中文版 | English

在结果中检索

CASIA OpenIR

浏览/检索结果: 共14条，第1-10条 帮助

限定条件    
已选(0)清除 条数/页：   排序方式：
VQAPT: A New visual question answering model for personality traits in social media images 期刊论文
PATTERN RECOGNITION LETTERS, 2023, 卷号: 175, 页码: 66-73
作者:  Biswas, Kunal;  Shivakumara, Palaiahnakote;  Pal, Umapada;  Liu, Cheng-Lin;  Lu, Yue
收藏  |  浏览/下载：66/0  |  提交时间：2024/02/22
Personality trait images  Multimodal concept  Text recognition  Social media images  Natural language processing  Visual question answering  
Deformable scene text detection using harmonic features and modified pixel aggregation network 期刊论文
PATTERN RECOGNITION LETTERS, 2021, 卷号: 152, 页码: 135-142
作者:  Jain, Tanmay;  Palaiahnakote, Shivakumara;  Pal, Umapada;  Liu, Cheng-Lin
收藏  |  浏览/下载：223/0  |  提交时间：2021/12/28
Natural scene text detection  Maximally stable extremal region  Fourier harmonic features  Pixel aggregation network  Deep learning models  Deformable text detection  
Unconstrained end-to-end text reading with feature rectification 期刊论文
PATTERN RECOGNITION LETTERS, 2021, 卷号: 149, 页码: 1-8
作者:  Du, Chen;  Wang, Yanna;  Wang, Chunheng;  Xiao, Baihua;  Shi, Cunzhao
Adobe PDF(1133Kb)  |  收藏  |  浏览/下载：337/71  |  提交时间：2021/11/02
Text recognition  Text detection  Position-sensitive network  Features incompatibility  End-to-end  
Adversarial learning based attentional scene text recognizer 期刊论文
PATTERN RECOGNITION LETTERS, 2020, 卷号: 138, 期号: 1, 页码: 217-222
作者:  Zhao, Jinyuan;  Wang, Yanna;  Xiao, Baihua;  Shi, Cunzhao;  Jiang, Jingzhong;  Wang, Chunheng
Adobe PDF(1152Kb)  |  收藏  |  浏览/下载：390/91  |  提交时间：2021/01/07
Scene text recognition  Generative adversarial network  Image rectification  
Selective feature connection mechanism: Concatenating multi-layer CNN features with a feature selector 期刊论文
PATTERN RECOGNITION LETTERS, 2020, 卷号: 129, 页码: 108-114
作者:  Du, Chen;  Wang, Chunheng;  Wang, Yanna;  Shi, Cunzhao;  Xiao, Baihua
Adobe PDF(2583Kb)  |  收藏  |  浏览/下载：389/59  |  提交时间：2020/03/30
Feature combination  Network architecture  Selective feature connection mechanism  Convolutional neural network  
Pyrboxes: An efficient multi-scale scene text detector with feature pyramids 期刊论文
PATTERN RECOGNITION LETTERS, 2019, 卷号: 125, 期号: 2019, 页码: 228-234
作者:  Sheng, Fenfen;  Chen, Zhineng;  Zhang, Wei;  Xu, Bo
浏览  |  Adobe PDF(1558Kb)  |  收藏  |  浏览/下载：356/59  |  提交时间：2019/12/16
Scene text detection  Multi-scale text detection  Grouped pyramid module  Efficient and effective  
Robust offline handwritten character recognition through exploring writer-independent features under the guidance of printed data 期刊论文
PATTERN RECOGNITION LETTERS, 2018, 卷号: 106, 期号: 无, 页码: 20-26
作者:  Zhang, Yaping;  Liang, Shan;  Nie, Shuai;  Liu, Wenju;  Peng, Shouye
浏览  |  Adobe PDF(756Kb)  |  收藏  |  浏览/下载：322/80  |  提交时间：2018/10/10
Handwritten Character Recognition  Writer-independent Features  Adversarial Feature Learning  Convolutional Neural Network  
Multi-order co-occurrence activations encoded with Fisher Vector for scene character recognition 期刊论文
PATTERN RECOGNITION LETTERS, 2017, 卷号: 97, 期号: 2017, 页码: 69-76
作者:  Wang, Yanna;  Shi, Cunzhao;  Wang, Chunheng;  Xiao, Baihua;  Qi, Chengzuo
浏览  |  Adobe PDF(1105Kb)  |  收藏  |  浏览/下载：324/92  |  提交时间：2018/01/03
Scene Character Recognition  Multi-order Co-occurrence Activations  Fisher Vector  Cnn  
A key binding system based on n-nearest minutiae structure of fingerprint 期刊论文
PATTERN RECOGNITION LETTERS, 2011, 卷号: 32, 期号: 5, 页码: 666-675
作者:  Liu, Eryun;  Zhao, Heng;  Liang, Jimin;  Pang, Liaojun;  Xie, Min;  Chen, Hongtao;  Li, Yanhua;  Li, Peng;  Tian, Jie
浏览  |  Adobe PDF(1164Kb)  |  收藏  |  浏览/下载：305/80  |  提交时间：2015/08/12
Key Binding  Nearest Minutiae Structure  Wrap-around  Secure Sketch  Pinsketch  Shamir's Secret Sharing  
Scene text detection using graph model built upon maximally stable extremal regions 期刊论文
PATTERN RECOGNITION LETTERS, 2013, 卷号: 34, 期号: 2, 页码: 107-116
作者:  Shi, Cunzhao;  Wang, Chunheng;  Xiao, Baihua;  Zhang, Yang;  Gao, Song
收藏  |  浏览/下载：211/0  |  提交时间：2015/08/12
Scene Text Detection  Mser  Graph Model  Cost Function  Graph Cut