35th IEEE Applied Imagery and Pattern Recognition Workshop (AIPR'06)最新文献_第3页

Nonlinear 3D and 2D Transforms for Image Processing and Surveillance 用于图像处理和监控的非线性三维和二维变换

35th IEEE Applied Imagery and Pattern Recognition Workshop (AIPR'06) Pub Date : 2006-10-11 DOI: 10.1109/AIPR.2006.28

Y. Tirat-Gefen

引用次数: 1

An Automatic Target Classifier using Model Based Image Processing 基于模型图像处理的自动目标分类器

35th IEEE Applied Imagery and Pattern Recognition Workshop (AIPR'06) Pub Date : 2006-10-11 DOI: 10.1109/AIPR.2006.12

D. Haanpaa, G. Beach, C. Cohen

引用次数: 0

Semi-automated 3-D Building Extraction from Stereo Imagery 基于立体图像的半自动化三维建筑提取

35th IEEE Applied Imagery and Pattern Recognition Workshop (AIPR'06) Pub Date : 2006-10-11 DOI: 10.1109/AIPR.2006.36

S. Lee, K. Price, R. Nevatia, T. Heinze, J. Irvine

引用次数: 2

Some Unmixing Problems and Algorithms in Spectroscopy and Hyperspectral Imaging 光谱学和高光谱成像中的若干解混问题及算法

35th IEEE Applied Imagery and Pattern Recognition Workshop (AIPR'06) Pub Date : 2006-10-11 DOI: 10.1109/AIPR.2006.37

M. Berman

引用次数: 8

Cramer-Rao Lower Bound Calculations for Registration of Linearly-Filtered Images 线性滤波图像配准的Cramer-Rao下界计算

35th IEEE Applied Imagery and Pattern Recognition Workshop (AIPR'06) Pub Date : 2006-10-11 DOI: 10.1109/AIPR.2006.19

D. Tyler

引用次数: 1

Automatic Alignment of Color Imagery onto 3D Laser Radar Data 彩色图像与三维激光雷达数据的自动对齐

35th IEEE Applied Imagery and Pattern Recognition Workshop (AIPR'06) Pub Date : 2006-10-11 DOI: 10.1109/AIPR.2006.16

A. Vasile, Frederick R. Waugh, Daniel Greisokh, R. Heinrichs

{"title":"Automatic Alignment of Color Imagery onto 3D Laser Radar Data","authors":"A. Vasile, Frederick R. Waugh, Daniel Greisokh, R. Heinrichs","doi":"10.1109/AIPR.2006.16","DOIUrl":"https://doi.org/10.1109/AIPR.2006.16","url":null,"abstract":"We present an algorithm for the automatic fusion of city-sized, 2D color imagery to 3D laser radar imagery collected from distinct airborne platforms at different times. Our approach is to derive pseudo-intensity images from ladar imagery and to align these with color imagery using conventional 2D registration algorithms. To construct a pseudo-intensity image, the algorithm uses the color imagery's time of day and location to predict shadows in the 3D image, then determines ambient and sun lighting conditions by histogram matching the 3D-derived shadowed and non-shadowed regions to their 2D counterparts. A projection matrix is computed to bring the pseudo- image into 2D image coordinates, resulting in an initial alignment of the imagery to within 200 meters. Finally, the 2D intensity image and 3D generated pseudo-intensity image are registered using a modified normalized correlation algorithm to solve for rotation, translation, scale and lens distortion, resulting in a fused data set that is aligned to within 1 meter. Applications of the presented work include the areas of augmented reality and scene interpretation for persistent surveillance in heavily cluttered and occluded environments.","PeriodicalId":375571,"journal":{"name":"35th IEEE Applied Imagery and Pattern Recognition Workshop (AIPR'06)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116217287","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: 13

Automatic Identification of Shot Body Region from Clinical Photographies 临床影像中射击体区域的自动识别

35th IEEE Applied Imagery and Pattern Recognition Workshop (AIPR'06) Pub Date : 2006-10-11 DOI: 10.1109/AIPR.2006.17

H. Iyatomi, H. Oka, Masaru Tanaka, K. Ogawa

{"title":"Automatic Identification of Shot Body Region from Clinical Photographies","authors":"H. Iyatomi, H. Oka, Masaru Tanaka, K. Ogawa","doi":"10.1109/AIPR.2006.17","DOIUrl":"https://doi.org/10.1109/AIPR.2006.17","url":null,"abstract":"Administration of clinical photographs taken by commonly used digital camera often requires troublesome manual operation. In this paper, we made a prototype scheme of automatic photographed area identification from clinical images to help or reduce administration task. A total of 8047 clinical photographs taken in department of dermatology, Keio University Hospital, were classified into 11 categories; head, hair, upper limb, lower limb, trunk, palm, sole, back of hand, back of foot, finger & detent and genital; to meet request by several dermatologists and we developed separate linear classifiers for each body region. The developed classifiers achieved an 82.8% in sensitivity (SE) and an 82.0% of specificity (SP) in average. In addition, integration of these classifiers with consideration of the feature space of each body region improved SP of 2.3% and precision (PR) of 3.0% at a maximum when the classification threshold was set to around 75% in SE. The proposed scheme requires only photographs to identify the photographed area and therefore it can be easily applied for DICOM (digital image and communication in medicine) system that is commonly used in clinical practice or other medical database systems.","PeriodicalId":375571,"journal":{"name":"35th IEEE Applied Imagery and Pattern Recognition Workshop (AIPR'06)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126706879","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

Advanced Techniques for Watershed Visualization 流域可视化的高级技术

35th IEEE Applied Imagery and Pattern Recognition Workshop (AIPR'06) Pub Date : 2006-10-11 DOI: 10.1109/AIPR.2006.10

V. J. Alarcon, C. O'Hara

引用次数: 3

Validation Techniques for Image-Based Simulations 基于图像的仿真验证技术

35th IEEE Applied Imagery and Pattern Recognition Workshop (AIPR'06) Pub Date : 2006-10-11 DOI: 10.1109/AIPR.2006.39

D. Fraedrich

引用次数: 1

Robust Adjusted Likelihood Function for Image Analysis 图像分析的鲁棒调整似然函数

35th IEEE Applied Imagery and Pattern Recognition Workshop (AIPR'06) Pub Date : 2006-10-11 DOI: 10.1109/AIPR.2006.34

Rong Duan, Wei Jiang, H. Man

{"title":"Robust Adjusted Likelihood Function for Image Analysis","authors":"Rong Duan, Wei Jiang, H. Man","doi":"10.1109/AIPR.2006.34","DOIUrl":"https://doi.org/10.1109/AIPR.2006.34","url":null,"abstract":"Model misspecification has been a major concern in practical model based image analysis. The underlying assumptions of generative processes usually can not exactly describe real-world data samples, which renders the maximum likelihood estimation (MLE) and the Bayesian decision methods unreliable. In this work we study a robust adjusted likelihood (RAL) function that can improve image classification performance under misspecified models. The RAL is calculated by raising the conventional likelihood function to a positive power and multiplying it with a scaling factor. Similar to model parameter estimation, these two new RAL parameters, i.e. the power and the scaling factor, are estimated from the training data using minimum error rate method. In two-category classification case, this RAL is equivalent to a linear discriminant function in log-likelihood space. To demonstrate the effectiveness of this RAL, we first simulate a model misspecification scenario, in which two Rayleigh sources are misspecified as Gaussian distributions. The Gaussian parameters and the RAL parameters are estimated accordingly from the training data, and the two RAL parameters are studied separately. The simulation results show that the Bayes decisions based on maximum-RAL yield higher classification accuracy than the decisions based on conventional maximum-likelihood. We further apply the RAL in automatic target recognition (ATR) of SAR images. Two target classes, i.e. t72 and bmp2, from MSTAR SAR target dataset are used in this study. The target signatures are modeled using Gaussian mixture models (GMMs) with five mixtures for each class. Image classification results again demonstrate a clear advantage of the proposed approach.","PeriodicalId":375571,"journal":{"name":"35th IEEE Applied Imagery and Pattern Recognition Workshop (AIPR'06)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127192698","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