机构

• 自动化研究所 [7]
• 长春光学精密机械与物... [2]
• 西安光学精密机械研究... [1]

采集方式

• OAI收割 [10]

内容类型

• 期刊论文 [8]
• 会议论文 [2]

发表日期

• 2023 [1]
• 2020 [1]
• 2019 [2]
• 2018 [1]
• 2016 [1]
• 2012 [1]
• 更多

学科主题

筛选

浏览/检索结果: 共10条，第1-10条 帮助 条数/页： 5101520253035404550556065707580859095100 排序方式： 请选择题名升序题名降序提交时间升序提交时间降序作者升序作者降序发表日期升序发表日期降序 Deep Gradient Learning for Efficient Camouflaged Object Detection 期刊论文  OAI收割 Machine Intelligence Research, 2023, 卷号: 20, 期号: 1, 页码: 92-108 作者:  Ge-Peng Ji;  Deng-Ping Fan;  Yu-Cheng Chou;  Dengxin Dai;  Alexander Liniger   |  收藏  |  浏览/下载：12/0  |  提交时间：2024/04/23 Camouflaged object detection (COD)  object gradient  soft grouping  efficient model  image segmentation   An Ultrahigh-Speed Object Detection Method With Projection-Based Position Compensation 期刊论文  OAI收割 IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT, 2020, 卷号: 69, 期号: 7, 页码: 4796-4806 作者:  Li, Jianquan;  Long, Xianlei;  Xu, De;  Gu, Qingyi;  Ishii, Idaku   |  收藏  |  浏览/下载：46/0  |  提交时间：2020/08/03 Field-programmable gate array (FPGA)  hardware implementation  histogram of oriented gradient (HOG)  object detection  pixel projection  ultrahigh-speed vision   An FPGA-Based Ultra-High-Speed Object Detection Algorithm with Multi-Frame Information Fusion 期刊论文  OAI收割 SENSORS, 2019, 卷号: 19, 期号: 17, 页码: 16 作者:  Long, Xianlei   |  收藏  |  浏览/下载：78/0  |  提交时间：2019/12/16 ultra-high-speed vision  object detection  field-programmable gate array  histogram of oriented gradient  multi-frame information fusion model   A Hardware-Oriented Algorithm for Ultra-High-Speed Object Detection 期刊论文  OAI收割 IEEE SENSORS JOURNAL, 2019, 卷号: 19, 期号: 10, 页码: 3818-3831 作者:     |  收藏  |  浏览/下载：76/0  |  提交时间：2019/07/12 Hardware implementation  high-frame-rate vision  field-programmable gate array  multi-object detection  histograms of oriented gradient  support vector machine   Ship Detection in Optical Remote Sensing Images Based on Saliency and a Rotation-Invariant Descriptor 期刊论文  OAI收割 Remote Sensing, 2018, 卷号: 10, 期号: 3, 页码: 19 作者:  Dong, C.;  Liu, J. H.;  Xu, F.   |  收藏  |  浏览/下载：12/0  |  提交时间：2019/09/17 remote sensing  visual saliency  radial gradient transform  covariance  matrix  Gaussian SVM  detection scheme  object detection  model  shape  Remote Sensing   Study on the deep neural network of intelligent image detection and the improvement of elastic momentum on image recognition 期刊论文  OAI收割 journal of computational and theoretical nanoscience, 2016, 卷号: 13, 期号: 5, 页码: 3326-3330 作者:  Yue, Qi;  Ma, Caiwen 收藏  |  浏览/下载：25/0  |  提交时间：2016/10/12 Gradient methods  Image recognition  Object recognition  Target tracking   Online multiple instance gradient feature selection for robust visual tracking 期刊论文  OAI收割 PATTERN RECOGNITION LETTERS, 2012, 卷号: 33, 期号: 9, 页码: 1075-1082 作者:  Xie, Yuan;  Qu, Yanyun;  Li, Cuihua;  Zhang, Wensheng 收藏  |  浏览/下载：30/0  |  提交时间：2015/09/18 Gradient-based feature selection  HOG  Multiple Instance Learning  Online object tracking   Boosting part-sense multi-feature learners toward effective object detection 期刊论文  OAI收割 COMPUTER VISION AND IMAGE UNDERSTANDING, 2011, 卷号: 115, 期号: 3, 页码: 364-374 作者:  Chen, Shi;  Wang, Jinqiao;  Ouyang, Yi;  Wang, Bo;  Xu, Changsheng 收藏  |  浏览/下载：24/0  |  提交时间：2015/08/12 AdaBoost  Object detection  Multi-feature learners  L(1)-regularized gradient boosting   3D Model Based Vehicle Tracking by Optimizing Gradient Based Fitness Evaluation 会议论文  OAI收割 Istanbul, Turkey, 2010 作者:  Zhaoxiang Zhang;  Kaiqi Huang;  Tieniu Tan;  Yunhong Wang   |  收藏  |  浏览/下载：12/0  |  提交时间：2016/12/30 Gradient Methods    object Detection    particle Filtering    A new segmentation method of CR images based on discrete wavelet transform and mathematics morphology (EI CONFERENCE) 会议论文  OAI收割 ICO20: Optical Information Processing, August 21, 2005 - August 26, 2005, Changchun, China 作者:  Li Z.;  Li Z. 收藏  |  浏览/下载：68/0  |  提交时间：2013/03/25 In this paper  we propose a segmentation method of CR(computed radiography) images with being rid of under-segmentation and over-segmentation. An under-segmentation occurs when pixels belonging to different objects are grouped into a single region. Such errors are the most dangerous because they can invalidate the whole segmentation process. The phenomenon always takes place when we segment CR images because of the principle of CR. In order to depressed under-segmentation  we enhance the CR images using DWT (discrete wavelet transform) to get more detail of CR image features. As we enhance the CR image  the over-segmentation maybe occurs. Compared with under-segmentation  the over-segmentation occurs when a single objects is subdivided by segmentation into several region. For the purpose of preventing from the over-segmentation  we present a scheme for enhanced CR images based on watershed algorithm that solves over-segmentation problem. We use marker-based watershed algorithm. Together with gradient image and marker image  watershed segmentation can make sure to partition CR image into meaningful object and avoid further segmentation of homogeneous regions. The result of the proposed algorithm are compared with those of other standard methods. Experiments have shown a better result and indeed solved under-segmentation and over-segmentation problems.