Anil ResMed Digital Health Technology United States Graphic Era Deemed to Be University Department of Computer Science & Engineering Uttarakhand Dehradun248002 India Gla University Department of Computer Engineering and Applications Mathura India Lloyd Institute of Engineering and Technology Greater Noida India Quantum University Uttarakhand Roorkee India Lloyd Law College Greater Noida India

ISBN: (纸本)9798350322842

Early stage malignant cancer detection has gain utmost research importance in recent times. Carcinoma, which is currently acknowledged as the deadliest form of cancer in humans, appears in various types such as skin cancer, basal cell carcinoma, squamous cell carcinoma, and the relatively unexpected malignant melanoma. Timely identification of malignant melanoma is crucial for providing effective treatment. Computer vision systems have played a significant role in medical image classification, offering valuable contributions in this field. In this study, we propose a computer-aided method that relies on images to detect melanoma, a type of skin cancer. The system takes an image of a skin lesion as input and employs innovative image processing techniques to assess and determine the presence or absence of skin cancer. By analyzing factors such as texture, size, and segmentation, lesion image analysis tools examine a wide range of features associated with melanoma, including spatial characteristics, contours, color, diameter, and more. Based on derived feature parameters, the images are classified as either indicating melanoma or representing benign skin lesions. © 2023 IEEE.

关键词： image analysis

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Skin Cancer Detection using modified CNN with GLCME Features 3

Skin Cancer Detection using modified CNN with GLCME Features

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3rd Odisha international conference on Electrical Power engineering, Communication and Computing Technology, ODICON 2024

作者： Das, Subhadarshini Das, Goutami Mohanty, Mihir Narayan Applied Signal Processing Lab Iter Odisha Bhubaneswar India

ISBN: (纸本)9798350354379

Skin cancer detection is a critical application of computer vision in the medical field. Early detection of skin cancer, including melanoma and non-melanoma types, significantly improves treatment outcomes. Automated detection methods can assist dermatologists by providing consistent and objective analysis. Traditional methods rely on clinical expertise and manual examination, which can be subjective and error-prone. Machine learning is the subset of AI that offers promising results for automatic skin cancer detection using image analysis. This report explores a hybrid approach combining Convolutional Neural Networks (CNNs) with GLCM and Energy features as one set of feature to enhance diagnostic accuracy. It is standard clinical criteria for evaluating skin lesions. The combination of CNNs with GLCME features enhances diagnostic accuracy by leveraging both learned and handcrafted features. © 2024 IEEE.

关键词： Convolutional neural networks

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Learning Spatial and Geometric Information for Robust Features 3

Learning Spatial and Geometric Information for Robust Featur...

引用

3rd international conference on Consumer Electronics and Computer engineering, ICCECE 2023

作者： Zhou, Junqi Li, Yanfeng Chen, Houjin School of Electronic and Information Engineering Beijing Jiaotong University Beijing China

ISBN: (纸本)9798350331578

Robust local feature detection and description are crucial in a huge of computer vision tasks. However, current feature descriptors lack geometric information and spatial features. In this paper, we propose a novel spatial and geometric feature fusion architecture, which can effectively tackle these problems. The proposed architecture includes three aspects. 1) A multiple low-level feature fusion (ML2F2) subnetworks, which could improve the ability to extract geometric information through the weighted fusion features. 2) A subnetwork for gradient feature extraction (GFE) which could validly extract the spatial features by encoding the horizontal and vertical gradients of an image. 3) Effective spatial geometric feature fusion (SGFF) module to alternatively fuse the above subnetworks. Experiments on the task of image Matching and Long-Term Visual Localization show that the proposed method is superior to most advanced local feature descriptors. © 2023 IEEE.

关键词： image matching

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Brightness Attention Mechanism for Few-Shot Action Recognition 3

Brightness Attention Mechanism for Few-Shot Action Recogniti...

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3rd international conference on Electronic Information engineering and Computer Science, EIECS 2023

作者： Tian, Zhiyuan Xue, Wanli Zhang, Ying Tianjin University of Technology School of Computer Science and Engineering Tianjin300384 China

ISBN: (纸本)9798350316674

Few-shot action recognition remains a challenging problem in computer vision, particularly in scenarios where the model needs to generalize well from limited examples. In this study, we present a novel concept known as the Brightness Attention Mechanism(BAM), seamlessly integrated into the ResNet50 architecture, to enhance the model's sensitivity to brighter regions in the image. Our approach begins with the use of an image segmentation model that isolates human figures, followed by blending these segmented images with original images to accentuate the subject. We then apply BAM to give more attention to these highlighted regions during the action recognition task. The data from our experiments reveal a marked increase in the accuracy of few-shot action recognition, surpassing both conventional techniques and the latest attention-based methods. Our methodology advances the state of the art for few-shot scenarios across Kinetics, HMDB51, and UCF101. © 2023 IEEE.

关键词： action recognition brightness attention computer vision image fusion

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Real Time Moving Object Detection for Video Surveillance in Presence of Low Illumination-Cloudy Environment 3

Real Time Moving Object Detection for Video Surveillance in ...

引用

3rd international conference on Advancement in Electronics and Communication engineering, AECE 2023

作者： Sharma, Monika Kaswan, Kuldeep Singh Yadav, Dileep Kumar School of Computing Science and Engineering Galgotias University Greater Noida India Bennett University Department of Cse Scset Greater Noida India

ISBN: (纸本)9798350330724

Object Detection and Tracking is one of the important applications of computer vision nowadays. Indoor/Outdoor visual surveillance is most important concern in today's scenario. Indoor/Outdoor surveillance system is used to monitor the various security related problems inside and outside home, companies, shopping malls etc. Video Enhancement in cloudy environment is a computer vision task that involves improving the quality of images captured under low-light conditions. The goal of low-light image enhancement is to make images brighter and clearer. Various object detection methods are available but Basic Background subtraction method is widely used to detect the object from the image and video. This paper proposed a system which uses Guided filter along with BBS method to enhance the quality of video captured during surveillance in low light. © 2023 IEEE.

关键词： image enhancement

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Automatic Detection Method of Operation Error of Smart Electricity Meter Based on Machine vision 3

Automatic Detection Method of Operation Error of Smart Elect...

引用

3rd IEEE international conference on Intelligent Technologies, CONIT 2023

作者： Chen, Jueyu Yang, Zhou Zhou, Zhenglei Guangxi Zhuang Autonomous Region Measurement Center Guangxi Power Grid Co. Ltd Nanning530024 China

ISBN: (纸本)9798350338607

In order to improve the detection effect and reduce the number of electricity theft, an automatic detection method of operation error of intelligent electricity meter based on machine vision is proposed. Firstly, the smart meter image is collected, the Otsu threshold binarization method is used to extract the pulse lamp area image, and the improved lifting wavelet and morphology are used to preprocess the area image. Then, the pulse lamp in the reconstructed image is segmented by projection combined with a priori knowledge feedback method, and the template matching method is used to automatically detect whether the segmented pulse lamp is on, Finally, the experimental results show that this method can effectively detect the meter operation error under different line loss and power factor, effectively reduce the occurrence times of power theft, and has high detection accuracy. When different image brightness, this method has high dice value and high segmentation effect quality. © 2023 IEEE.

关键词： Crime

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A Real-Time Multimodal Deep Learning for image-to-Cartoon Conversion 3

A Real-Time Multimodal Deep Learning for Image-to-Cartoon Co...

引用

3rd international conference on Innovative Mechanisms for Industry Applications, ICIMIA 2023

作者： Karthik, Raja Pavan Vamsi, Kalla Yadu Reddy, Veeramreddy Sourya Tejarsha Abhishek, S. Anjali, T. Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Amritapuri India Rolls-Royce Inc Bangalore India

ISBN: (纸本)9798350343632

In the era of digital imagery, there is a great interest in finding new and creative ways to express ourselves and make our images look beautiful. One such fascinating method is cartoonization, a process that transforms ordinary images into visually appealing cartoon images. This paper explores the integration of cutting-edge computer vision algorithms, traditional image processing methods, and Neural Networks to achieve cartoonization. The main focus is on combining object segmentation with cartoonization in a smooth and seamless way, which offers a unique and innovative approach to improving images. By thoroughly considering various techniques and how they can be used together, our research not only gives a complete understanding of these methods but also highlights how they can transform the field of digital artistry. By exploring the integration between methods, the study sheds light on how these techniques contribute to the evolving landscape of digital artistry. The research suggests that the fusion of computer vision, traditional image processing, and Machine Learning techniques holds promising potential for pushing the boundaries of creative expression in the digital realm, offering new ways for creating efficient cartoon images. © 2023 IEEE.

关键词： Cartoonification Cartooning Color Quantization Edge Detection Region-Based Convolutional Neural Networks (R-CNN)

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Quality Analysis and improvement of 3D Panoramic Stitching Sequence image 3

Quality Analysis and Improvement of 3D Panoramic Stitching S...

引用

3rd international conference on Consumer Electronics and Computer engineering, ICCECE 2023

作者： Zhang, Jiapei Wu, Jingbin Wang, Hanxi School of Mechanical and Engineering Wuhan University of Technology Key Laboratory of Digital Stage Design and Service Culture and Tourism Ministry of the PRC Wuhan China School of Mechanical and Engineering Wuhan University of Technology Wuhan China

ISBN: (纸本)9798350331578

Using a computer to splice images with overlapping parts is the current mainstream method for 3D panorama construction. However, the quality of the spliced sequence images cannot be guaranteed due to the uneven quality of the resulting panorama. Aiming at quality problems such as inconsistent brightness and contrast of stitched sequence images and easy noise generation during the shooting process, a batch quality improvement process for multiple sequence images required for 3D panorama construction is proposed, which is different from single image processing;firstly, the median value is used The filtering method is used to denoise multiple sequential images, secondly to make the gray range of multiple images consistent, and finally to improve the contrast of the sequential images by using histogram equalization. Experimental results show that this method has a good effect on improving the quality of 3D panorama stitching sequence images. © 2023 IEEE.

关键词： Median filters

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Air-Pen: Revolutionizing Virtual Teaching With Air-Based Writing Solutions 3

Air-Pen: Revolutionizing Virtual Teaching With Air-Based Wri...

引用

3rd international conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Patil, Neha Anandhalli, Mallikarjun Patil, Abhishek Mutgekar, Harshally Kls Gogte Institute of Technology Department of Electronics and Communication Engineering Belagavi India Central University of Karnataka Department of Electronics and Communication Engineering India

ISBN: (纸本)9798350319125

In today's tech-driven education landscape, many teachers are relying on digital writing pads to help students grasp complex concepts. There is a growing use of digital writing pads by teachers to help students understand their lessons. This research introduces a novel video image processing algorithm that excels at detecting and tracking specific objects used as visual markers. To achieve this, the algorithm converts the RGB video frames into the HSV color space, making it easier to distinguish marker colors accurately. It also effectively removes any unwanted noise from each frame. The marker detection and tracking processes primarily leverage the marker's color features, ensuring a robust and dependable performance. The proposed innovation results in replacing traditional digital pads and offers a promising path for future research and educational applications. The proposed technology, allows the user to draw with gestures with desired objects used as markers. This model provides accuracy of 93%. © 2023 IEEE.

关键词： Color space-HSV Computer vision Gesture-controlled learning image processing Object Tracking Virtual content display

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Underwater image color correction and adaptive contrast algorithm improvement based on fusion algorithm 3

Underwater image color correction and adaptive contrast algo...

引用

3rd international conference on image processing and Intelligent Control, IPIC 2023

作者： Zhu, Hengjun Wang, Tianluo Ma, Lihao School of Communication and Electronic Engineering Qiqihar University Qiqihar China

ISBN: (纸本)9781510668140

Due to the characteristics of water and the particles in water, the underwater images will have problems of color deviation, low contrast and uneven brightness. Therefore, a statistical approach based on variance is proposed in this paper to correct color-biased images and then equalize the image brightness using the Gamma algorithm. Secondly, a local adaptive contrast enhancement algorithm improved by contrast code images and a restricted contrast adaptive histogram equalization algorithm are used to improve the contrast of underwater images. Finally, two different contrast-enhanced images after color correction are fused by a multi-scale fusion algorithm to obtain high-quality underwater images. The results of the comparison experiments with some existing underwater image enhancement algorithms show that our algorithm can effectively improve the quality of underwater images. © 2023 SPIE.

关键词： Luminance

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