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Improving Education for Visually Challenged Students through image Captioning and Automated Reading

Improving Education for Visually Challenged Students through...

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Emerging Frontiers in Electrical and Electronic Technologies (ICEFEET), international conference on

作者： Shirin Jain Snigdha Jain Anirban Dasgupta Anwesha Sengupta Shubhobrata Bhattacharya Department of EEE IIT Guwahati Guwahati India Dept. of Electrical Engineering NIT Rourkela Rourkela India MathWorks India Hyderabad India

ISBN: (数字)9798331528614

ISBN: (纸本)9798331528621

the integration of advanced technologies into education has emerged as a vital solution to bridge the accessibility gap for visually challenged students. this paper introduces an innovative system that uses image captioning and automated reading to enhance learning experiences for such students. A combination of natural language processing (NLP) along with computer vision has been employed to transform the images into descriptive text, which is further converted to audio for auditory learning. the present work attempts to circumvent the limitations of current image captioning models. A major one include these models being biased on generic real-world images, instead of educational material. A custom dataset has been created with diagrams of various subjects from standard textbooks with an aim to improve classroom comprehension for students, thereby addressing educational content. Optical Character recognition (OCR) has also been incorporated into the proposed system for reading text embedded in images, thereby providing a comprehensive tool for visually impaired students to access complex visual and textual information.

关键词： Deep learning Visualization Electric potential computer vision Computational modeling Optical character recognition Education Transforms Natural language processing Standards

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Study of the influence of RGB and Lab color spaces on the performance of multifocus image fusion techniques 4

Study of the influence of RGB and Lab color spaces on the pe...

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4th international conference on pattern Analysis and Intelligent Systems, PAIS 2022

作者： Babahenini, Sarra Charif, Fella Taleb-Ahmed, Abdelmalik Biskra University Lesia Laboratory Computer Science Department Algeria Ouargla University Dept. of Elect. & Telecommunications Algeria Polytechnic University of Hauts-de-France Iemn Lab. Valenciennes France

ISBN: (数字)9781665461610

ISBN: (纸本)9781665461610

In order to create a single image, multifocus image fusion brings together the important details and focused areas of the input multifocus images. Diverse camera depths of the field are used to capture these multi-focus images. Spatial domain focusing measurement has been used to introduce various multifocus image fusion algorithms. In this paper, we focus on the implementation of three multifocus color image fusion techniques: salience detection based multifocus image fusion (SDMF), salience detection using the technique of contourlet transformation (CT) and multi-scale guided filter (MGF) based fusion technique, using various color space models to improve the fusion result. the main objective is to examine whether the effects of R.G.B and L.A.B spaces have an influence on their recognition rate. We evaluated the performance of these techniques based on SSIM metrics, Q AB F, L AB F) N(ABF) and FMI. the experimental results on the test set show that the RGB space performs better than the LAB space. © 2022 IEEE.

关键词： image fusion

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A Practical Approach of Recognizing and Detecting Traffic Signs using Deep Neural Network Model 4

A Practical Approach of Recognizing and Detecting Traffic Si...

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4th international conference on Emerging Research in Electronics, computer Science and Technology, ICERECT 2022

作者： Geetha Rani, E. Bellam, Tanuep Mounika, E. Bhuvaneswari, P. Gopala Krishnan, C. Anusha, D. Department of CSE India Department of CSE India Department of CSE India Department of CSE India Department of CSE India

ISBN: (纸本)9781665456357

As a result of many technological developments and growing usage of artificial intelligence in our daily routines, the number of autonomous and self-driving vehicles has expanded dramatically. To be effective and efficient, autonomous cars must be able to recognize and interpret a variety of traffic signs and take appropriate responses. A technology known as Traffic Sign recognition can be used to determine a large number of different traffic signs. Traffic Sign recognition is a technique that enables self-driving or autonomous vehicles to recognize traffic signs on the road. We must classify the images into their appropriate categories or groupings once they have been recognized. We accomplish this by creating a Convolutional Neural Network model. We must apply a discipline of artificial intelligence known as computer vision to derive information from the images acquired and recognized by the Traffic Sign recognition and make recommendations based on that knowledge. In our paper, we'll use a Convolutional Neural Network model to create distinct indications in the image that may be sorted into several categories. As a result, the system can read and understand traffic signs, which is a crucial duty in the creation and improvement of autonomous vehicles. Because this would be used in a real-time setting, we have included blurry images in our dataset to emulate real-time capturing scenarios like as when the vehicle is moving or when direct light is shining on the subject. © 2022 IEEE.

关键词： image classification

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A Comprehensive Study on Geometric, Appearance, and Deep Feature based Methods for Automatic Facial Expression recognition 4

A Comprehensive Study on Geometric, Appearance, and Deep Fea...

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4th international conference on Cognitive Computing and Information processing, CCIP 2022

作者： Naveen Kumar, H.N. Patil, Chandrashekar M Jain, Amith K Sudheesh, K.V. Mahadevaswamy Vidyavardhaka College of Engineering Department of ECE Karnataka Mysuru India SDM Institute of Technology Department of ECE Karnataka Ujire India

ISBN: (纸本)9781665456487

Facial Expression (FE) is one kind of communication that, despite its non-verbal nature, predates verbal communication in terms of both its genesis and its conception. Automatic Facial Expression recognition (AFER) is a predominant facet in analyzing facial images and thus has been an in-demand research problem for decades in the emerging field of computer vision (CV) and Artificial Intelligence (AI). Recent works on AFER systems focused on the following issues: insufficient training data which causes overfitting;robustness to identity bias, illumination and head pose variation, partial occlusion;generalization power;transformation from controlled to uncontrolled environments;cross dataset experiments. A comprehensive study on existing methods for the design and development of AFER systems is presented in the proposed study. the benchmark datasets and its characteristics are summarized. the advantages and limitations of the existing methods to extract the highly discriminative and abstract distributions are discussed. the evaluation methods to assess the performance of AFER systems, along with comparative analysis of various methods implemented on benchmark datasets are summarized. Furthermore, unresolved challenging issues in the field of AFER are presented in detail, which serves as an open-ended research area concerning the AFER problem. © 2022 IEEE.

关键词： Human computer interaction

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Semantic Segmentation of Fire and Smoke images Based on Dual Attention Mechanism 4

Semantic Segmentation of Fire and Smoke Images Based on Dual...

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4th international conference on Frontiers Technology of Information and computer, ICFTIC 2022

作者： Wang, Yunpeng Luo, Zhenbao Chen, Daizhong Li, Yiqiang Southwest Institute of Technical Physics Information Processing Technology Research Department Chengdu China

ISBN: (纸本)9798350321951

Fire and smoke recognition has a wide range of applications in fire warning, anti-jamming, battlefield situational awareness and other fields. Using the method of semantic segmentation for fire and smoke recognition can obtain the fire and smoke area of the image more accurately and comprehensively. Although the DeepLabV3+ network based on the Encoder-decoder structure has achieved excellent performance in semantic segmentation in many fields, it still has some shortcomings, such as slow fitting, blurred edge segmentation, and holes in the feature map. In order to address the deficiencies of DeepLabV3+,In this paper we propose a novel method of introducing parallel dual attention mechanism network(DAMN). We designed a parallel connection between DAMN and atous spatial pyramid pooling(ASPP). DAMN and ASPP process the feature map generated by the network in parallel, and then fuse the feature map information generated by DAMN and ASPP module. Meanwhile, we built a multi-scene fire and smoke dataset named 'Fire-Smoke' for comprehensive assessment in fire and smoke image recognition. Fire-Smoke contains over 8,000 hand-labeled smoke labels. We achieve better segmentation performance on Fire-Smoke dataset. In particular, it achieves an average IoU score of 85.77% on the Fire-Smoke test set, an improvement of 2.18% over the DeepLabV3+. © 2022 IEEE.

关键词： Smoke

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Statistical Characteristics of 3-D PET Imaging: A Comparison Between Conventional and Total-Body PET Scanners 5th

Statistical Characteristics of 3-D PET Imaging: A Comparison...

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5th Chinese conference on pattern recognition and computer vision (PRCV)

作者： Lin, Yuejie Liu, En-tao Mou, Tian Shenzhen Univ Hlth Sci Ctr Sch Biomed Engn Shenzhen Peoples R China Guangdong Acad Med Sci Guangdong Prov Peoples Hosp WeiLun PET Ctr Dept Nucl Med Guangzhou Peoples R China

ISBN: (纸本)9783031189098;9783031189104

the performance of an operational PET scanner is affected by many factors, thus the detailed evaluation of imaging characteristics could contribute to ensuring the image quality. the routine quality assurance (QA) measurement of PET scanners can be used to provide an operational understanding of scanning properties. this study aims to compare the results obtained by the procedure for qualifying PET/CT scanners for clinical trials adopted by Guangdong Provincial People's Hospital on both conventional (Biograph 16HR, Siemens) and total-body (uEXPLORER) PET/CT scanners. Uniform phantoms filled with 18F-FDG used in static mode and Ge-68 PET cylinders & transmission rod source used in dynamic mode have both been analyzed. this comparison study focuses on statistical characteristics and spatial correlation within the region of interest. We evaluate axial and transaxial patterns in mean, variance, and covariance. In general, voxel values of dynamic images are more skewed than static images, indicating that the Normal-based image post-processing algorithms might not be applicable for dynamic PET scans. Since variance characteristics are dominated by effective counts-these are lower in the axial extremes of the scanner bed, there is always a U-shaped of variance over axial locations in conventional PET scanners, while the static mode of the uEXPLORER scanner does not have. the spatial correlation between adjacent slices shows a potential connection with scanning settings. therefore, there is a certain difference in imaging characteristics between conventional and whole-body PET scanners. In addition, information from routine phantom scanning would enrich the knowledge of image processing and give a novel method of comparative studies. Investigation of this approach is currently underway and will be reported in future work.

关键词： PET/CT image processing Whole-body PET Quality assurance Imaging characteristic

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MultiShadow: Shadow Synthesis for Multiple Virtual Objects 4

MultiShadow: Shadow Synthesis for Multiple Virtual Objects

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4th international conference on Data Intelligence and Security, ICDIS 2022

作者： Ding, Ye Wan, Qi Liu, Ziyuan Wang, Jie School of Cyberspace Security Dongguan University of Technology China

ISBN: (数字)9781665459686

ISBN: (纸本)9781665459686

image synthesis adds synthetic objects to existing images and makes them visually hard to distinguish. In order to make the synthetic objects more realistic, shadows are also required to be generated in the synthesis process. However, most existing shadow synthetic methods work poorly when there is no fixed lighting source or there are multiple objects that already existed in the original image. Hence in this paper, we propose a novel method called MultiShadow, which generates realistic shadows for virtual objects with better recognition of illumination information based on the existing objects in the original image. the experiments on benchmark datasets show that MultiShadow outperforms the state-of-the-art. © 2022 IEEE.

关键词： computer vision

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MS-Net: A CNN Architecture for Agriculture pattern Segmentation in Aerial images 6th

MS-Net: A CNN Architecture for Agriculture Pattern Segmenta...

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6th international conference on computer vision and image processing, CVIP 2021

作者： Bhagat, Sandesh Kokare, Manesh Haswani, Vineet Hambarde, Praful Kamble, Ravi SGGS Institute of Engineering and Technology Nanded431606 India Indian Institute of Technology Ropar140001 India

ISBN: (纸本)9783031113451

computer vision for crop sciences is overgrowing with the advancement in pattern recognition and deep learning. Agriculture pattern segmentation is an important application such as segmentation of cloud shadow, waterway, standing water, weed cluster, planter skip, double plant, etc. However, the segmentation of agriculture patterns is challenging due to multi-scale variations of patterns and considerable overlap between classes. Furthermore, size and shape variation, unclear boundaries, and missing edges make this task more complex. To address this problem, we proposed the encoder-decoder architecture with input as a multi-scale image pyramid to improve the multi-scale feature extraction of agriculture patterns. EfficientNetB7 is utilized as an encoder for efficient feature extraction. In addition, the proposed Lateral-output layer shows rich semantic features by aggregating low level and high level features from the decoder, shows improvement in dice score. the proposed research uses the Agriculture vision dataset with aerial farmland images. the proposed method outperformed different state-of-the-art methods in the literature for six agricultural patterns segmentation with a mean dice score of 74.78, 68.11, and 84.23 for RGB, NIR, and RGB + NIR images, respectively. © 2022, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： computer vision

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Review on image Segmentation Methods Using Deep Learning 4

Review on Image Segmentation Methods Using Deep Learning

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4th international conference on Advanced Science and Engineering, ICOASE 2022

作者： Ali, Nabeel N. Kako, Najdavan A. Abdi, Abdo Sulaiman Duhok Polytechnic University Management Information Systems Dept. Kurdistan Region Duhok Iraq Duhok Polytechnic University Information Technology Dept. Kurdistan Region Duhok Iraq

ISBN: (纸本)9781665472227

In recent years, the machine learning field has been inundated with a variety of deep learning methods. Different deep learning model types, including recurrent neural networks (RNNs), convolutional neural networks (CNNs), adversarial neural networks (ANNs), and autoencoders, are successfully tackling challenging computer vision problems including image detection and segmentation in an unconstrained environment. Although image segmentation has received a lot of interest, there have been several new deep learning methods discovered with regard to object detection and recognition. An academic review of deep learning image segmentation methods is presented in this article. In this study, the major goal is to offer a sensible comprehension of the basic approaches that have already made a substantial contribution to the domain of image segmentation throughout the years. the article describes the existing state of image segmentation, and goes on to make the argument that deep learning has revolutionized this field. Afterwards, segmentation algorithms have been scientifically classified and optimized, each with their own special contribution. With a variety of informative narratives, the reader may be able to understand the internal workings of these processes more quickly. © 2022 IEEE.

关键词： Semantic Segmentation

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Object recognition in High-Resolution 360° Panoramic images Using Spherical Grids

Object Recognition in High-Resolution 360° Panoramic Images...

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Electrical, computer, Communications and Mechatronics Engineering (ICECCME), international conference on

作者： Federico Candela Andrea Francesco Morabito Francesco Carlo Morabito DIlES Department University Mediterranea Reggio Calabria Italy DICEAM Department University Mediterranea Reggio Calabria Italy

ISBN: (数字)9798350391183

ISBN: (纸本)9798350391190

this paper presents an innovative method for object recognition in high-resolution $360^{\circ}$ panoramic images using a VGGl6 convolutional neural network pre-trained on imageNet. By employing a spherical grid, the method segments panoramic images into sub-images, enabling precise classification of objects such as people and vehicles. After classification, a reconstruction process is used to reassemble the $360^{\circ}$ image, preserving its panoramic properties. Experimental results demonstrate a 98 % accuracy in object recognition, with a mean squared error (MSE) of 0.0096. this method shows potential for object labeling and recognition in ultra-high-resolution images (SK, 8K, IlK), with applications in immersive computer vision, particularly using $360^{\circ}$ camera technology.

关键词： image segmentation Accuracy image recognition Mechatronics Streaming media Cameras Object recognition Labeling image reconstruction Sports

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