2012 IEEE Applied Imagery Pattern Recognition Workshop (AIPR)最新文献

Video track screening using syntactic activity-based methods 基于句法活动的视频轨迹筛选方法

2012 IEEE Applied Imagery Pattern Recognition Workshop (AIPR) Pub Date : 2012-10-09 DOI: 10.1109/AIPR.2012.6528201

Richard J. Wood, C. McPherson, J. Irvine

{"title":"Video track screening using syntactic activity-based methods","authors":"Richard J. Wood, C. McPherson, J. Irvine","doi":"10.1109/AIPR.2012.6528201","DOIUrl":"https://doi.org/10.1109/AIPR.2012.6528201","url":null,"abstract":"The adversary in current threat situations can no longer be identified by what they are, but by what they are doing. This has lead to a large increase in the use of video surveillance systems for security and defense applications. With the quantity of video surveillance at the disposal of organizations responsible for protecting military and civilian lives come the issues regarding storage and screening of this data. This paper defines a screening and classification method based upon activity-based screening and recognition to provide that filtering mechanism. Activity recognition from video for such applications seeks to develop semi-automated screening of video based upon the recognition of activities of interest rather than merely the presence of specific persons or vehicle classes developed for the Cold War problem of “Find the T72 Tank.” This paper examines the approach to activity recognition, consisting of heuristic, semantic, and syntactic methods, based upon tokens derived from the video as applied to relevant scenarios involving behavior as captured from entity tracks. The proposed architecture discussed herein uses a multi-level approach that divides the problem into three or more tiers of recognition, each employing different techniques according to their appropriateness to strengths at each tier using heuristics, syntactic recognition, and Hidden Markov Model's of token strings to form higher level interpretations. Performance of activity-based screening and recognition as applied to example scenarios has been demonstrated to reduce the quantity of tracks (analogous to video frames) by orders of magnitude with little loss of relevant information.","PeriodicalId":406942,"journal":{"name":"2012 IEEE Applied Imagery Pattern Recognition Workshop (AIPR)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124428301","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

Novel features for silhouette based gait recognition systems 基于轮廓的步态识别系统的新特征

2012 IEEE Applied Imagery Pattern Recognition Workshop (AIPR) Pub Date : 2012-10-09 DOI: 10.1109/AIPR.2012.6528205

Arihant Kochhar, Divyesh Gupta, M. Hanmandlu, S. Vasikarla

引用次数: 0

Path-based constraints for accurate scene reconstruction from aerial video 航拍视频精确场景重建的路径约束

2012 IEEE Applied Imagery Pattern Recognition Workshop (AIPR) Pub Date : 2012-10-09 DOI: 10.1109/AIPR.2012.6528221

Mauricio Hess-Flores, M. Duchaineau, K. Joy

{"title":"Path-based constraints for accurate scene reconstruction from aerial video","authors":"Mauricio Hess-Flores, M. Duchaineau, K. Joy","doi":"10.1109/AIPR.2012.6528221","DOIUrl":"https://doi.org/10.1109/AIPR.2012.6528221","url":null,"abstract":"This paper discusses the constraints imposed by the path of a moving camera in multi-view sequential scene reconstruction scenarios such as in aerial video, which allow for an efficient detection and correction of inaccuracies in the feature tracking and structure computation processes. The main insight is that for short, planar segments of a continuous camera trajectory, parallax movement corresponding to a viewed scene point should ideally form a scaled and translated version of this trajectory when projected onto a parallel plane. Two inter-camera and intra-camera constraints arise, which create a prediction of where all feature tracks should be located given the consensus information of all accurate tracks and cameras, which allows for the detection and correction of inaccurate feature tracks, as well as a very simple update of scene structure. This procedure differs from classical approaches such as factorization and RANSAC. In both aerial video and turntable sequences, the use of such constraints was proven to correct outlier tracks, detect and correct tracking drift, allow for a simple updating of scene structure, and improve bundle adjustment convergence.","PeriodicalId":406942,"journal":{"name":"2012 IEEE Applied Imagery Pattern Recognition Workshop (AIPR)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132901454","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

Action classification in polarimetric infrared imagery via diffusion maps 利用扩散图进行偏振红外图像的动作分类

2012 IEEE Applied Imagery Pattern Recognition Workshop (AIPR) Pub Date : 2012-10-09 DOI: 10.1109/AIPR.2012.6528218

W. Sakla

{"title":"Action classification in polarimetric infrared imagery via diffusion maps","authors":"W. Sakla","doi":"10.1109/AIPR.2012.6528218","DOIUrl":"https://doi.org/10.1109/AIPR.2012.6528218","url":null,"abstract":"This work explores the application of a nonlinear dimensionality reduction technique known as diffusion maps for performing action classification in polarimetric infrared video sequences. The diffusion maps algorithm has been used successfully in a variety of applications involving the extraction of low-dimensional embeddings from high-dimensional data. Our dataset is composed of eight subjects each performing three basic actions: walking, walking while carrying an object in one hand, and running. The actions were captured with a polarized microgrid sensor operating in the longwave portion of the electromagnetic (EM) spectrum with a temporal resolution of 24 Hz, yielding the Stokes traditional intensity (S0) and linearly polarized (S1, S2) components of data. Our work includes the use of diffusion maps as an unsupervised dimensionality reduction step prior to action classification with three conventional classifiers: the linear perceptron algorithm, the k nearest neighbors (KNN) algorithm, and the kernel-based support vector machine (SVM). We present classification results using both the low-dimensional principal components via PCA and the low-dimensional diffusion map embedding coordinates of the data for each class. Results indicate that the diffusion map lower-dimensional embeddings provide a salient feature space for action classification, yielding an increase of overall classification accuracy by ~40% compared to PCA. Additionally, we examine the utility that the polarimetric sensor may provide by concurrently performing these analyses in the polarimetric feature spaces.","PeriodicalId":406942,"journal":{"name":"2012 IEEE Applied Imagery Pattern Recognition Workshop (AIPR)","volume":"133 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123165454","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: 2

Centralized multi-scale singular value decomposition for feature construction in LIDAR image classification problems 集中多尺度奇异值分解在激光雷达图像分类问题中的特征构建

2012 IEEE Applied Imagery Pattern Recognition Workshop (AIPR) Pub Date : 2012-10-09 DOI: 10.1109/AIPR.2012.6528195

D. Bassu, R. Izmailov, A. McIntosh, Linda Ness, D. Shallcross

{"title":"Centralized multi-scale singular value decomposition for feature construction in LIDAR image classification problems","authors":"D. Bassu, R. Izmailov, A. McIntosh, Linda Ness, D. Shallcross","doi":"10.1109/AIPR.2012.6528195","DOIUrl":"https://doi.org/10.1109/AIPR.2012.6528195","url":null,"abstract":"Creation and selection of relevant features for machine learning applications (including image classification) is typically a process requiring significant involvement of domain knowledge. It is thus desirable to cover at least part of that process with semi-automated techniques capable of discovering and visualizing those geometric characteristics of images that are potentially relevant to the classification objective. In this work, we propose to utilize multi-scale singular value decomposition (MSVD) along with approximate nearest neighbors algorithm: both have been recently realized using the randomized approach, and can be efficiently run on large, high-dimensional datasets (sparse or dense). We apply this technique to create a multi-scale view of every point in a publicly available set of LIDAR data of riparian images, with classification objective being separating ground from vegetation. We perform “centralized MSVD” for every point and its neighborhood generated by an approximate nearest neighbor algorithm. After completion of this procedure, the original set of 3-dimensional data is augmented by 36 dimensions generated by MSVD (in three different scales), which is then processed using a novel discretization pre-processing method and the SVM classification algorithm with RBF kernel. The result is two times better that the one previously obtained (in terms of its classification error level). The generic nature of the MSVD mechanism and standard mechanisms used for classification (SVM) suggest a wider utility of the proposed approach for other problems as well.","PeriodicalId":406942,"journal":{"name":"2012 IEEE Applied Imagery Pattern Recognition Workshop (AIPR)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128827074","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: 14

Unsupervised land cover classification in multispectral imagery with sparse representations on learned dictionaries 基于学习字典稀疏表示的多光谱图像无监督土地覆盖分类

2012 IEEE Applied Imagery Pattern Recognition Workshop (AIPR) Pub Date : 2012-10-09 DOI: 10.1109/AIPR.2012.6528190

D. Moody, S. Brumby, J. Rowland, C. Gangodagamage

{"title":"Unsupervised land cover classification in multispectral imagery with sparse representations on learned dictionaries","authors":"D. Moody, S. Brumby, J. Rowland, C. Gangodagamage","doi":"10.1109/AIPR.2012.6528190","DOIUrl":"https://doi.org/10.1109/AIPR.2012.6528190","url":null,"abstract":"Techniques for automated feature extraction, including neuroscience-inspired machine vision, are of current interest in the areas of climate change monitoring, change detection, and Land Use/Land Cover classification using satellite image data. We describe an approach for automatic classification of land cover in multispectral satellite imagery of the Arctic using sparse representations over learned dictionaries. We demonstrate our method using DigitalGlobe Worldview-2 visible/near infrared high spatial resolution imagery. We use a Hebbian learning rule to build spectral-textural dictionaries that are adapted to the data. We learn our dictionaries from millions of overlapping image patches and then use a pursuit search to generate sparse classification features. These sparse representations of pixel patches are used to perform unsupervised k-means clustering into land-cover categories. Our approach combines spectral and spatial textural characteristics to detect geologic, vegetative, and hydrologic features. We compare our technique to standard remote sensing classification algorithms. Our results suggest that neuroscience-based models are a promising approach to practical pattern recognition problems in remote sensing, even for datasets using spectral bands not found in natural visual systems.","PeriodicalId":406942,"journal":{"name":"2012 IEEE Applied Imagery Pattern Recognition Workshop (AIPR)","volume":"40 8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124918705","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: 11

Spatial oversampling in imaging sensors: Benefits in sensitivity and detection 成像传感器的空间过采样:在灵敏度和检测方面的好处

2012 IEEE Applied Imagery Pattern Recognition Workshop (AIPR) Pub Date : 2012-10-09 DOI: 10.1117/12.2019328

J. Caulfield, Jerry A. Wilson, N. Dhar

引用次数: 9

Deriving economic and social indicators from imagery 从图像中得出经济和社会指标

2012 IEEE Applied Imagery Pattern Recognition Workshop (AIPR) Pub Date : 2012-10-09 DOI: 10.1109/AIPR.2012.6528213

J. Irvine, J. Lepanto, J. Regan, M. Young

{"title":"Deriving economic and social indicators from imagery","authors":"J. Irvine, J. Lepanto, J. Regan, M. Young","doi":"10.1109/AIPR.2012.6528213","DOIUrl":"https://doi.org/10.1109/AIPR.2012.6528213","url":null,"abstract":"The application of remote sensing to the social sciences is an emerging research area. People's behavior and values shape the environment in which they live. Similarly, values and behaviors are influenced by the environment. This study explores the relationship between features observable in overhead imagery and direct measurements of attitudes obtained through surveys. We focus on three topic areas: (1) Income and Economic Development (2) Centrality and Decision Authority (3) Social Capital Using commercial satellite imagery data from rural Afghanistan, we present an exploration of the direct and indirect indicators derived from the imagery. We demonstrate a methodology for extracting relevant measures from the imagery, using a combination of human-guided and automated methods. These imagery observables indicate characteristics of the villages which will be compared to survey data in future modeling work. Preliminary survey modeling, based on data from sub-Saharan Africa, suggests that modeling of the Afghan data will also demonstrate a relationship between remote sensing data and survey-based measures of economic and social phenomena. We conclude with a discussion of the next steps, which include extensions to new regions of the world.","PeriodicalId":406942,"journal":{"name":"2012 IEEE Applied Imagery Pattern Recognition Workshop (AIPR)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129708468","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: 5

Toward a common lexicon for exploiting activity data 开发活动数据的通用词汇

2012 IEEE Applied Imagery Pattern Recognition Workshop (AIPR) Pub Date : 2012-10-09 DOI: 10.1109/AIPR.2012.6528217

S. Israel

引用次数: 1

Simulating vision through time: Hierarchical, sparse models of visual cortex for motion imagery 通过时间模拟视觉:运动图像视觉皮层的分层稀疏模型

2012 IEEE Applied Imagery Pattern Recognition Workshop (AIPR) Pub Date : 2012-10-09 DOI: 10.1109/AIPR.2012.6528200

A. Galbraith, S. Brumby, R. Chartrand

{"title":"Simulating vision through time: Hierarchical, sparse models of visual cortex for motion imagery","authors":"A. Galbraith, S. Brumby, R. Chartrand","doi":"10.1109/AIPR.2012.6528200","DOIUrl":"https://doi.org/10.1109/AIPR.2012.6528200","url":null,"abstract":"Efficient pattern recognition in motion imagery has become a growing challenge as the number of video sources proliferates worldwide. Historically, automated analysis of motion imagery, such as object detection, classification and tracking, has been accomplished using hand-designed feature detectors. Though useful, these feature detectors are not easily extended to new data sets or new target categories since they are often task specific, and typically require substantial effort to design. Rather than hand-designing filters, recent advances in the field of image processing have resulted in a theoretical framework of sparse, hierarchical, learned representations that can describe video data of natural scenes at many spatial and temporal scales and many levels of object complexity. These sparse, hierarchical models learn the information content of imagery and video from the data itself and lead to state-of-the-art performance and more efficient processing. Processing efficiency is important as it allows scaling up of research to work with dataset sizes and numbers of categories approaching real-world conditions. We now describe recent work at Los Alamos National Laboratory developing hierarchical sparse learning computer vision models that can process high definition color video in real time. We present preliminary results extending our prior work on object classification in still imagery [1] to discovery of useful features at different time scales in motion imagery for detection, classification and tracking of objects.","PeriodicalId":406942,"journal":{"name":"2012 IEEE Applied Imagery Pattern Recognition Workshop (AIPR)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132854821","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