• Proceedings of the IEEK Conference
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• 2000.11d
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• pp.41-44
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• 2000

In this paper, we present an efficient face detection algorithm for locating vertical views of human faces in complex scenes. The algorithm models the distribution of human skin color in YCbCr color space and find various ace candidate regions. Face candidate regions are found by thresholding with predetermined thresholds. For each of these face candidate regions, The sobel edge operator is used to find edge regions. For each edge region, we used an ellipse detection algorithm which is similar to hough transform to refine the candidate region. Finally if a substantial number of he facial features (eye, mouth) are found successfully in the candidate region, we determine he ace candidate region as a face region. e show empirically that the presented algorithm an find the face region very well in the complex scenes.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.41 no.1
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• pp.1-12
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• 2004

In this paper, we present an identificable face (n face) detection method robust to color degradation and partial veiling. This method is composed of three parts: segmentation of face candidate regions, extraction of face candidate windows, and decision of veiling. In the segmentation of face candidate regions, face candidate regions are detected by finding skin color regions and facial components such as eyes, a nose and a mouth, which may have degraded colors, from an input image. In the extraction of face candidate windows, face candidate windows which have high potentials of faces are extracted in face candidate regions. In the decision of veiling, using an eigenface method, a face candidate window whose similarity with eigenfaces is maximum is determined and whether facial components of the face candidate window are veiled or not is determined in the similar way. Experimental results show that the proposed method yields better the detection rate by about $11.4\%$ in test DB containing color-degraded faces and veiled ones than a conventional method without considering color degradation and partial veiling.

• Journal of the Korea Society of Computer and Information
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• v.29 no.2
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• pp.79-86
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• 2024

In this paper, a novel visual tracking method which can utilize the context of object regions is presented. Conventional methods have the inherent problem of treating all candidate regions independently, where the tracker could not successfully discriminate regions with similar appearances. This was due to lack of contextual modeling in a given scene, where all candidate object regions should be taken into consideration when choosing a single region. The goal of the proposed method is to encourage feature exchange between candidate regions to improve the discriminability between similar regions. It improves upon conventional methods that only consider a single region, and is implemented by employing the MLP-Mixer model for enhanced feature exchange between regions. By implementing channel-wise, inter-region interaction operation between candidate features, contextual information of regions can be embedded into the individual feature representations. To evaluate the performance of the proposed tracker, the large-scale LaSOT dataset is used, and the experimental results show a competitive AUC performance of 0.560 while running at a real-time speed of 65 fps.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.3
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• pp.1301-1314
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• 2018

In this paper, we present a low-cost fire detection system using a thermal camera and a smartphone. The developed system collects thermal and RGB videos from the developed camera. To detect fire, candidate fire regions are extracted from videos obtained using a thermal camera. The block mean of variation of adjacent frames is measured to analyze the dynamic characteristics of the candidate fire regions. After analyzing the dynamic characteristics of regions of interest, a fire is determined by the candidate fire regions. In order to evaluate the performance of our system, we compared with a smoke detector, a heat detector, and a flame detector. In the experiments, our fire detection system showed the excellent performance in detecting fire with an overall accuracy rate of 97.8 %.

• Proceedings of the IEEK Conference
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• 2000.06d
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• pp.47-49
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• 2000

In this paper we propose the multiple local search method(MLSM) based on the motion information of the neighbor blocks. In the proposed method motions are estimated from the multiple searches of many candidate local search regions. To reduce the additional search points we avoid to search the same candidate regions previously visited using the distance from the initial search point to the recently found vector points. In the simulation the proposed method shows more excellent results than that of other gradient based method especially in the search of motion boundary.

• Journal of the Institute of Convergence Signal Processing
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• v.10 no.3
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• pp.164-169
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• 2009

This paper proposes a road detection method using BP(Back-Propagation) neural network based on texture information of the each candidate road region segmented for satellite images. To segment the candidate road regions, the histogram-based binarization method proposed by N.Otsu is firstly performed and the neighboring regions surrounding road regions are then removed. And after extracting the principal color using the histogram of the segmented foreground, the candidate road regions are classified into the regions within ${\pm}25$ of the principal color. Finally, the road regions are segmented using BP neural network based on texture information of the candidate regions. The texture information in this paper is calculated using co-occurrence matrix and is used as an input data of the BP neural network. The proposed method is based on the fact that the road has the constant intensity and shape. The experiment demonstrated the validity of the proposed method and showed 90% detection accuracy for the various images.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.14 no.2
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• pp.885-892
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• 2013

In this paper, we propose a method of effectively segmenting character regions by using texture and depth features in 3D stereoscopic images. The suggested method is mainly composed of four steps. The candidate character region extraction step extracts candidate character regions by using texture features. The character region localization step obtains only the string regions in the candidate character regions. The character/background separation step separates characters from background in the localized character areas. The verification step verifies if the candidate regions are real characters or not. In experimental results, we show that the proposed method can extract character regions from input images more accurately compared to other existing methods.

• Proceedings of the IEEK Conference
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• 2000.07b
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• pp.906-909
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• 2000

Conventional caption extraction methods use the difference between frames or color segmentation methods from the whole image. Because these methods depend heavily on heuristics, we should have a priori knowledge of the captions to be extracted. Also they are difficult to implement. In this paper, we propose a method that uses little heuristics and simplified algorithm. We use topographical features of characters to extract the character points and use KMST(Kruskal minimum spanning tree) to extract the candidate regions for captions. Character regions are determined by testing several conditions and verifying those candidate regions. Experimental results show that the candidate region extraction rate is 100%, and the character region extraction rate is 98.2%. And then we can see the results that caption area in complex images is well extracted.

• Asian-Australasian Journal of Animal Sciences
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• v.25 no.5
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• pp.613-620
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• 2012

Marbling from intramuscular fat is an important trait of meat quality and has an economic benefit for the beef industry. Quantitative trait loci (QTL) fine mapping was performed to identify the marbling trait in 266 Hanwoo steers using a 10K single nucleotide polymorphism panel with the combined linkage and linkage disequilibrium method. As a result, we found nine putative QTL regions for marbling: three on BTA6, two on BTA17, two on BTA22, and two on BTA29. We detected candidate genes for marbling within 1 cM of either side of the putative QTL regions. Additionally, to understand the functions of these candidate genes at the molecular level, we conducted a functional categorization using gene ontology and pathway analyses for those genes involved in lipid metabolism or fat deposition. In these putative QTL regions, we found 95 candidate genes for marbling. Using these candidate genes, we found five genes that had a direct interaction with the candidate genes. We also found SCARB1 as a putative candidate gene for marbling that involves fat deposition related to cholesterol transport.

• Proceedings of the KSRS Conference
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• 2005.10a
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• pp.571-574
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• 2005

In this paper, we propose an efficient image segmentation technique for large-sized monochrome images using a hybrid approach which combines threshold and region-based techniques. First, an image is partitioned into fixed-size blocks and for each block the representative intensity is determined by averaging pixel intensities within the block. Next, the neighborhood blocks that have similar characteristics with respect to a specific threshold are merged in order to form candidate regions. Finally, those candidate regions are refined to get final target object regions by merging regions considering the spatial locality and certain criteria. We have performed experiments on images selected from various domains and showed that our technique was able to extract target object regions appropriately from most images.