2009 IEEE Applied Imagery Pattern Recognition Workshop (AIPR 2009)最新文献

A comparison of bicubic and biquintic interpolators suitable for real-time hardware implementation 适合于实时硬件实现的双三次和双五次插值器的比较

2009 IEEE Applied Imagery Pattern Recognition Workshop (AIPR 2009) Pub Date : 2009-10-01 DOI: 10.1109/AIPR.2009.5466320

Jonathan Fry, M. Pusateri

{"title":"A comparison of bicubic and biquintic interpolators suitable for real-time hardware implementation","authors":"Jonathan Fry, M. Pusateri","doi":"10.1109/AIPR.2009.5466320","DOIUrl":"https://doi.org/10.1109/AIPR.2009.5466320","url":null,"abstract":"Digital multispectral night vision goggles incorporate both imagers and displays that often have different resolutions. While both thermal imager and micro-display technologies continue to produce larger arrays, thermal imagers still lag well behind displays and can require interpolation by a factor of 2.5 in both horizontal and vertical directions. In goggle applications, resizing the imagery streams to the size of the display must occur in real-time with minimal latency. In addition to low latency, a resizing algorithm must produce acceptable imagery, necessitating an understanding of the resized image fidelity and spatial smoothness. While both spatial and spatial frequency domain resizing techniques are available, most spatial frequency techniques require a complete frame for operation introducing unacceptable latency. Spatial domain techniques can be implemented on a neighborhood basis allowing latencies equivalent to several row clock pulses to be achieved. We have already implemented bilinear re-sampling in hardware and, while bilinear re-sampling supports moderate up-sizes with reasonable image quality, its deficiencies are apparent at interpolation ratios of two and greater. We are developing hardware implementations of both bicubic and biquintic resizing algorithms. We present the results of comparison between hardware ready versions of the bicubic and biquintic algorithms with the existing bilinear. We also discuss the hardware requirements for bicubic and biquintic compared to the existing bilinear resizing.","PeriodicalId":266025,"journal":{"name":"2009 IEEE Applied Imagery Pattern Recognition Workshop (AIPR 2009)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2009-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121338951","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

Evaluation methods for curvilinear feature extraction 曲线特征提取的评价方法

2009 IEEE Applied Imagery Pattern Recognition Workshop (AIPR 2009) Pub Date : 2009-10-01 DOI: 10.1109/AIPR.2009.5466322

Peter Doucette, Ann Martin, Chris Kavanagh, Tim McIntyre, Steven Barton, J. Grodecki, S. Malitz, Matthew Tang, J. Nolting

引用次数: 0

Advanced hyperspectral detection based on elliptically contoured distribution models and operator feedback 基于椭圆轮廓分布模型和算子反馈的高级高光谱检测

2009 IEEE Applied Imagery Pattern Recognition Workshop (AIPR 2009) Pub Date : 2009-10-01 DOI: 10.1109/AIPR.2009.5466308

A. Schaum

引用次数: 9

MRI brain image segmentation for spotting tumors using improved mountain clustering approach 基于改进山聚类方法的MRI脑图像分割

2009 IEEE Applied Imagery Pattern Recognition Workshop (AIPR 2009) Pub Date : 2009-10-01 DOI: 10.1109/AIPR.2009.5466301

N. Verma, Payal Gupta, P. Agrawal, Yan Cui

引用次数: 13

User guided visualization for target search 用户引导的可视化目标搜索

2009 IEEE Applied Imagery Pattern Recognition Workshop (AIPR 2009) Pub Date : 2009-10-01 DOI: 10.1109/AIPR.2009.5466318

J. Irvine

引用次数: 0

Biologically inspired motion detection neural network models evolved using genetic algorithms 生物启发的运动检测神经网络模型使用遗传算法进化

2009 IEEE Applied Imagery Pattern Recognition Workshop (AIPR 2009) Pub Date : 2009-10-01 DOI: 10.1109/AIPR.2009.5466326

S. Azary, P. Anderson, R. Gaborski

{"title":"Biologically inspired motion detection neural network models evolved using genetic algorithms","authors":"S. Azary, P. Anderson, R. Gaborski","doi":"10.1109/AIPR.2009.5466326","DOIUrl":"https://doi.org/10.1109/AIPR.2009.5466326","url":null,"abstract":"In this paper we describe a method to evolve biologically inspired motion detection systems utilizing artificial neural networks (ANN's). Previously, the evolution of neural networks has focused on feed-forward neural networks or networks with predefined architectures. The purpose of this paper is to present a novel method for evolving neural networks with no predefined architectures to solve various problems including motion detection models. The neural network models are evolved with genetic algorithms using an encoding that defines a functional network with no restriction on recurrence, activation function types, or the number of nodes that compose the final ANN. The genetic algorithm operates on a population of potential solutions where each potential network is represented in a chromosome. The structure of each chromosome in the population is defined with a weight matrix which allows for efficient simulation of outputs. Each chromosome is evaluated by a fitness function that scores how well the actual output of an ANN compares to the expected output. Crossovers and mutations are made with specified probabilities between population members to evolve new members of the population. After a number of iterations a near optimal network is evolved that solves the problem at hand. The approach has proven to be sufficient to create biologically realistic motion detection neural network models with results that are comparable to results obtained from the standard Reichardt model.","PeriodicalId":266025,"journal":{"name":"2009 IEEE Applied Imagery Pattern Recognition Workshop (AIPR 2009)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2009-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131292015","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

Face recognition using a hybrid model 人脸识别使用混合模型

2009 IEEE Applied Imagery Pattern Recognition Workshop (AIPR 2009) Pub Date : 2009-10-01 DOI: 10.1109/AIPR.2009.5466296

Yuheng Wang, P. Anderson, R. Gaborski

引用次数: 4

Robust object recognition using a cascade of geometric consistency filters 鲁棒目标识别使用级联的几何一致性滤波器

2009 IEEE Applied Imagery Pattern Recognition Workshop (AIPR 2009) Pub Date : 2009-10-01 DOI: 10.1109/AIPR.2009.5466299

Yuetian Xu, R. Madison

引用次数: 3

Data driven approach to estimating fire danger from satellite images and weather information 从卫星图像和天气信息估计火灾危险的数据驱动方法

2009 IEEE Applied Imagery Pattern Recognition Workshop (AIPR 2009) Pub Date : 2009-10-01 DOI: 10.1109/AIPR.2009.5466309

N. Markuzon, S. Kolitz

引用次数: 6

Biologically-inspired visible and infrared camera technology development 受生物启发的可见光和红外相机技术的发展

2009 IEEE Applied Imagery Pattern Recognition Workshop (AIPR 2009) Pub Date : 2009-10-01 DOI: 10.1109/AIPR.2009.5466298

E. Williams, M. Pusateri, J. Scott

{"title":"Biologically-inspired visible and infrared camera technology development","authors":"E. Williams, M. Pusateri, J. Scott","doi":"10.1109/AIPR.2009.5466298","DOIUrl":"https://doi.org/10.1109/AIPR.2009.5466298","url":null,"abstract":"Visible band and Infrared (IR) band camera and vision system development has been inspired by the human and animal vision systems. This paper will discuss the development of the Electro-Optical/Infrared (EO/IR) spectrum cameras from the front end optics, the detector or photon to electron convertor, preprocessing such as non-uniformity correction, automatic gain control, foveal vision processing done by the human eye, the gimbal system (human or animal eye ball and head motion), and the analog and digital paths of the data (optic nerve in humans). The computer vision algorithms (human or animal brain vision processing) will not be discussed in this paper. The Integrated Design Services in the College of Engineering at Penn State University has been developing EO/IR camera and sensor based computer vision systems for several years and combined with more than twenty years of developing imaging sensor stabilized platforms will use this imaging system development expertise to describe how the human and animal vision systems inspired the design and development of the computer based vision system. This paper will illustrate a block diagram of both the human eye and a typical EO/IR camera while comparing the two imaging systems.","PeriodicalId":266025,"journal":{"name":"2009 IEEE Applied Imagery Pattern Recognition Workshop (AIPR 2009)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2009-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127364078","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