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2013 6th International Congress on Image and Signal Processing (CISP)最新文献

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Super-resolution image reconstruction via adaptive sparse representation and joint dictionary training 基于自适应稀疏表示和联合字典训练的超分辨率图像重建
2013 6th International Congress on Image and Signal Processing (CISP) Pub Date : 2013-12-01 DOI: 10.1109/CISP.2013.6744051
Di Zhang, Minghui Du
{"title":"Super-resolution image reconstruction via adaptive sparse representation and joint dictionary training","authors":"Di Zhang, Minghui Du","doi":"10.1109/CISP.2013.6744051","DOIUrl":"https://doi.org/10.1109/CISP.2013.6744051","url":null,"abstract":"Recently, sparse representation has emerged as a powerful technique for solving various image restoration applications. In this paper, we investigate the application of sparse representation on single-image super-resolution problems. Considering that the quality of the super-resolved images largely depends on whether the employed sparse domain can represent well the target image, we propose to seek a sparse representation adaptively for each patch of the low-resolution image, and then use the coefficients in the low-resolution domain to reconstruct the high-resolution counterpart. By jointly training the low- and high-resolution dictionaries and choosing the best set of bases to characterize the local patch, we can tighten the similarity between the low-resolution and high-resolution local patches. Experimental results on single-image super-resolution demonstrate the effectiveness of the proposed method.","PeriodicalId":442320,"journal":{"name":"2013 6th International Congress on Image and Signal Processing (CISP)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129201366","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 10
A novel multivariant optimization algorithm for multimodal optimization 一种新的多变量多模态优化算法
2013 6th International Congress on Image and Signal Processing (CISP) Pub Date : 2013-12-01 DOI: 10.1109/CISP.2013.6743936
Changxing Gou, Xinling Shi, Baolei Li, Tiansong Li, Lan-juan Liu, Qinhu Zhang, Yajie Liu
{"title":"A novel multivariant optimization algorithm for multimodal optimization","authors":"Changxing Gou, Xinling Shi, Baolei Li, Tiansong Li, Lan-juan Liu, Qinhu Zhang, Yajie Liu","doi":"10.1109/CISP.2013.6743936","DOIUrl":"https://doi.org/10.1109/CISP.2013.6743936","url":null,"abstract":"This paper provides a detailed description of a novel multivariant optimization algorithm (MOA) for multi-modal optimization with the main idea to share search information by organizing all search atoms into a special designed structure. Its multiple and variant group property make MOA capable on multi-modal optimization problems. The capability of the MOA method in locating and maintaining multi optima in one execution is discussed in details in this paper and two experiments are carried out to validate its feasibility in multi-modal optimization problems. The experimental results are also compared with those obtained by the species-based PSO, the adaptive sequential niche PSO and the memetic PSO. The experiment results show that MOA has high success rate and convergence speed in multi-modal optimization problems.","PeriodicalId":442320,"journal":{"name":"2013 6th International Congress on Image and Signal Processing (CISP)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122257928","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
On the unbiasedness of Multivariant Optimization Algorithm 多变量优化算法的无偏性
2013 6th International Congress on Image and Signal Processing (CISP) Pub Date : 2013-12-01 DOI: 10.1109/CISP.2013.6743864
Baolei Li, Xinling Shi, Jianhua Chen, Yajie Liu, Qinhu Zhang, Lan-juan Liu, Yufeng Zhang, Danjv Lv
{"title":"On the unbiasedness of Multivariant Optimization Algorithm","authors":"Baolei Li, Xinling Shi, Jianhua Chen, Yajie Liu, Qinhu Zhang, Lan-juan Liu, Yufeng Zhang, Danjv Lv","doi":"10.1109/CISP.2013.6743864","DOIUrl":"https://doi.org/10.1109/CISP.2013.6743864","url":null,"abstract":"Multivariant Optimization Algorithm (MOA) is proposed to effectively solve complex multimodal optimization problems through tracking the history information by multiple variant search groups based on a structure. The proposed method has the ability to locate optimum through global-local search iterations which are carried out by a global exploration group and local exploitation groups which are not only multiple but also variant. In this paper, we study the unbiasedness property of MOA and prove that MOA provides an unbiased estimate of the optimal solution for identification problem on an AR model where the outputs are corrupted by noises. The comparison experiments on the identifications of AR model by (Finite Impulse Response) FIR filter shows that MOA is superior to recursive least squares (RLS) and the particle swarm optimization (PSO) in unbiasedness property.","PeriodicalId":442320,"journal":{"name":"2013 6th International Congress on Image and Signal Processing (CISP)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130671131","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
Video flame detection algorithm based on region growing 基于区域增长的视频火焰检测算法
2013 6th International Congress on Image and Signal Processing (CISP) Pub Date : 2013-12-01 DOI: 10.1109/CISP.2013.6745204
Ligang Miao, Aizhong Wang
{"title":"Video flame detection algorithm based on region growing","authors":"Ligang Miao, Aizhong Wang","doi":"10.1109/CISP.2013.6745204","DOIUrl":"https://doi.org/10.1109/CISP.2013.6745204","url":null,"abstract":"This paper proposes a region growing based video flame detection algorithm. Firstly, it estimates class-conditional probability density of flame and background with hand-labeled samples, and five discrimination models are proposed using maximum a-posteriori theory. Secondly, it proposes four rules for flame detection with difference of RGB channels, and ROC analysis is used to estimate rule parameters. Finally, it combines the detection results of these models and rules to detect the candidate flame regions. Region growing uses the high belief region as seed points, and some middle belief regions are classified as flame region if they are adjacent to high belief region, while other regions are classified as background regions. Experiments show that this method can achieve desired flame region in various scenes with high true positive rate and low false detection rate.","PeriodicalId":442320,"journal":{"name":"2013 6th International Congress on Image and Signal Processing (CISP)","volume":"67 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130709973","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 6
A GM-PHD filter for new appearing targets tracking GM-PHD滤波器用于新出现的目标跟踪
2013 6th International Congress on Image and Signal Processing (CISP) Pub Date : 2013-12-01 DOI: 10.1109/CISP.2013.6745230
Hongjian Zhang, Jin Wang, B. Ye, Yuewu Zhang
{"title":"A GM-PHD filter for new appearing targets tracking","authors":"Hongjian Zhang, Jin Wang, B. Ye, Yuewu Zhang","doi":"10.1109/CISP.2013.6745230","DOIUrl":"https://doi.org/10.1109/CISP.2013.6745230","url":null,"abstract":"Simulations reveal that the usual implementations of the Gaussian Mixture PHD filter can detect new targets only if its target-birth model is based on a priori knowledge of where new targets might appear. Otherwise, it cannot detect new targets (unless they happen to be near existing tracks) since it prunes Gaussian components that are not associated with existing tracks. In this paper, this problem is remedied by reserving at least one Gaussian component corresponding to each measurement in the revised Gaussian components pruning approach. Simulations involving four targets show that the proposed approach successfully deals with newly appearing targets.","PeriodicalId":442320,"journal":{"name":"2013 6th International Congress on Image and Signal Processing (CISP)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130970900","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 8
An empirical envelope estimation algorithm 一种经验包络估计算法
2013 6th International Congress on Image and Signal Processing (CISP) Pub Date : 2013-12-01 DOI: 10.1109/CISP.2013.6745226
Q. Meng, Meng Yuan, Zhenya Yang, Haihong Feng
{"title":"An empirical envelope estimation algorithm","authors":"Q. Meng, Meng Yuan, Zhenya Yang, Haihong Feng","doi":"10.1109/CISP.2013.6745226","DOIUrl":"https://doi.org/10.1109/CISP.2013.6745226","url":null,"abstract":"Envelope is the vital part of one-dimensional data. The estimation of envelope can be treated as a demodulation problem. However, the definition of envelope is ambiguous and lack of an exact mathematical definition. It is commonly agreed that envelope varies slowly and in some empirical view it should pass the prominent peaks of the data smoothly. In this study, we propose an algorithm to directly use the prominent peaks and finally get the envelope by interpolation. We term it as empirical envelope estimation algorithm (EEEA). Envelope derived by EEEA has steerable smoothness and is adaptive to the data. It has clear physical meaning and has great potential in some off-line signal analysis applications, such as musical sound analysis and heart sound analysis.","PeriodicalId":442320,"journal":{"name":"2013 6th International Congress on Image and Signal Processing (CISP)","volume":"124 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128136858","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 8
Hierarchical sparse representation with adaptive dictionaries for image super-resolution 基于自适应字典的图像超分辨率分层稀疏表示
2013 6th International Congress on Image and Signal Processing (CISP) Pub Date : 2013-12-01 DOI: 10.1109/CISP.2013.6744001
Xuelian Wu, Daiguo Deng, Jianhong Li, Xiaonan Luo, K. Zeng
{"title":"Hierarchical sparse representation with adaptive dictionaries for image super-resolution","authors":"Xuelian Wu, Daiguo Deng, Jianhong Li, Xiaonan Luo, K. Zeng","doi":"10.1109/CISP.2013.6744001","DOIUrl":"https://doi.org/10.1109/CISP.2013.6744001","url":null,"abstract":"This paper presents an image hierarchical super-resolution (SR) method with adaptive dictionaries, based on signal sparse representation. It can not only improve image detail quality but also reduce computational cost. Research on the human visual system suggests that our eyes are mainly sensitive to high-frequency contents. Inspired by this observation, we implemented a hierarchical process where an image was decomposed into a detail layer and a base layer. The detail layer is reconstructed through an over-complete dictionary while the base layer is interpolated by bi-cubic. Through these, we can keep the HR details better. Next is how to accelerate while keeping good quality. In our method, adaptive dictionaries are trained by feature clustering. Firstly, we train low dimension sub-dictionaries to reduce time complexity. Secondly, then we apply overlapping feature clustering to the training. Thus dictionaries can be adaptive and more complete. All these can also prevent sub-dictionaries with over strong independence but less compatibility. Besides, initializing the sparse coefficients also plays an important role in our acceleration. Experimental results validate that ours are competitive or even superior in quality than those produced by other methods and our test data indicates substantial reduction in processing time over other similar SR methods.","PeriodicalId":442320,"journal":{"name":"2013 6th International Congress on Image and Signal Processing (CISP)","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128371920","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
SAR automatic target recognition based on a visual cortical system 基于视觉皮层系统的SAR自动目标识别
2013 6th International Congress on Image and Signal Processing (CISP) Pub Date : 2013-12-01 DOI: 10.1109/CISP.2013.6745270
J. Ni, Yue Xu
{"title":"SAR automatic target recognition based on a visual cortical system","authors":"J. Ni, Yue Xu","doi":"10.1109/CISP.2013.6745270","DOIUrl":"https://doi.org/10.1109/CISP.2013.6745270","url":null,"abstract":"Human Vision system is the most complex and accurate system. In order to extract better features about Synthetic Aperture Radar (SAR) targets, a SAR automatic target recognition (ATR) algorithm based on human visual cortical system is proposed. This algorithm contains three stages: (1) Image preprocessing (we use a Kuan filter to do the enhancement and an adaptive Intersecting Cortical Model (ICM) to do the segmentation) (2) Feature extraction using a sparse autoencoder. (3) Classification using a softmax regression classifier. Experiment result of MSTAR public data shows a better performance of recognition.","PeriodicalId":442320,"journal":{"name":"2013 6th International Congress on Image and Signal Processing (CISP)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131732599","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 22
Image segmentation in weld defect detection based on modified background subtraction 基于改进背景减法的焊缝缺陷检测图像分割
2013 6th International Congress on Image and Signal Processing (CISP) Pub Date : 2013-12-01 DOI: 10.1109/CISP.2013.6745239
Zhichao Liao, Jun Sun
{"title":"Image segmentation in weld defect detection based on modified background subtraction","authors":"Zhichao Liao, Jun Sun","doi":"10.1109/CISP.2013.6745239","DOIUrl":"https://doi.org/10.1109/CISP.2013.6745239","url":null,"abstract":"In computer vision, the background subtraction is an important method to detect moving objects. The background reconstruction algorithm is based on the hypotheses that the background pixels intensity appears in image sequence with maximum probability. This paper proposes a real-time weld defect detection algorithm using a modified background subtraction method based on the assumption that the background pixel intensity appears in image sequence with maximum probability and the distribution of the pixels of background conforms to the Gaussian distribution. The algorithm has been successfully applied to the on-line weld defect detection. Our approach can perfectly extract and roughly classify the weld defects. Experimental results show that the proposed algorithm can meet the requirement of the efficiency of on-line continuous detection of weld defects and detect weld defects automatically and successfully.","PeriodicalId":442320,"journal":{"name":"2013 6th International Congress on Image and Signal Processing (CISP)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134405327","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 9
Kernel density feature based improved Chan-Vese Model for image segmentation 基于核密度特征的改进Chan-Vese模型图像分割
2013 6th International Congress on Image and Signal Processing (CISP) Pub Date : 2013-12-01 DOI: 10.1109/CISP.2013.6745240
Jin Li, Shoudong Han, Yong Zhao
{"title":"Kernel density feature based improved Chan-Vese Model for image segmentation","authors":"Jin Li, Shoudong Han, Yong Zhao","doi":"10.1109/CISP.2013.6745240","DOIUrl":"https://doi.org/10.1109/CISP.2013.6745240","url":null,"abstract":"In this paper, an interactive image segmentation method is proposed base on the kernel density feature estimation. Compared with the traditional RGB value, it could be more accurate to model the color feature of pixel using corresponding kernel density estimation. To obtain the regional color feature, the mean of kernel densities of all pixels in this region is applied, and Bhattacharyya distance is used to measure the differences between two kernel densities. Consequently, an energy function is constructed according to the main idea of Chan-Vese Model, and it is optimized using the graph cuts technique. Experimental results demonstrate the advantages of our proposed method in terms of robustness and accuracy, especially for objects with thin elongated or concave parts.","PeriodicalId":442320,"journal":{"name":"2013 6th International Congress on Image and Signal Processing (CISP)","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122940590","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
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