Network For Recognition Research Articles

A Practical Unified Network for Localization and Recognition of Arbitrary-Oriented Container Code and Type

A Practical Unified Network for Localization and Recognition of Arbitrary-Oriented Container Code and Type

A Low-Cost Dual-Path Feature Fusion Network for Omnidirectional Human Motion Recognition Using Monostatic Radar

A Low-Cost Dual-Path Feature Fusion Network for Omnidirectional Human Motion Recognition Using Monostatic Radar

Converting Artificial Neural Networks to Ultra-Low-Latency Spiking Neural Networks for Action Recognition

Converting Artificial Neural Networks to Ultra-Low-Latency Spiking Neural Networks for Action Recognition

An Attention-Based Convolutional Recurrent Neural Networks for Scene Text Recognition

An Attention-Based Convolutional Recurrent Neural Networks for Scene Text Recognition

A Novel Sequence Modeling Network for Multiview SAR Target Recognition

A Novel Sequence Modeling Network for Multiview SAR Target Recognition

A Graph Neural Network for EEG-Based Emotion Recognition With Contrastive Learning and Generative Adversarial Neural Network Data Augmentation

A Graph Neural Network for EEG-Based Emotion Recognition With Contrastive Learning and Generative Adversarial Neural Network Data Augmentation

Spatial-Temporal Adaptive Metric Learning Network for One-Shot Skeleton-Based Action Recognition

Spatial-Temporal Adaptive Metric Learning Network for One-Shot Skeleton-Based Action Recognition

Multi-View Time-Series Hypergraph Neural Network for Action Recognition.

Recently, action recognition has attracted considerable attention in the field of computer vision. In dynamic circumstances and complicated backgrounds, there are some problems, such as object occlusion, insufficient light, and weak correlation of human body joints, resulting in skeleton-based human action recognition accuracy being very low. To address this issue, we propose a Multi-View Time-Series Hypergraph Neural Network (MV-TSHGNN) method. The framework is composed of two main parts: the construction of a multi-view time-series hypergraph structure and the learning process of multi-view time-series hypergraph convolutions. Specifically, given the multi-view video sequence frames, we first extract the joint features of actions from different views. Then, limb components and adjacent joints spatial hypergraphs based on the joints of different views at the same time are constructed respectively, temporal hypergraphs are constructed joints of the same view at continuous times, which are established high-order semantic relationships and cooperatively generate complementary action features. After that, we design a multi-view time-series hypergraph neural network to efficiently learn the features of spatial and temporal hypergraphs, and effectively improve the accuracy of skeleton-based action recognition. To evaluate the effectiveness and efficiency of MV-TSHGNN, we conduct experiments on NTU RGB+D, NTU RGB+D 120 and imitating traffic police gestures datasets. The experimental results indicate that our proposed method model achieves the new state-of-the-art performance.

Synchrosqueezing-Based Shallow Neural Network for Near-Surface Void Recognition by Ground Penetrating Radar

Synchrosqueezing-Based Shallow Neural Network for Near-Surface Void Recognition by Ground Penetrating Radar

RAFA-Net: Region Attention Network for Food Items and Agricultural Stress Recognition

RAFA-Net: Region Attention Network for Food Items and Agricultural Stress Recognition

Automatic Segmentation of Oracle Bone Inscriptions Using YOLOv8

Oracle bones inscription is an important part of Chinese cultural heritage, and the automatic segmentation technology of oracle bones is of great significance to promote the research of oracle bones. The main challenges for automatic segmentation of oracle bone inscriptions include the interference of complex backgrounds, the extraction of text regions of tiny sizes, and the problems of noise and cracks in images, which are hard to deal with using traditional image processing and machine learning techniques. With the development of artificial intelligence technology, especially the breakthrough of deep learning and convolutional neural network (CNN) in image recognition, new solutions are provided for automatic segmentation of oracle bone inscriptions. In this paper, we introduce the YOLOv8 model to the automatic segmentation task of oracle bone images. The experimental results show that the YOLOv8 model achieves satisfactory performance in this task, which verifies the effectiveness of advanced deep learning models, and may provide technical support for the digitization and cultural inheritance of oracle bones in future work.

MEN: Mutual Enhancement Networks for Sign Language Recognition and Education.

The performance of existing sign language recognition approaches is typically limited by the scale of training data. To address this issue, we propose a mutual enhancement network (MEN) for joint sign language recognition and education. First, a sign language recognition system built upon a spatial-temporal network is proposed to recognize the semantic category of a given sign language video. Besides, a sign language education system is developed to detect the failure modes of learners and further guide them to sign correctly. Our theoretical contribution lies in formulating the above two systems as an estimation-maximization (EM) framework, which can progressively boost each other. The recognition system could become more robust and accurate with more training data collected by the education system, while the education system could guide the learners to sign more precisely, benefiting from the hand shape analysis module of the recognition system. Experimental results on three large-scale sign language recognition datasets validate the superiority of the proposed framework.

Interactive Feature Extraction Network for Target Intention Recognition

Interactive Feature Extraction Network for Target Intention Recognition

Hierarchical Aggregated Graph Neural Network for Skeleton-based Action Recognition

Hierarchical Aggregated Graph Neural Network for Skeleton-based Action Recognition

Intelligent Character Recognition Framework for Kannada Scripts via Long Short Term Memory with Thresholding-based Segmentation

Various opinions were made by the researchers to develop an automatic network for Optical Character Recognition (OCR). Still, character recognition in handwritten scripts is an unsolved task. In this paper, two efficient techniques are developed an effective character recognition technique for the handwritten Kannada scripts. The Kannada Character Recognition (KCR) techniques faced several challenges due to the different writing styles of people and the absence of fixed spacing among alphabets, words and lines. Another complication in the KCR model is the absence of large datasets to train the network, and it isn’t easy to write the Kannada script by combining the Kannada alphabets. Therefore, a new handwritten KCR approach is developed to identify the characters from the ancient Kannada scripts. The required Kannada script images are gathered from various online databases. The garnered images are preprocessed and segmented using morphological operation and thresholding. The relevant features from the images are achieved by the geometric feature extraction method. Finally, the characters are recognized by utilizing the Long Short Term Memory (LSTM) network, and the experimental results will be analyzed over the traditional optimization strategies and baseline works to evaluate the efficiency of the proposed network.

UNet Based on Multi-Object Segmentation and Convolution Neural Network for Object Recognition

UNet Based on Multi-Object Segmentation and Convolution Neural Network for Object Recognition

PSPNet: Pretraining and Self-Supervised Fine-Tuning-Based Prototypical Network for Radar Active Deception Jamming Recognition With Few Shots

PSPNet: Pretraining and Self-Supervised Fine-Tuning-Based Prototypical Network for Radar Active Deception Jamming Recognition With Few Shots

Multimodal Decoupled Distillation Graph Neural Network for Emotion Recognition in Conversation

Multimodal Decoupled Distillation Graph Neural Network for Emotion Recognition in Conversation

Towards AI-Enabled Approach for Urdu Text Recognition: A Legacy for Urdu Image Apprehension

Recognizing Urdu text in natural images is more challenging as compared to other languages, such as English, due to the cursive nature of Urdu script. However, Urdu scene text has not received enough attention from both industry and academia due to the lack of the dataset of Urdu text. We propose a large-scale Urdu Scene Text Dataset (USTD) to address this problem, which is designed for Urdu scene text detection and recognition. The proposed dataset contains 29674 text annotations (17877 Urdu and 11797 English), 749725 characters in 6389 images. It covers a wide variety of text images with both Nastaleeq and Naskh writing styles, taken from different streets and roads of Pakistan. The vast diversity of this dataset makes it a benchmark to work on and train robust neural networks for the detection and recognition of cursive text. Besides, baseline results are also provided with several state-of-the-art networks, including TextBoxes++, Seglink, DB(ResNet-50) and EAST for text localization and Convolutional Recurrent Neural Network (CRNN) for text recognition. To further evaluate the performance of these models, we have used the most popular evaluation matrices of precision, recall, and F-measure. Our experimental outputs reveal that an end-to-end combination of DB(ResNet-50) and CRNN provides the best results with precision, recall, and F-measure of 0.7526, 0.5974, and 0.6660, respectively.

Evaluating Noise-Robustness of Convolutional and Recurrent Neural Networks for Baby Cry Recognition

Evaluating Noise-Robustness of Convolutional and Recurrent Neural Networks for Baby Cry Recognition