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Blood Cell Classification and Counting in Hematology through Deep Learning 4

Blood Cell Classification and Counting in Hematology through...

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4th international conference on Sustainable Expert Systems, ICSES 2024

作者： Ananthi, P. Devi, K. Nirmala Ramya, T.E. Shanmugapriya, P. Aradhya, T. Kongu Engineering College Department of Computer Technology Perundurai Tamil Nadu Erode India Madanapalle Institute of Technology & Science Department of Computer Applications India Kongu Engineering College Perundurai Tamil Nadu Erode India

ISBN: (纸本)9798331540364

Medical diagnosis is one of the areas that has been greatly influenced by the progress of computer vision technologies. this study presents a unique approach for the detection and counting of blood cells in hematology: You Only Look One algorithm version 7 (YOLOv7). YOLO is designed for real-time object detection, making it ideal for applications such as blood vessel detection where speed is critical. the architecture allows the model to process images faster than other recognition models, such as R-CNN or SSD, which is very important for situations that require fast results, such as analysis automatic blood at clinical sites. the purpose of the proposed method is to overcome the shortcomings of conventional methods and to accurately and quickly identify blood cells in mechanical images. the YOLOv7 model has been utilizedbecause it can detect objects in real-time with highprecision and speed. the methodology for bloodcell counting involves the use of YOLOv7 togenerate bounding boxes around each detectedblood cell in an image, where the count of these bounding boxes directly corresponds to the number of cells. this advancement in hematology not onlyimproves blood cell analysis efficiency but also expedites the process of diagnosing and planningtreatment in various medical situations. this paper details how YOLOv7 was modified and adjusted to meet the specific requirements of hematologicalimage analysis, including training models, preparing datasets, and evaluating performance. the study also addresses the potential impact of this technology on clinical workflows, highlightinghow it might help medical practitioners make decisions more quickly and intelligently. Hematology analysis systems including YOLOv7 are able to improve laboratory diagnostics and help patients by providing better care and results. this workconcludes by demonstrating the revolutionary potential of YOLOv7 in blood cell identificationand counting in hematology, paving the way for accurate and better

关键词： Diagnosis

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MiVOLO: Multi-input Transformer for Age and Gender Estimation 11th

MiVOLO: Multi-input Transformer for Age and Gender Estimat...

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11th international conference on Analysis of images, Social Networks and Texts, AIST 2023

作者： Kuprashevich, Maksim Tolstykh, Irina Layer Team SaluteDevices Moscow Russia

ISBN: (纸本)9783031545337

Age and gender recognition in the wild is a highly challenging task: apart from the variability of conditions, pose complexities, and varying image quality, there are cases where the face is partially or completely occluded. We present MiVOLO (Multi Input VOLO), a straightforward approach for age and gender estimation using the latest vision transformer. Our method integrates both tasks into a unified dual input/output model, leveraging not only facial information but also person image data. this improves the generalization ability of our model and enables it to deliver satisfactory results even when the face is not visible in the image. To evaluate our proposed model, we conduct experiments on five popular benchmarks and achieve state-of-the-art performance, while demonstrating real-time processing capabilities. Additionally, we introduce a novel benchmark based on images from the Open images Dataset. the ground truth annotations for this benchmark have been meticulously generated by human annotators, resulting in high accuracy answers due to the smart aggregation of votes. Furthermore, we compare our model’s age recognition performance with human-level accuracy and demonstrate that it significantly outperforms humans across a majority of age ranges. Finally, we grant public access to our models, along with the code for validation and inference. In addition, we provide extra annotations for used datasets and introduce our new benchmark. the source code and data can be accessed at https://***/WildChlamydia/***. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： computer vision

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Normalization attention and lightweight convolution-based network for traffic sign detection 4

Normalization attention and lightweight convolution-based ne...

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4th international conference on Telecommunications, Optics, and computer Science, TOCS 2023

作者： Zhao, Meng-Lei Department of Computer Science and Technology Yanbian University 977 Gongyuan Road Yanji133002 China

ISBN: (数字)9781510679849

ISBN: (纸本)9781510679832

Object detection and image recognition are considered to be the most critical and challenging aspects of computer vision. the rapid advancement of driverless technology and assisted driving systems further emphasizes the importance of traffic sign detection and recognition. Detecting small objects against complex backgrounds in practical assisted driving tasks remains a formidable challenge. Many existing target detection methods struggle to strike a balance between performance and parameters. In response, we propose a novel traffic sign detection model based on YOLOv8s—A Traffic Sign Detection Network incorporating Normalization-based Attention and Lightweight Convolution (YOLOv8-NL). the objective is to optimize the performance and address the challenge of balancing model parameters in traffic sign recognition within the transportation domain. Firstly, Normalization-based Attention (NAM) is introduced to address issues related to inconsistent input dimensions, uneven target distribution, and complex background of optimization. Secondly, lightweight Global Sparse Convolution (GSConv) is integrated to reduce the model parameters and enhance model generalization performance. Finally, to further enhance detection accuracy, the Wise-IOUv3 loss function is introduced to tackle difficulties associated with low-quality labeling of data. the effectiveness of the novel YOLOv8 model, which integrates Normalization-based Attention and Lightweight Convolution, is demonstrated through experiments conducted on two publicly available datasets. these experiments showcase the model's ability to significantly reduce parameters without compromising its guaranteed performance. It is noteworthy to note that significant improvements were observed in the CCTSDB2021 dataset, with a reduction of 1.2 million model parameters, resulting in 1% increase in mean average precision (mAP) compared to the YOLOv8s model. Furthermore, the mAP for the three-class target detection task on the TT00K dat

关键词： Convolution

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AI-Based Real Time Object Measurement Using YOLO V5 Algorithm 4th

AI-Based Real Time Object Measurement Using YOLO V5 Algorith...

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4th international conference on Machine Intelligence and Signal processing, MISP 2022

作者： Anand, R. Sabeenian, R.S. Ganesh Kumar, S. Logeshwaran, V. Soundarrajan, B. Department of Electronics and Communication Engineering Sona College of Technology Salem India

ISBN: (纸本)9789819900848

In our paper various object’s size will be measured from an input image and also by means of camera. We had chosen open computer vision algorithm and Yolo v5 algorithm for faster and simpler process. Open cv has large open-source library which will be useful for the machine learning and image processing users. It can be able to process the images and videos to identify the face of a person, objects and etc. Main aim of this project is to measure the dimension (width and height) of an object from a given image and the distance between the objects will be automatically computed and stored in the memory. It has a necessary ways to obtain the object dimension, first we need to find a pixel value, to find the pixel value we need to select one reference object from an image by considering the width & height of an image from distance and also the measuring parameter units such as inches /mm/cm. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： computer vision

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3D facial animation generation technology based on 2D image 4

3D facial animation generation technology based on 2D image

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4th international conference on Machine Learning and Big Data Analytics for IoT Security and Privacy, SPIoT 2023

作者： Li, Jian School of Art Design Shandong Business Institute YanTai264000 China

In order to improve the modeling efficiency and ensure the authenticity of the model, this paper proposes a 3D facial animation generation method based on front and side images. First, the feature point information reflecting the shape characteristics of the face is extracted from the front and side photos of a given face, and then these feature point information is used to modify the general face model to obtain a specific face model. then, the face texture information in the front and side photos is used to texture map the specific face model, and finally a realistic virtual 3D face animation is obtained. In the aspect of feature extraction, this paper uses an improved color Snake model to accurately locate contour lines. the experimental results show that the system still has good processing performance, and the average processing time is less than 0.1s. On the set V, the light source angle is greater than 78 °, the illumination is seriously uneven, and the recognition rate is very low, which indicates that the texture features are not well extracted. But even in this case, the recognition rate after light compensation has doubled, which proves that the two-dimensional image can realize the three-dimensional facial animation generation technology. © 2024 the Authors.

关键词： 3D modeling

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Frequency Modulated Deformable Transformer for Underwater image Enhancement 27th

Frequency Modulated Deformable Transformer for Underwater I...

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27th international conference on pattern recognition, ICPR 2024

作者： Dukre, Adinath Deshmukh, Vivek Kulkarni, Ashutosh Phutke, Shruti Vipparthi, Santosh Kumar Gonde, Anil B. Murala, Subrahmanyam Shri Guru Gobind Singhji Institute of Engineering and Technology Nanded India CVPR Lab Indian Institute of Technology Ropar Rupnagar India ETI Lab Yamaha Motor Solutions Faridabad India School of Computer Science and Statistics Trinity College Dublin Dublin Ireland

ISBN: (纸本)9783031781247

Underwater images frequently experience quality degradation due to refraction, back-scattering, and absorption, leading to color distortion, blurriness, and reduced visibility. Such degradation present in the underwater images can cause inaccuracies while functioning with higher advanced level computer vision applications, equipped for autonomous underwater vehicles. Despite the ability of enhancing the degraded images, existing approaches fail at preserving the localized fine edges also producing the true colors. therefore, an effective pre-processing network is necessary for underwater image enhancement. With this motivation, we propose a frequency modulated deformable transformer network for underwater image enhancement. Initially, the features are extracted with the proposed multi-scale feature fusion feed-forward module. Further, the frequency modulated deformable attention module is proposed to reconstruct fine-level texture in the restored image. Here, we propose a spatio-channel attentive offset extractor in the modulated deformable convolution for focusing on relevant contextual information. Also, adaptive edge-preserving skip connections are proposed for propagating prominent edge features from the network’s shallow layers to its deeper layers. A comprehensive evaluation of the proposed method on synthetic and real-world datasets and extensive ablation analysis demonstrates that the proposed approach shows superior performance than existing state-of-the-art methods. the testing code is provided at https://***/adinathdukre/FMDTUIE. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Frequency modulation

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Enhanced YOLOv7-Based Construction Site Environmental Detection Method 4

Enhanced YOLOv7-Based Construction Site Environmental Detect...

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4th international conference on Big Data, Artificial Intelligence and Internet of things Engineering, ICBAIE 2023

作者： Liang, Siheng Huang, Wenming Xiao, Yannan Guilin University of Electronic Technology School of Computer and Information Security Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin China Guilin University of Electronic Technology School of Computer and Information Security Guilin China

ISBN: (纸本)9798350343618

this paper proposes an enhanced approach utilizing the YOLOv7 model for automated construction site environment detection and management. the method leverages high-resolution surveillance cameras and computer vision techniques to achieve this goal. the proposed method starts with data collection of the construction site environment. then produce accurate bounding boxes. these bounding boxes serve as input to an improved YOLOv7 network model, enabling feature extraction and generating high-scoring feature maps with associated weights. Utilizing these weights, image segmentation is performed to obtain the desired detection heads. Experimental results demonstrate the effectiveness of the method in identifying and annotating various environmental features within the construction site, leading to significant improvements in detection accuracy. Compared to similar techniques, this method offers faster detection speed, higher accuracy, and reduced error rates. the proposed construction site environment detection method provides an efficient and automated solution for on-site management in the construction industry, with practical applications and value. © 2023 IEEE.

关键词： Construction industry

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Scene Text recognition with image-Text Matching-Guided Dictionary 17th

Scene Text Recognition with Image-Text Matching-Guided Dicti...

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17th international conference on Document Analysis and recognition (ICDAR)

作者： Wei, Jiajun Zhan, Hongjian Tu, Xiao Lu, Yue Pal, Umapada East China Normal Univ Shanghai Key Lab Multidimens Informat Proc Shanghai Peoples R China East China Normal Univ Chongqing Inst Chongqing 401120 Peoples R China Indian Stat Inst CVPR Unit Kolkata India

ISBN: (纸本)9783031417306;9783031417313

Employing a dictionary can efficiently rectify the deviation between the visual prediction and the ground truth in scene text recognition methods. However, the independence of the dictionary on the visual features may lead to incorrect rectification of accurate visual predictions. In this paper, we propose a new dictionary language model leveraging the Scene image-Text Matching(SITM) network, which avoids the drawbacks of the explicit dictionary language model: 1) the independence of the visual features;2) noisy choice in candidates etc. the SITM network accomplishes this by using image-Text Contrastive (ITC) Learning to match an image with its corresponding text among candidates in the inference stage. ITC is widely used in vision-language learning to pull the positive image-text pair closer in feature space. Inspired by ITC, the SITM network combines the visual features and the text features of all candidates to identify the candidate with the minimum distance in the feature space. Our lexicon method achieves better results(93.8% accuracy) than the ordinary method results(92.1% accuracy) on six mainstream benchmarks. Additionally, we integrate our method with ABINet and establish new state-of-the-art results on several benchmarks.

关键词： Dictionary Language Model Scene image-Text Matching image-Text Contrastive Learning Scene Text recognition

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Design of rhubarb processing control system based on Internet of things 4

Design of rhubarb processing control system based on Interne...

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4th international conference on Telecommunications, Optics, and computer Science, TOCS 2023

作者： Sun, Lijuan Wei, Jiansheng Li, Xiangwei Zhao, Xiaoqiang Tang, Jiawei School of Computer Science and Artificial Intelligence Lanzhou Institute of Technology Lanzhou730000 China Li County Rhubarb technology Co. LTD Gansu province Li County Industrial Park Longnan Longnan730000 China

ISBN: (数字)9781510679849

ISBN: (纸本)9781510679832

In order to improve the processing quality of traditional Chinese medicine rhubarb and control the processing process of rhubarb in real time, a programmable logic control system with Ethernet and Internet of things remote communication function was designed, and the corresponding programming software and configuration software were developed, which had important engineering application value for the transformation and upgrading of traditional processing equipment of rhubarb. In this paper, based on 5G Narrowband Internet of things (NBiot) technology and industrial Ethernet technology, a programmable logic control system with remote wireless communication function is designed, and the supporting programming software platform is developed to connect to the cloud wireless network. the research results were tested in Li County Rhuai Technology Co., LTD. the rhuai production yield was increased by 30%, the processing quality was effectively improved, and the effectiveness of the rhuai processing process control was guaranteed. In addition, the results can provide reference for the research and development of intelligent control systems in traditional Chinese medicine preparation industry, and realize the large-scale and industrialized development of traditional Chinese medicine processing equipment. © 2024 SPIE.

关键词： Internet of things

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A System for processing and recognition of Greek Byzantine and Post-Byzantine Documents 17th

A System for Processing and Recognition of Greek Byzantine a...

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17th international conference on Document Analysis and recognition (ICDAR)

作者： Kaddas, Panagiotis Palaiologos, Konstantinos Gatos, Basilis Katsouros, Vassilis Christopoulou, Katerina NCSR Demokritos Computat Intelligence Lab Inst Informat & Telecommun Athens 15310 Greece Univ Athens Dept Informat & Telecommun Athens 15784 Greece Royal Holloway Univ London Hellen Inst Egham Hill Egham TW20 0EX Surrey England Athena Res Ctr Inst Language & Speech Proc Athens Greece Harokopio Univ Sch Environm Geog & Appl Econ Dept Econ & Sustainable Dev Athens 17676 Greece

ISBN: (纸本)9783031416842;9783031416859

processing and recognition of Greek Byzantine and Post-Byzantine (old Greek) Documents has been proven to be a tedious task in the domain of Historical Document image processing. Several unique characteristics of these documents (existence of character ligatures, abbreviations, lack of clear word division, existence of symbols or punctuations in an arbitrary position) impose significant difficulties for current processing and recognition tools. In this work, we introduce a system for processing and recognition of old Greek documents and give details about all the components that comprise it. these include an image pre-processing, a text line segmentation and a recognition module. In order to test the proposed system, we introduce and provide publicly a newdataset of old GreekDocuments that includes text line images and the corresponding transcription. Using this dataset, we evaluate the embedded recognition engine of the proposed system which is the open-source Calamari-OCR engine employing a variety of configurations. the best result corresponded to a character error rate less than 1.5% which is acceptable and promising. Finally, we also achieved promising results when comparing the embedded OCR engine with other recognition methods already proposed for the recognition of old Greek Documents.

关键词： Document Analysis System Deep Neural Networks Calamari-OCR Greek Byzantine and Post-Byzantine Documents Text Line recognition

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