• Journal of KIISE:Software and Applications
• /
• v.36 no.11
• /
• pp.936-946
• /
• 2009

A graph cuts method has recently attracted a lot of attentions for image segmentation, as it can globally minimize energy functions composed of data term that reflects how each pixel fits into prior information for each class and smoothness term that penalizes discontinuities between neighboring pixels. In previous approaches to graph cuts-based automatic image segmentation, GMM(Gaussian mixture models) is generally used, and means and covariance matrixes calculated by EM algorithm were used as prior information for each cluster. However, it is practicable only for clusters with a hyper-spherical or hyper-ellipsoidal shape, as the cluster was represented based on the covariance matrix centered on the mean. For arbitrary-shaped clusters, this paper proposes graph cuts-based image segmentation using mean shift analysis. As a prior information to estimate the data term, we use the set of mean trajectories toward each mode from initial means randomly selected in $L^*u^*{\upsilon}^*$ color space. Since the mean shift procedure requires many computational times, we transform features in continuous feature space into 3D discrete grid, and use 3D kernel based on the first moment in the grid, which are needed to move the means to modes. In the experiments, we investigate the problems of mean shift-based and normalized cuts-based image segmentation methods that are recently popular methods, and the proposed method showed better performance than previous two methods and graph cuts-based automatic image segmentation using GMM on Berkeley segmentation dataset.

• Journal of KIISE:Information Networking
• /
• v.35 no.6
• /
• pp.497-509
• /
• 2008

Various algorithms and architectures for efficient packet classification have been widely studied. Packet classification algorithms based on a decision tree structure such as HiCuts and HyperCuts are known to be the best by exploiting the geometrical representation of rules in a classifier. However, the algorithms are not practical since they involve complicated heuristics in selecting a dimension of cuts and determining the number of cuts at each node of the decision tree. Moreover, the cutting is not efficient enough since the cutting is based on regular interval which is not related to the actual range that each rule covers. In this paper, we proposed a new efficient packet classification algorithm using a range cutting. The proposed algorithm primarily finds out the ranges that each rule covers in 2-dimensional prefix plane and performs cutting according to the ranges. Hence, the proposed algorithm constructs a very efficient decision tree. The cutting applied to each node of the decision tree is optimal and deterministic not involving the complicated heuristics. Simulation results for rule sets generated using class-bench databases show that the proposed algorithm has better performance in average search speed and consumes up to 3-300 times less memory space compared with previous cutting algorithms.

• The Bulletin of The Korean Astronomical Society
• /
• v.42 no.2
• /
• pp.74.2-74.2
• /
• 2017

Faint quasars are important to test the possibility that quasars are the main contributor to the cosmic reionization. However, it has been difficult to find faint quasars due to the lack of deep, wide-field imaging data. In this poster, we present our efforts to find faint quasars in the ELAIS-N1 field through the deep data (iAB ~ 25) obtained by the Subaru Hyper Suprime-Cam (HSC) Strategic Program survey. To select reliable quasar candidate, we also use the near-infrared (NIR) data of the Infrared Medium-deep Survey (IMS) and the UKIRT Infrared Deep Sky Survey (UKIDSS) - Deep Extragalactic Survey (DXS). Using multiple-band color cuts, we select high redshift quasar candidates. To confirm them as high redshift quasars, candidates are observed by the SED camera for QUasars in EArly uNiverse (SQUEAN) instrument in several medium band filters that can sample the redshifted Lyman break efficiency. The quasar sample will be used to study the growth of BH and stellar mass, the relation between the quasar activity and the host galaxy, and their contribution to the cosmic re-ionization.