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Cross-Modal Perception for Customer Service 23

Cross-Modal Perception for Customer Service

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29th Annual international conference on Mobile Computing and Networking, MobiCom 2023

作者： Maj, Michal Rymarczyk, Tomasz MacIura, Lukasz Cieplak, Tomasz Pliszczuk, Damian Faculty of Computer Science Wsei University Lublin60274650 Poland Research and Development Center Netrix S.A. Lublin60274777 Poland Faculty of Management Lublin University of Technology Lublin60011739 Poland

ISBN: (纸本)9781450399906

Artificial Intelligence offers cost-effective solutions to improve business processes and ensure more satisfying customer service. The advantage of solutions based on artificial intelligence is the possibility of using the API with mobile or stationary applications and cloud services. The research presented here aims to develop a deep learning model using cross-modal techniques on the example of a multi-tasking network. The main task is to use computer vision on IoT devices using sensors for customer service. Additionally, the solution will be based on distributed systems. Finally, the method of building the multi-tasking model, which will be designed to determine the person in the image and their emotional state, will be verified. © 2023 ACM.

关键词： Cost effectiveness

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A Review on computer-assisted Techniques to Analyze Histopathological images of the Breast

A Review on Computer-assisted Techniques to Analyze Histopat...

引用

2023 international conference on Power, Instrumentation, Energy and Control, PIECON 2023

作者： Nagdeote, Sushma Prabhu, Sapna Fr. Conceicao Rodrigues College of Engineering Department of Computer Engineering Mumbai India

ISBN: (纸本)9798350399769

This article provides an overview of computer-assisted techniques (CAT) used to assess histopathological images for Breast cancer. The Histopathological images analysis (HIPA) is time consuming and challenging. The shape, size, color and crowding of nuclei disclose important statistics about the tissue health. For diagnosis of cancer, nuclei are an important attribute which has to be isolated from other healthy tissues followed by feature extraction which is the primary and the most critical step. In order to distinguish between tumor or cancer types, experts assess morphometric features of each and every cell and their nuclei. The most familiar steps in histopathological image analysis like stain normalization, segmentation, feature extraction and classification are covered in this review. The segmentation methods of H & E images and several challenges related to stain normalization are discussed. Since stain normalization is a stage in the pre-processing of H & E images and the first step of feature extraction. In a nutshell, this article will outline different methods for analysing breast pathology images. © 2023 IEEE.

关键词： Feature extraction

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Research on forest land classification method of remote sensing images based on boundary information 4

Research on forest land classification method of remote sens...

引用

4th international conference on Machine Learning and computer Application, ICMLCA 2023

作者： Yuan, Zhengwu Wang, Yang Yang, Aixia Chen, Qiang School of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing China State Key Laboratory of Remote Sensing Science Aerospace Information Research Institute Chinese Academy of Sciences Beijing China

ISBN: (数字)9781510680265

ISBN: (纸本)9781510680258

Semantic segmentation of remote sensing image is a key technology in the field of remote sensing image processing, and its segmentation results can be used in land resource management, yield estimation, disaster evaluation and many other aspects. However, the forest land is widely distributed and the tree species are diverse, which brings difficulties to the extraction of forest land. Different from traditional manual investigation, semantic segmentation can quickly extract forest land from remote sensing images. U-Net is a deep codec structure, which has been frequently used for high-precision image segmentation. In this paper, Multi-scale features of different levels of U-Net are used to extract forest land from high-resolution remote sensing images. Multi-scale features can capture features of different scales for fusion, and note the importance of boundary information. A boundary attention module is added to explicitly use boundary information for context aggregation, which makes the extracted boundary effect more remarkable Attention module is designed to enhance the learning of vegetation characteristics, so as to improve the segmentation performance. This study effectively improves the problems of fuzzy boundary of semantic segmentation, large intra-class differences and small inter-class differences, and can quickly extract forest land. © 2024 SPIE.

关键词： Extraction

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Quaternion Orthogonal Transformer for Facial Expression Recognition in the Wild 48

Quaternion Orthogonal Transformer for Facial Expression Reco...

引用

48th IEEE international conference on Acoustics, Speech and Signal processing, ICASSP 2023

作者： Zhou, Yu Guo, Liyuan Jin, Lianghai Huazhong University of Science and Technology School of Computer Science and Technology China Huazhong University of Science and Technology Institute of Artificial Intelligence China

ISBN: (纸本)9781728163277

Facial expression recognition (FER) is a challenging topic in artificial intelligence. Recently, many researchers have attempted to introduce vision Transformer (ViT) to the FER task. However, ViT cannot fully utilize emotional features extracted from raw images and requires a lot of computing resources. To overcome these problems, we propose a quaternion orthogonal transformer (QOT) for FER. Firstly, to reduce redundancy among features extracted from pre-trained ResNet-50, we use the orthogonal loss to decompose and compact these features into three sets of orthogonal sub-features. Secondly, three orthogonal sub-features are integrated into a quaternion matrix, which maintains the correlations between different orthogonal components. Finally, we develop a quaternion vision transformer (Q-ViT) for feature classification. The Q-ViT adopts quaternion operations instead of the original operations in ViT, which improves the final accuracies with fewer parameters. Experimental results on three in-the-wild FER datasets show that the proposed QOT outperforms several state-of-the-art models and reduces the *** are available at https://***/Gabrella/QOT. © 2023 IEEE.

关键词： Face recognition

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An Innovative New Open computer vision Framework Via Artificial Intelligence with Python

An Innovative New Open Computer Vision Framework Via Artific...

引用

international conference on Data Science and Network Engineering, ICDSNE 2023

作者： Bonkra, Anupam Dhiman, Pummy Goyal, Shanky Islam, Sardar M. N. Rana, Arun Kumar Sharma, Naman Information Technology Department Chandigarh Engineering College-CGC Punjab India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India ISILC & Decision Sciences and Modelling Program Victoria University Footscra Australia Department of Computer Science Galgotias College of Engineering of Technology Uttar Pradesh Greater Noida India

ISBN: (纸本)9789819967544

computer vision has emerged as an important subject of study, with several practical applications in a wide range of domains. OpenCV, a widely used framework, has played an important role in allowing computer vision tasks. This study presents an AI-driven Python implementation of the OpenCV framework to expand its capabilities. Data collecting and pre-processing, feature extraction, model selection and training, and Python-based system implementation are all part of the proposed system. A thorough examination of system performance indicates its advantage over competing approaches. The system’s flexibility enables it to handle a variety of computer vision tasks, such as gesture recognition, face detection, and object recognition. This paper advances AI-powered computer vision systems by providing significant insights into the implementation of OpenCV with Python. The focus of future work will be on improving system accuracy and broadening its functional range. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Python

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Classification of Subspecies of Honey Bees using Convolutional Neural Network 6

Classification of Subspecies of Honey Bees using Convolution...

引用

6th international conference on Applied Computational Intelligence in Information Systems, ACIIS 2023

作者： Ratnayake, Ashan Yasin, Hartini M. Ghani Naim, Abdul Abas, Pg Emeroylariffion Universiti Brunei Darussalam Faculty of Integrated Technologies Brunei Uva Wellassa University Dept of Computer Science & Informatics Sri Lanka Universiti Brunei Darussalam Faculty of Science Brunei Universiti Brunei Darussalam School of Digital Science Brunei

ISBN: (纸本)9798350317428

Honey bees are vital for humans and the ecosystem;humans use bee products for various purposes and honey bees play an essential role in pollination. Identifying the subspecies of honey bees is essential for beekeepers, bee breeders, and researchers. Conventional methods of identification are time-consuming and expensive, as such, a more efficient and accurate technique is required. This paper proposes a computer vision-based method to classify subspecies of honey bees, with a dataset consisting of seven honey bee subspecies utilised. Due to the unavailability of a sufficient amount of labelled images to train a model, Transfer Learning (TL) technique has been employed. The Inception v3 model has been utilized as the main Convolutional Neural Network architecture;trained initially using the imageNet dataset and then fine-tuned using the honey bee dataset. image augmentation technique has also been used to reduce the impact of an imbalanced and small dataset. The Transfer Learning-based Inception v3 model was trained using the Sigmoid Focal loss function and demonstrated an accuracy of 94% with a weighted average F1 score of 0.94. This demonstrates the applicability of computer vision in the classification of honey bees. © 2023 IEEE.

关键词： Convolutional neural networks

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A Comparison between Interpolation Method and Neural Network Approach in 3D Digital Imaging and Communications in Medicine

A Comparison between Interpolation Method and Neural Network...

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2023 international conference on computer Science, Information Technology and Engineering, ICCoSITE 2023

作者： Supriyanto, Muhammad Ibadurrahman Arrasyid Sarno, Riyanarto Fatichah, Chastine Fajar, Aziz Institut Teknologi Sepuluh November Surabaya Indonesia

ISBN: (纸本)9798350320954

Higher image reconstruction with excellent structural detail allows experts to perform accurate analysis, especially on the smallest organ details. The interpolation method that approaches the problem of medical image reconstruction, especially 3D, still causes serious problems. The medical image produced by the interpolation method produces blurred or smooth lines on some parts of the organ. This can cause errors in the medical analysis that will be carried out if the reconstruction results are problematic. For this reason, a method is needed that can reconstruct images well without producing blur but does not require very large computer resources. This study aims to evaluate and compare the quality of 3D magnetic resonance imaging medical images reconstructed using interpolation methods and artificial neural network architectures in the DICOM data format. This study evaluates and compares the quality of 3D magnetic resonance imaging medical images reconstructed using interpolation methods and artificial neural network architectures. The test scenario was performed using images from the ADNI dataset and comparing the output results using a variational autoencoder and a multi-level densely connected super-resolution network on 3D data with existing interpolation methods. The evaluation was done using two metrics, i.e., SSIM and PSNR. The results showed that the variational autoencoder method has the highest SSIM and PSNR values, indicating it has the highest image quality among the three methods, while the mDCSRN method has the lowest SSIM and PSNR values, meaning it has the lowest image quality. © 2023 IEEE.

关键词： computer vision

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Adaptive Vehicle Headlights Direction System based on Road Lane Detection

Adaptive Vehicle Headlights Direction System based on Road L...

引用

2023 international conference on Informatics Engineering, Science and Technology, INCITEST 2023

作者： Nizar, Taufiq Nuzwir Hartono, Rodi Jatmiko, Didit Andri Universitas Komputer Indonesia Department of Computer Engineering Bandung Indonesia Universitas Komputer Indonesia Department of Electrical Engineering Bandung Indonesia Universitas Komputer Indonesia Department of Informatics Engineering Bandung Indonesia

ISBN: (纸本)9798350317862

Almost half of the vehicle accident fatalities occur at night. Driving at night is riskier. Even the most familiar roads can look distant in the dark. It becomes difficult to see other vehicles, pedestrians, and other objects. One of the problems is the lack of light, especially at road bends, because the vehicle headlights are always straight. The purpose of the research is to design the adaptive vehicle headlights direction that is always facing the road even if it is on the curving road. There are several processes to implement the system are detecting roads, processing detected road conditions and controlling the direction of the headlights. To detect the road using the camera sensor and image processing based the road lane and recognizing it using Hough Transform algorithm. the results of road detection are processed to drive the servo motor which adjust the direction of the lights. The implemented system works with the success rate 76.63% with accuracy error rate 1.28 degrees for the left bend and 80% with accuracy error rate 1.45 degrees to the right bend. © 2023 IEEE.

关键词： Headlights

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Accelerating Large Kernel Convolutions with Nested Winograd Transformation 31st

Accelerating Large Kernel Convolutions with Nested Winograd ...

引用

31st IFIP/IEEE international conference on Very Large Scale Integration (VLSI-SoC)

作者： Jiang, Jingbo Chen, Xizi Tsui, Chi-Ying Hong Kong Univ Sci & Technol Dept Elect & Comp Engn Hong Kong Peoples R China Huazhong Agr Univ Coll Informat Wuhan Peoples R China

ISBN: (纸本)9798350325997

Recent literature has shown that convolutional neural networks (CNNs) with large kernels outperform vision transformers (ViTs) and CNNs with stacked small kernels in many computer vision tasks, such as object detection and image restoration. The Winograd transformation helps reduce the number of repetitive multiplications in convolution and is widely supported by many commercial AI processors. Researchers have proposed accelerating large kernel convolutions by linearly decomposing them into many small kernel convolutions and then sequentially accelerating each small kernel convolution with the Winograd algorithm. This work proposes a nested Winograd algorithm that iteratively decomposes a large kernel convolution into small kernel convolutions and proves it to be more effective than the linear decomposition Winograd transformation algorithm. Experiments show that compared to the linear decomposition Winograd algorithm, the proposed algorithm reduces the total number of multiplications by 1.4 to 10.5 times for computing 4x4 to 31x31 convolutions.

关键词： image reconstruction

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Smart vision and Intelligent Object Recognition System for the Visually Impaired 15th

Smart Vision and Intelligent Object Recognition System for t...

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15th international conference on Soft Computing and Pattern Recognition, SoCPaR 2023 and 14th World Congress on Nature and Biologically Inspired Computing, NaBIC 2023

作者： Suresh, Merugu Sandhyarani Shaik, Abdul Subhani Premalatha, B. Ghinea, George Reddy, Avala Raji Raju, K. Srujan Kumar, Voruganti Naresh Department of ECE CMR College of Engineering and Technology Telangana Hyderabad India CMR Technical Campus Telangana Hyderabad India Mulsemedia Computing Brunal University London Wilfred Brown Building 215 Uxbridge United Kingdom

ISBN: (纸本)9783031810824

In an effort to increase the functional dependence for the visually impaired people, have identified the disadvantages and drawbacks in the present existing solutions and have tried to include the loopholes in the existing systems. Identified and addressed the problems like: object recognition, identification of packaged goods, identification of currency coins and notes, ability to read texts recognize people and faces. This paper provides complete support to the visually impaired friends. The idea of this paper is an augmented reality wearable spectacle along with a wearable smart vision band on the wrist. The Spectacles are equipped with camera, earphones, GPS module and Internet connection (Wi-Fi &4G). The wearable smart vision system contains ultrasonic sensors, IR sensors to recognize the objects that are present before/behind the visually impaired person and can alert using a vibration. The Spectacles and the band are connected using Bluetooth. The outcome of the assistive device should be in the form of audible signal received using the earphones, that can be clearly usable for a visually impaired friend. This research paper works on the principle of computer vision for image processing using OpenCV and Google Cloud vision API. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： image coding

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