检索结果-内蒙古大学图书馆

TechRxiv 2022年

作者： Rabhi, Besma Elbaati, Abdelkarim Boubaker, Houcine Pal, Umapada Alimi, Adel M. University of Sfax National Engineering School of Sfax REGIM-Lab.: REsearch Groups in Intelligent Machines LR11ES48 Sfax3038 Tunisia University of Monastir Higher Institute of Applied Sciences and Technology of Mahdia REGIM-Lab.: REsearch Groups in Intelligent Machines LR11ES48 Mahdia5121 Tunisia Computer Vision and Pattern Recognition Unit Indian Statistical Institute 203 B. T. Road Kolkata700108 India Department of Electrical and Electronic Engineering Science Faculty of Engineering and the Built Environment University of Johannesburg Johannesburg South Africa

For several decades, the offline handwriting recognition problem has escaped a satisfactory solution. In the field of online recognition, researchers have had more successful performance, but the ability to extract dynamic information from static images has not been well explored yet. In this paper, we introduce a novel multi-lingual word handwriting recovery framework based on a convolutional denoising autoencoder with an attention model for pen up / down, velocity and temporal order recovery. The proposed framework consists of extracting robust features from a handwriting image using a stacked denoising autoencoder and an encoder Bidirectional Gated Recurrent Unit (BGRU) model. Then, the obtained vectors are decoded to produce an online script with dynamic characteristics using a BGRU with temporal attention. Evaluation is done on a Latin and Arabic Online and offline handwriting character / word databases and the proposed framework achieves high competitive results. To the best of our knowledge, this is the first work of its kind. © 2022, CC BY.

关键词： Recovery

来源：评论

学校读者我要写书评

暂无评论

GPU & CPU cooperative accelerated pedestrian and vehicle detection

GPU & CPU cooperative accelerated pedestrian and vehicle det...

引用

International Conference on Computer Vision Workshops (ICCV Workshops)

作者： Takashi Machida Takashi Naito Road Environment Recognition Lab Toyota Central Research and Development Laboratories Inc. Nagakute Aichi Japan

This paper presents a fast pedestrian and vehicle detection framework that integrates GPU (graphics processing unit) and CPU implementations. We employ the Histograms of Oriented Gradients (HoG) and the Feature Interaction Descriptor (FIND) as object descriptors. FIND describes the high-level properties of an object's appearance by computing pair-wise interactions of adjacent region-level features. We also employ the cascade approach in a sliding-window manner with multi-classifiers specialized for both the direction of a pedestrian and the distance of the pedestrian to a camera installed on a vehicle. Although our detection framework can detect pedestrians and vehicles in images, it wastes computational cost on calculating high-dimensional FIND features. Therefore, to realize real-time processing, we utilize the NVIDIA CUDA framework for pedestrian and vehicle detection. Three parallelization techniques implemented on both a GPU and a CPU are incorporated in our detection framework. As a result, our proposed implementation can perform detection more than 30 times faster than can a conventional implementation running on a CPU. For a 640×480 image, our parallel techniques attain a processing speed of 23.8 fps (42 [ms]) and detects both pedestrians and vehicles.

关键词： Instruction sets Graphics processing unit

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：