ADORE: Adaptive Object Recognition Export

@incollection{citeulike:4450776, abstract = {Many modern computer vision systems are built by chaining together standard vision procedures, often in graphical programming environments such as Khoros, CVIPtools or IUE. Typically, these procedures are selected and sequenced by an ad-hoc combination of programmer's intuition and trial-and-error. This paper presents a theoretically sound method for constructing object recognition strategies by casting object recognition as a Markov Decision Problem (MDP). The result is a system called ADORE (Adaptive Object Recognition) that automatically learns object recognition control policies from training data. Experimental results are presented in which ADORE is trained to recognize five types of houses in aerial images, and where its performance can be (and is) compared to optimal.}, author = {Draper, Bruce and Bins, Jose and Baek, Kyungim}, citeulike-article-id = {4450776}, citeulike-linkout-0 = {http://dx.doi.org/10.1007/3-540-49256-9_31}, citeulike-linkout-1 = {http://www.springerlink.com/content/re11ycle4qhhl929}, doi = {10.1007/3-540-49256-9_31}, journal = {Computer Vision Systems}, keywords = {cognitive\_vision}, pages = {522--537}, posted-at = {2009-11-05 21:45:31}, priority = {2}, title = {ADORE: Adaptive Object Recognition}, url = {http://dx.doi.org/10.1007/3-540-49256-9_31}, year = {1999} }

TY - CHAP ID - citeulike:4450776 L3 - citeulike-article-id:4450776 N2 - Many modern computer vision systems are built by chaining together standard vision procedures, often in graphical programming environments such as Khoros, CVIPtools or IUE. Typically, these procedures are selected and sequenced by an ad-hoc combination of programmer’s intuition and trial-and-error. This paper presents a theoretically sound method for constructing object recognition strategies by casting object recognition as a Markov Decision Problem (MDP). The result is a system called ADORE (Adaptive Object Recognition) that automatically learns object recognition control policies from training data. Experimental results are presented in which ADORE is trained to recognize five types of houses in aerial images, and where its performance can be (and is) compared to optimal. JF - Computer Vision Systems EP - 537 TI - ADORE: Adaptive Object Recognition SP - 522 KW - cognitive_vision AU - Draper, Bruce AU - Bins, Jose AU - Baek, Kyungim PY - 1999/// UR - http://dx.doi.org/10.1007/3-540-49256-9_31 ER -