• Journal of Korean Society for Geospatial Information Science
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• v.21 no.4
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• pp.151-159
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• 2013

The building information should be up-to-date information and propagated rapidly for urban modeling, terrain analysis, life information, navigational system, and location-based services(LBS), hence the most recent and updated data of the building information have been required of researchers. This paper presents the developed system to extract the 3-dimension spatial information from aerial orthoimage and LiDAR data of HSI color model. In particular, this paper presents the image processing algorithm to extract the outline of specific buildings and generate the building polygon from the image using HIS color model, recursive backtracking algorithm and the search maze algorithm. Also, this paper shows the effectivity of the HIS color model in the image segmentation.

• Journal of Information Processing Systems
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• v.8 no.4
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• pp.685-692
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• 2012

B frame bi-directional predictions and the DIRECT mode coding of the H.264 video compression standard necessitate a complex mode decision process, resulting in a long computation time. To make H.264 feasible, this paper proposes an image backtrack-based fast (IBFD) algorithm and evaluates the performances of two promising fast algorithms (i.e., AFDM and IBFD). Evaluation results show that an image backtrack-based fast (IBFD) algorithm can determine DIRECT mode macroblocks with 13% higher accuracy, as compared with the AFDM. Furthermore, IBFD is shown to reduce the motion estimation time of B frames by up to 23% with a negligible quality degradation.

• Journal of the Institute of Convergence Signal Processing
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• v.16 no.1
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• pp.1-8
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• 2015

In many image processing tasks, connected component labeling (CCL) is performed to extract regions of interest. CCL was usually done in a sequential fashion when image resolution was relatively low and there are small number of input channels. As image resolution gets higher up to HD or Full HD and as the number of input channels increases, sequential CCL is too time-consuming to be used in real time applications. To cope with this situation, parallel CCL framework was introduced where multiple cores are utilized simultaneously. Several parallel CCL methods have been proposed in the literature. Among them are NSZ label equivalence (NSZ-LE) method[1], modified 8 directional label selection (M8DLS) method[2], and HYBRID1 method[3]. Soh [3] showed that HYBRID1 outperforms NSZ-LE and M8DLS, and argued that HYBRID1 is by far the best. In this paper we propose an improved hybrid parallel CCL algorithm termed as HYBRID2 that hybridizes M8DLS with label backtracking (LB) and show that it runs around 20% faster than HYBRID1 for various kinds of images.

• Journal of the Institute of Convergence Signal Processing
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• v.16 no.3
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• pp.83-89
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• 2015

Connected component labeling (CCL) is a mandatory step in image segmentation where objects are extracted and uniquely labeled. CCL is a computationally expensive operation and thus is often done in parallel processing framework to reduce execution time. Various parallel CCL methods have been proposed in the literature. Among them are NSZ label equivalence (NSZ-LE) method, modified 8 directional label selection (M8DLS) method, HYBRID1 method, and HYBRID2 method. Soh et al. showed that HYBRID2 outperforms the others and is the best so far. In this paper we propose a new hybrid parallel CCL algorithm termed as HYBRID3 that combines selective four directional label search (S4DLS) with label backtracking (LB). We show that the average percentage speedup of the proposed over M8DLS is around 60% more than that of HYBRID2 over M8DLS for various kinds of images.