中国科学院机构知识库网格系统: 检索

Enhancing outdoor long-distance matching in mobile AR: A continuous and real-time geo-registration approach 期刊论文 OAI收割
INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION, 2025, 卷号: 137, 页码: 104422
作者: Huang, Kejia; Liu, Di; Zlatanova, Sisi; Lu, Yue; Wang, Yiwen

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收藏 | 浏览/下载：6/0 | 提交时间：2025/04/21

Mobile Augmented Reality (MAR) Geo-registration Sensor fusion Geodesic equations Rotation invariance estimation

OSN: Onion-ring support neighbors for correspondence selection 期刊论文 OAI收割
INFORMATION SCIENCES, 2021, 卷号: 560, 页码: 331-346
作者: Gong, Cheng; Lu, Ye; Song, Chunying; Li, Tao; Wang, Kai

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收藏 | 浏览/下载：100/0 | 提交时间：2021/12/01

Correspondence selection Rotation invariance Support neighbors Coherence constraints

Texture Classification in Extreme Scale Variations Using GANet 期刊论文 OAI收割
IEEE TRANSACTIONS ON IMAGE PROCESSING, 2019, 卷号: 28, 期号: 8, 页码: 3910-3922
作者: Chen, Xilin; Liu, Li; Chen, Jie; Zhao, Guoying; Fieguth, Paul

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收藏 | 浏览/下载：61/0 | 提交时间：2019/08/16

Texture descriptors rotation invariance local binary pattern (LBP) feature extraction texture analysis

Top-Down Visual Saliency Detection in Optical Satellite Images Based on Local Adaptive Regression Kernel 期刊论文 OAI收割
Journal of Multimedia, 2014, 卷号: 9, 期号: 1, 页码: 173-180
作者: Cui, Xiaoguang; Tian, Yuan

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收藏 | 浏览/下载：23/0 | 提交时间：2016/10/26

Visual Saliency Top-down Kernel Regression Rotation Invariance Bounded Partial Correlation

Rotationally Invariant Descriptors Using Intensity Order Pooling 期刊论文 OAI收割
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 2012, 卷号: 34, 期号: 10, 页码: 2031-2045
作者: Fan, Bin; Wu, Fuchao; Hu, Zhanyi

收藏 | 浏览/下载：34/0 | 提交时间：2015/08/12

Local image descriptor rotation invariance monotonic intensity invariance image matching intensity orders SIFT

图像特征匹配研究 学位论文 OAI收割
工学博士, 中国科学院自动化研究所: 中国科学院研究生院, 2011
作者: 樊彬

收藏 | 浏览/下载：42/0 | 提交时间：2015/09/02

特征描述特征匹配重复性纹理直线匹配点线不变量旋转不变性光照不变性 feature description feature matching repetitive patterns line matching line-point invariant rotation invariance illumination invariance

Rotation and scaling invariant feature lines for image matching (EI CONFERENCE) 会议论文 OAI收割
2011 International Conference on Mechatronic Science, Electric Engineering and Computer, MEC 2011, August 19, 2011 - August 22, 2011, Jilin, China
作者: Wang Y.; Wang Y.; Wang Y.; Wang Y.; Wang Y.

收藏 | 浏览/下载：25/0 | 提交时间：2013/03/25

Image matching has been one of the most fundamental issues computer vision over the decades. In this paper we propose a novel method based on making use of feature lines in order to achieve more robust image matching. The feature lines have the properties of rotation and scaling invariance coined RIFLT(Rotation invariant feature line transform). Experimental results demonstrate the effectiveness and efficiency of the proposed method. Compare with the famous powerful algorithm Scale Invariant Feature Transform(SIFT) the proposed method is more insensitive to noise. And for certain sequence of images which contain clear lines the proposed method is more efficiency. Using the feature lines obtained by our method it is possible to matching two scene images with different rotation angles scale and light distort. 2011 IEEE.

Scene matching based on directional keylines and polar transform (EI CONFERENCE) 会议论文 OAI收割
2010 IEEE 10th International Conference on Signal Processing, ICSP2010, October 24, 2010 - October 28, 2010, Beijing, China
作者: Wang Y.; Wang Y.; Wang Y.; Wang Y.; Wang Y.

收藏 | 浏览/下载：21/0 | 提交时间：2013/03/25

Scene matching under complex background is a priority and difficulty in the field of computer vision it has the characteristics of rotation and scaling invariance commonly used in matching real-time collected images and photos for navigation. Scene matching techniques are faced with complex natural scenes anti-light and anti-slight-distortion the image distortion exist applicable for complex scene matching. The project has a new idea: combining the keylines with the vectors description based on polar image translation such as light and utilize the rotation-scale-invariance vectors to describe the extracted keylines change of gray levels this method includes three steps: keylines extraction perspective description and matching. Preliminary experiments show that this keylines-based scene matching algorithm is applicable for image matching under complex background. 2010 IEEE. scaling and other differences which cause matching difficult. This paper aims to find a scene matching algorithm

Target track system design based on circular projection (EI CONFERENCE) 会议论文 OAI收割
ICO20: Optical Information Processing, August 21, 2005 - August 26, 2005, Changchun, China
Song H.-J.; Zhu M.; Hu S.; Shen M.-L.

收藏 | 浏览/下载：21/0 | 提交时间：2013/03/25

Template matching is the process of searching the present and the location of a reference image or an object in a scene image. Template matching is a classical problem in a scene analysis: given a reference image of an object decide whether that object exists in a scene image under analysis and find its location if it does. The template matching process involves cross-correlating the template with the scene image and computing a measure of similarity between them to determine the displacement. The conventional matching method used the spatial cross-correlation process which is computationally expensive. Some algorithms are proposed for this speed problem such as pyramid algorithm but it still can't reach the real-time for bigger model image. Moreover the cross-correlation algorithm can't be effective when the object in the image is rotated. Therefore the conventional algorithms can't be used for practical purpose. In this paper an algorithm for a rotation invariant template matching method based on different value circular projection target tracking algorithm is proposed. This algorithm projects the model image as circular and gets the radius and the sum of the same radius pixel value. The sum of the same radius pixel value is invariable for the same image and the any rotated angle image. Therefore this algorithm has the rotation invariant property. In order to improve the matching speed and get the illumination invariance the different value method is combined with circular projection algorithm. This method computes the different value between model image radius pixel sum and the scene image radius pixel sum so that it gets the matching result. The pyramid algorithm also is been applied in order to improve the matching speed. The high speed hardware system also is been design in order to meet the real time requirement of target tracking system. The results show that this system has the good rotate invariance and real-time property.

Efficient rotation invariant texture features for content-based image retrieval 期刊论文 OAI收割
PATTERN RECOGNITION, 1998, 卷号: 31, 期号: 11, 页码: 1725-1732
作者: Fountain, SR; Tan, TN

收藏 | 浏览/下载：34/0 | 提交时间：2015/11/08

image database rotation invariance content-based retrieval texture analysis