Abstract book (pdf) - ICPR 2010

15:00-17:10, Paper MoBT8.48
A Unified Probabilistic Approach to Feature Matching and Object Segmentation
Kim, Tae Hoon, Seoul National Univ.
Lee, Kyoung Mu, Seoul National Univ.
Lee, Sang Uk, Seoul National Univ.
This paper deals with feature matching and segmentation of common objects in a pair of images, simultaneously. For the
feature matching problem, the matching likelihoods of all feature correspondences are obtained by combining their discriminative
power with the spatial coherence constraint that favors their spatial aggregation via object segmentation. At
the same time, for the object segmentation problem, our algorithm estimates the object likelihood that each subregion is
a commonly existing part in two images by the affinity propagation of the resulted matching likelihoods. Since these two
problems are related to each other, our main idea to solve them is to integrate all the priors about them into a unified framework,
that consists of several correlated quadratic cost functions. Eventually, all matching and object likelihoods are estimated
simultaneously as a solution of linear system of equations. Based on these likelihoods, we finally recover the optimal
feature matches and the common object parts by imposing simple sequential mapping and thresholding techniques, respectively.
The experiments demonstrate the superiority of our algorithm compared with the conventional methods.
15:00-17:10, Paper MoBT8.49
Automatic Restoration of Scratch in Old Archive
Kim, Kyung-Tai, Konkuk Univ.
Kim, Byunggeun, Konkuk Univ.
Kim, Eun Yi, Konkuk Univ.
This paper presents scratch restoration method that can deal with scratches of various lengths and widths in old film. The
proposed method consists of detection and reconstruction. The detection is performed using texture and shape properties
of the scratches: first, each pixel is classified as scratches and non-scratches using a neural network (NN)-based texture
classifier, and then some false alarms are removed by shape filtering. Thereafter, the detected region is reconstructed.
Here, the reconstruction is formulated as energy minimization problem, thus genetic algorithm is used as optimization algorithm.
The experimental result with well-known old films showed the effectiveness of the proposed method.
15:00-17:10, Paper MoBT8.50
Automatic Building Detection in Aerial Images using a Hierarchical Feature based Image Segmentation
Izadi, Mohammad, Simon Fraser Univ.
Saeedi, Parvaneh, Simon Fraser Univ.
This paper introduces a novel automatic building detection method for aerial images. The proposed method incorporates
a hierarchical multilayer feature based image segmentation technique using color. A number of geometrical/regional attributes
are defined to identify potential regions in multiple layers of segmented images. A tree-based mechanism is utilized
to inspect segmented regions using their spatial relationships with each other and their regional/geometrical characteristics.
This process allows the creation of a set of candidate regions that are validated as rooftops based on the overlap between
existing and predicted shadows of each region according to the image acquisition information. Experimental results show
an overall shape accuracy and completeness of 96%.
15:00-17:10, Paper MoBT8.51
Making Visual Object Categorization More Challenging: Randomized Caltech-101 Data Set
Kinnunen, Juha Teemu Ensio, Lappeenranta Univ. of Tech.
Kamarainen, Joni-Kristian, Lappeenranta Univ. of Tech.
Lensu, Lasse, Lappeenranta Univ. of Tech.
Lankinen, Jukka, Lappeenranta Univ. of Tech.
Kalviainen, Heikki, Lappeenranta Univ. of Tech.
Visual object categorization is one of the most active research topics in computer vision, and Caltech-101 data set is one
of the standard benchmarks for evaluating the method performance. Despite of its wide use, the data set has certain weaknesses:
I) the objects are practically in a standard pose and scale in the middle of the images and ii) background varies too
little in certain categories making it more discriminative than the foreground objects. In this work, we demonstrate how
these weaknesses bias the evaluation results in an undesired manner. In addition, we reduce the bias effect by replacing
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