2016 12th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS)最新文献_第9页

A Semi-Automated Segmentation Framework for MRI Based Brain Tumor Segmentation Using Regularized Nonnegative Matrix Factorization 基于正则化非负矩阵分解的MRI脑肿瘤半自动化分割框架

2016 12th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS) Pub Date : 1900-01-01 DOI: 10.1109/SITIS.2016.23

N. Sauwen, D. Sima, M. Acou, E. Achten, F. Maes, U. Himmelreich, S. Huffel

{"title":"A Semi-Automated Segmentation Framework for MRI Based Brain Tumor Segmentation Using Regularized Nonnegative Matrix Factorization","authors":"N. Sauwen, D. Sima, M. Acou, E. Achten, F. Maes, U. Himmelreich, S. Huffel","doi":"10.1109/SITIS.2016.23","DOIUrl":"https://doi.org/10.1109/SITIS.2016.23","url":null,"abstract":"Segmentation plays an important role in the clinical management of brain tumors. Clinical practice would benefit from accurate and automated volumetric delineation of the tumor and its subcompartments. We present a semi-automated framework for brain tumor segmentation based on regularized nonnegative matrix factorization (NMF). L1-regularization is incorporated into the NMF objective function to promote spatial consistency and sparseness of the tissue abundance maps. The pathological sources are initialized through user-defined voxel selection. Knowledge about the spatial location of the selected voxels is combined with tissue adjacency constraints in a post-processing step to enhance segmentation quality. The method is applied to the BRATS 2013 Leaderboard dataset, consisting of publicly available multi-sequence MRI data of brain tumor patients. Our method performs well in comparison with state-of-the-art, in particular for the enhancing tumor region, for which we reach the highest Dice score among all participants.","PeriodicalId":403704,"journal":{"name":"2016 12th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123284802","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: 7

Motion Analysis for Cooking Motion Recognition 烹饪动作识别的运动分析

2016 12th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS) Pub Date : 1900-01-01 DOI: 10.1109/SITIS.2016.93

Yuma Hijioka, Makoto Murakami, T. Kimoto

引用次数: 0

Implementation of a Coin Recognition System for Mobile Devices with Deep Learning 基于深度学习的移动设备硬币识别系统的实现

2016 12th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS) Pub Date : 1900-01-01 DOI: 10.1109/SITIS.2016.37

N. Capece, U. Erra, A. Ciliberto

引用次数: 12

An Efficient Classification Method for Knee MR Image Segmentation 一种有效的膝关节MR图像分割分类方法

2016 12th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS) Pub Date : 1900-01-01 DOI: 10.1109/SITIS.2016.15

Yukiko Yamamoto, S. Tsuruta, Syoji Kobashi, Yoshitaka Sakurai, R. Knauf

{"title":"An Efficient Classification Method for Knee MR Image Segmentation","authors":"Yukiko Yamamoto, S. Tsuruta, Syoji Kobashi, Yoshitaka Sakurai, R. Knauf","doi":"10.1109/SITIS.2016.15","DOIUrl":"https://doi.org/10.1109/SITIS.2016.15","url":null,"abstract":"Aiming at application to automated recognition of knee bone magnetic resonance (MR) images, an evolutional classification method called CBGA-LDIC is proposed. CBGA-LDIC finds an appropriate cell set towards efficient image segmentation. This method uses location-dependent image classification (LDIC), which is integrated by genetic algorithm (GA) combined with case based reasoning (CB). LDIC introduces a new but local heuristics for image segmentation, and defines multiple classifiers dependent on location. Each classifier is trained by Gaussian mixture model. CBGA-LDIC decomposes the whole image into some cells, makes a set of cells, and then trains classifiers. Since the knee bones and/or their formations are similar in their location, good combinations of cells seem useful for other clients and are stored in case bases. Thus this method is expected to produce the better results when good combinations of cells are selected from cases as initial individuals of GA, especially through its repetition on restarting GA. This is verified by some experimentations shown in this paper.","PeriodicalId":403704,"journal":{"name":"2016 12th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126711601","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: 4

Face Recognition Using Multiple Histogram Features in Spatial and Frequency Domains 基于空间域和频域多个直方图特征的人脸识别

2016 12th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS) Pub Date : 1900-01-01 DOI: 10.1109/SITIS.2016.40

Qiu Chen, K. Kotani, Feifei Lee

{"title":"Face Recognition Using Multiple Histogram Features in Spatial and Frequency Domains","authors":"Qiu Chen, K. Kotani, Feifei Lee","doi":"10.1109/SITIS.2016.40","DOIUrl":"https://doi.org/10.1109/SITIS.2016.40","url":null,"abstract":"In this paper, we propose an efficient algorithm for facial image recognition using multiple histogram features from spatial and frequency domains, respectively. In spatial domain, we utilize Local Binary Pattern (LBP) histogram due to its excellent robustness and strong discriminative power. In frequency domain, we utilize two types of histogram named binary vector quantization (BVQ) histogram and energy histogram extracted from low-frequency DCT domain. The former histogram feature is essential for utilizing the phase information of DCT coefficients by applying binary vector quantization (BVQ) on DCT coefficient blocks. The latter is energy histogram which can be considered to add magnitude information of DCT coefficients. These two histograms then contain both phase and magnitude information of a DCT transformed facial image. These 3 types of histograms described above, which contain both spatial and frequency domain information of a facial image, are utilized as a very effective personal feature. Publicly available AT&T database is used for the evaluation of our proposed algorithm, which is consisted of 40 subjects with 10 images per subject containing variations in lighting, posing, and expressions. Experimental results demonstrated that face recognition using multiple histogram features can achieve higher recognition rate.","PeriodicalId":403704,"journal":{"name":"2016 12th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121705237","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: 4

Numerical Stability Analysis of the Centered Log-Ratio Transformation 中心对数比变换的数值稳定性分析

2016 12th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS) Pub Date : 1900-01-01 DOI: 10.1109/SITIS.2016.119

A. Galletti, A. Maratea

引用次数: 5

Algorithm Design in Leaf Surface Separation by Degree in HSV Color Model and Estimation of Leaf Area by Linear Regression HSV颜色模型中叶面按度分离算法设计及线性回归估计叶面积

2016 12th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS) Pub Date : 1900-01-01 DOI: 10.1109/SITIS.2016.104

Narumol Chumuang, Sattarpoom Thaiparnit, M. Ketcham

引用次数: 20

Classification of Indoor Actions through Deep Neural Networks 基于深度神经网络的室内动作分类

2016 12th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS) Pub Date : 1900-01-01 DOI: 10.1109/SITIS.2016.22

Filippo Vella, A. Augello, U. Maniscalco, Vincenzo Bentivenga, S. Gaglio

引用次数: 3

A Hierarchical Scale-and-Stretch Approach for Image Retargeting 图像重定位的分级缩放和拉伸方法

2016 12th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS) Pub Date : 1900-01-01 DOI: 10.1109/SITIS.2016.11

Stavros Papadopoulos, A. Drosou, D. Tzovaras

{"title":"A Hierarchical Scale-and-Stretch Approach for Image Retargeting","authors":"Stavros Papadopoulos, A. Drosou, D. Tzovaras","doi":"10.1109/SITIS.2016.11","DOIUrl":"https://doi.org/10.1109/SITIS.2016.11","url":null,"abstract":"Automated image retargeting techniques are becoming important with the proliferation of different display units, such as cell phones, notebooks, televisions etc. Scale-and-stretch techniques have been successfully used for resizing images into different aspect ratios, while also preserving the most prominent visual features. The main idea of scale-and-stretch techniques is to utilize a single grid with predefined resolution, and map onto it the single significance map. The problem of image resizing is subsequently formulated as an optimization problem, which determines the optimal deformation for every local region of the image based on their underling significance. The use, however, of a single grid with specific distribution is not robust with respect to the size of the significant regions that exist in the image. In this respect, this paper extents the scale-and-stretch techniques with the use of a hierarchical grid that incorporates the significance maps of different grid resolutions in a single grid. The proposed hierarchical approach surpasses current methods and manages to efficiently identify significant regions and achieve better results.","PeriodicalId":403704,"journal":{"name":"2016 12th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122770543","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

A Method to Validate the Insertion of a New Concept in an Ontology 一种在本体中插入新概念的验证方法

2016 12th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS) Pub Date : 1900-01-01 DOI: 10.1109/SITIS.2016.52

Aly Ngoné Ngom, Papa Fary Diallo, Fatou Kamara-Sangaré, Moussa Lo

{"title":"A Method to Validate the Insertion of a New Concept in an Ontology","authors":"Aly Ngoné Ngom, Papa Fary Diallo, Fatou Kamara-Sangaré, Moussa Lo","doi":"10.1109/SITIS.2016.52","DOIUrl":"https://doi.org/10.1109/SITIS.2016.52","url":null,"abstract":"This paper presents a method to validate the insertion of a new concept in an ontology. This method is based on our previous works which add new concepts in a basic ontology using a general ontology (genaral ontology contains all the concepts of the basic ontology). To verify the semantic relevance of an ontology, we have proposed a method with three steps. First, we have found the neighborhood of the concept C in the basic ontology Ob and we store their semantic similarity values in a stack. The neighbourhood represents the concepts which are more similar to C in Ob. Secondly, we have assessed in the general ontology Og the semantic similarity between C and its neighbourhood found in the first step. Finally, we have evaluated the correlation between values found in the previous steps. We have considered the basic ontology as ontology with which we work and the general ontology as ontology used to align concepts with the basic ontology. The result obtained thanks to the method is a validated ontology after an update by adding a new concept. To illustrate our method, we have used the whole WordNet as the reference ontology and a branch of WordNet as basic ontology.","PeriodicalId":403704,"journal":{"name":"2016 12th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128352498","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