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Hybrid CNN-RNN Model for Accurate image Captioning with Age and Gender Detection 2

Hybrid CNN-RNN Model for Accurate Image Captioning with Age ...

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2nd international conference on Automation, Computing and Renewable Systems, ICACRS 2023

作者： Jain, Bhavik Doshi, Khushi Dwivedi, Pulkit Chandigarh University Chandigarh India

ISBN: (纸本)9798350340235

Age and gender detection is a computer vision process that aims to identify the age and gender of individuals based on their physical characteristics, primarily facial features. This research paper presents a novel approach to age and gender detection using Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN). Our proposed method combines CNN and RNN models to accurately detect age and gender from input images. To address the limitations of existing datasets and the scarcity of data, we have developed a unique dataset that includes diverse facial images annotated with age and gender information. Our approach leverages CNN to extract region-based visual features from input images, while the RNN-based model decodes output captions word by word. Attention mechanisms are incorporated to highlight relevant regions in the input image during the detection process. Our results demonstrate high accuracy rates for both age and gender detection, showcasing the potential of the proposed method in various domains such as security, healthcare, and entertainment. This study offers a promising solution to the age and gender detection challenge, underscoring the effectiveness of CNN and RNN models in image analysis. © 2023 IEEE.

关键词： Convolutional neural network(CNN) Deep learning Long short-term memory(LSTM) Natural language processing(CNN) Recurrent neural network(RNN) VGG-16

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image Registration using Shi-Tomasi and SIFT 2

Image Registration using Shi-Tomasi and SIFT

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2nd international conference for Innovation in Technology, INOCON 2023

作者： Patankar, Sanika S Kadam, Sara Ganesh Jadhav, Aishwarya Gore, Maitrayee Vishwakarma Institute of Technology Department of Instrumentation and Control Engineering Pune India

ISBN: (纸本)9798350320923

The procedure of merging data from various sources into a single reference system/ reference frame is known as 'image Registration'. The data can be images taken from the sensors, multiple different images taken from different angles, images from different lapses of time, images from a different point of view/frame of reference, etc. This type of data is used in numerous applications such as medical imaging, computer vision, target detection in the military, analyzing and processing of satellite data, etc. To merge and differentiate this data obtained from various different sources, image registration is used. This paper introduces two algorithms well-balanced together for image registration. The two algorithms used are Shi-Tomasi Corner Detection Algorithm and Scale-Invariant Feature Transform (SIFT) Algorithm. The Shi-Tomasi Corner Detection Algorithm issued by *** and *** in the year 1994 is used for the corner detection in the reference image. The Scale-Invariant Feature Transform (SIFT) Algorithm is used for searching, outlining and equivalenting the corners/features in the images. The average error obtained after the estimation of the TRS for, Translation of (500,500), Rotation of angle -10, and Scale of 0.7 is θ=0.18%, Δx=17.14% Δ y=7.69%,S=0.01%, and the average error obtained after the estimation of the TRS for, Translation of (500,500), Rotation of angle 10 and Scale of 0.8 is Θ=0.14%Δx=10.76%Δy=7.95%,S=0.01% © 2023 IEEE.

关键词： image registration

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Design and Implementation of Smart Text Reader System for People with vision Impairment 2nd

Design and Implementation of Smart Text Reader System for Pe...

引用

2nd international conference on Signal and Data processing, ICSDP 2022

作者： Gangiredla, Sandeep Naidu Yadav, Mukesh Kumar Tiwari, Vaibhav Ghai, Deepika Mandal, Danvir School of Electronics and Electrical Engineering Lovely Professional University Punjab Jalandhar India

ISBN: (纸本)9789819914098

This paper proposes a smart text reader system which is designed to help people with vision impairment. The Microsoft Azure Cognitive Services Technology is used in this system for recognition of print or written character by taking input image and using computer programming. This technology converts the image of printed or handwritten characters to machine-coded characters. These files are then converted to acoustic outcome, which is speech by employing Microsoft Azure Cognitive Services and Google Text-to-Speech (gTTS) Converter. The transformation of the image into text file is performed by employing Microsoft Azure Cognitive Services which further utilizes the computer vision Client, Cognitive Services Credentials, Operation Status Codes, json and gTTS library, and Python programming language. The Microsoft Azure Cognitive Services further processes the character files using its library and Python programming language and that is how the acoustic outcome speech is attained. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： High level languages

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Scale Space Mining Algorithm and Application Analysis Based on computer image processing 2

Scale Space Mining Algorithm and Application Analysis Based ...

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2nd IEEE international conference on Advanced Technologies in Intelligent Control, Environment, Computing and Communication Engineering, ICATIECE 2022

作者： Fu, Rong City Institute Dalian University of Technology Dalian China

ISBN: (纸本)9781665493963

In the process of computer IP(CIP), computer vision is used as the basis of image processing(IP). images contain a lot of information, which is the basis for people to analyze image data. However, because images contain noise that affects the analysis results, intelligent extraction of image information has become the key to computer vision analysis of image data. According to the SS theory, this paper proposes three CIP algorithms, such as the EM image segmentation algorithm based on one-dimensional and multi-dimensional feature scale space(SS) and the IP algorithm based on adaptive multi-scale wavelet transform(WT). Compared with the other two algorithms, the three algorithms obtained the MSE value, PSNR value and SSIM value of the Q1 image. With the increase of the denoising level, the WT IP algorithm has the best denoising effect on the image, and with the increase of the denoising level. With the increase of sampling rate, the algorithm also has the best effect on image reconstruction. © 2022 IEEE.

关键词： Wavelet transforms

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Toward Automatic Number Plate Recognition: A Deep Learning Based Study for Macau 2

Toward Automatic Number Plate Recognition: A Deep Learning B...

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2nd IEEE international conference on Software Services Engineering (SSE)

作者： Dai, Fansong Mondal, Subrota Kumar Macau Univ Sci & Technol Sch Comp Sci & Engn Taipa 999078 Macao Peoples R China

ISBN: (纸本)9798350368529;9798350368512

Automatic Number Plate Recognition (ANPR) is a computer vision technology using optical character recognition to identify and read vehicle number plates to obtain vehicle data. ANPR is closely related to our daily life, it is widely used in applications like parking lot management, automatic toll payment system, and traffic violation detection. In recent years ANPR has made great progress, thanks to the advancement of algorithms and techniques especially deep learning methods, which show great success in a wide range of tasks. To this, in the paper, we implement Macau ANPR system with deep learning-based methods. We create a new process: vehicle detection, and a new function: detecting number plate characters' color whether the vehicle is for commercial use. We provide complete and detailed explanations of these two new functions and the two main processes of ANPR: vehicle and number plate detection and character recognition. We make use of five state-of-the-art algorithms for detection, including four deep learning-based methods, and three tools for character recognition. To this, we holistically present the steps of character separation and recognition. Besides, we illustrate the comparison of different deep learning models and show all the outcomes of the sample pictures with all the mentioned methods. Moreover, at the end of this paper, we highlight merits and drawbacks of all these methods and tools. We believe that our study can help grow the community in a better way.

关键词： Automatic Number Plate Recognition (ANPR) computer vision Deep Learning image processing Optical Character Recognition (OCR) Object Detection

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Enhancing Scene Text Segmentation through Subtask Decomposition 2

Enhancing Scene Text Segmentation through Subtask Decomposit...

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2nd international conference on image processing, computer vision and Machine Learning, ICICML 2023

作者： Wang, Yong Chen, Youguang East China Normal University School of Data Science and Engineering Shanghai China

ISBN: (纸本)9798350331417

The field of image processing widely utilizes scene text segmentation technology, with applications extending to image editing and font style transfer. These applications enhance image understanding quality and aid in boosting the performance of numerous computer vision tasks. The advent and progression of deep learning have led to substantial advancements in scene text segmentation technology. However, the limited size of existing scene text segmentation datasets constrains the performance of models. Therefore, we propose an algorithm for synthetic segmentation data. We first pretrain the model using large-scale synthetic data, then fine-tune it on the target dataset to address the issue of limited dataset size. Existing models employ end-to-end segmentation, which presents challenges in segmentation. We propose a scene text segmentation method. By decomposing the segmentation task into subtasks and solving them one by one, the complexity of the task can be reduced compared to direct segmentation of the entire image significantly improving the segmentation effect. The proposed method consists of three modules: a fragment crop module, a fragment segmentation module, and a fragment combination module. The fragment crop module is composed of an additional corp layer added after DBnet. The fragment segmentation module can be embedded with various segmentation methods. The fragment combination module uses the maximum pixel value pasting algorithm to combine the segmented fragments. We call this method Crop-Segmentation-Combination Framework (CSCF). We conducted experiments on the ICDAR 2013 and TextSeg datasets. The CSCF, embedded in Unet within the segment segmentation module, enhanced the text segmentation IoU by 5.80% on the ICDAR 2013 test dataset. Our suggested approach has been shown to notably enhance the efficiency of scene text segmentation. © 2023 IEEE.

关键词： image segmentation Scene text segmentation Synthetic data synthetic image generations Text segmentation

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Efficient Detection and Categorization of Thyroid Nodules from Medical Ultrasound images 2

Efficient Detection and Categorization of Thyroid Nodules fr...

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2nd DMIHER international conference on Artificial Intelligence in Healthcare, Education and Industry, IDICAIEI 2024

作者： Deshkar, Prarthana A. Fulzele, Punit Yeshwantrao Chavan College of Engineering Department of Computer Technology Nagpur India Datta Meghe Institute of Higher Education and Research Sawangi Wardha India

ISBN: (纸本)9798331528713

The use of ML and DL in medical imaging for early illness symptom prediction is substantial. Since 2013, DL has been one of the rising trends in general data analysis. With its multiple hidden layers that allow for a high degree of data abstraction, it is an enhancement of artificial neural networks (ANN). One promising approach for computer vision applications is the use of convolutional neural networks (CNNs). Automatic learning of raw data, particularly photographs, is a capability of deep CNNs. Both the capture and interpretation of images are crucial for the correct diagnosis or evaluation of illness. Devices are able to gather data at a rapid pace with improved resolution because to advancements in image acquisition over the last decade. Nevertheless, computer technology has only just started to improve picture interpretation. Since they are subjective and need highly trained doctors, they are often created by radiologist, physicians, and senior doctors. In medical imaging, computerised tools play a crucial role in facilitating results and improving diagnosis. Early detection and categorization of thyroid nodules by visual inspection and manual study of thyroid ultrasonography (USG) images has traditionally been a tedious process. Identifying benign from malignant thyroid nodules requires manual evaluation of thyroid USG images, which may be a time-consuming and laborious process. The medical industry has seen the emergence of many deep learning models, particularly for the categorization of thyroid nodules, thanks to the growth in processing resources and the fast improvement in technology. Clinical interventions and therapies may be more successful when these nodules are detected early. Thus, a growing number of academics are pushing for the use of computer diagnostic systems (CDS) as a means to statistically and scientifically evaluate USG pictures of thyroid nodules. Creating effective models for identifying and classifying thyroid nodules using ML and DL

关键词： Medical imaging

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A 3D object detection strategy based on YOLOv8 combined with CaDDN for vehicle images 2

A 3D object detection strategy based on YOLOv8 combined with...

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2nd international conference on image processing, computer vision and Machine Learning, ICICML 2023

作者： Liu, Shaojiang Xu, Zhiming Wang, Feng Qiu, Zemin Guangzhou Xinhua University Guangzhou China

ISBN: (纸本)9798350331417

With the rapid development of deep learning and computer vision technologies, 3D object detection has become more and more widely used in fields such as autonomous driving, robotics and augmented reality. However, 3D object detection from monocular images is still a challenging problem. To solve this problem, this paper proposes a new method combining YOLOv8 and CaDDN (abbreviated as YO-Ca). First, we use YOLOv8 to perform 2D object category detection on monocular images, providing a 2D bounding box for each detected vehicle. Then, we employ the linear increasing discretization (LID) approach in the CaDDN method to predict the depth distribution of each pixel. In this way, we can accurately localize the image information in 3D space. Then, we convert the 2D bounding box into a 3D bounding box in the bird's-eye view, and refine and verify the 3D bounding box in the bird's-eye view. The experimental results show that our proposed YO-Ca method achieves better performance on the KITTI dataset, especially in terms of 3D intersection and merger ratio (IoU=0.7). © 2023 IEEE.

关键词： 3D object detection CaDDN monocular image YOLOv8

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A Novel Annotation Method on RGB images for Head Pose Estimation 2

A Novel Annotation Method on RGB Images for Head Pose Estima...

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2nd international conference on Signal processing, computer Networks and Communications, SPCNC 2023

作者： Zhou, Jiakai Wang, Yang The College of Mechanical & Electrical Engineering Nanjing University of Aeronautics and Astronautics Nanjing210016 China

ISBN: (纸本)9798400716430

Head pose estimation, a critical task in computer vision, involves predicting the head's Euler angles in an image. Recent advancements have seen CNN-based methods excel in this area, typically trained using RGB images with facial landmarks or depth data from RGBD cameras. In this paper, we introduce an innovative and user-friendly approach for annotating head poses' Euler angles in RGB images. This technique employs a 3D virtual human head model to replicate head poses in RGB images. By tracking the coordinate changes of the 3D virtual head, we can accurately determine the corresponding Euler angles in the image. Utilizing Unity 3D for implementation, we applied this method to annotate a standard RGB dataset with head pose Euler angles, resulting in the creation of a 2DHeadpose dataset. Our experimental results confirm the effectiveness of both our annotation approach and the datasets generated. © 2023 ACM.

关键词： image annotation

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Deep Curriculum Learning for PolSAR image Classification 12

Deep Curriculum Learning for PolSAR Image Classification

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12th Iranian/2nd international conference on Machine vision and image processing, MVIP 2022

作者： Mousavi, Hamidreza Imani, Maryam Ghassemian, Hassan Image Processing and Information Analysis Lab Faculty of Electrical and Computer Engineering Tehran Iran

ISBN: (纸本)9781665412162

Following the great success of curriculum learning in the area of machine learning, a novel deep curriculum learning method proposed in this paper, entitled DCL, particularly for the classification of fully polarimetric synthetic aperture radar (PolSAR) data. This method utilizes the entropy-alpha target decomposition method to estimate the degree of complexity of each PolSAR image patch before applying it to the convolutional neural network (CNN). Also, an accumulative mini-batch pacing function is used to introduce more difficult patches to CNN. Experiments on the widely used data set of AIRSAR Flevoland reveal that the proposed curriculum learning method can not only increase classification accuracy but also lead to faster training convergence. © 2022 IEEE.

关键词： Synthetic aperture radar

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