• IEIE Transactions on Smart Processing and Computing
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• v.5 no.3
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• pp.153-163
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• 2016

Local feature extraction methods for images and videos are widely applied in the fields of image understanding and computer vision. However, robust features are detected differently when using the latest feature detectors and descriptors because of diverse image environments. This paper analyzes various feature extraction methods by summarizing algorithms, specifying properties, and comparing performance. We analyze eight feature extraction methods. The performance of feature extraction in various image environments is compared and evaluated. As a result, the feature detectors and descriptors can be used adaptively for image sequences captured under various image environments. Also, the evaluation of feature detectors and descriptors can be applied to driving assistance systems, closed circuit televisions (CCTVs), robot vision, etc.

• Journal of Institute of Control, Robotics and Systems
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• v.20 no.1
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• pp.37-41
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• 2014

FFT (Fast Fourier Transform) has been widely used in various fields such as image processing, voice processing, physics, astronomy, applied mathematics and so forth. Much research has been conducted due to the importance of the FFT and recently new FFT algorithms using a GPU (Graphics Processing Unit) have been developed for the purpose of much faster processing. In this paper, the new optimal FFT algorithm using the Pease FFT algorithm has been proposed reflecting the hardware configuration of a GPGPU (General Purpose computing of GPU). According to the experiments, the proposed algorithm outperformed by between 3% to 43% compared to the CUFFT algorithm.

• Herbal Formula Science
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• v.7 no.1
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• pp.77-87
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• 1999

We studied medicinal theory on processing drugs of Wang-Ang. Wang-Ang used various methods of medicinal processing. He used such as soaking(浸), maceration(泡), washing(洗), elutriation(淘), mixing together(拌), refining powder with water(水飛) and floating(水漂) in medicinal processing with water. He used such as making hot(?), roasting in ashes(?), stir-frying with liquid(炙), parching(炒), heating(燒), baking(焙), stirring-baking(?) and distilling(煉) in medicinal processing with fire. He used such as steaming(蒸), boiling(煮), making hot and soaking(??) in medicinal processing with water and fire. And He used such as cooking(熟), making drugs into gelatin(制膠), making drugs into frostlike powder(制霜法), making drugs into bead(制珠), fermentation(生衣) and compound medicinal processing(複制法).

• Preventive Nutrition and Food Science
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• v.6 no.4
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• pp.257-262
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• 2001

Large amounts of poultry processing wastes including blood, feathers, offal, bones and manure are produced annually from the poultry industry. Over the past years, these products have been wasted and now there is a need for the treatment of these processing wastes. These processing wastes could be either discarded, a rather expensive option considering the cost of sewage disposal, or processed into animal feed or food for human consumption. This paper mainly deals with the various methods through which the different poultry processing wastes have been further processed and/or utilized for human flood or animal consumption. This paper also reviews steps involved in general poultry processing.

• Journal of the Institute of Convergence Signal Processing
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• v.16 no.1
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• pp.9-14
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• 2015

Outlier detection and removal is a crucial step needed for various image processing applications such as image registration. Random Sample Consensus (RANSAC) is known to be the best algorithm so far for the outlier detection and removal. However RANSAC requires a cosiderable computation time. To drastically reduce the computation time while preserving the comparable quality, a outlier detection and removal method based on modified K-means is proposed. The original K-means was conducted first for matching point pairs and then cluster merging and member exclusion step are performed in the modification step. We applied the methods to various images with highly repetitive patterns under several geometric distortions and obtained successful results. We compared the proposed method with RANSAC and showed that the proposed method runs 3~10 times faster than RANSAC.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.1
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• pp.54-62
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• 2018

According to evolution of technologies, many devices related to various applications were researched. The advanced driver assistance system is a famous technique effected from the evolution. The technique of driver assistance uses image recognition methods to collect exactly information around the vehicle. The computing power of driver assistance device has become more improved than in the past. However, it's difficult that processed various recognition methods at real-time. We propose new frame skip method to process various recognition methods at real-time in the limited hardware. In the previous researches, frame skip rate was set up static values, thus the number of processed frames through recognition methods was smaller. We set up the frame skip rate dynamically using a driving condition of vehicle through speed and acceleration value, in addition, the number of processed frames was maximized. The performance is improved more 32.5% than static frame skip method.

• Journal of IKEEE
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• v.26 no.4
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• pp.742-745
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• 2022

In this paper, we propose a hybrid image processing and deep learning-based method for detecting the presence of power lines in infrared images. Deep learning-based methods can learn feature vectors from a large number of data without much effort, resulting in outstanding performances in various fields. However, it is difficult to apply human intuition to the deep learning-based methods while image processing techniques can be used to apply human intuition. Based on these, we propose a method that exploits both advantages to detect the existence of power lines in infrared images. To this end, five methods have been applied and compared to find the most effective image processing technique for detecting the presence of power lines. As a result, the proposed method achieves 99.48% of accuracy which is higher than those of methods based on either image processing or deep learning.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.10
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• pp.4908-4928
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• 2017

Recent research into wireless sensor network (WSN)-related technology that senses various data has recognized the need for spatio-temporal queries for searching necessary data from wireless sensor nodes. Answers to the queries are transmitted from sensor nodes, and for the efficient transmission of the sensed data to the application server, research on index processing methods that increase accuracy while reducing the energy consumption in the node and minimizing query delays has been conducted extensively. Previous research has emphasized the importance of accuracy and energy efficiency of the sensor node's routing process. In this study, we propose an itinerary-based R-tree (IR-tree) to solve the existing problems of spatial query processing methods such as efficient processing and expansion of the query to the spatio-temporal domain.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1993.06a
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• pp.981-984
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• 1993

During the past decade, several specific hardwares for fast fuzzy inference have been developed. Most of them are dedicated to a specific inference method and thus cannot support other inference methods. In this paper, we present a hardware architecture called KAFA(KAist Fuzzy Accelerator) which provides various fuzzy inference methods and fuzzy set operators. The architecture has SIMD structure, which consists of two parts; system control/interface unit(Main Controller) and arithmetic units(FPEs). Using the parallel processing technology, the KAFA has the high performance for fuzzy information processing. The speed of the KAFA holds promise for the development of the new fuzzy application systems.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2015.05a
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• pp.281-284
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• 2015

Digital image processing is utilized in various application fields by rapid development of memory cell. However, the noise occurs with various causes in the process of data processing process and various methods have been studied in order to remove such noises. In general, the image is damaged by the mixed noise which has different characteristics each other. This paper proposed a filter algorithm which processes the data according to shape of noise in order to mitigate the impact of the mixed noise added to the image. In addition, this paper compared this filter algorithm with the current methods and used PSNR(peak signal to noise ratio) as a criterion of judgment.