• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.2
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• pp.186-191
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• 2020

In this paper, we proposed an optimization technique for efficiently placing vehicles on decks in a vehicle-carrying ship to efficiently handle loading and unloading. For this purpose, we utilized the transformation method of the XML data representing the ship's spatial information, merging and branching algorithm and genetic algorithm, and implemented the function to visualize the optimized vehicle placement results. The techniques of selection, crossover, mutation, and elite preservation, which are used in the conventional genetic algorithms, are used. In particular, the vehicle placement optimization method is proposed by merging and branching the ship space for the vehicle loading. The experimental results show that the proposed merging and branching method is effective for the optimization process that is difficult to optimize with the existing genetic algorithm alone. In addition, visualization results show vehicle layout results in the form of drawings so that experts can easily determine the efficiency of the layout results.

• The KIPS Transactions:PartB
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• v.9B no.2
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• pp.231-236
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• 2002

The classification of the speech-sound block comes from the multi-resolution analysis property of the discrete wavelet transform, which is used to reduce the computational time for the pre-processing of speech recognition. The merging algorithm is proposed to extract vapid speech-sounds in terms of position and frequency range. It performs unvoiced/voiced classification and denoising. Since the merging algorithm can decide the processing parameters relating to voices only and is independent of system noises, it is useful for extracting valid speech-sounds. The merging algorithm has an adaptive feature for arbitrary system noises and an excellent denoising signal-to-noise ratio and a useful system tuning for the system implementation.

• Journal of Positioning, Navigation, and Timing
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• v.6 no.4
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• pp.135-140
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• 2017

This paper proposes a signal merging algorithm to increase the signal-to-noise ratio (SNR) of a GPS correlator output to estimate the roll angle of a rotating vehicle in a weak GPS signal environment. Rotation Locked Loop (RLL) algorithm is used to estimate a roll angle using the characteristics that the power of the GPS signal measured at the receiver of a rotating vehicle varies periodically. First, delay times are calculated to synchronize GPS signals using satellites' and receiver's positions and the rotation frequency of a vehicle, and then correlator outputs are delayed in time and merged with each other, resulting in the increase of an SNR in a correlator output. Finally, simulations are conducted and the performance of the proposed algorithm is validated.

• Journal of Korea Multimedia Society
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• v.19 no.7
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• pp.1116-1126
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• 2016

Image segmentation is widely used in the pre-processing stage of image analysis and, therefore, the accuracy of image segmentation is important for performance of an image-based analysis system. An efficient image segmentation method is proposed, including a filtering process for super-pixels, improved saliency map information, and a merge process. The proposed algorithm removes areas that are not equal or of small size based on comparison of the area of smoothed superpixels in order to maintain generation of a similar size super pixel area. In addition, application of a bilateral filter to an existing saliency map that represents human visual attention allows improvement of separation between objects and background. Finally, a segmented result is obtained based on the suggested merging process without any prior knowledge or information. Performance of the proposed algorithm is verified experimentally.

• Journal of information and communication convergence engineering
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• v.9 no.5
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• pp.487-490
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• 2011

This paper presents splitting and merging algorithm based on adaptive thresholding. The algorithm first divides the image into blocks, and then compares each block using the calculated thresholding value. The blocks which are same are merged using the certain threshold value and different blocks are split unless it satisfies the threshold value. When the block has been merged, maximum and minimum block sizes are determined then the average block size is determined. After the average block size is determined the average intensity and standard deviation of average block is calculated. The process of thresholding is applied to binarize the image. Finally, the experimental results show that the proposed method distinguishes clearly the background with text in the document image.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.60 no.5
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• pp.1023-1029
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• 2011

In the industrial vision systems, image correction has great influence on the overall performance of measurement or inspection. The overall area of distorted image is usually splitted into small control areas, and each area is corrected by its control points. The performance of correction methods using control points can be improved by reduction of control areas because the computational time and memory highly depend on the number of control areas. We develop a merging algorithm that reduces control areas and preserves the correction accuracy. The algorithm merges the splitted control areas by use of quad tree method. Experimental results are presented to verify the usefulness of the proposed method.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.21 no.10
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• pp.2552-2562
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• 1996

In this paper, we propose a segmentation algorithm that can reliably separate moving objects from noisy background in the image sequance received from a camera at the fixed position. The proposed algorithm consists of three processes:generation of the difference image between the input image and the reference image, multilevel quantization of the difference image, and multistagemerging in the quantized image. The quantization process requantizes the difference image based on the multiple threshold values determined bythe histogram analysis. The merging starts from the seed region which created by using the highest threshold value and ends when termination conditions are met. the proposed method has been tested with various real imge sequances containing intruders. The test results show that the proposed algorithm can detect moving objects like intruders very effectively in the noisy environment.

• Journal of the Korea Society of Computer and Information
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• v.11 no.4 s.42
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• pp.283-289
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• 2006

The present image segmentation is what user want to segment image and has been studied for technology in composition of segment object with other images. In this paper, we propose a method of novel semi-automatic image segmentation using gradual region merging and genetic algorithm. Proposed algorithm is edge detection of object using genetic algorithm after selecting object which user want. We segment region of object which user want to based on detection edge using watershed algorithm. We separated background and object in indefinite region using gradual region merge from Segment object. And, we have applicable value which user want by making interface based on GUI for efficient perform of algorithm development. In the experiments, we analyzed various images for proving superiority of the proposed method.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.7
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• pp.2400-2419
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• 2014

Pre-segmented pixels can reduce the difficulty of segmentation and promote the segmentation performance. This paper proposes a novel segmentation method based on merging texture superpixels by computing inner similarity. Firstly, we design a set of Gabor filters to compute the amplitude responses of original image and compute the texture map by a salience model. Secondly, we employ the simple clustering to extract superpixles by affinity of color, coordinates and texture map. Then, we design a normalized histograms descriptor for superpixels integrated color and texture information of inner pixels. To obtain the final segmentation result, all adjacent superpixels are merged by the homogeneity comparison of normalized color-texture features until the stop criteria is satisfied. The experiments are conducted on natural scene images and synthesis texture images demonstrate that the proposed segmentation algorithm can achieve ideal segmentation on complex texture regions.

• Korean Journal of Remote Sensing
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• v.26 no.6
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• pp.731-740
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• 2010

A region merging technique is suggested in this paper for the segmentation of high-spatial resolution imagery. It employs a region growing scheme based on the region adjacency graph (RAG). The proposed algorithm uses directional neighbor-line average feature vectors to improve the quality of segmentation. The feature vector consists of 9 components which includes an observation and 8 directional averages. Each directional average is the average of the pixel values along the neighbor line for a given neighbor line length at each direction. The merging coefficients of the segmentation process use a part of the feature components according to a given merging coefficient order. This study performed the extensive experiments using simulation data and a real high-spatial resolution data of IKONOS. The experimental results show that the new approach proposed in this study is quite effective to provide segments of high quality for the object-based analysis of high-spatial resolution images.