The Kips Transactions:partb最新文献_第9页

Active Contour Model for Boundary Detection of Multiple Objects 多目标边界检测的主动轮廓模型

The Kips Transactions:partb Pub Date : 2010-10-31 DOI: 10.3745/KIPSTB.2010.17B.5.375

Jong-Whan Jang

{"title":"Active Contour Model for Boundary Detection of Multiple Objects","authors":"Jong-Whan Jang","doi":"10.3745/KIPSTB.2010.17B.5.375","DOIUrl":"https://doi.org/10.3745/KIPSTB.2010.17B.5.375","url":null,"abstract":"ABSTRACT Most of previous algorithms of object boundary extraction have been studied for extracting the boundary of single object. However, multiple objects are much common in the real image. The proposed algorithm of extracting the boundary of each of multiple objects has two steps. In the first step, we propose the fast method using the outer and inner products; the initial contour including multiple objects is split and connected and each of new contours includes only one object. In the second step, an improved active contour model is studied to extract the boundary of each object included each of contours. Experimental results with various test images have shown that our algorithm produces much better results than the previous algorithmsKey words : Snake, Active Contour Model, Multiple Objects, Boundary Extraction, Highly Irregular Boundary, Splitting and Connecting of Snake Points 1. 서 론 1) 객체윤곽 추출은 내용기반 검출시스템 및 대화형 멀티미디어시스템에서 매우 중요하다[1, 2]. 이러한 시스템을 사용하여 서비스를 성공적으로 제공하기 위해서는 영상질의를 하는데 기본 정보로 객체 모양을 사용한다. 실 영상에서는 단일객체 보다는 복수 객체가 일반적이고 복수객체 윤곽을 효율적으로 추출하면 활용되는 분야가 다양화 될 것으로 기대된다. 복수 객체의 윤곽추출의 일반적인 방법은 먼저 복수객체를 단일객체로 분리하고, 그 다음 단일 객체의 모양 즉 윤곽을 추출하여야 한다.복수객체의 윤곽추출에는 여러 방법이 제안되었다. 예를 들면, 영역분할이나, Watershed 등의 알고리즘이 제안되었","PeriodicalId":122700,"journal":{"name":"The Kips Transactions:partb","volume":"11 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120857027","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

Gradient Descent Approach for Value-Based Weighting 基于值的加权梯度下降法

The Kips Transactions:partb Pub Date : 2010-10-31 DOI: 10.3745/KIPSTB.2010.17B.5.381

Chang-Hwan Lee, Joohyun Bae

引用次数: 0

Robust AAM-based Face Tracking with Occlusion Using SIFT Features 基于SIFT特征的人脸遮挡鲁棒跟踪

The Kips Transactions:partb Pub Date : 2010-10-31 DOI: 10.3745/KIPSTB.2010.17B.5.355

Sungeun Eom, Jun-Su Jang

引用次数: 1

Panoramic Image Composition Algorithm through Scaling and Rotation Invariant Features 基于缩放和旋转不变性特征的全景图像合成算法

The Kips Transactions:partb Pub Date : 2010-10-31 DOI: 10.3745/KIPSTB.2010.17B.5.333

Ki-Won Kwon, Hae-Yeoun Lee, Duk-Hwan Oh

引用次数: 1

An Ant Colony Optimization Heuristic to solve the VRP with Time Window 求解带时间窗VRP的蚁群算法

The Kips Transactions:partb Pub Date : 2010-10-31 DOI: 10.3745/KIPSTB.2010.17B.5.389

Myung-Duk Hong, Young-Hoon Yu, Geun-Sik Jo

引用次数: 1

A Method for Precision Improvement Based on Core Query Clusters and Term Proximity 一种基于核心查询聚类和词邻近度的精度提高方法

The Kips Transactions:partb Pub Date : 2010-10-31 DOI: 10.3745/KIPSTB.2010.17B.5.399

Kye-Hun Jang, Kyung-Soon Lee

引用次数: 0

Fast Natural Feature Tracking Using Optical Flow 使用光流快速自然特征跟踪

The Kips Transactions:partb Pub Date : 2010-10-31 DOI: 10.3745/KIPSTB.2010.17B.5.345

Byung-Jo Bae, Jong-Seung Park

{"title":"Fast Natural Feature Tracking Using Optical Flow","authors":"Byung-Jo Bae, Jong-Seung Park","doi":"10.3745/KIPSTB.2010.17B.5.345","DOIUrl":"https://doi.org/10.3745/KIPSTB.2010.17B.5.345","url":null,"abstract":"Visual tracking techniques for Augmented Reality are classified as either a marker tracking approach or a natural feature tracking approach. Marker-based tracking algorithms can be efficiently implemented sufficient to work in real-time on mobile devices. On the other hand, natural feature tracking methods require a lot of computationally expensive procedures. Most previous natural feature tracking methods include heavy feature extraction and pattern matching procedures for each of the input image frame. It is difficult to implement real-time augmented reality applications including the capability of natural feature tracking on low performance devices. The required computational time cost is also in proportion to the number of patterns to be matched. To speed up the natural feature tracking process, we propose a novel fast tracking method based on optical flow. We implemented the proposed method on mobile devices to run in real-time and be appropriately used with mobile augmented reality applications. Moreover, during tracking, we keep up the total number of feature points by inserting new feature points proportional to the number of vanished feature points. Experimental results showed that the proposed method reduces the computational cost and also stabilizes the camera pose estimation results.","PeriodicalId":122700,"journal":{"name":"The Kips Transactions:partb","volume":"110 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128415417","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

Automatic Method for Extracting Homogeneity Threshold and Segmenting Homogeneous Regions in Image 图像均匀性阈值提取与均匀区域分割的自动方法

The Kips Transactions:partb Pub Date : 2010-10-31 DOI: 10.3745/KIPSTB.2010.17B.5.363

Gi-Tae Han

{"title":"Automatic Method for Extracting Homogeneity Threshold and Segmenting Homogeneous Regions in Image","authors":"Gi-Tae Han","doi":"10.3745/KIPSTB.2010.17B.5.363","DOIUrl":"https://doi.org/10.3745/KIPSTB.2010.17B.5.363","url":null,"abstract":"In this paper, we propose the method for extracting Homogeneity Threshold() and for segmenting homogeneous regions by USRG(Unseeded Region Growing) with . The is a criterion to distinguish homogeneity in neighbor pixels and is computed automatically from the original image by proposed method. Theoretical background for proposed method is based on the Otsu`s single level threshold method. The method is used to divide a small local part of original image int o two classes and the sum() of standard deviations for the classes to satisfy special conditions for distinguishing as different regions from each other is used to compute . To find validity for proposed method, we compare the original image with the image that is regenerated with only the segmented homogeneous regions and show up the fact that the difference between two images is not exist visually and also present the steps to regenerate the image in order the size of segmented homogeneous regions and in order the intensity that includes pixels. Also, we show up the validity of proposed method with various results that is segmented using the homogeneity thresholds() that is added a coefficient for adjusting scope of . We expect that the proposed method can be applied in various fields such as visualization and animation of natural image, anatomy and biology and so on.","PeriodicalId":122700,"journal":{"name":"The Kips Transactions:partb","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126510670","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

Model based Facial Expression Recognition using New Feature Space 基于模型的新特征空间面部表情识别

The Kips Transactions:partb Pub Date : 2010-08-31 DOI: 10.3745/KIPSTB.2010.17B.4.309

Jin-Ok Kim

引用次数: 2

Example-based Super Resolution Text Image Reconstruction Using Image Observation Model 基于实例的基于图像观测模型的超分辨率文本图像重建

The Kips Transactions:partb Pub Date : 2010-08-31 DOI: 10.3745/KIPSTB.2010.17B.4.295

Gyu-Ro Park, In-Jung Kim

{"title":"Example-based Super Resolution Text Image Reconstruction Using Image Observation Model","authors":"Gyu-Ro Park, In-Jung Kim","doi":"10.3745/KIPSTB.2010.17B.4.295","DOIUrl":"https://doi.org/10.3745/KIPSTB.2010.17B.4.295","url":null,"abstract":"Example-based super resolution(EBSR) is a method to reconstruct high-resolution images by learning patch-wise correspondence between high-resolution and low-resolution images. It can reconstruct a high-resolution from just a single low-resolution image. However, when it is applied to a text image whose font type and size are different from those of training images, it often produces lots of noise. The primary reason is that, in the patch matching step of the reconstruction process, input patches can be inappropriately matched to the high-resolution patches in the patch dictionary. In this paper, we propose a new patch matching method to overcome this problem. Using an image observation model, it preserves the correlation between the input and the output images. Therefore, it effectively suppresses spurious noise caused by inappropriately matched patches. This does not only improve the quality of the output image but also allows the system to use a huge dictionary containing a variety of font types and sizes, which significantly improves the adaptability to variation in font type and size. In experiments, the proposed method outperformed conventional methods in reconstruction of multi-font and multi-size images. Moreover, it improved recognition performance from 88.58% to 93.54%, which confirms the practical effect of the proposed method on recognition performance.","PeriodicalId":122700,"journal":{"name":"The Kips Transactions:partb","volume":"136 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127295052","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