Abstract book (pdf) - ICPR 2010

This contribution presents a method for incorporating information given by a lane estimation system into the pedestrian
recognition task. The lane in front of the vehicle is represented by a three dimensional set of points belonging to the middle
of the road. A cascaded classifier solves the first stage of pedestrian recognition task delivering a list of detections in a camera
image. We present a fusion system which combines the information provided by the cascaded classifier and the lane estimation.
The fusion system delivers a probability map of the environment in front of the vehicle. The map indicates regions in
front of the vehicle which with a certain probability contain a relevant detected pedestrian.
14:30-14:50, Paper WeBT2.4
PILL-ID: Matching and Retrieval of Drug Pill Imprint Images
Lee, Young-Beom, Korea Univ.
Park, Unsang, Michigan State Univ.
Jain, Anil, Michigan State Univ.
Automatic illicit drug pill matching and retrieval is becoming an important problem due to an increase in the number of
tablet type illicit drugs being circulated in our society. We propose an automatic method to match drug pill images based on
the imprints appearing on the tablet. This will help identify the source and manufacturer of the illicit drugs. The feature
vector extracted from tablet images is based on edge localization and invariant moments. Instead of storing a single template
for each pill type, we generate multiple templates during the edge detection process. This circumvents the difficulties during
matching due to variations in illumination and viewpoint. Experimental results using a set of real drug pill images (822 illicit
drug pill images and 1,294 legal drug pill images) showed 76.74% (93.02%) rank one (rank-20) matching accuracy.
14:50-15:10, Paper WeBT2.5
Identifying Gender from Unaligned Facial Images by Set Classification
Chu, Wen-Sheng, Acad. Sinica
Huang, Chun-Rong, Acad. Sinica
Chen, Chu-Song, Acad. Sinica
Rough face alignments lead to suboptimal performance of face identification systems. In this study, we present a novel approach
for identifying genders from facial images without proper face alignments. Instead of using only one input for test,
we generate an image set by randomly cropping out a set of image patches from a neighborhood of the face detection region.
Each image set is represented as a subspace and compared with other image sets by measuring the canonical correlation between
two associated subspaces. By finding an optimal discriminative transformation for all training subspaces, the proposed
approach with unaligned facial images is shown to outperform the state-of-the-art methods with face alignment.
WeBT3 Dolmabahçe Hall A
Shape Modeling - II Regular Session
Session chair: Imiya, Atsushi (Chiba Univ.)
13:30-13:50, Paper WeBT3.1
Detection of Shapes in 2D Point Clouds Generated from Images
Su, Jingyong, Florida State Univ.
Zhu, Zhiqiang, Florida State Univ.
Srivastava, Anuj, Florida State Univ.
Huffer, Fred W., Florida State Univ.
We present a novel statistical framework for detecting pre-determined shape classes in 2D cluttered point clouds, which are
in turn extracted from images. In this model based approach, we use a 1D Poisson process for sampling points on shapes, a
2D Poisson process for points from background clutter, and an additive Gaussian model for noise. Combining these with a
past stochastic model on shapes of continuous 2D contours, and optimization over unknown pose and scale, we develop a
generalized likelihood ratio test for shape detection. We demonstrate the efficiency of this method and its robustness to clutter
using both simulated and real data.
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