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image processing in Industrial Design 2

Image Processing in Industrial Design

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2nd international conference on Computing and Data Science, CONF-CDS 2021

作者： Zang, Suran Xu, Lianjiang Li, Xinze College of Mechanical and Electrical Engineering Yunnan Open University Kunming650500 China

image processing is the core of visual culture. Visual culture is formed and developed around image processing, which is analyzed from the essence of visual design. It can be understood that visualization constitutes the external form of image processing, the visual regime constitutes the internal mechanism of image processing, among which the industrial design should grasp the image processing. Shape constitutes visual cognition, color arouses visual emotion, texture can obtain visual extension. Industrial design is the rational integration of product practicability, beauty and environment. The research on the aesthetic form and visual characteristics of industrial design is the main content of industrial design research [1]. Using computer vision expression method to evaluate the rationality of industrial design has application prospects. Using computer vision expression method, combined with graphics and image processing, the optimization design of the structure, structure, shape and color of workpiece products can be realized. © Published under licence by IOP Publishing Ltd.

关键词： Product design

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A Hybrid Pipeline for the Segmentation of Eye Regions from Video Frames 2nd

A Hybrid Pipeline for the Segmentation of Eye Regions from V...

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2nd international conference on computer vision, High-Performance Computing, Smart Devices, and Networks, CHSN 2021

作者： Rao, Adish Mysore, Aniruddha Guragol, Abhishek Shetty, Rajath Ajri, Siddhanth Sarkar, Poulami Srinivasa, Gowri PES Center for Pattern Recognition PES University Electronic City Campus Bengaluru560100 India Department of Computer Science and Engineering PES Center for Pattern Recognition PES University Bengaluru India

ISBN: (纸本)9789811698842

An accurate tracking and, in turn, an accurate segmentation of key regions of the eye are a sine qua non to provide users with superior quality of immersive experiences in augmented reality (AR), virtual reality (VR), and mixed reality (MR) applications. In this paper, we present the detailed rationale and research behind the design of an image processing pipeline to perform pixel-wise segmentation of eye from the background and labeling key structures, viz. sclera, iris and pupil, of the eye. The images used in this study are from a dataset provided by Facebook as a part of the OpenEDS Challenge 2020 and are sampled frames from a video capture of the eye. The pipeline we present is a hybrid of traditional image preprocessing techniques (such as histogram equalization) with application-specific augmentation (such as emulating a glare pattern) and an ensemble of five powerful, deep learning-based segmentation networks derived from the U-Net and LinkNet, followed by post-processing that harnesses temporal information. With this pipeline, we obtain a final evaluation score of 0.9641, which is well above the baseline score of 0.840 (provided by Facebook as a part of the challenge) and comparable to the top scores reported in the public domain. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Pipelines

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Face Detection System for Health Care Units Using Raspberry PI 2

Face Detection System for Health Care Units Using Raspberry ...

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2nd international conference on Intelligent Engineering and Management, ICIEM 2021

作者： Bhattacharya, Pronaya Handa, Anand Zuhair, Mohd Nirma University Institute of Technology Department of Cse Ahmedabad India Dr. A.P.J Abdul Kalam Technical University Lucknow India Nirma University Institute of Technology Department of Cse Ahmedabad India

ISBN: (纸本)9781665414500

computer vision has one of its primary applications as facial detection and recognition system, which can perform two essential functions of identifying and verifying an individual. An image capturing system with embedded computing as one of its significant parts extracts image information and requires no external processing system. To deliver results to other devices for the corresponding inputs, we use an interface device. The designed system is fast and efficient. It coordinates efficiently with the image recognition unit and the recognition algorithm. The system ensures a smooth flow of data stream between the two devices, namely, the Raspberry PI board and the camera. It also handles the data stream flow to occur smoothly between the camera and the Raspberry PI board. During this pandemic era, the application has a wide range of applicability for healthcare systems, helping in a contactless authentication of patients. © 2021 IEEE.

关键词： image enhancement

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Inappropriate Content Classification based on Video Rating: A Preliminary Study 2

Inappropriate Content Classification based on Video Rating: ...

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2nd international conference on computer Science and Engineering, IC2SE 2021

作者： Yong, Zuo Jun Lam Hoo, Wai University of Malaya Faculty of Computer Science and Information Technology Centre of Image and Signal Processing Department of Information Systems Kuala Lumpur Malaysia

ISBN: (纸本)9781665400459

With the rise of social media, video analysis becomes more important in understanding user behavior. Video analysis has been a challenging problem in the computer vision domain compared to image analysis, considering the number of image frames per second a video can have, and the time-series information that underlying within the video. While recent advancements in video analysis improved gradually, the inappropriate content classification remains challenging, mainly due to the limited number of frames to be detected that is contributing to the inappropriate context of the video. In this work, we tackled this problem by addressing the current algorithm in video analysis problems and investigating the majority algorithm that is widely used in video analysis problems and is heavily affected by the issues in inappropriate content classification. A weighted score strategy is proposed to overcome the limitation of the small number of image frame detection in terms of inappropriate content so that inappropriate content classification problems can be optimized. To prove the concept, a video dataset labeled with inappropriate content inspired by Malaysia movie rating has been collected. The proposed strategy can outperform the majority algorithm in a good margin, showing that the proposed solution helps get more effective in inappropriate content classification. © 2021 IEEE.

关键词： computer vision

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Poverty Mapping with Satellite vision for Social Good Using R-CNN Algorithm 2

Poverty Mapping with Satellite Vision for Social Good Using ...

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2nd international conference on Machine Learning and Autonomous Systems, ICMLAS 2025

作者： Nandhini, S. Kumar, Gerigi Charan Karthik, M. Jeeva, S. Karpagam Academy of Higher Education Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798331505745

Poverty mapping using satellite imagery data has appeared as a revolutionary methodology for addressing challenges related to socio-economic in underserved areas. This idea influences the deep learning models, particularly R-CNNs (Region-based Convolutional Neural Networks) for examining high-quality satellite imagery and extracting features that represent poverty levels like agricultural patterns, urbanization, and infrastructure quality. By combining advanced image processing methods and geospatial datasets, this proposed algorithm detects the level of poverty with better accuracy requiring the least ground survey data. This proposed solution not only allows organizations and policymakers to allot resources more effectively, enhancing life's quality and driving sustainable growth in poor communities but also provides scalable and cost-effective alternatives for traditional techniques. The ability of this model generates comprehensive poverty mapping that empowers NGOs (Non-Government Organizations), government, and policymakers with actionable visions towards resource allocation effectively and prioritizes growth interventions. The model, which builds on a modified ResNet50 (Residual Network) framework, attained an R2 value of 93% and a low MAE (Mean Absolute Error) of 13%, showing its ability to map poverty patterns effectively. © 2025 IEEE.

关键词： Cost effectiveness

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Detection of Diabetic Retinopathy from fundus images using Resnet50

Detection of Diabetic Retinopathy from fundus images using R...

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international conference on the Paradigm Shifts in Communication, Embedded Systems, Machine Learning and Signal processing (PCEMS)

作者： Sarvat Ali Shital Raut Department of Computer Science and Engineering Visvesvaraya National Institute of Technology Nagpur India

High blood glucose levels cause lesions on the retina of the eye, resulting in a degenerative condition known as diabetic retinopathy (DR), which impacts vision and can cause irreversible vision loss. The most common cause of blindness in diabetic people is thought to be diabetic retinopathy. Early diagnosis of diabetic retinopathy is essential to efficiently maintaining the patient’s vision. We attempted to give first-hand verification to this fundamental problem of DR detection to save time, money and efforts of ophthalmologists. The latter also proved to be more challenging, especially early on in the disease, when disease characteristics are less obvious in the fundus images. Deep learning algorithms and machine learning-based medical image analysis have aided in the early identification of diabetic retinopathy along with the evaluation of retinal fundus images. This paper attempts to preprocess and binary classify fundus images from the famous Aptos dataset using finetuned ResNet50 as well as features extraction from ResNet50 and later classifying using machine learning models. We have achieved an accuracy of 0.9802, an AUC score of 0.9937, F1 score of 0.9870, a precision of 0.9890, a recall as 0.9845 and kappa score of 0.9481 on the evaluation data by fine-tuning of ResNet50.

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Remote Sensing image Detection Method Based on Contrastive Learning

Remote Sensing Image Detection Method Based on Contrastive L...

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Artificial Intelligence and Intelligent Information processing (AIIIP), international conference on

作者： Yongjin Zhang Qiu Cheng Zhongke Zhu Jian Jiao Siyu Qu Fujun Zhang The 52nd Research Institute of China Electronics Technology Corporation Hangzhou China

In recent years, with rapid development of artificial intelligence technology, object detection in remote sensing images has attracted wide attention in the field of computer vision. Different from ordinary image object detection, low proportion of object semantic information and unbalanced number of object categories. To solve these problems, an object detection method based on contrastive learning representation is proposed to improve the performance of remote sensing image object detection. The design of backbone network is based on the image-text pair contrast language-image pretrained model, which can effectively increase the number of target detection types in remote sensing images by using its representation learning in zero-shot and transfer learning Settings. The RCNN structure is added by extension design to achieve a region-level visual representation of model learning, enabling fine-grained alignment between image regions and text concepts. The problem of missing detection is solved, which is caused by many kinds of objects, low proportion of object semantic information and unbalanced number of object categories. The experimental results show that the target detection method based on image-text representation learning can effectively detect multiple targets with low information proportion in remote sensing images. Compared with the original RCNN, this method can detect more types of objects in remote sensing images, and the number of objects detected is also improved compared with other typical remote sensing object detection methods, which proves the effectiveness of the remote sensing image object detection method.

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Enhancing Safety Perception in Autonomous Driving Systems Through 3D Object Detection and Neural Network Regression

Enhancing Safety Perception in Autonomous Driving Systems Th...

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Algorithm, image processing and Machine vision (AIPMV), international conference on

作者： Yuhang Wang Yue Zhang Shutong Liu Chuangqi Li College of Computer and Information Engineering Henan Normal University Xinxiang Henan China School of Liberal Arts' ZhouKou Normal University Zhoukou Henan China

ISBN: (数字)9798350390254

ISBN: (纸本)9798350390261

With the rapid development of autonomous driving technology, safety perception has become a critical component in ensuring the reliability and safety of these systems. However, challenges persist, such as traffic accidents caused by the Ideal L9, which misidentified billboard images as real vehicles, leading to erroneous emergency stops. This paper proposes a novel method to address such safety perception issues by enhancing 3D object detection accuracy. Utilizing the mmdetection framework and the SparseBEV algorithm, we identify and address deficiencies in distinguishing planar images from stereo objects. Our approach combines deep neural network regression learning with geometric constraints, introducing a hybrid discrete-continuous loss function to improve the 3D direction regression of objects. Experimental results demonstrate significant improvements in detection accuracy and safety awareness, providing a robust solution to mitigate misidentification risks. However, further optimization is needed to ensure comprehensive safety enhancement in autonomous driving systems.

关键词： Adaptation models Three-dimensional displays Accuracy Object detection Artificial neural networks Robustness Safety Object recognition Autonomous vehicles Optimization

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image Pre-processing for Differential Diagnosis of Multiple Sclerosis using Brain MRI

Image Pre-processing for Differential Diagnosis of Multiple ...

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international conference on vision Towards Emerging Trends in Communication and Networking (ViTECoN)

作者： Khuhed Memon Norashikin Yahya Mohd Zuki Yusoff Hilwati Hashim Syed Saad Azhar Ali Shahabuddin Siddiqui Dept. of Electrical & Electronic Engineering Universiti Teknologi PETRONAS Seri Iskandar Perak Malaysia Dept. of Electrical & Electronic Engineering HiCoE-CISIR Universiti Teknologi PETRONAS Seri Iskandar Perak Malaysia Department of Radiology Faculty of Medicine Universiti Teknologi MARA Sungai Buloh Selangor Malaysia Aerospace Engineering Dept. Center for Smart Mobility & Logistics King Fahd University of Petroleum & Minerals Dhahran Saudi Arabia Department of Radiology Advanced International Hospital Pakistan Institute of Medical Sciences Islamabad Pakistan

Multiple sclerosis (MS) is a type of auto-immune disease affecting the human brain. One of the challenges in clinical diagnosis of MS lies in its close similarity to another type of brain auto-immune disease known as neuromyelitis optica (MNO). Hence, various researchers are looking into the development of computer-aided differential diagnosis (CADD) of MS vs. MNO. Generally, the development of the CADD, involves image pre-processing, feature extraction, statistical analysis and training and testing of the classification model. The focus of this paper is on the 2 critical pre-processing steps prior to classification of MS vs. MNO, namely skull stripping and lesion segmentation. In particular, both skull stripping and brain lesion segmentation are tested with a popular semantic segmentation technique, DeepLabV3. For comparison, a recently published technique, SynthStrip from FreeSurfer [1], developed by Harvard University is also tested for brain extraction / skull removal. Similarly for segmentation of brain lesions, Lesion Segmentation Tool in SPM Toolbox is tested and compared. The results from this study provides insight on what are the appropriate pre-processing techniques for the development of an reliable automatic differential diagnosis tool for multiple sclerosis conditions.

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Enhancing Multimodal Named Entity Recognition via Entity Span Detection and Graph Networks

Enhancing Multimodal Named Entity Recognition via Entity Spa...

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computer, vision and Intelligent Technology (ICCVIT), international conference on

作者： Yupeng Ma Ming Zhang Zhiqiang Li Shihong Wu School of Computer Science and Engineering Wuhan Institute of Technology Wuhan China Yuanguang Software Co. Ltd Wuhan China

ISBN: (数字)9798331540043

ISBN: (纸本)9798331540050

Multimodal Named Entity Recognition (MNER) focuses on utilizing multiple modalities, including text and images, to recognize specific types of entities within text. Despite recent advancements, existing methods still face several challenges: (1) How to create a unified representation that connects and integrates different modalities, and (2) how to better interact between different modalities based on the unified representation while fully capturing both intra-modal and inter-modal information. In order to address these challenges, we design a new model, enhancing multimodal named entity recognition via entity span detection and graph networks (GENT-Net), which leverages entity recognition and graph networks to enhance MNER. Specifically, we employ a graph-based structure to model text and images, use a graph attention network for multimodal fusion, and introduce entity span detection using a graph convolutional network to assist the MNER task. We perform comprehensive experiments on the publicly available datasets, Twitter2015 and Twitter2017, showcasing the effectiveness of our proposed approach.

关键词： computer vision image recognition Graph convolutional networks Text recognition Face recognition Computational modeling Named entity recognition Feature extraction

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