Abstract book (pdf) - ICPR 2010

This paper presents a new approach for hand rotation and grasping tracking from a single IR camera. For the complexity and
ambiguity of hand pose, it is difficult to track hand pose and view variations simultaneously from a single camera. We propose
a cylindrical manifold embedding for one dimensional hand pose variation and cyclic viewpoint variation. A hand pose shape
from a specific viewpoint can be generated from an embedding point on the cylindrical manifold after learning nonlinear
generative models from the embedding space to the corresponding observed shape. Hand grasping with simultaneous hand
rotation is tracked using particle filter on the manifold space. Experimental results for synthetic and real data show accurate
tracking of grasping hand with rotation. The proposed approach shows potentials for advanced user interface in dark environments.
14:50-15:10, Paper WeBT1.5
Particle Filter Tracking with Online Multiple Instance Learning
Ni, Zefeng, Univ. of California, Santa Barbara
Sunderrajan, Santhoshkumar, Univ. of California, Santa Barbara
Rahimi, Amir, Univ. of California, Santa Barbara
Manjunath, B. S., Univ. of California, Santa Barbara
This paper addresses the problem of object tracking by learning a discriminative classifier to separate the object from its
background. The online-learned classifier is used to adaptively model object’s appearance and its background. To solve the
typical problem of erroneous training examples generated during tracking, an online multiple instance learning (MIL) algorithm
is used by allowing false positive examples. In addition, particle filter is applied to make best use of the learned classifier
and help to generate a better representative set of training examples for the online MIL learning. The effectiveness of the
proposed algorithm is demonstrated in some challenging environments for human tracking.
WeBT2 Topkapı Hall A
Pattern Recognition Systems and Applications - I Regular Session
Session chair: Fred, Ana Luisa Nobre (Instituto Superior Técnico)
13:30-13:50, Paper WeBT2.1
A Test of Granger Non-Causality based on Nonparametric Conditional Independence
Seth, Sohan, Univ. of Florida
Principe, Jose, Univ. of Florida
In this paper we describe a test of Granger non-causality from the perspective of a new measure of nonparametric conditional
independence. We apply the proposed test on two synthetic nonlinear problems where linear Granger causality fails and
show that the proposed method is able to derive the true causal connectivity effectively.
13:50-14:10, Paper WeBT2.2
Haar Random Forest Features and SVM Spatial Matching Kernel for Stonefly Species Identification
Larios, Natalia, Univ. of Washington
Soran, Bilge, Univ. of Washington
Shapiro, Linda,
Martinez-Muñoz, Gonzalo, Univ. Autonoma de Madrid
Lin, Junyuan, Oregon State Univ.
Dietterich, Thomas G., Oregon State Univ.
This paper proposes an image classification method based on extracting image features using Haar random forests and combining
them with a spatial matching kernel SVM. The method works by combining multiple efficient, yet powerful, learning
algorithms at every stage of the recognition process. On the task of identifying aquatic stonefly larvae, the method has stateof-the-art
or better performance, but with much higher efficiency.
14:10-14:30, Paper WeBT2.3
Incorporating Lane Estimation as Context Source in Pedestrian Recognition Task
Szczot, Magdalena, Daimler AG
Dannenmann, Iris, Daimler AG
Löhlein, Otto, Daimler AG
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