2015 IEEE Applied Imagery Pattern Recognition Workshop (AIPR)最新文献_第2页

Image reconstruction using the reconfiguration technique 利用重构技术进行图像重建

2015 IEEE Applied Imagery Pattern Recognition Workshop (AIPR) Pub Date : 2015-10-01 DOI: 10.1109/AIPR.2015.7444543

Etienne Aubin Mbe Mbock

引用次数: 0

Bandelet transformation based image registration 基于Bandelet变换的图像配准

2015 IEEE Applied Imagery Pattern Recognition Workshop (AIPR) Pub Date : 2015-10-01 DOI: 10.1109/AIPR.2015.7444530

Adam Lutz, K. Grace, Neal Messer, Soundararajan Ezekiel, Erik Blasch, M. Alford, A. Bubalo, Maria Scalzo-Cornacchia

{"title":"Bandelet transformation based image registration","authors":"Adam Lutz, K. Grace, Neal Messer, Soundararajan Ezekiel, Erik Blasch, M. Alford, A. Bubalo, Maria Scalzo-Cornacchia","doi":"10.1109/AIPR.2015.7444530","DOIUrl":"https://doi.org/10.1109/AIPR.2015.7444530","url":null,"abstract":"Perfect image registration is an unsolved challenge that has been attempted in a multitude of different ways. This paper presents an approach for single-modal, multi-view registration of aerial imagery data that uses bandelets in the preprocessing phase to extract key geometric features and limit the amount of details in the image that must be considered during the feature matching process. Applying the bandelet decomposition on both the reference and target images before feature extraction will limit the control point selection process to only those points with the most relevant geometric data. The approach uses a multi-scale approach to estimate a transformation that converges to an optimal solution as well as reduce the computation time for real-time image registration. The bandelet basis also provides for a more effective feature (e.g. corner) detection and extraction method because it determines the geometric flow and allows for shifted patches in the orthogonal direction of the geometric flow. Theoretically the bandelet results in less false positives and better detection rates than existing methods. The Bandelet-based Image Registration (BIR) method has applications in image fusion, change detection, object recognition, autonomous navigation, and target tracking.","PeriodicalId":440673,"journal":{"name":"2015 IEEE Applied Imagery Pattern Recognition Workshop (AIPR)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127945947","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

Unsupervised approach for object matching using Speeded Up Robust Features 基于加速鲁棒特征的无监督对象匹配方法

2015 IEEE Applied Imagery Pattern Recognition Workshop (AIPR) Pub Date : 2015-10-01 DOI: 10.1109/AIPR.2015.7444541

A. Vardhan, N. Verma, R. K. Sevakula, A. Salour

{"title":"Unsupervised approach for object matching using Speeded Up Robust Features","authors":"A. Vardhan, N. Verma, R. K. Sevakula, A. Salour","doi":"10.1109/AIPR.2015.7444541","DOIUrl":"https://doi.org/10.1109/AIPR.2015.7444541","url":null,"abstract":"Autonomous object counting system is of great use in retail stores, industries and also in research processes. In this paper, a Speeded Up Robust Feature (SURF) based robust algorithm for identifying, counting and locating all instances of a defined object in any image, has been proposed. The defined object is referred to as prototype and the image in which one wishes to count the prototype is referred to as scene image. The algorithm starts by detecting the interest points for SURF in both, prototype and scene images. The SURF points on prototype are first clustered using density based clustering; then SURF points in each cluster are matched with those in scene image. The SURF points in scene image that have been matched w.r.t. a single cluster, are clustered using the same clustering algorithm. Each cluster formed in scene image represents an instance of prototype object in the image. Homography transforms are further used to give exact location and span of each prototype object in the scene image. Once the span of each prototype is defined, SURF points within this span are matched with the prototype image and then Homography transform is once again applied while considering the newly matched SURF points; thus eliminating noisy detection/s of prototype. While the same process is repeated with each cluster, a novel centroid based algorithm for merging repeated detections of same prototype instance is used. Carrying the benefits of SURF and Homography transforms, the algorithm is capable of detecting all prototype instances present in scene image, irrespective of their scale and orientation. The complete algorithm has also been integrated into a desktop application, which uses camera feed to report the real time count of the prototype in the scene image.","PeriodicalId":440673,"journal":{"name":"2015 IEEE Applied Imagery Pattern Recognition Workshop (AIPR)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128140747","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: 18

Online signature verification using the entropy function 使用熵函数的在线签名验证

2015 IEEE Applied Imagery Pattern Recognition Workshop (AIPR) Pub Date : 2015-10-01 DOI: 10.1109/AIPR.2015.7444522

M. Hanmandlu, Farrukh Sayeed, S. Vasikarla

引用次数: 2

Suspicious Face Detection based on Eye and other facial features movement monitoring 基于眼睛和其他面部特征运动监测的可疑人脸检测

2015 IEEE Applied Imagery Pattern Recognition Workshop (AIPR) Pub Date : 2015-10-01 DOI: 10.1109/AIPR.2015.7444523

Chandan Tiwari, M. Hanmandlu, S. Vasikarla

{"title":"Suspicious Face Detection based on Eye and other facial features movement monitoring","authors":"Chandan Tiwari, M. Hanmandlu, S. Vasikarla","doi":"10.1109/AIPR.2015.7444523","DOIUrl":"https://doi.org/10.1109/AIPR.2015.7444523","url":null,"abstract":"Visual surveillance and security applications were never more important than now more so due to the overwhelming ever-growing threat of terrorism. Till date the large scale video surveillance systems mostly work as a passive system in which the videos are simply stored without being monitored. Such system will be useful for post event investigation. In order to make a system that is capable of real-time monitoring, we need to develop algorithms which can analyze and understand the scene that is being monitored. Generally, humans express their intention explicitly through facial expressions, speech, eye movement, and hand gesture. According to cognitive visiomotor theory, the human eye movements are rich source of information about the human intention and behavior. If we monitor the eye movement of a person, we will be able to describe him as an abnormal suspicious person or a normal person. We track his/her Eyes and based upon the eye movement in successive frames of the input videos using the Non-linear Entropy of eyes. Results of our experiments show that Non-linear Entropy of Eyes of an abnormal person is much higher than the eye's entropy of any normal person.","PeriodicalId":440673,"journal":{"name":"2015 IEEE Applied Imagery Pattern Recognition Workshop (AIPR)","volume":"755 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133846560","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: 9

Runway assessment via remote sensing 通过遥感评估跑道

2015 IEEE Applied Imagery Pattern Recognition Workshop (AIPR) Pub Date : 2015-10-01 DOI: 10.1109/AIPR.2015.7444545

Lalitha Dabbiru, Pan Wei, A. Harsh, Julie White, J. Ball, J. Aanstoos, P. Donohoe, J. Doyle, Sam Jackson, J. Newman

{"title":"Runway assessment via remote sensing","authors":"Lalitha Dabbiru, Pan Wei, A. Harsh, Julie White, J. Ball, J. Aanstoos, P. Donohoe, J. Doyle, Sam Jackson, J. Newman","doi":"10.1109/AIPR.2015.7444545","DOIUrl":"https://doi.org/10.1109/AIPR.2015.7444545","url":null,"abstract":"Airport pavements are constructed to provide adequate support for the loads and traffic volume imposed by aircrafts. One aspect of pavement evaluation is the pavement condition which is determined by the types and extent of distresses. These include cracking, rutting, weathering, and others that may affect pavement surface roughness and the potential for FOD (Foreign Object Debris). Pavement evaluations are necessary to assess the ability to safely operate aircraft on an airfield. The purpose of this study is to explore the potential use of microwave remote sensing to assess the pavement surface roughness. Radar backscatter responds to surface roughness as well as dielectric constant. The resulting changes in backscatter can convey information about the degree of cracking and surface roughness of the runway. In this study, we develop a relation between the Terrain Ruggedness Index (TRI) of the runway and radar backscatter magnitudes. Radar data from the TerraSAR-X satellite is used, along with airborne LiDAR data (30 cm spacing). Modest linear correlation was found between the vertical co-polarization channel of the radar data and TRI values computed in 5 by 5 pixel windows from the LiDAR elevation data. Over four different test areas on the runway, the coefficients of determination ranged from 0.12 to 0.46.","PeriodicalId":440673,"journal":{"name":"2015 IEEE Applied Imagery Pattern Recognition Workshop (AIPR)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124119936","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

Applying attributes to improve human activity recognition 应用属性提高人类活动识别

2015 IEEE Applied Imagery Pattern Recognition Workshop (AIPR) Pub Date : 2015-10-01 DOI: 10.1109/AIPR.2015.7444553

D. Tahmoush, Claire Bonial

{"title":"Applying attributes to improve human activity recognition","authors":"D. Tahmoush, Claire Bonial","doi":"10.1109/AIPR.2015.7444553","DOIUrl":"https://doi.org/10.1109/AIPR.2015.7444553","url":null,"abstract":"Activity and event recognition from video has utilized low-level features over higher-level text-based class attributes and ontologies because they traditionally have been more effective on small datasets. However, by including human knowledge-driven associations between actions and attributes while recognizing the lower-level attributes with their temporal relationships, we can learn a much greater set of activities as well as improve low-level feature-based algorithms by incorporating an expert knowledge ontology. In an event ontology, events can be broken down into actions, and these can be decomposed further into attributes. For example, throwing events can include throwing of stones or baseballs with the object being relocated from a hand through the air to a location of interest. The throwing can be broken down into the many physical attributes that can be used to describe the motion like BodyPartsUsed = Hands, BodyPartArticulation-Arm = OneArmRaisedOverHead, and many others. Building general attributes from video and merging them into an ontology for recognition allows significant reuse for the development of activity and event classifiers. Each activity or event classifier is composed of interacting attributes the same way sentences are composed of interacting letters to create a complete language.","PeriodicalId":440673,"journal":{"name":"2015 IEEE Applied Imagery Pattern Recognition Workshop (AIPR)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123163277","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

Machine vision algorithms for robust animal species identification 鲁棒动物物种识别的机器视觉算法

2015 IEEE Applied Imagery Pattern Recognition Workshop (AIPR) Pub Date : 2015-10-01 DOI: 10.1109/AIPR.2015.7444526

C. Cohen, D. Haanpaa, James P. Zott

{"title":"Machine vision algorithms for robust animal species identification","authors":"C. Cohen, D. Haanpaa, James P. Zott","doi":"10.1109/AIPR.2015.7444526","DOIUrl":"https://doi.org/10.1109/AIPR.2015.7444526","url":null,"abstract":"Numerous military bases have a requirement, based on the Sikes Act, to maintain the base's natural environment while still meeting military mission objectives. One method used to accomplish this is by working towards the goal of achieving habitat and species sustainability. One difficulty is that there is currently no baseline of the ecosystem. Specifically, a critical need is the detection and identification of animals on Federal and State endangered lists. For instance, the U.S. Fish and Wildlife Service lists 130 animals as either endangered or threatened, including the desert tortoise, the Mohave ground squirrel, various species of fox, jaguar, mountain beaver, and wolf. In order to even begin to form an appropriate natural environmental baseline, the location and movements of these animals must be acquired, recorded, and made available for review. To this end, in this presentation we detail technology and machine vision algorithms that can be used to: 1.) Recognize animals that are on the endangered or threatened lists, 2.) Identification of animals without the need to track them in sequential image frames, 3.) Provide continual animal census surveillance for weeks at a time in operational environments, and 4.) Record video and still-image data along with annotations for later analysis. Specifically, present an extendable architecture for species identification and identification software truthing/training, and populate this architecture with three recognition modules: a Haar Cascade classifier, a Local Binary Pattern cascade classifier, and a neural network. We also detail the results of our work, current challenges, and future approaches we are taking with our research.","PeriodicalId":440673,"journal":{"name":"2015 IEEE Applied Imagery Pattern Recognition Workshop (AIPR)","volume":"85 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133251971","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: 8

3D camera identification for enabling robotic manipulation 用于实现机器人操作的3D相机识别

2015 IEEE Applied Imagery Pattern Recognition Workshop (AIPR) Pub Date : 2015-10-01 DOI: 10.1109/AIPR.2015.7444549

G. Beach, C. Cohen, D. Haanpaa, Steven C. Rowe, Pritpaul Mahal

引用次数: 0

Hybrid sensing face detection and recognition 混合传感人脸检测与识别

2015 IEEE Applied Imagery Pattern Recognition Workshop (AIPR) Pub Date : 2015-10-01 DOI: 10.1109/AIPR.2015.7444542

Mingyuan Zhou, Haiting Lin, Jingyi Yu, S. Young

引用次数: 3