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基于背景估计和边缘检测的文档图像二值化 期刊论文  OAI收割
计算机应用与软件, 2014, 卷号: 31, 期号: 8, 页码: 196-200
许海洋; 马龙龙; 吴健
  |  收藏  |  浏览/下载:41/0  |  提交时间:2014/12/16
An improved hyperspectral classification algorithm based on back-propagation neural networks (EI CONFERENCE) 会议论文  OAI收割
2012 2nd International Conference on Remote Sensing, Environment and Transportation Engineering, RSETE 2012, June 1, 2012 - June 3, 2012, Nanjing, China
作者:  
Yu P.;  Yu P.
收藏  |  浏览/下载:35/0  |  提交时间:2013/03/25
In this paper  a new method is proposed to improve the classification performance of hyperspectral images by combining the principal component analysis (PCA)  genetic algorithm (GA)  and artificial neural networks (ANNs). First  some characteristics of the hyperspectral remotely sensed data  such as high correlation  high redundancy  etc.  are investigated. Based on the above analysis  we propose to use the principal component analysis to capture the main information existing in the hyperspectral images and reduce its dimensionality consequently. Next  we use neural networks to classify the reduced hyperspectral data. Since the back-propagation neural network we used is easy to suffer from the local minimum problem  we adopt a genetic algorithm to optimize the BP network's weights and the threshold. Experimental results show that the classification accuracy is improved and the time of calculation is reduced as well. 2012 IEEE.  
Extracting sea-sky-line based on improved local complexity (EI CONFERENCE) 会议论文  OAI收割
2010 International Conference on Computer, Mechatronics, Control and Electronic Engineering, CMCE 2010, August 24, 2010 - August 26, 2010, Changchun, China
作者:  
Zhang Y.-F.
收藏  |  浏览/下载:24/0  |  提交时间:2013/03/25
Sea-sky-line extraction under complicated sea-sky background is an important aspect of long-range target tracking research. An algorithm based on improved local complexity is proposed according to the feature that the sky and the sea usually show up at different gray levels in sea-sky background images. Median filter is applied first to remove peak noise  and then the point set of sea-sky-line region which has the greatest change in image is obtained by calculating the local complexity of image and selecting a segmentation threshold. Finally  Hough transform is used to extract the sea-sky-line. The experiment results indicate that this method can extract sea-sky-line under simple and complicated sea-sky backgrounds  which is robust and can improve noise immunity of the algorithm. 2010 IEEE.  
Real time tracking by LOPF algorithm with mixture model (EI CONFERENCE) 会议论文  OAI收割
MIPPR 2007: Automatic Target Recognition and Image Analysis; and Multispectral Image Acquisition, November 15, 2007 - November 17, 2007, Wuhan, China
Meng B.; Zhu M.; Han G.; Wu Z.
收藏  |  浏览/下载:27/0  |  提交时间:2013/03/25
A new particle filter-the Local Optimum Particle Filter (LOPF) algorithm is presented for tracking object accurately and steadily in visual sequences in real time which is a challenge task in computer vision field. In order to using the particles efficiently  we first use Sobel algorithm to extract the profile of the object. Then  we employ a new Local Optimum algorithm to auto-initialize some certain number of particles from these edge points as centre of the particles. The main advantage we do this in stead of selecting particles randomly in conventional particle filter is that we can pay more attentions on these more important optimum candidates and reduce the unnecessary calculation on those negligible ones  in addition we can overcome the conventional degeneracy phenomenon in a way and decrease the computational costs. Otherwise  the threshold is a key factor that affecting the results very much. So here we adapt an adaptive threshold choosing method to get the optimal Sobel result. The dissimilarities between the target model and the target candidates are expressed by a metric derived from the Bhattacharyya coefficient. Here  we use both the counter cue to select the particles and the color cur to describe the targets as the mixture target model. The effectiveness of our scheme is demonstrated by real visual tracking experiments. Results from simulations and experiments with real video data show the improved performance of the proposed algorithm when compared with that of the standard particle filter. The superior performance is evident when the target encountering the occlusion in real video where the standard particle filter usually fails.  
A segment detection method based on improved Hough transform (EI CONFERENCE) 会议论文  OAI收割
ICO20: Optical Information Processing, August 21, 2005 - August 26, 2005, Changchun, China
作者:  
Yao Z.-J.
收藏  |  浏览/下载:27/0  |  提交时间:2013/03/25
Hough transform is recognized as a powerful tool in shape analysis which gives good results even in the presence of noise and the disconnection of edge. However  3. applying the standard Hough transform equation to every point of the input image edge  4. according to the local threshold  6. merging the segments whose extreme points are near. Experiment results show the approach not only can recognize regular geometric object but also can extract the segment feature of real targets in complex environment. So the proposed method can be used in the target detection of complicated scenes  traditional Hough transform can only detect the lines  2. quantizing the parameter space  and extracting a group of maximums according to the global threshold  eliminating spurious peaks which are caused by the spreading effects  and will improve the precision of tracking.  cannot give the endpoints and length of the line segments and it is vulnerable to the quantization errors. Based on the analysis of its limitations  Hough transform has been improved in order to detect line segment feature of targets. The algorithm aims to avoid the loss of spatial information  as well as to eliminate the spurious peaks and fix on the line segments endpoints accurately  5. fixing on the endpoints of the segments according to the dynamic clustering rule  which can expediently be used for the description and classification of regular objects. The method consists of 6 steps: 1. setting up the image  parameter and line-segment spaces  
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