2014 IEEE Applied Imagery Pattern Recognition Workshop (AIPR)最新文献_第5页

Accurate coverage summarization of UAV videos 无人机视频准确覆盖汇总

2014 IEEE Applied Imagery Pattern Recognition Workshop (AIPR) Pub Date : 2014-10-01 DOI: 10.1109/AIPR.2014.7041923

Chung-Ching Lin, Sharath Pankanti, John R. Smith

{"title":"Accurate coverage summarization of UAV videos","authors":"Chung-Ching Lin, Sharath Pankanti, John R. Smith","doi":"10.1109/AIPR.2014.7041923","DOIUrl":"https://doi.org/10.1109/AIPR.2014.7041923","url":null,"abstract":"A predominant fraction of UAV videos are never watched or analyzed and there is growing interest in having a summary view of the UAV videos for obtaining a better overall perspective of the visual content. Real time summarization of the UAV video events is also important from tactical perspective. Our research focuses on developing resilient algorithms for summarizing videos that can be efficiently processed either onboard or offline. Our previous work [2] on the video summarization has focused on the event summarization. More recently, we have investigated the challenges in providing the coverage summarization of the video content from UAV videos. Different from the traditional coverage summarization taking SfM approach (e.g., [7]) on SIFT-based [14] feature points, there are several additional challenges including jitter, low resolution, contrast, lack of salient features in UAV videos. We propose a novel correspondence algorithm that exploits the 3D context that can potentially alleviate the correspondence ambiguity. Our results on VIRAT dataset shows that our algorithm can find many correct correspondences in low resolution imagery while avoiding many false positives from the traditional algorithms.","PeriodicalId":210982,"journal":{"name":"2014 IEEE Applied Imagery Pattern Recognition Workshop (AIPR)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2014-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127526508","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

High dynamic range (HDR) video processing for the exploitation of high bit-depth sensors in human-monitored surveillance 高动态范围(HDR)视频处理技术是高位深传感器在人类监控中的应用

2014 IEEE Applied Imagery Pattern Recognition Workshop (AIPR) Pub Date : 2014-10-01 DOI: 10.1109/AIPR.2014.7041912

D. Natale, Matthew S. Baran, R. Tutwiler

{"title":"High dynamic range (HDR) video processing for the exploitation of high bit-depth sensors in human-monitored surveillance","authors":"D. Natale, Matthew S. Baran, R. Tutwiler","doi":"10.1109/AIPR.2014.7041912","DOIUrl":"https://doi.org/10.1109/AIPR.2014.7041912","url":null,"abstract":"High bit-depth video data is becoming more common in imaging and remote sensing because higher bit-depth cameras are becoming more affordable. Displays often represent images in lower bit-depths, and human vision is not able to completely exploit this additional information in its native form. These problems are addressed with High Dynamic Range (HDR) tone mapping, which nonlinearly maps lightness levels from a high bit-depth image into a lower bit-depth representation in a way that attempts to retain and accentuate the maximum amount of useful information therein. We have adapted the well-known Contrast Limited Adaptive Histogram Equalization (CLAHE) algorithm into the application of HDR video tone mapping by using time-adaptive local histogram transformations. In addition to lightness contrast, we use the transformations in the L*a*b* color space to amplify color contrast in the video stream. The transformed HDR video data maintains important details in local contrast while maintaining relative lightness levels locally through time. Our results show that time-adapted HDR tone mapping methods can be used in real-time video processing to store and display HDR data in low bit-depth formats with less loss of useful information compared to simple truncation.","PeriodicalId":210982,"journal":{"name":"2014 IEEE Applied Imagery Pattern Recognition Workshop (AIPR)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2014-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114271401","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

Evaluating the Lidar/HSI direct method for physics-based scene modeling 评估基于物理场景建模的Lidar/HSI直接方法

2014 IEEE Applied Imagery Pattern Recognition Workshop (AIPR) Pub Date : 2014-10-01 DOI: 10.1109/AIPR.2014.7041906

Ryan N. Givens, K. Walli, M. Eismann

{"title":"Evaluating the Lidar/HSI direct method for physics-based scene modeling","authors":"Ryan N. Givens, K. Walli, M. Eismann","doi":"10.1109/AIPR.2014.7041906","DOIUrl":"https://doi.org/10.1109/AIPR.2014.7041906","url":null,"abstract":"Recent work has been able to automate the process of generating three-dimensional, spectrally attributed scenes for use in physics-based modeling software using the Lidar/Hyperspectral Direct (LHD) method. The LHD method autonomously generates three-dimensional Digital Imaging and Remote Sensing Image Generation (DIRSIG) scenes from input high-resolution imagery, lidar data, and hyperspectral imagery and has been shown to do this successfully using both modeled and real datasets. While the output scenes look realistic and appear to match the input scenes under qualitative comparisons, a more quantitative approach is needed to evaluate the full utility of these autonomously generated scenes. This paper seeks to improve the evaluation of the spatial and spectral accuracy of autonomously generated three-dimensional scenes using the DIRSIG model. Two scenes are presented for this evaluation. The first is generated from a modeled dataset and the second is generated using data collected over a real-world site. DIRSIG-generated synthetic imagery over the recreated scenes are then compared to the original input imagery to evaluate how well the recreated scenes match the original scenes in spatial and spectral accuracy and to determine the ability of the recreated scenes to produce useful outputs for algorithm development.","PeriodicalId":210982,"journal":{"name":"2014 IEEE Applied Imagery Pattern Recognition Workshop (AIPR)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2014-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116467454","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

Adaptive automatic object recognition in single and multi-modal sensor data 单模态和多模态传感器数据的自适应自动目标识别

2014 IEEE Applied Imagery Pattern Recognition Workshop (AIPR) Pub Date : 2014-10-01 DOI: 10.1109/AIPR.2014.7041915

T. Khuon, R. Rand

{"title":"Adaptive automatic object recognition in single and multi-modal sensor data","authors":"T. Khuon, R. Rand","doi":"10.1109/AIPR.2014.7041915","DOIUrl":"https://doi.org/10.1109/AIPR.2014.7041915","url":null,"abstract":"For single-modal data, object recognition and classification in a 3D point cloud is a non-trivial process due to the nature of the data collected from a sensor system where the signal can be corrupted by noise from the environment, electronic system, A/D converter, etc. Therefore, an adaptive system with a specific desired tolerance is required to perform classification and recognition optimally. The feature-based pattern recognition algorithm described below, is generalized for solving a particular global problem with minimal change. Since for the given class set, a feature set must be extracted accordingly. For instance, man-made urban object classification, rural and natural objects, and human organ classification would require different and distinct feature sets. This study is to compare the adaptive automatic object recognition in single sensor and the distributed adaptive pattern recognition in multi-sensor fusion. The similarity in automatic object recognition between single-sensor and multi-sensor fusion is the ability to learn from experiences and decide on a given pattern. Their main difference is that the sensor fusion makes a decision from the decisions of all sensors whereas the single sensor requires a feature extraction for a decision.","PeriodicalId":210982,"journal":{"name":"2014 IEEE Applied Imagery Pattern Recognition Workshop (AIPR)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2014-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122238144","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

Change detection and classification of land cover in multispectral satellite imagery using clustering of sparse approximations (CoSA) over learned feature dictionaries 基于稀疏逼近聚类的多光谱卫星影像土地覆盖变化检测与分类

2014 IEEE Applied Imagery Pattern Recognition Workshop (AIPR) Pub Date : 2014-10-01 DOI: 10.1109/AIPR.2014.7041921

D. Moody, S. Brumby, J. Rowland, G. Altmann, Amy E. Larson

{"title":"Change detection and classification of land cover in multispectral satellite imagery using clustering of sparse approximations (CoSA) over learned feature dictionaries","authors":"D. Moody, S. Brumby, J. Rowland, G. Altmann, Amy E. Larson","doi":"10.1109/AIPR.2014.7041921","DOIUrl":"https://doi.org/10.1109/AIPR.2014.7041921","url":null,"abstract":"Neuromimetic machine vision and pattern recognition algorithms are of great interest for landscape characterization and change detection in satellite imagery in support of global climate change science and modeling. We present results from an ongoing effort to extend machine vision methods to the environmental sciences, using adaptive sparse signal processing combined with machine learning. A Hebbian learning rule is used to build multispectral, multiresolution dictionaries from regional satellite normalized band difference index data. Land cover labels are automatically generated via our CoSA algorithm: Clustering of Sparse Approximations, using a clustering distance metric that combines spectral and spatial textural characteristics to help separate geologic, vegetative, and hydrologie features. We demonstrate our method on example Worldview-2 satellite images of an Arctic region, and use CoSA labels to detect seasonal surface changes. Our results suggest that neuroscience-based models are a promising approach to practical pattern recognition and change detection problems in remote sensing.","PeriodicalId":210982,"journal":{"name":"2014 IEEE Applied Imagery Pattern Recognition Workshop (AIPR)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2014-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128407586","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: 3

Against conventional wisdom: Longitudinal inference for pattern recognition in remote sensing 反对传统智慧:遥感模式识别的纵向推理

2014 IEEE Applied Imagery Pattern Recognition Workshop (AIPR) Pub Date : 2014-10-01 DOI: 10.1109/AIPR.2014.7041932

D. Rosario, Christoph Borel-Donohue, J. Romano

{"title":"Against conventional wisdom: Longitudinal inference for pattern recognition in remote sensing","authors":"D. Rosario, Christoph Borel-Donohue, J. Romano","doi":"10.1109/AIPR.2014.7041932","DOIUrl":"https://doi.org/10.1109/AIPR.2014.7041932","url":null,"abstract":"In response to Democratization of Imagery, a recent leading theme in the scientific community, we discuss a persistent imaging experiment dataset, which is being considered for public release in a foreseeable future, and present our observations analyzing a subset of the dataset. The experiment is a long-term collaborative effort among the Army Research Laboratory, Army Armament RDEC, and Air Force Institute of Technology that focuses on the collection and exploitation of longwave infrared (LWIR) hyperspectral and polarimetric imagery. In this paper, we emphasize the inherent challenges associated with using remotely sensed LWIR hyperspectral imagery for material recognition, and argue that the idealized data assumptions often made by the state of the art methods are too restrictive for real operational scenarios. We treat LWIR hyperspectral imagery for the first time as Longitudinal Data and aim at proposing a more realistic framework for material recognition as a function of spectral evolution over time. The defining characteristic of a longitudinal study is that objects are measured repeatedly through time and, as a result, data are dependent. This is in contrast to cross-sectional studies in which the outcomes of a specific event are observed by randomly sampling from a large population of relevant objects, where data are assumed independent. The scientific community generally assumes the problem of object recognition to be cross-sectional. We argue that, as data evolve over a full diurnal cycle, pattern recognition problems are longitudinal in nature and that by applying this knowledge it may lead to better algorithms.","PeriodicalId":210982,"journal":{"name":"2014 IEEE Applied Imagery Pattern Recognition Workshop (AIPR)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2014-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127878526","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

Human activity detection using sparse representation 基于稀疏表示的人类活动检测

2014 IEEE Applied Imagery Pattern Recognition Workshop (AIPR) Pub Date : 2014-10-01 DOI: 10.1109/AIPR.2014.7041933

D. Killedar, S. Sasi

引用次数: 2

Performance benefits of sub-diffraction sized pixels in imaging sensors 成像传感器中亚衍射尺寸像素的性能优势

2014 IEEE Applied Imagery Pattern Recognition Workshop (AIPR) Pub Date : 2014-05-28 DOI: 10.1117/12.2053443

J. Caulfield, J. Curzan, N. Dhar

引用次数: 4

Physical modeling of nuclear detonations in DIRSIG DIRSIG核爆物理模拟

2014 IEEE Applied Imagery Pattern Recognition Workshop (AIPR) Pub Date : 2013-10-01 DOI: 10.1109/AIPR.2014.7041907

Ashley E. Green, T. Peery, Robert C. Slaughter, J. McClory

引用次数: 0