Abstract book (pdf) - ICPR 2010

PASCAL VOC 2007 image benchmark show significant accuracy improvement by the proposed operators as compared
with both the original LBP and other popular texture descriptors such as Gabor filter.
13:30-16:30, Paper WeBCT8.49
Object Localization by Propagating Connectivity via Superfeatures
Chakraborty, Ishani, Rutgers Univ.
Elgammal, Ahmed, Rutgers Univ.
In this paper, we propose a part-based approach to localize objects in cluttered images. We represent object parts as boundary
segments and image patches. A semi-local grouping of parts named superfeatures encodes appearance and connectivity
within a neighborhood. To match parts, we integrate inter-feature similarities and intra-feature connectivity via a relaxation
labeling framework. Additionally, we use a global elliptical shape prior to match the shape of the solution space to that of
the object. To this end, we demonstrate the efficacy of the method for detecting various objects in cluttered images by
comparing them to simple object models.
13:30-16:30, Paper WeBCT8.50
Efficient Object Detection and Matching using Feature Classification
Dornaika, Fadi, Univ. of the Basque Country
Chakik, Fadi, Lebanese Univ.
This paper presents a new approach for efficient object detection and matching in images and videos. We propose a stage
based on a classification scheme that classifies the extracted features in new images into object features and non-object
features. This binary classification scheme has turned out to be an efficient tool that can be used for object detection and
matching. By means of this classification not only the matching process becomes more robust and faster but also the robust
object registration becomes fast. We provide quantitative evaluations showing the advantages of using the classification
stage for object matching and registration. Our approach could lend itself nicely to real-time object tracking and detection.
13:30-16:30, Paper WeBCT8.51
A Discriminative Model for Object Representation and Detection via Sparse Features
Song, Xi, Beijing Inst. of Tech.
Luo, Ping, Sun Yat-Sen Univ.
Lin, Liang, Sun Yat-Sen Univ.
Jia, Yunde, Beijing Inst. of Tech.
This paper proposes a discriminative model that represents an object category with a batch of boosted image patches, motivated
by detecting and localizing objects with sparse features. Instead of designing features carefully and category-specifically
as in previous work, we extract a massive number of local image patches from the positive object instances and
quantize them as weak classifiers. Then we extend the Adaboost algorithm for learning the patch-based model integrating
object appearance and structure information. With the learned model, a few features are activated to localize instances in
the testing images. In the experiments, we apply the proposed method with several public datasets and achieve advancing
performance.
13:30-16:30, Paper WeBCT8.52
A Robust Recognition Technique for Dense Checkerboard Patterns
Dao, Vinh Ninh, The Univ. of Tokyo
Sugimoto, Masanori, The Univ. of Tokyo
The checkerboard pattern is widely used in computer vision techniques for camera calibration and simple geometry acquisition,
both in practical use and research. However, most of the current techniques fail to recognize the checkerboard
pattern under distorted, occluded or discontinuous conditions, especially when the checkerboard pattern is dense. This
paper proposes a novel checkerboard recognition technique that is robust to noise, surface distortion or discontinuity, supporting
checkerboard recognition in dynamic conditions for a wider range of applications. When the checkerboard pattern
is used in a projector camera system for geometry reconstruction, by using epipolar geometry, this technique can recognize
the corresponding positions of the crossing points, even if the checkerboard pattern is only partly detected.
- 224 -