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Interactive Sign Language Learning System using Deep Learning

Interactive Sign Language Learning System using Deep Learnin...

引用

Self Sustainable Artificial Intelligence Systems (ICSSAS), international conference on

作者： S Ravikumar P. Dimple Praharsha J. Sri Harsha Priya V. Naga Rohit Sai Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R & D Institute of Science and Technology Avadi Chennai Tamil Nadu

ISBN: (数字)9798350368413

ISBN: (纸本)9798350368420

The “Interactive Sign Language Learning System” is a sophisticated application designed to facilitate the learning process of sign language learners. This comprehensive system encompasses several key features, including sign language alphabet and word recognition, text-to-action conversion for learners, multi-language support, and integrated voice output functionality. The system utilizes advanced algorithms for sign language recognition, employing techniques such as image processing and machine learning to accurately interpret hand gestures and movements. For sign language alphabet and word recognition, a combination of computer vision algorithms, possibly including convolutional neural networks (CNNs) or recurrent neural networks (RNNs), may be employed to analyze input images or video streams and classify them into corresponding sign language symbols or words. Text-to-action conversion involves mapping textual input to corresponding sign language actions or gestures, possibly using natural language processing (NLP) algorithms to understand the semantics of the text and generate appropriate sign language representations. The system’s accuracy, measured in terms of correctly recognized sign language symbols or words, would depend on the effectiveness of the algorithms employed and the quality of the training data. With rigorous development and training, the system aims to achieve high accuracy levels, in sign language recognition and text-to-action conversion tasks.

关键词： Sign language Accuracy Machine learning algorithms image recognition Symbols Training data Speech recognition Natural language processing Classification algorithms Convolutional neural networks

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Color Code Structure Design and Recognition Using OpenCV

Color Code Structure Design and Recognition Using OpenCV

引用

computer Graphics, image and Virtualization (ICCGIV), international conference on

作者： Qintao Huang Chang Zhou Gefu Zhang College of Computer Science/Software School University of South China Hengyang China School of Economics Management and Law University of South China Hengyang China

Aiming at the inaccuracy of the traditional manual timing in track events, we propose a method that uses OpenCV-based image processing technology for athlete finish video processing. Specifically, we first extract all the frames from finished video streams and discard images with high similarity. Then, we select interesting areas and transform them into grayscale to enhance the image quality. After that, image processing and edge detection are strengthened to better identify color codes. These codes are specially designed and related to the identity of athletes. The combination of color codes determines the athletes and the track. Finally, computer vision is used to process and record the results of the competition. Compared with the traditional timing method, our method is more efficient and greatly reduces the error in the timing process.

关键词： Codes image color analysis Transforms Manuals Streaming media Gray-scale Libraries

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MiniDigPath: A New Standard For Pathology images Few-Shot Learning Classification

MiniDigPath: A New Standard For Pathology Images Few-Shot Le...

引用

Cloud Computing, Big Data Applications and Software Engineering (CBASE), international conference on

作者： Yandong Du Xinlei Liu Ling Yue Lin Feng Peng Tao Qinlu Jing College of Computer Science Sichuan Normal University Chengdu China

Recently, few-shot learning(FSL) has received widespread attention. With the continuous development of machine learning and deep learning, the classification problem based on FSL has been deeply studied in fields such as computer vision and natural language processing. This greatly alleviates the problem of insufficient labeled data. So far, the publicly available datasets in FSL include Omniglot, miniimageNet, CUB-200, and CIFAR 100, etc. These datasets contain handwritten characters from different languages and natural images. No dataset in the medical field yet that can be applied to FSL despite massive of unlabeled data available. Therefore, the miniDigPath dataset is proposed by our work for FSL, which composed by 6 public pathology image datasets. It consists 67 different diseases and tissue types, and every type has 48-500 tissue image blocks. In total, there are 21165 histopathology images. Importantly, miniDigPath is available publicly for every researcher. It explores a new idea to solve pathological images annotation using FSL, which is the importance and originality of the dataset we proposed. Experimental evaluation on the classical FSL algorithm shows that the miniDigPath dataset can accomplish the task of FSL. Besides, FSL has good advantage for classification of digital pathology images.

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Improved YOLOv5 for Aerial images Object Detection with the Introduction of Attention Mechanism

Improved YOLOv5 for Aerial Images Object Detection with the ...

引用

Data Analytics, Computing and Artificial Intelligence (ICDACAI), international conference on

作者： Linhao Xu International College Zhengzhou University Zhengzhou China

Due to the development of drone technology and researches on deep learning in computer vision, object detection of aerial images from UAVs has a wide range of applications in practical production. Aerial images have problems such as large scale variations, small and dense objects and background disturbance. Traditional object detection methods are difficult to apply in practical detection scenarios, while deep learning-based methods each has its pros and cons. In this paper, the YOLOv5 model, which is a good fit for UAVs, is chosen to deal with the challenges of aerial image object detection, and the CBAM attention module is introduced to improve the network of YOLOv5 to obtain better feature extraction capability. This paper uses dataset analysis to obtain insight into the characteristics of objects in different datasets, and uses DOTA dataset for training, combining image pre-processing and data augmentation techniques to improve the training process and enhance the robustness of the model. In addition, this paper uses Grad-CAM visualisation method in an attempt to analyse and interpret the detection results. Compared to the original YOLOv5, the improved model has an accuracy rate of 75.8%, gaining a 2.2% increase. This shows the model performs well and the attention mechanism can successfully assist in resolving the challenging issues with object detection in aerial photography scenarios.

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Natural image matting based on image inpainting

Natural image matting based on image inpainting

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computer Graphics, image and Virtualization (ICCGIV), international conference on

作者： Yuan Zhang Mian Tan Zhulian Zhou Yuan Yang Yihui Liang Fujian Feng Guizhou Key Laboratory of Pattern Recognition and Intelligent System Guizhou Minzu University Guiyang China School of Computer Science University of Electronic Science and Technology China Zhongshan China

Deep image matting is a hot problem with applications in computer vision and image processing. It has been widely used in image composition, film production and video editing etc. The current matting method based on image inpainting uses a deep neural network to inpaint the foreground target and background region to further improve the accuracy of alpha matte. However, when the trimap contains large unknown regions, the excessive inpainting of foreground and background produce a lot of redundant information, which leads to a degradation of alpha matte quality. Therefore, to address this issue, a natural image matting method based on image inpainting is designed. This method involves the refinement process for trimap, which improves the quality of the trimap, enlarges the foreground and background regions providing additional information for image matting. Extensive experimental results on the composition-1k dataset demonstrate that the presented method provide high-quality alpha mattes not only in the case that the trimap contains small unknown regions, but also in the case that the trimap contains large unknown regions.

关键词： Degradation Deep learning computer vision image processing Refining Neural networks Production

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Remote Sensing image Segmentation and Captioning Using Deep Learning

Remote Sensing Image Segmentation and Captioning Using Deep ...

引用

Smart Cities 4.0, international conference on

作者： Reem Mostafa Elsady Youssef Abdelrahman Ahmed Mohammed Abdel-Megeed Salem Media Engineering and Technology German University in Cairo Cairo Egypt

ISBN: (数字)9781665474306

ISBN: (纸本)9781665474313

The analysis of remote-sensing images can be of great importance, as it can directly impact people's lives such as monitoring environmental changes, or even traffic congestion in cities. Describing the content of a remote-sensing image using natural languages and segmenting the image into different semantic classes can help in the analysis process. Deep learning architectures have proved their effectiveness when used for many applications including computer vision tasks. For segmentation, we have used the U-net deep learning model. Regarding captioning, a CNN-Transformer-based model for image captioning has been tested with different CNN configurations and model architectures. We have also used an image processing technique to be applied to the segmented image produced by the U-net to further analyze the image and augment the predicted caption to give more useful information about the image. Our approach has allowed enriching the captions for the remote-sensing images by using the segmentation output.

关键词： Deep learning Training image segmentation Smart cities Computational modeling Semantics computer architecture

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Tomographic Object Detection by Unsupervised Clustering

Tomographic Object Detection by Unsupervised Clustering

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

作者： Fengyuan Wang Zheng Wang Yimin Wu Yandan Jiang Haifeng Ji Baoliang Wang State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Zhejiang University Hangzhou China

ISBN: (数字)9798331540043

ISBN: (纸本)9798331540050

This work introduces clustering to electrical tomography (ET) and first compares the effectiveness of different unsupervised clustering categories in tomographic object detection. First, the one-step linear back projection (LBP) algorithm is applied to reconstruct a rough image. Then, clustering is introduced to detect and refine the objects in the image, and a higher-quality image is obtained based on the clustering results. Six unsupervised clustering algorithms belonging to different categories, including K-means, Mini Batch K-means, Agglomerative, density peak clustering (DPC), statistical information grid (STING), gaussian mixture model (GMM), are compared from the aspects of clustering evaluation index, reconstruction quality and reconstruction efficiency. Simulation was carried out based on a capacitively coupled electrical resistance tomography (CCERT) system to collect the projection data under the cases of one to three objects to be detected in the sensing region. The research results show that the quality of object detection and reconstruction can be effectively improved by post-processing the image with clustering. It is found that for the single-object distribution, the grid-based clustering algorithm STING provides images with the highest quality. While for the multi-objects distributions (two or three objects), the Agglomerative algorithm belonging to the hierarchy clustering category shows advantage in achieving good images. Concerning the real-time performance, Agglomerative clustering algorithm is also preferred with less computational time than most of the other clustering algorithms. Therefore, among the combinations investigated in this work, LBP + Agglomerative clustering has the overall best performance in topographic object detection, in regarding of the reconstruction quality and efficiency.

关键词： image quality Resistance Clustering algorithms Object detection Tomography Real-time systems Sensors Pattern recognition Indexes image reconstruction

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Design of an Improved Dish Recognition Algorithm Based on image Retrieval

Design of an Improved Dish Recognition Algorithm Based on Im...

引用

Machine vision, image processing and Imaging Technology (MVIPIT), international conference on

作者： Yi Yu Jie Liu Archival Sebial Angie Ceniza Canillo Jiangxi Vocational College of Industry and Engineering China Department of Computer Information Sciences and Mathematics School of Arts and Sciences University of San Carlos Cebu Philippines

ISBN: (数字)9798331543037

ISBN: (纸本)9798331543044

With the booming of the new retail industry, unmanned billing systems have become the key to improve the operational efficiency of restaurants and reduce labour costs. In the field of payment and billing, traditional recognition systems based on deep neural networks often rely on a single network to achieve the detection and classification of Chinese dishes, but in the face of the increase or decrease in the number of dishes, it is necessary to frequently adjust the type of the network output and re-collect a large amount of image data for training, which leads to high update costs. In this study, a deep learning EfficientNet convolutional neural network combined with image retrieval for dish recognition is used to solve this problem. Firstly, EfficientNet, an advanced dish detection network, is used to accurately locate the positions of multiple dishes in an image, determine their orientations, and effectively separate the dishes from the background. Subsequently, the EfficientNet based feature extraction network calculates the image features of the separated dishes and uses cosine similarity as a metric to efficiently retrieve the dishes in the reference dish library, thus obtaining the classification results of the dishes. Finally, the system outputs the precise location and category information of the dishes. Experimental results show that the EfficientNet and cosine similarity approach achieves an average accuracy of 98.3% on the dish image dataset of China Food Network, which not only significantly shortens the updating time compared with traditional algorithms, but also requires only 1/24 of the amount of image data of the traditional deep learning classification methods. This breakthrough effectively solves the problem of the difficulty and high cost of updating the current dish recognition algorithms. This breakthrough effectively solves the current problem of difficult and costly updating of dish recognition algorithms, and is expected to significantly red

关键词： Industries Deep learning Training image recognition Costs Accuracy Face recognition image retrieval Feature extraction Classification algorithms

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Hexagonal image Compressionusing Singular Value Decomposition in Python 2

Hexagonal Image Compressionusing Singular Value Decompositio...

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2nd international conference on Advances in Computing, Communication, Embedded and Secure Systems, ACCESS 2021

作者： Varghese, Prathibha Selva Saroja, G. Arockia Electronics and Communication Department Noorul Islam Centre for Higher Education Tamil Nadu India Dept. Electronics and Communication Engineering Noorul Islam Centre for Higher Education Tamil Nadu India

ISBN: (纸本)9781728171364

With the advent of multimedia technologies in last two decades, there is a widespread need for efficient storage and transmission of data. Dealing with the vast information interchangein this digital era, image compression for reduction in byte size of graphics image file without loss of image quality to an acceptable level becomes the large interest area. Inspired from the biological models of human fovea, hexagonal image processing has gained a lot of attention in artificial intelligence era that deals with the application of image processing system that combines the benefits of biologically motivated structures. In this paper a singular value decomposition (SVD) over hexagonal image compression which is a missing stone in computer vision which provides higher packing density, higher angular symmetry and uniform connectivity. Due to lack of developments in hexagonal imaging devices, different resampling methods like alternate pixel shift method, half pixel shift method, pseudo hexagonal pixel method for sourcing hexagonal images. SVD is one of the powerful cutting-edge technology for image compression algorithms. SVD based image compression is performed on hexagonal grid and is compared with square grid using different parameters like compression ratio, compression size, PSNR and MSE using PYTHON’s SVD function. SVD based hexagonal image achieves the goal of compression by preserving good image quality at higher compression ratios, high computational efficiency, provides low mean square error (MSE), acceptable compression size depending on application and high peak to signal ratio (PSNR). © 2021 IEEE

关键词： Singular value decomposition

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Improved SIFT image feature matching algorithm

Improved SIFT image feature matching algorithm

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computer Graphics, image and Virtualization (ICCGIV), international conference on

作者： Zhonghao Wu Hengyang Wu Resource and Environmental Engineering Shanghai Polytechnic University Shanghai China School of Computer and Information Engineering Shanghai Polytechnic University Shanghai China

image matching technology is one of the important research problems in the field of computer vision. Scale invariant feature transform (SIFT) is a widely used algorithm in image matching, but the SIFT algorithm has problems such as long matching time and incorrect image matching. In order to improve the image matching accuracy, this paper proposes an improved SIFT image feature matching algorithm. First, for the detection of image feature points, the original image is subjected to grayscale conversion and binarization processing, and the image contour point information is extracted to remove background interference; secondly, the Even-odd algorithm is used to extract the point information from the original image and the target. The feature point sets extracted from the images are compared to screen out unnecessary background feature point sets, thereby reducing the number of matching feature point sets and reducing the probability of misidentification. The experimental results show that the algorithm can effectively reduce the interference caused by the image background, thereby improving the accuracy of image matching.

关键词： computer vision image matching Interference Transforms computer graphics Gray-scale Feature extraction

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