Abstract book (pdf) - ICPR 2010

15:00-17:10, Paper MoBT8.55
Task-Oriented Evaluation of Super-Resolution Techniques
Tian, Li, NTT Corp.
Suzuki, Akira, NTT Cyber Space Lab.
Koike, Hideki, NTT Corp.
The goal of super-resolution (SR) techniques is to enhance the resolution of low-resolution (LR) images. How to evaluate
the performance of an SR algorithm seems to be forgotten when researchers keep producing algorithms. This paper presents
a task-oriented method for evaluating SR techniques. Our method includes both objective and subjective measures and is
designed from the viewpoint of how SR impacts many essential image processing and vision tasks. We evaluate some
state-of-the-art SR algorithms and the results suggest that different SR algorithms should be utilized for different applications.
In general, they reflect the consistency and conflict between objective and subjective measures as well as computer
vision systems and human vision systems do.
15:00-17:10, Paper MoBT8.56
FeEval – a Dataset for Evaluation of Spatio-Temporal Local Features
Stoettinger, Julian, TU Vienna
Zambanini, Sebastian, TU Vienna
Khan, Rehanullah, TU Vienna
Hanbury, Allan, Information Retrieval Facility
The most successful approaches to video understanding and video matching use local spatio-temporal features as a sparse
representation for video content. Until now, no principled evaluation of these features has been done. We present FeEval,
a dataset for the evaluation of such features. For the first time, this dataset allows for a systematic measurement of the stability
and the invariance of local features in videos. FeEval consists of 30 original videos from a great variety of different
sources, including HDTV shows, 1080p HD movies and surveillance cameras. The videos are iteratively varied by increasing
blur, noise, increasing or decreasing light, median filter, compression quality, scale and rotation leading to a total
of 1710 video clips. Homography matrices are provided for geometric transformations. The surveillance videos are taken
from 4 different angles in a calibrated environment. Similar to prior work on 2D images, this leads to a repeatability and
matching measurement in videos for spatio-temporal features estimating the overlap of features under increasing changes
in the data.
15:00-17:10, Paper MoBT8.57
Performance Evaluation Tools for Zone Segmentation and Classification (PETS)
Seo, Wontaek, Univ. of Maryland
Agrawal, Mudit, Univ. of Maryland
Doermann, David, Univ. of Maryland
This paper describes a set of Performance Evaluation Tools (PETS) for document image zone segmentation and classification.
The tools allow researchers and developers to evaluate, optimize and compare their algorithms by providing a
variety of quantitative performance metrics. The evaluation of segmentation quality is based on the pixel-based overlaps
between two sets of zones proposed by Randriamasy and Vincent. PETS extends the approach by providing a set of metrics
for overlap analysis, RLE and polygonal representation of zones and introduces type-matching to evaluate zone classification.
The software is available for research use.
MoBT9 Upper Foyer
Feature Extraction; Classification; Clustering; Bayesian Methods Poster Session
Session chair: Pietikäinen, Matti (Univ of Oulu)
15:00-17:10, Paper MoBT9.1
Shape Filling Rate for Silhouette Representation and Recognition
An, Guocheng, Chinese Acad. of Sciences
Zhang, Fengjun, Chinese Acad. of Sciences
Wang, Hong’An, Chinese Acad. of Sciences
Dai, Guozhong, Chinese Acad. of Sciences
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