Face Hallucination and Recognition

Save this PDF as:
 WORD  PNG  TXT  JPG

Size: px
Start display at page:

Download "Face Hallucination and Recognition"

Transcription

1 Face Haucination and Recognition Xiaogang Wang and Xiaoou Tang Department of Information Engineering, The Chinese University of Hong Kong {xgwang1, Abstract. In video surveiance, the faces of interest are often of sma size. Image resoution is an important factor affecting face recognition by human and computer. In this paper, we study the face recognition performance using different image resoutions. For automatic face recognition, a ow resoution bound is found through experiments. We use an eigentransformation based haucination method to improve the image resoution. The haucinated face images are not ony much hepfu for recognition by human, but aso make the automatic recognition procedure easier, since they emphasize the face difference by adding some high frequency detais. 1 Introduction In video surveiance, the faces of interest are often of sma size because of the great distance between the camera and the objects. Image resoution is a potentia factor affecting face recognition performance. In the ow-resoution face images, many detaied facia features are ost and faces are indiscernibe to human. We aso notice that in many automatic face recognition systems, face images are down samped to sma size, and aso achieve satisfied performance. But how wi the image resoution affect recognition accuracy is sti open to discussion. Severa agorithms have been proposed to render a high-resoution face image from the ow-resoution one. This technique is caed haucination [4]. Since face images are we structured and have simiar appearance, they span a sma subset in the high dimensiona image space [3]. This impies that the high frequency detai can be inferred from the ow frequency components, utiizing the face structura simiarity. The simpest way to increase resoution is direct interpoation of input images with such agorithms as nearest neighbour, cubic spine. But its performance is poor if the image size is too sma. Baker and Kanade [4] deveop a haucination method based on the property of face image. It infers the high frequency component from a parent structure by recognizing the oca features from the training set. Liu et. a. [1] deveop a two-step statistica modeing approach integrating goba and oca parameter modes. Haucination has effectivey improved the resoution of face images thus makes it much easier for a human being to recognize a face. However, how much information has been extracted from the ow-resoution image by the haucination process and its contribution to automatic face recognition has not been studied in previous works.

2 g g + + B 1 Low frequency B 0 B K B 1 B K High frequency Figure 1. Muti-resoution anaysis in spatia domain. g is the smoothing function, and B 0,, B K, are different frequency bands In this paper, we study the face recognition performance using different image resoutions. We use a nove haucination method based on eigentransformation [6]. It is cosey reated to the work in [5], in which an eigentransformation approach was deveoped for sketch recognition. In our method, PCA is appied to the ow-resoution face image. In the PCA space, different frequency components are independent. By seecting the number of eigenfaces, we coud extract the maximum amount of facia information from the ow-resoution face image and remove the noise. The new haucinated face image is rendered by mapping between the ow- and high- resoution training pairs. We aso study the effect of haucination on automatic face recognition. Since haucination emphasizes the face difference by adding some high frequency detais, it may hep the automatic recognition process. Experiments are conducted on a database containing images of 188 peope and the XM2VTS face database [2]. 2 Mutiresoution Anaysis Viewing a 2D image as a vector, the process of getting a ow-resoution face image from the high-resoution face image can be formuated as I = HI h + n. (1) Here, I h and I represent the high- and ow-resoution face image vectors respectivey. H is the transformation matrix invoving burring and downsamping process, and n is the noise perturbation to the ow-resoution face image captured by camera. As shown in Figure 1, a process of iterative smoothing and downsamping decomposes the face image into different bands, B 0,, BK. In this decomposition, different frequency bands are not independent. Some components of the high-frequency bands, B 1,, B K, can be inferred from the ow frequency band B 0. This is a starting point for haucination. Many super-resoution agorithms assume the dependency as homogeneous Markov Random Fieds (MRFs), i.e. the pixe ony reies on the pixes in its neighborhood. This is an assumption for genera images. It is not optima for the face

3 e 1 e 2 e 3 e 5 Information on facia feature e 10 e 50 e 100 e 500 Figure 2. Eigenfaces sorted by eigenvaues. e i is the ith eigenface. Noise K Eigenfaces Figure 3. Extract facia information in the PCA space of ow-resoution face images. cass without considering face structura simiarity. A better way to address the dependency is using PCA, in which different frequency components are independent. 3 Haucination and Recognition Face image can be reconstructed from some eigenfaces in the PCA representation. PCA aso decomposes face image into different frequency components, but encoding facia information in a more compact way, since it takes into account of the face distribution. Our agorithm first empoys PCA to extract as much usefu information from ow-resoution face image as possibe, and then renders a high-resoution face image by eigentransformation. A detaied description for eigentransformation can be found in [5]. 3.1 Principe Component Anaysis We represent a set of face images by a N by M matrix, [, ] 1 M, where i is the image vector, N is the number of image pixe, and M is the number of the training sampes ( N >> M ). In PCA, a set of eigenvectors E = [ e1, h, e K ], aso caed eigenfaces, are computed from the ensembe covariance matrix, M T T C = ( i m )( i m ) = LL, (2) i=1 where m is the mean face computed from the sampe set, and L is the sampe matrix, L = [ 1 m1,, M mm ] = [ ' 1, ' M ]. (3) For a face image x, a weight vector is computed by projecting it onto eigenfaces, w = E. (4) T ( x m )

4 This is a face representation based on eigenfaces. A face can be reconstructed from the K eigenfaces, r = E w + m. (5) Figure. 2 shows some eigenfaces sorted by eigenvaues. Eigenfaces with arge eigenvaues are face-ike, and characterize ow frequency components. Eigenfaces with sma eigenvaues are noise-ike, and characterize high frequency detais. 3.2 Eigentransformation Given the ow-resoution sampe set L, according to singuar vaue decomposition theorem, E aso can be computed from, 1/ 2 E = LV Λ, (6) where V and Λ are the eigenvector and eigenvaue matrix for L T L. From (5) and (6), the reconstructed face image can be represented by 1/ 2 r = LV Λ w + m = Lc + m, (7) c = V Λ w = c1, c2,, c 1/ 2 where [ ] T M. Equation (7) can be rewritten as, M = Lc + m = i=1 r i i c ' + m. (8) This shows that the input ow-resoution face image can be reconstructed from the optima inear combination of the M ow-resoution training face images. Repacing each ow-resoution image ' i by its high-resoution sampe h' i, and repacing m with the high-resoution mean face m h, we get x h, which is expected to be an approximation to the rea high-resoution face image. 3.3 Recognition In our agorithm, the haucinated face image is synthesized by the inear combination of high-resoution training images and the coefficients come from the ow-resoution face images using the PCA method. Because of the structura simiarity among face images, in mutiresoution anaysis, there exists strong correation between the high frequency band and ow frequency band. For high-resoution face images, PCA can compact these correated information onto a sma number of principe components. Then, in the eigentransformation process, these principe components can be inferred from those of the ow-resoution face image by mapping between the high- and owresoution training pairs. Therefore, some information in the high frequency band bands are partiay recovered. In practice, the ow-resoution image is often disturbed by noise which has a fat distribution on a the axes. For ow-resoution face images, the energy on sma ei-

5 genvectors is sma, thus is overwhemed by noise. By seecting an optima eigenface number K, we can extract the facia information and remove the noise. The information on these noisy components (eigenfaces after K in Fig. 3) is ost, and cannot be recovered since the components on different eigenvectors are independent in PCA space. In this sense, our haucination method has extracted the maximum amount of facia information exists in the ow-resoution face images. Given the significant improvement of the face appearance by the haucination process, it is interesting to investigate whether the haucination heps automatic recognition. Since more high frequency detais are recovered, we expect the baucination process to hep the recognition performance. 4 Experiment 4.1 Haucination Experiment Our haucination experiment is conducted on a data set containing 188 individuas with one face image for each individua. Using the eave-one-out methodoogy, at each time, one image is seected for testing and the remaining are used for training. In preprocessing, the face images are aigned by the two eyes. The distance between the eye centers is fixed at 50 pixes, and the image size is fixed at Images are burred by averaging neighbour pixes and down samped to ow-resoution images. Here, we use the eye center distance de to measure the face resoution. Some haucination resuts are shown in Fig. 4. The input face images are down samped to 23 25, with de equa to 10. Compared with the Cubic B-Spine interpoation resut, the haucinated face images have much cearer detai features. They are good approximation to the origina high-resoution images. Figure 5 reports the haucination performance for different input resoutions. The eye center distance is down samped to 20, 10, 7, and 5. Figure 6 repots the average RMS error per pixe in intensity for the 188 face images. Under a very ow resoution, the ow-resoution and direct interpoated face images are amost indiscernibe, and the RMS error of Cubic B-spine interpoation increases quicky. The performance of haucination by eigentransformation is much better. When de is down samped to 10, the resut of eigentransformation is sti satisfactory. For further ower resoutions, there are some distortions on the eyes and mouth. As discussed in Section 3, some high frequency detai is ost in the process of bur and downsamping, or is overwhemed by noise. Seecting the eigenface number in eigentransformation, we coud contro the detai eve by keeping maximum facia information whie removing the noise. This point can be iustrated in the experiment reported by Figure 7. We add zero mean, white Gaussian noise with five different standard deviations (σ ) to the ow-resoution face image, and then use different eigenface number (K) for haucination. The optima eigenface number decreases as the increase of noise. Using 180 eigenfaces, the haucinated face images are noisy and distorted for a the five eves of noise. When K is reduced to 100, face images under

6 (a) input (b) Cubic B-Spine (c) Haucinated (d) Origina Figure 4. Haucinated face images by eigentransformation. sma noise ( σ = 0.03,0. 05 ) are we haucinated. but resuts under more noise ( σ = 0.07,0.1,0. 12 ) have a arger distortion. Using 50 eigenfaces, a of the images show itte noise effect. So eigenface number can contro the detai eve to make the haucinated face images robust to noise. 4.2 Recognition Experiment We study the recognition performance using ow-resoution face images and haucinated face images. Two hundred and ninety five individuas from the XM2VTS face database are seected, with two face images in different sessions for each individua. One image is used as reference, and the other is used for testing. We use direct correation for recognition, which is perhaps the simpest face recognition agorithm. The recognition accuracies over different resoutions are potted in Figure 8. When de is reduced from 50 to 10, there is ony sight fuctuation on recognition accuracy using ow-resoution face images. When de is further reduced to 7 and 5, the recognition accuracy for ow-resoution face images drops greaty. Resoution with de equa to 10 is perhaps a ower bound for recognition. Beow this eve there may not be enough information for recognition. This is aso consistent with the haucination experiment in 4.1. Satisfactory haucination resuts can be obtained when de is arger than 10. We aso try to expore whether haucination can contribute to automatic face recognition. We expect haucination make the recognition procedure easier, since it emphasizes the face difference by adding some high frequency detais. In this experiment, the ow-resoution testing image is haucinated by reference face images, but the face image of the testing individua is excuded from the training set. As shown in Figure 8, the haucination improved the recognition accuracy when the input face images have very ow resoutions.

7 (a) Origina 50 ( ) 20 ( ) 10 ( ) 7 ( ) 5 ( ) (b) The first row is the input face images, for which de is 20, 10, 7, 5 respectivey; the second row is the haucinated face images. Figure 5. Haucinated face images using input images of different resoutions. 5 Concusion Our haucination method based on eigentransformation coud extract the maximum facia information from the ow-resoution face images and render some high frequency facia feature to make the face image more discernibe. It aso makes the automatic face recognition more easier. We aso study the face recognition performance over different resoutions. A ow resoution bound for recognition is found in the experiment. This is ony a preiminary study. The resuts need to be further confirmed using more face recognition agorithms and data sets. Acknowedgement This work was supported by the Research Grants Counci of the Hong Kong SAR under Projects CUHK 4190/01E and AOE/E-01/99. Reference 1. C. Liu, H. Shum, and C. Zhang, " A Two-Step Approach to Haucinating Faces: Goba Parametric Mode and Loca Nonparametric Mode," Proc. of IEEE Internationa Conference on Computer Vision and Pattern Recognition, pp , K. Messer, J. Matas, J. Kitter, J. Luettin, and G. Maitre, XM2VTSDB: The Extended M2VTSDB, In the Second Internationa Conference on Audio and Video-Based Biometric Person Authentication, pp , March P. S. Penev, and L. Sirovich, The Goba Dimensionaity of Face Space, Proc. of IEEE Internationa Conference on Automatic Face and Gesture Recognition, pp , S. Baker, and T. Kanade, "Haucinating Faces," Proceedings IEEE Internationa Conference on Automatic Face and Gesture Recognition, pp , X. Tang, and X. Wang, Face Photo Recognition Using Sketch, Proc. of ICIP, X. Wang and X. Tang, Haucinating Face by Eigentransformation, ICIP 2003.

8 Figure 6. RMS error per pixe in intensity using Cubic-spine interpoation and haucination by eigentransformation. The intensity is between 0 and 1. Figure 8. Recognition accuracy using owresoution face images and haucinated face images based on XM2VTS database. ( σ = ) ( σ = ) ( σ = ) ( σ = 0. 1) ( σ = ) (a) (K=50, σ = ) (K=50, σ = ) (K=50, σ = ) (K=50, σ = 0. 1 ) (K=50, σ = ) (K=100, σ = )(K=100, σ = )(K=100, σ = )(K=100, σ = 0. 1 )(K=100, σ = ) (K=180, σ = )(K=180, σ = )(K=180, σ = )(K=180, σ = 0. 1 ) (K=180, σ = ) (b) Figure 7. Haucinating face with additive Gaussian noise. (a): Low-resoution face images with noise, (b) Haucinated faces. K is the eigenface number, and σ is the standard deviation of Gaussian noise (Image intensity is between 0 and 1). The origina high-resoution face image is referred to Fig. 7 (a).

A Similarity Search Scheme over Encrypted Cloud Images based on Secure Transformation

A Similarity Search Scheme over Encrypted Cloud Images based on Secure Transformation A Simiarity Search Scheme over Encrypted Coud Images based on Secure Transormation Zhihua Xia, Yi Zhu, Xingming Sun, and Jin Wang Jiangsu Engineering Center o Network Monitoring, Nanjing University o Inormation

More information

Math: Fundamentals 100

Math: Fundamentals 100 Math: Fundamentas 100 Wecome to the Tooing University. This course is designed to be used in conjunction with the onine version of this cass. The onine version can be found at http://www.tooingu.com. We

More information

Learning framework for NNs. Introduction to Neural Networks. Learning goal: Inputs/outputs. x 1 x 2. y 1 y 2

Learning framework for NNs. Introduction to Neural Networks. Learning goal: Inputs/outputs. x 1 x 2. y 1 y 2 Introduction to Neura Networks Learning framework for NNs What are neura networks? Noninear function approimators How do they reate to pattern recognition/cassification? Noninear discriminant functions

More information

Secure Network Coding with a Cost Criterion

Secure Network Coding with a Cost Criterion Secure Network Coding with a Cost Criterion Jianong Tan, Murie Médard Laboratory for Information and Decision Systems Massachusetts Institute of Technoogy Cambridge, MA 0239, USA E-mai: {jianong, medard}@mit.edu

More information

Determining the User Intent of Chinese-English Mixed Language Queries Based On Search Logs

Determining the User Intent of Chinese-English Mixed Language Queries Based On Search Logs Determining the User Intent of Chinese-Engish Mixed Language Queries Based On Search Logs Hengyi Fu, Forida State University, City University of New York Abstract With the increasing number of mutiingua

More information

COMPARISON OF DIFFUSION MODELS IN ASTRONOMICAL OBJECT LOCALIZATION

COMPARISON OF DIFFUSION MODELS IN ASTRONOMICAL OBJECT LOCALIZATION COMPARISON OF DIFFUSION MODELS IN ASTRONOMICAL OBJECT LOCALIZATION Františe Mojžíš Department of Computing and Contro Engineering, ICT Prague, Technicá, 8 Prague frantise.mojzis@vscht.cz Abstract This

More information

A Latent Variable Pairwise Classification Model of a Clustering Ensemble

A Latent Variable Pairwise Classification Model of a Clustering Ensemble A atent Variabe Pairwise Cassification Mode of a Custering Ensembe Vadimir Berikov Soboev Institute of mathematics, Novosibirsk State University, Russia berikov@math.nsc.ru http://www.math.nsc.ru Abstract.

More information

ONE of the most challenging problems addressed by the

ONE of the most challenging problems addressed by the IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, VOL. 44, NO. 9, SEPTEMBER 2006 2587 A Mutieve Context-Based System for Cassification of Very High Spatia Resoution Images Lorenzo Bruzzone, Senior Member,

More information

Australian Bureau of Statistics Management of Business Providers

Australian Bureau of Statistics Management of Business Providers Purpose Austraian Bureau of Statistics Management of Business Providers 1 The principa objective of the Austraian Bureau of Statistics (ABS) in respect of business providers is to impose the owest oad

More information

The Radix-4 and the Class of Radix-2 s FFTs

The Radix-4 and the Class of Radix-2 s FFTs Chapter 11 The Radix- and the Cass of Radix- s FFTs The divide-and-conuer paradigm introduced in Chapter 3 is not restricted to dividing a probem into two subprobems. In fact, as expained in Section. and

More information

Simultaneous Routing and Power Allocation in CDMA Wireless Data Networks

Simultaneous Routing and Power Allocation in CDMA Wireless Data Networks Simutaneous Routing and Power Aocation in CDMA Wireess Data Networks Mikae Johansson *,LinXiao and Stephen Boyd * Department of Signas, Sensors and Systems Roya Institute of Technoogy, SE 00 Stockhom,

More information

HYBRID FUZZY LOGIC PID CONTROLLER. Abstract

HYBRID FUZZY LOGIC PID CONTROLLER. Abstract HYBRID FUZZY LOGIC PID CONTROLLER Thomas Brehm and Kudip S. Rattan Department of Eectrica Engineering Wright State University Dayton, OH 45435 Abstract This paper investigates two fuzzy ogic PID controers

More information

Fast Robust Hashing. ) [7] will be re-mapped (and therefore discarded), due to the load-balancing property of hashing.

Fast Robust Hashing. ) [7] will be re-mapped (and therefore discarded), due to the load-balancing property of hashing. Fast Robust Hashing Manue Urueña, David Larrabeiti and Pabo Serrano Universidad Caros III de Madrid E-89 Leganés (Madrid), Spain Emai: {muruenya,darra,pabo}@it.uc3m.es Abstract As statefu fow-aware services

More information

FRAME BASED TEXTURE CLASSIFICATION BY CONSIDERING VARIOUS SPATIAL NEIGHBORHOODS. Karl Skretting and John Håkon Husøy

FRAME BASED TEXTURE CLASSIFICATION BY CONSIDERING VARIOUS SPATIAL NEIGHBORHOODS. Karl Skretting and John Håkon Husøy FRAME BASED TEXTURE CLASSIFICATION BY CONSIDERING VARIOUS SPATIAL NEIGHBORHOODS Kar Skretting and John Håkon Husøy University of Stavanger, Department of Eectrica and Computer Engineering N-4036 Stavanger,

More information

Distribution of Family Income: Improved Estimates

Distribution of Family Income: Improved Estimates Distribution of Famiy Income: Improved Estimates by Danie B. Radner * his artice describes the resuts of research to improve estimates of the distribution of famiy income. In this research, a microdata

More information

3.3 SOFTWARE RISK MANAGEMENT (SRM)

3.3 SOFTWARE RISK MANAGEMENT (SRM) 93 3.3 SOFTWARE RISK MANAGEMENT (SRM) Fig. 3.2 SRM is a process buit in five steps. The steps are: Identify Anayse Pan Track Resove The process is continuous in nature and handed dynamicay throughout ifecyce

More information

Bayesian Tensor Inference for Sketch-based Facial Photo Hallucination

Bayesian Tensor Inference for Sketch-based Facial Photo Hallucination Bayesian ensor Inference for Sketch-based Facial Photo Hallucination Wei Liu Xiaoou ang Jianzhuang Liu Dept. of Information Engineering, he Chinese University of Hong Kong, Hong Kong Visual Computing Group,

More information

Leakage detection in water pipe networks using a Bayesian probabilistic framework

Leakage detection in water pipe networks using a Bayesian probabilistic framework Probabiistic Engineering Mechanics 18 (2003) 315 327 www.esevier.com/ocate/probengmech Leakage detection in water pipe networks using a Bayesian probabiistic framework Z. Pouakis, D. Vaougeorgis, C. Papadimitriou*

More information

Probabilistic Systems Analysis Autumn 2016 Tse Lecture Note 12

Probabilistic Systems Analysis Autumn 2016 Tse Lecture Note 12 EE 178 Probabiistic Systems Anaysis Autumn 2016 Tse Lecture Note 12 Continuous random variabes Up to now we have focused excusivey on discrete random variabes, which take on ony a finite (or countaby infinite)

More information

REAL TIME IMPLEMANTATION OF LMS BEAMFORMER FOR cdma2000 3G SYSTEM USING TI TMS320C6701 DSP

REAL TIME IMPLEMANTATION OF LMS BEAMFORMER FOR cdma2000 3G SYSTEM USING TI TMS320C6701 DSP REAL TIME IMPLEMANTATION OF LMS BEAMFORMER FOR cdma2000 3G SYSTEM USING TI TMS320C6701 DSP Kerem Küçük, Mustafa Karakoç, and Adnan Kavak + Kocaei University + Kocaei University Eectronics and Computer

More information

ASYMPTOTIC DIRECTION FOR RANDOM WALKS IN RANDOM ENVIRONMENTS arxiv:math/0512388v2 [math.pr] 11 Dec 2007

ASYMPTOTIC DIRECTION FOR RANDOM WALKS IN RANDOM ENVIRONMENTS arxiv:math/0512388v2 [math.pr] 11 Dec 2007 ASYMPTOTIC DIRECTION FOR RANDOM WALKS IN RANDOM ENVIRONMENTS arxiv:math/0512388v2 [math.pr] 11 Dec 2007 FRANÇOIS SIMENHAUS Université Paris 7, Mathématiques, case 7012, 2, pace Jussieu, 75251 Paris, France

More information

Artificial neural networks and deep learning

Artificial neural networks and deep learning February 20, 2015 1 Introduction Artificia Neura Networks (ANNs) are a set of statistica modeing toos originay inspired by studies of bioogica neura networks in animas, for exampe the brain and the centra

More information

AUSTRALIA S GAMBLING INDUSTRIES - INQUIRY

AUSTRALIA S GAMBLING INDUSTRIES - INQUIRY Mr Gary Banks Chairman Productivity Commission PO Box 80 BELCONNEN ACT 2616 Dear Mr Banks AUSTRALIA S GAMBLING INDUSTRIES - INQUIRY I refer to the Issues Paper issued September 1998 seeking submissions

More information

Practicing Reference... Learning from Library Science *

Practicing Reference... Learning from Library Science * Practicing Reference... Learning from Library Science * Mary Whisner ** Ms. Whisner describes the method and some of the resuts reported in a recenty pubished book about the reference interview written

More information

3.14 Lifting Surfaces

3.14 Lifting Surfaces .0 - Marine Hydrodynamics, Spring 005 Lecture.0 - Marine Hydrodynamics Lecture 3.4 Lifting Surfaces 3.4. D Symmetric Streamined Body No separation, even for arge Reynods numbers. stream ine Viscous effects

More information

Multi-Robot Task Scheduling

Multi-Robot Task Scheduling Proc of IEEE Internationa Conference on Robotics and Automation, Karsruhe, Germany, 013 Muti-Robot Tas Scheduing Yu Zhang and Lynne E Parer Abstract The scheduing probem has been studied extensivey in

More information

The Computation of the Inverse of a Square Polynomial Matrix

The Computation of the Inverse of a Square Polynomial Matrix The Computation of the Inverse of a Square Poynomia Matrix Ky M. Vu, PhD. AuLac Technoogies Inc. c 2008 Emai: kymvu@auactechnoogies.com Abstract An approach to cacuate the inverse of a square poynomia

More information

Integrating Risk into your Plant Lifecycle A next generation software architecture for risk based

Integrating Risk into your Plant Lifecycle A next generation software architecture for risk based Integrating Risk into your Pant Lifecyce A next generation software architecture for risk based operations Dr Nic Cavanagh 1, Dr Jeremy Linn 2 and Coin Hickey 3 1 Head of Safeti Product Management, DNV

More information

Face Recognition Using Line Edge Map

Face Recognition Using Line Edge Map 764 IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, VOL. 4, NO. 6, JUNE 00 Face Recognition Using Line Edge Map Yongsheng Gao, Member, IEEE, and Mayor K.H. Leung, Member, IEEE AbstractÐThe

More information

Betting Strategies, Market Selection, and the Wisdom of Crowds

Betting Strategies, Market Selection, and the Wisdom of Crowds Betting Strategies, Market Seection, and the Wisdom of Crowds Wiemien Kets Northwestern University w-kets@keogg.northwestern.edu David M. Pennock Microsoft Research New York City dpennock@microsoft.com

More information

Vision Helpdesk Client Portal User Guide

Vision Helpdesk Client Portal User Guide Hepdesk Hepdesk Vision Hepdesk Cient Porta User Guide VISION HELPDESK v3 User Guide (for Cient) CLIENT PORTAL DETAILS VISION HELPDESK v3 User Guide (for Cient) Hepdesk Index Cient Porta.....................................................

More information

Restoration of blue scratches in digital image sequences

Restoration of blue scratches in digital image sequences Avaiabe onine at www.sciencedirect.com Image and Vision Computing 26 (2008) 1314 1326 www.esevier.com/ocate/imavis Restoration of bue scratches in digita image sequences Lucia Maddaena a, *, Afredo Petrosino

More information

SELECTING THE SUITABLE ERP SYSTEM: A FUZZY AHP APPROACH. Ufuk Cebeci

SELECTING THE SUITABLE ERP SYSTEM: A FUZZY AHP APPROACH. Ufuk Cebeci SELECTING THE SUITABLE ERP SYSTEM: A FUZZY AHP APPROACH Ufuk Cebeci Department of Industria Engineering, Istanbu Technica University, Macka, Istanbu, Turkey - ufuk_cebeci@yahoo.com Abstract An Enterprise

More information

Section 1 : Exploring the visual arts

Section 1 : Exploring the visual arts Section 1 : Exporing the visua arts Copyright 2014 The Open University Contents Section 1 : Exporing the visua arts 3 1. Using brainstorming to think about oca art 3 2. Studying and making masks 4 3. Creating

More information

Certified Once Accepted Everywhere Why use an accredited certification body?

Certified Once Accepted Everywhere Why use an accredited certification body? Certified Once Accepted Everywhere Why use an accredited certification body? Third party management systems certification is a frequenty specified requirement to operate in the goba market pace. It can

More information

EE 178/278A Probabilistic Systems Analysis. Spring 2014 Tse/Hussami Lecture 11. A Brief Introduction to Continuous Probability

EE 178/278A Probabilistic Systems Analysis. Spring 2014 Tse/Hussami Lecture 11. A Brief Introduction to Continuous Probability EE 178/278A Probabiistic Systems Anaysis Spring 2014 Tse/Hussami Lecture 11 A Brief Introduction to Continuous Probabiity Up to now we have focused excusivey on discrete probabiity spaces Ω, where the

More information

ELEVATING YOUR GAME FROM TRADE SPEND TO TRADE INVESTMENT

ELEVATING YOUR GAME FROM TRADE SPEND TO TRADE INVESTMENT Initiatives Strategic Mapping Success in The Food System: Discover. Anayze. Strategize. Impement. Measure. ELEVATING YOUR GAME FROM TRADE SPEND TO TRADE INVESTMENT Foodservice manufacturers aocate, in

More information

Dynamic Pricing Trade Market for Shared Resources in IIU Federated Cloud

Dynamic Pricing Trade Market for Shared Resources in IIU Federated Cloud Dynamic Pricing Trade Market or Shared Resources in IIU Federated Coud Tongrang Fan 1, Jian Liu 1, Feng Gao 1 1Schoo o Inormation Science and Technoogy, Shiiazhuang Tiedao University, Shiiazhuang, 543,

More information

A Practical Framework for Privacy-Preserving Data Analytics

A Practical Framework for Privacy-Preserving Data Analytics A Practica Framework for Privacy-Preserving Data Anaytics ABSTRACT Liyue Fan Integrated Media Systems Center University of Southern Caifornia Los Angees, CA, USA iyuefan@usc.edu The avaiabiity of an increasing

More information

Teamwork. Abstract. 2.1 Overview

Teamwork. Abstract. 2.1 Overview 2 Teamwork Abstract This chapter presents one of the basic eements of software projects teamwork. It addresses how to buid teams in a way that promotes team members accountabiity and responsibiity, and

More information

On Capacity Scaling in Arbitrary Wireless Networks

On Capacity Scaling in Arbitrary Wireless Networks On Capacity Scaing in Arbitrary Wireess Networks Urs Niesen, Piyush Gupta, and Devavrat Shah 1 Abstract arxiv:07112745v3 [csit] 3 Aug 2009 In recent work, Özgür, Lévêque, and Tse 2007) obtained a compete

More information

500 IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS, VOL. 31, NO. 3, MARCH 2013

500 IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS, VOL. 31, NO. 3, MARCH 2013 500 IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS, VOL. 3, NO. 3, MARCH 203 Cognitive Radio Network Duaity Agorithms for Utiity Maximization Liang Zheng Chee Wei Tan, Senior Member, IEEE Abstract We

More information

ABSTRACT. Categories and Subject Descriptors. General Terms. Keywords 1. INTRODUCTION. Jun Yin, Ye Wang and David Hsu

ABSTRACT. Categories and Subject Descriptors. General Terms. Keywords 1. INTRODUCTION. Jun Yin, Ye Wang and David Hsu Jun Yin, Ye Wang and David Hsu ABSTRACT Prompt feedback is essentia for beginning vioin earners; however, most amateur earners can ony meet with teachers and receive feedback once or twice a week. To hep

More information

Art of Java Web Development By Neal Ford 624 pages US$44.95 Manning Publications, 2004 ISBN: 1-932394-06-0

Art of Java Web Development By Neal Ford 624 pages US$44.95 Manning Publications, 2004 ISBN: 1-932394-06-0 IEEE DISTRIBUTED SYSTEMS ONLINE 1541-4922 2005 Pubished by the IEEE Computer Society Vo. 6, No. 5; May 2005 Editor: Marcin Paprzycki, http://www.cs.okstate.edu/%7emarcin/ Book Reviews: Java Toos and Frameworks

More information

Experiment #11 BJT filtering

Experiment #11 BJT filtering Jonathan Roderick Hakan Durmus Experiment #11 BJT fitering Introduction: Now that the BJT has bn expored in static and dynamic operation, the BJT, combined with what has bn aready presented, may be used

More information

Packet Classification with Network Traffic Statistics

Packet Classification with Network Traffic Statistics Packet Cassification with Network Traffic Statistics Yaxuan Qi and Jun Li Research Institute of Information Technoogy (RIIT), Tsinghua Uniersity Beijing, China, 100084 Abstract-- Packet cassification on

More information

Managing Business Risks from Major Chemical

Managing Business Risks from Major Chemical Managing Business Risks from Major Chemica Process Accidents Mariana Bardy 1, Dr Luiz Fernando Oiveira 2, and Dr Nic Cavanagh 3 1 Head of Section, Risk Management Soutions Savador, DNV Energy Soutions

More information

High-order balanced M-band multiwavelet packet transform-based remote sensing image denoising

High-order balanced M-band multiwavelet packet transform-based remote sensing image denoising Wang et a. EURASIP Journa on Advances in Signa Processing (2016) 2016:10 DOI 10.1186/s13634-015-0298-7 RESEARCH High-order baanced M-band mutiwaveet packet transform-based remote sensing image denoising

More information

ENERGY AND BOLTZMANN DISTRIBUTIONS

ENERGY AND BOLTZMANN DISTRIBUTIONS MISN--159 NRGY AND BOLTZMANN DISTRIBUTIONS NRGY AND BOLTZMANN DISTRIBUTIONS by J. S. Kovacs and O. McHarris Michigan State University 1. Introduction.............................................. 1 2.

More information

Determination of Forward and Futures Prices. Chapter 5

Determination of Forward and Futures Prices. Chapter 5 Determination of Forward and Futures Prices Chapter 5 1 Consumption vs Investment Assets Investment assets are assets hed by a significant number of peope purey for investment purposes (Exampes: stocks,

More information

Avaya Remote Feature Activation (RFA) User Guide

Avaya Remote Feature Activation (RFA) User Guide Avaya Remote Feature Activation (RFA) User Guide 03-300149 Issue 5.0 September 2007 2007 Avaya Inc. A Rights Reserved. Notice Whie reasonabe efforts were made to ensure that the information in this document

More information

Spatio-Temporal Asynchronous Co-Occurrence Pattern for Big Climate Data towards Long-Lead Flood Prediction

Spatio-Temporal Asynchronous Co-Occurrence Pattern for Big Climate Data towards Long-Lead Flood Prediction Spatio-Tempora Asynchronous Co-Occurrence Pattern for Big Cimate Data towards Long-Lead Food Prediction Chung-Hsien Yu, Dong Luo, Wei Ding, Joseph Cohen, David Sma and Shafiqu Isam Department of Computer

More information

Network/Communicational Vulnerability

Network/Communicational Vulnerability Automated teer machines (ATMs) are a part of most of our ives. The major appea of these machines is convenience The ATM environment is changing and that change has serious ramifications for the security

More information

1##111##1111#1#111i#lllil

1##111##1111#1#111i#lllil 1##111##1111#1#111i#i 140334197x SWP 6/90 GROWTH AND PERFORMANCE CONTRASTS BETWEEN TYPES OF SMALL FIRMS PROFESSOR SUE BIRLEY & DR PAUL WESTHEAD Cranfieid Entrepreneurship Research Centre Cranfied Schoo

More information

Business Banking. A guide for franchises

Business Banking. A guide for franchises Business Banking A guide for franchises Hep with your franchise business, right on your doorstep A true understanding of the needs of your business: that s what makes RBS the right choice for financia

More information

Improving the error rates of the Begg and Mazumdar test for publication bias in fixed effects meta-analysis

Improving the error rates of the Begg and Mazumdar test for publication bias in fixed effects meta-analysis Gjerdevik and Heuch BMC Medica Research Methodoogy 2014, 14:109 http://www.biomedcentra.com/1471-2288/14/109 RESEARCH ARTICLE Improving the error rates of the Begg and Mazumdar test for pubication bias

More information

Recognition of Prior Learning

Recognition of Prior Learning Recognition of Prior Learning Information Guideines for Students EXTENDED CAMPUS This subject materia is issued by Cork Institute of Technoogy on the understanding that: Cork Institute of Technoogy expressy

More information

A Frame Synchronization Method with Robustness to the Effects of Initial SFO in DRM Systems

A Frame Synchronization Method with Robustness to the Effects of Initial SFO in DRM Systems Internationa Journa of Software Engineering and Its Appications Vo. 6, o. 3, Juy, 0 A Frame Synchronization Method with Robustness to the Effects of Initia SFO in DRM Systems Ki-Won Kwon, Seong-Jun Kim,Yong-Suk,

More information

Understanding. nystagmus. RCOphth

Understanding. nystagmus. RCOphth Understanding nystagmus RCOphth RNIB s understanding series The understanding series is designed to hep you, your friends and famiy understand a itte bit more about your eye condition. Other tites in the

More information

READING A CREDIT REPORT

READING A CREDIT REPORT Name Date CHAPTER 6 STUDENT ACTIVITY SHEET READING A CREDIT REPORT Review the sampe credit report. Then search for a sampe credit report onine, print it off, and answer the questions beow. This activity

More information

Message. The Trade and Industry Bureau is committed to providing maximum support for Hong Kong s manufacturing and services industries.

Message. The Trade and Industry Bureau is committed to providing maximum support for Hong Kong s manufacturing and services industries. Message The Trade and Industry Bureau is committed to providing maximum support for Hong Kong s manufacturing and services industries. With the weight of our economy shifting towards knowedge-based and

More information

GWPD 4 Measuring water levels by use of an electric tape

GWPD 4 Measuring water levels by use of an electric tape GWPD 4 Measuring water eves by use of an eectric tape VERSION: 2010.1 PURPOSE: To measure the depth to the water surface beow and-surface datum using the eectric tape method. Materias and Instruments 1.

More information

Business schools are the academic setting where. The current crisis has highlighted the need to redefine the role of senior managers in organizations.

Business schools are the academic setting where. The current crisis has highlighted the need to redefine the role of senior managers in organizations. c r o s os r oi a d s REDISCOVERING THE ROLE OF BUSINESS SCHOOLS The current crisis has highighted the need to redefine the roe of senior managers in organizations. JORDI CANALS Professor and Dean, IESE

More information

Learning from evaluations Processes and instruments used by GIZ as a learning organisation and their contribution to interorganisational learning

Learning from evaluations Processes and instruments used by GIZ as a learning organisation and their contribution to interorganisational learning Monitoring and Evauation Unit Learning from evauations Processes and instruments used by GIZ as a earning organisation and their contribution to interorganisationa earning Contents 1.3Learning from evauations

More information

No. of Pages 15, Model 5G ARTICLE IN PRESS. Contents lists available at ScienceDirect. Electronic Commerce Research and Applications

No. of Pages 15, Model 5G ARTICLE IN PRESS. Contents lists available at ScienceDirect. Electronic Commerce Research and Applications Eectronic Commerce Research and Appications xxx (2008) xxx xxx 1 Contents ists avaiabe at ScienceDirect Eectronic Commerce Research and Appications journa homepage: www.esevier.com/ocate/ecra 2 Forecasting

More information

Sketch to Photo Matching: A Feature-based Approach

Sketch to Photo Matching: A Feature-based Approach Sketch to Photo Matching: A Feature-based Approach Brendan Klare a and Anil K Jain a,b a Department of Computer Science and Engineering Michigan State University East Lansing, MI, U.S.A b Department of

More information

Software Quality - Getting Right Metrics, Getting Metrics Right

Software Quality - Getting Right Metrics, Getting Metrics Right Software Quaity - Getting Right Metrics, Getting Metrics Right How to set the right performance metrics and then benchmark it for continuous improvement? Whie metrics are important means to quantify performance

More information

Lecture 5: Solution Method for Beam Deflections

Lecture 5: Solution Method for Beam Deflections Structura Mechanics.080 Lecture 5 Semester Yr Lecture 5: Soution Method for Beam Defections 5.1 Governing Equations So far we have estabished three groups of equations fuy characterizing the response of

More information

Restoration of blue scratches in digital image sequences

Restoration of blue scratches in digital image sequences Consigio Nazionae dee Ricerche Istituto di Cacoo e Reti ad Ate Prestazioni Restoration of bue scratches in digita image sequences Lucia Maddaena, Afredo Petrosino RT-ICAR-NA-05-2 December 2005 Consigio

More information

A Learning Based Method for Super-Resolution of Low Resolution Images

A Learning Based Method for Super-Resolution of Low Resolution Images A Learning Based Method for Super-Resolution of Low Resolution Images Emre Ugur June 1, 2004 emre.ugur@ceng.metu.edu.tr Abstract The main objective of this project is the study of a learning based method

More information

Gateshead Carers Strategy

Gateshead Carers Strategy Gateshead Carers Strategy 2014-17 Recognised, Vaued and Supported Contents Page Foreword...3 Executive summary...4 Introduction...5 Strategic Priorities Nationa Drivers...5 Loca Drivers...6 Adut Socia

More information

Leadership & Management Certificate Programs

Leadership & Management Certificate Programs MANAGEMENT CONCEPTS Leadership & Management Certificate Programs Programs to deveop expertise in: Anaytics // Leadership // Professiona Skis // Supervision ENROLL TODAY! Contract oder Contract GS-02F-0010J

More information

Fixed income managers: evolution or revolution

Fixed income managers: evolution or revolution Fixed income managers: evoution or revoution Traditiona approaches to managing fixed interest funds rey on benchmarks that may not represent optima risk and return outcomes. New techniques based on separate

More information

Best Practices for Push & Pull Using Oracle Inventory Stock Locators. Introduction to Master Data and Master Data Management (MDM): Part 1

Best Practices for Push & Pull Using Oracle Inventory Stock Locators. Introduction to Master Data and Master Data Management (MDM): Part 1 SPECIAL CONFERENCE ISSUE THE OFFICIAL PUBLICATION OF THE Orace Appications USERS GROUP spring 2012 Introduction to Master Data and Master Data Management (MDM): Part 1 Utiizing Orace Upgrade Advisor for

More information

Protection Against Income Loss During the First 4 Months of Illness or Injury *

Protection Against Income Loss During the First 4 Months of Illness or Injury * Protection Against Income Loss During the First 4 Months of Iness or Injury * This note examines and describes the kinds of income protection that are avaiabe to workers during the first 6 months of iness

More information

Face Recognition using Principle Component Analysis

Face Recognition using Principle Component Analysis Face Recognition using Principle Component Analysis Kyungnam Kim Department of Computer Science University of Maryland, College Park MD 20742, USA Summary This is the summary of the basic idea about PCA

More information

Vibration Reduction of Audio Visual Device Mounted on Automobile due to Gap Vibration

Vibration Reduction of Audio Visual Device Mounted on Automobile due to Gap Vibration Vibration Reduction of Audio Visua Device Mounted on Automobie due to Gap Vibration Nobuyuki OKUBO, Shinji KANADA, Takeshi TOI CAMAL, Department of Precision Mechanics, Chuo University 1-13-27 Kasuga,

More information

Pricing Internet Services With Multiple Providers

Pricing Internet Services With Multiple Providers Pricing Internet Services With Mutipe Providers Linhai He and Jean Warand Dept. of Eectrica Engineering and Computer Science University of Caifornia at Berkeey Berkeey, CA 94709 inhai, wr@eecs.berkeey.edu

More information

Sketch-based Network-wide Traffic Anomaly Detection

Sketch-based Network-wide Traffic Anomaly Detection Sketch-based Network-wide Traffic Anomay Detection Yang Liu, Linfeng Zhang, and Yong Guan Department of Eectrica and Computer Engineering Iowa State University, Ames, Iowa 500 Emai: {yang, zhangf, guan}@iastate.edu

More information

INTERNATIONAL PAYMENT INSTRUMENTS

INTERNATIONAL PAYMENT INSTRUMENTS INTERNATIONAL PAYMENT INSTRUMENTS Dr Nguyen Minh Duc 2009 1 THE INTERNATIONAL CHAMBER OF COMMERCE THE ICC AT A GLANCE represent the word business community at nationa and internationa eves promotes word

More information

(David H T Lan) Secretary for Home Affairs

(David H T Lan) Secretary for Home Affairs Message We sha make every effort to strengthen the community buiding programme which serves to foster among the peope of Hong Kong a sense of beonging and mutua care. We wi continue to impement the District

More information

Design of Follow-Up Experiments for Improving Model Discrimination and Parameter Estimation

Design of Follow-Up Experiments for Improving Model Discrimination and Parameter Estimation Design of Foow-Up Experiments for Improving Mode Discrimination and Parameter Estimation Szu Hui Ng 1 Stephen E. Chick 2 Nationa University of Singapore, 10 Kent Ridge Crescent, Singapore 119260. Technoogy

More information

Let s get usable! Usability studies for indexes. Susan C. Olason. Study plan

Let s get usable! Usability studies for indexes. Susan C. Olason. Study plan Let s get usabe! Usabiity studies for indexes Susan C. Oason The artice discusses a series of usabiity studies on indexes from a systems engineering and human factors perspective. The purpose of these

More information

Distribution of Income Sources of Recent Retirees: Findings From the New Beneficiary Survey

Distribution of Income Sources of Recent Retirees: Findings From the New Beneficiary Survey Distribution of Income Sources of Recent Retirees: Findings From the New Beneficiary Survey by Linda Drazga Maxfied and Virginia P. Rena* Using data from the New Beneficiary Survey, this artice examines

More information

Comparison of Misspecification Tests Designed for Non-linear Time Series Models

Comparison of Misspecification Tests Designed for Non-linear Time Series Models ömmföäfsäafaäsfassfassfas fffffffffffffffffffffffffffffffffff Discussion Papers Comparison of Misspecification Tests Designed for Non-inear Time Series Modes Leena Kaiovirta University of Hesinki and HECER

More information

CARBON FOOTPRINT REPORT

CARBON FOOTPRINT REPORT CARBON FOOTPRINT REPORT 07.03.2012 ATEA ASA REPORT: CARBON FOOTPRINT ANALYSIS 2011 PROVIDED BY: CO2FOCUS Content Introduction... 2 Method... 2 Resuts... 4 Atea Group... 4 Atea Norway... 5 Atea Denmark...

More information

A quantum model for the stock market

A quantum model for the stock market A quantum mode for the stock market Authors: Chao Zhang a,, Lu Huang b Affiiations: a Schoo of Physics and Engineering, Sun Yat-sen University, Guangzhou 5175, China b Schoo of Economics and Business Administration,

More information

Automatic Structure Discovery for Large Source Code

Automatic Structure Discovery for Large Source Code Automatic Structure Discovery for Large Source Code By Sarge Rogatch Master Thesis Universiteit van Amsterdam, Artificia Inteigence, 2010 Automatic Structure Discovery for Large Source Code Page 1 of 130

More information

The Web Insider... The Best Tool for Building a Web Site *

The Web Insider... The Best Tool for Building a Web Site * The Web Insider... The Best Too for Buiding a Web Site * Anna Bee Leiserson ** Ms. Leiserson describes the types of Web-authoring systems that are avaiabe for buiding a site and then discusses the various

More information

Subspace Analysis and Optimization for AAM Based Face Alignment

Subspace Analysis and Optimization for AAM Based Face Alignment Subspace Analysis and Optimization for AAM Based Face Alignment Ming Zhao Chun Chen College of Computer Science Zhejiang University Hangzhou, 310027, P.R.China zhaoming1999@zju.edu.cn Stan Z. Li Microsoft

More information

Overview of Health and Safety in China

Overview of Health and Safety in China Overview of Heath and Safety in China Hongyuan Wei 1, Leping Dang 1, and Mark Hoye 2 1 Schoo of Chemica Engineering, Tianjin University, Tianjin 300072, P R China, E-mai: david.wei@tju.edu.cn 2 AstraZeneca

More information

Adaptive Face Recognition System from Myanmar NRC Card

Adaptive Face Recognition System from Myanmar NRC Card Adaptive Face Recognition System from Myanmar NRC Card Ei Phyo Wai University of Computer Studies, Yangon, Myanmar Myint Myint Sein University of Computer Studies, Yangon, Myanmar ABSTRACT Biometrics is

More information

Vision based Vehicle Tracking using a high angle camera

Vision based Vehicle Tracking using a high angle camera Vision based Vehicle Tracking using a high angle camera Raúl Ignacio Ramos García Dule Shu gramos@clemson.edu dshu@clemson.edu Abstract A vehicle tracking and grouping algorithm is presented in this work

More information

Bite-Size Steps to ITIL Success

Bite-Size Steps to ITIL Success 7 Bite-Size Steps to ITIL Success Pus making a Business Case for ITIL! Do you want to impement ITIL but don t know where to start? 7 Bite-Size Steps to ITIL Success can hep you to decide whether ITIL can

More information

Breakeven analysis and short-term decision making

Breakeven analysis and short-term decision making Chapter 20 Breakeven anaysis and short-term decision making REAL WORLD CASE This case study shows a typica situation in which management accounting can be hepfu. Read the case study now but ony attempt

More information

Efficient Attendance Management: A Face Recognition Approach

Efficient Attendance Management: A Face Recognition Approach Efficient Attendance Management: A Face Recognition Approach Badal J. Deshmukh, Sudhir M. Kharad Abstract Taking student attendance in a classroom has always been a tedious task faultfinders. It is completely

More information

Recent Trends in Workers Compensation Coverage by Brian Z. Brown, FCAS Melodee J. Saunders, ACAS

Recent Trends in Workers Compensation Coverage by Brian Z. Brown, FCAS Melodee J. Saunders, ACAS Recent Trends in Workers Compensation Coverage by Brian Z. Brown, FCAS Meodee J. Saunders, ACAS TITLE: RECENT TRENDS IN WORKERS COMPENSATION COVERAGE BY: Ms. Meodee J. Saunders, A.C.A.S., M.A.A.A. Mr.

More information

Energy Density / Energy Flux / Total Energy in 3D

Energy Density / Energy Flux / Total Energy in 3D Lecture 5 Phys 75 Energy Density / Energy Fux / Tota Energy in D Overview and Motivation: In this ecture we extend the discussion of the energy associated with wave otion to waves described by the D wave

More information

An Idiot s guide to Support vector machines (SVMs)

An Idiot s guide to Support vector machines (SVMs) An Idiot s guide to Support vector machines (SVMs) R. Berwick, Viage Idiot SVMs: A New Generation of Learning Agorithms Pre 1980: Amost a earning methods earned inear decision surfaces. Linear earning

More information

This paper considers an inventory system with an assembly structure. In addition to uncertain customer

This paper considers an inventory system with an assembly structure. In addition to uncertain customer MANAGEMENT SCIENCE Vo. 51, No. 8, August 2005, pp. 1250 1265 issn 0025-1909 eissn 1526-5501 05 5108 1250 informs doi 10.1287/mnsc.1050.0394 2005 INFORMS Inventory Management for an Assemby System wh Product

More information