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3. Three-Dimensional Correspondence - Shelton, Christian R. This paper describes the problem of three-dimensional object correspondence and presents an algorithm for matching two three-dimensional colored surfaces using polygon reduction and the minimization of an energy function.
6. Intelligent Market-Making in Artificial Financial Markets - Das, Sanmay This thesis describes and evaluates a market-making algorithm for setting prices in financial markets with asymmetric information, and analyzes the properties of artificial markets in which the algorithm is used.
7. A Biological Model of Object Recognition with Feature Learning - Louie, Jennifer Previous biological models of object recognition in cortex have been evaluated using idealized scenes and have hard-coded features, such as the HMAX model by Riesenhuber and Poggio [10].
10. A Note on Object Class Representation and Categorical Perception - Riesenhuber, Maximilian; Poggio, Tomaso We present a novel scheme ("Categorical Basis Functions", CBF) for object class representation in the brain and contrast it to the "Chorus of Prototypes" scheme recently proposed by Edelman.
12. Learning-Based Approach to Real Time Tracking and Analysis of Faces - Kumar, Vinay P.; Poggio, Tomaso This paper describes a trainable system capable of tracking faces and facialsfeatures like eyes and nostrils and estimating basic mouth features such as sdegrees of openness and smile in real time.
13. A Trainable Object Detection System: Car Detection in Static Images - Papageorgiou, Constantine P.; Poggio, Tomaso This paper describes a general, trainable architecture for object detection that has previously been applied to face and peoplesdetection with a new application to car detection in static images.
15. Pre-Attentive Segmentation in the Primary Visual Cortex - Li, Zhaoping Stimuli outside classical receptive fields have been shown to exert significant influence over the activities of neurons in primary visual cortexWe propose that contextual influences are used for pre-attentive visual segmentation, in a new framework called segmentation without classification.
16. Triangulation by Continuous Embedding - Meila, Marina; Jordan, Michael I. When triangulating a belief network we aim to obtain a junction tree of minimum state space.
17. A View on Dyslexia - Geiger, Gad; Lettvin, Jerome Y. More importantly, we describe a regimen of practice with which dyslexics learn a new perceptual strategy for reading.
19. Image-Based View Synthesis - Avidan, Shai; Evgeniou, Theodoros; Shashua, Amnon; Poggio, Tomaso We present a new method for rendering novel images of flexible 3D objects from a small number of example images in correspondence.
20. Comparing Support Vector Machines with Gaussian Kernels to Radial Basis Function Classifiers - Schoelkopf, B.; Sung, K.; Burges, C.; Girosi, F.; Niyogi, P.; Poggio, T.; Vapnik, V. The Support Vector (SV) machine is a novel type of learning machine, based on statistical learning theory, which contains polynomial classifiers, neural networks, and radial basis function (RBF) networks as special cases.
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