Abstract book (pdf) - ICPR 2010

15:00-17:10, Paper MoBT8.24 CANCELED
Image Feature Associations via Local Semantic Structure
Parrish, Nicholas, Colorado State Univ.
Draper, Bruce A., Colorado State Univ.
Most research in object recognition suffers from two distinct weaknesses that limits its effectiveness in natural environments.
First, it tends to rely on labeled training images to learn object models. Second, it tends to assume that the goal is
to recognize a single, dominant foreground object. This paper presents a different method of object recognition that learns
to recognize objects in natural scenes without supervision. The approach uses semantic co-occurance information of local
image features to form object models (called percepts) from groups of image features. These percepts are used to recognize
objects in novel images. It will be shown that this approach is capable of learning object categories without supervision,
and of recognizing objects in complex multi-object scenes. It will also be shown that it outperforms nearest-neighbor
scene recognition.
15:00-17:10, Paper MoBT8.25
Unifying Approach for Fast License Plate Localization and Super-Resolution
Nguyen, Chu Duc, Ec. Centrale de Lyon
Ardabilian, Mohsen, Ec. Centrale de Lyon
Chen, Liming, Ec. Centrale de Lyon
This paper addresses the localization and super resolution of license plate in a unifying approach. Higher quality license
plate can be obtained using super resolution on successive lower resolution plate images. All existing methods assume that
plate zones are correctly extracted from every frame. However, the accurate localization needs a sufficient quality of the
image, which is not always true in real video. Super-resolution on all pixels is a possible but much time consuming alternative.
We propose a framework which interlaces successfully these two modules. First, coarse candidates are found by an weak
but fast license plate detection based on edge map sub-sampling. Then, an improved fast MAP-based super-resolution, using
local phase accurate registration and edge preserving prior, applied on these regions of interest. Finally, our robust ICHTbased
localizer rejects false-alarms and localizes the high resolution license plate more accurately. Experiments which were
conducted on synthetic and real data, proved the robustness of our approach with real-time possibility.
15:00-17:10, Paper MoBT8.26
Dimensionality Reduction for Distributed Vision Systems using Random Projection
Sulic, Vildana, Univ. of Ljubljana
Pers, Janez, Univ. of Ljubljana
Kristan, Matej, Univ. of Ljubljana
Kovacic, Stanislav, Univ. of Ljubljana
Dimensionality reduction is an important issue in the context of distributed vision systems. Processing of dimensionality
reduced data requires far less network resources (e.g., storage space, network bandwidth) than processing of original data.
In this paper we explore the performance of the random projection method for distributed smart cameras. In our tests, random
projection is compared to principal component analysis in terms of recognition efficiency (i.e., object recognition).
The results obtained on the COIL-20 image data set show good performance of the random projection in comparison to
the principal component analysis, which requires distribution of a subspace and therefore consumes more resources of the
network. This indicates that random projection method can elegantly solve the problem of subspace distribution in embedded
and distributed vision systems. Moreover, even without explicit orthogonalization or normalization of random
projection transformation subspace, the method achieves good object recognition efficiency.
15:00-17:10, Paper MoBT8.27
Sensor Fusion for Cooperative Head Localization
Del Bimbo, Alberto, Univ. of Florence
Dini, Fabrizio, Univ. of Florence
Lisanti, Giuseppe, Univ. of Florence
Pernici, Federico, Univ. of Florence
In modern video surveillance systems, pan; tilt; zoom (PTZ) cameras certainly have the potential to allow the coverage of
wide areas with a much smaller number of sensors, compared to the common approach of fixed camera networks. This
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