Jong-Eun Ha

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This paper describes a method for image classification and retrieval for natural and urban scenes. The proposed algorithm is based on hierarchical image contents analysis. First image is classified as urban or natural according to color and edge distribution properties. Additionally scene is classified according to its conditions: illumination, weather,(More)
This paper deals with visual place recognition by adopting visual categorization algorithm of bag of words. We aim to recognize different categories of room by applying bag of words algorithm. Experiments are done using real indoor images and it is shown that it can be used in the visual place recognition.
Localization and mapping is important in autonomous navigation of mobile robot. In this paper, we deal with localization using 2D laser range finder and IR positioning sensor. 2D grid map is built in local coordinate using data from laser range finder. And it is integrated into global coordinate using information from IR landmark. It can provide consistent(More)
In this paper, we present a new algorithm for the calibration of a camera and the recovery of 3D scene structure up to a scale from image sequences using known angles between lines in the scene. The proposed method computes the intrinsic parameters of camera using the invariance of angles under the similarity transformation. Speci"cally, we recover the(More)
This paper proposes a method to detect calibration patterns for accurate camera calibration under complicated backgrounds and uneven lighting conditions of industrial fields. Required to measure object dimensions, the preprocessing of camera calibration must be able to extract calibration points from a calibration pattern. However, industrial fields for(More)
Recently, self-calibration algorithms that use only the information in the image have been actively researched. But most algorithms require bundle adjustment in the projective reconstruction or in the nonlinear minimization. We propose a practical self-calibration algorithm that only requires a linear projective reconstruction. We overcome the sensitivity(More)