• Journal of the Institute of Convergence Signal Processing
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• v.12 no.1
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• pp.7-12
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• 2011

In this paper, we propose the technique detecting interest object region effectively in the images from periscope of submarine based on self-generated template. First, we extract the sea-sky line, and divide it into sky and sea area from background region based on the sea-sky line. In each divided background region, the blocks which can be represented in each background region are set as a reference template. After dividing an image into several same size of blocks, we apply multi template matching to the divided search blocks and histogram template to divide the image into object region and background region. Proposed algorithm is adapted to various images in which objects exist in the background of sea and sky. We verified that proposed algorithm performed properly without given informmed prby prior learning.ropso, regardless of the slope of sea-sky line and the locmed p of object based on sea-sky line, we verified that the objects region was segmented effectively from the input image.

• Journal of Korea Multimedia Society
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• v.17 no.2
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• pp.132-142
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• 2014

Stereo camera is drawing attention as an essential sensor for future intelligence robot system since it has the advantage of acquiring not only distance but also other additive information for an object. However, it cannot match correlated point on target image for low textured region or periodic patterned region such as wall of building or room. In this paper, we propose a stereo matching technique that increase the matching performance by fusing belief propagation stereo matching algorithm and local distance measurements of 2D-laser range finder in order to overcome this kind of limitation. The proposed technique adds laser measurements by referring quad-tree based segment information on to the local-evidence of belief propagation stereo matching algorithm, and calculates compatibility function by reflecting over-segmented information. Experimental results of the proposed method using simulation and real test images show that the distance information for some low textured region can be acquired and the discontinuity of depth information is preserved by using segmentation information.

• Korean Journal of Remote Sensing
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• v.38 no.5_1
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• pp.597-608
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• 2022

Remotely sensed data have been used in various fields, such as disasters, agriculture, urban planning, and the military. Recently, the demand for the multitemporal dataset with the high-spatial-resolution has increased. This manuscript proposed an automatic image matching algorithm using a deep learning technique to utilize a multitemporal remotely sensed dataset. The proposed deep learning model was based on High Resolution Net (HRNet), widely used in image segmentation. In this manuscript, denseblock was added to calculate the correlation map between images effectively and to increase learning efficiency. The training of the proposed model was performed using the multitemporal orthophotos of the National Geographic Information Institute (NGII). In order to evaluate the performance of image matching using a deep learning model, a comparative evaluation was performed. As a result of the experiment, the average horizontal error of the proposed algorithm based on 80% of the image matching rate was 3 pixels. At the same time, that of the Zero Normalized Cross-Correlation (ZNCC) was 25 pixels. In particular, it was confirmed that the proposed method is effective even in mountainous and farmland areas where the image changes according to vegetation growth. Therefore, it is expected that the proposed deep learning algorithm can perform relative image registration and image matching of a multitemporal remote sensed dataset.

• Korean Journal of Remote Sensing
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• v.39 no.2
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• pp.223-232
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• 2023

A widely used method for monitoring land cover using high-resolution satellite images is to classify the images based on the colors of the objects of interest. In urban areas, not only major objects such as buildings and roads but also vegetation such as trees frequently appear in high-resolution satellite images. However, the colors of vegetation objects often resemble those of other objects such as buildings, roads, and shadows, making it difficult to accurately classify objects based solely on color information. In this study, we propose a method that can accurately classify not only objects with various colors such as buildings but also vegetation objects. The proposed method uses the normalized difference vegetation index (NDVI) image, which is useful for detecting vegetation objects, along with the RGB image and classifies objects into subclasses. The subclass classification results are fused, and the final classification result is generated by combining them with the image segmentation results. In experiments using Compact Advanced Satellite 500-1 imagery, the proposed method, which applies the NDVI and subclass classification together, showed an overall accuracy of 87.42%, while the overall accuracy of the subchannel classification technique without using the NDVI and the subclass classification technique alone were 73.18% and 81.79%, respectively.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.39 no.3
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• pp.11-22
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• 2002

The design of optimal filter yielding optimal texture feature separation is a most effective technique in many torture analyzing areas, such as perception of surface, object, shape and depth. But, most optimal filter design approaches are restricted to the issue of computational complexity and supervised problems. In this paper, Our proposed method yields new insight into the design of optimal Gabor filters for segmenting multiple texture images. The optimal frequency of Gator filter is turned to the optimal frequency of the distinct texture in frequency domain. In order to show the performance of the designed filters, we have attempted to build a various texture images. Our experimental results show that the performance of the system is very successful.

• Journal of the Korean Institute of Intelligent Systems
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• v.13 no.4
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• pp.461-468
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• 2003

In a sequence of images obtained by surveillance cameras, facial regions appear very small and their colors change abruptly by lighting condition. This paper proposes a new face detection scheme, robust on complex background, small size, and lighting conditions. The proposed method is consisted of three processes. In the first step, the candidates for the face regions are selected using face color distribution and motion information. In the second stage, the non-face regions are removed using face color ratio, boundary ratio, and average of column-wise intensity variation in the candidates. The face regions containing eyes and mouth are segmented and classified, and then they are scored using their topological relations in the last step. To speed up and improve a performance the above process, a block based image segmentation technique is used. The experiments have shown that the proposed algorithm detects faced regions with more than 91% of accuracy and less than 4.3% of false alarm rate.

• Journal of Korea Multimedia Society
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• v.10 no.2
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• pp.218-225
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• 2007

This paper presents a technique to detect a document skew that often occurs during document scanning. To correct a skewed document is essential for automatic processing system including character segmentation, character recognition and so on. The proposed algorithm can detect a skew angle exactly by searching characters baselines that have slant information of the document within a candidated area. To reduce processing time, we resized the image small and then established a ROI (region of interest) by morphology operations and connected components analysis. We compared our method with the existing method based on morphology operations and proved correctness and efficiency of the proposed algorithm through experiments and analysis with various kind of document images.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2021.11a
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• pp.217-218
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• 2021

The development of artificial intelligence in the field of construction and construction is revitalizing. The performance and development techniques of artificial intelligence are changing rapidly, but if you look at the cases of domestic construction sites, they are using technologies from 5 to 7 years ago. It is right to follow a stable method in consideration of commercialization, but the previous AI development method requires more manpower and time to develop than the current technology. In addition, in order to actively utilize artificial intelligence technology, customized artificial intelligence is required to be applied to ever-changing changes in construction sites. it is the reality As a result, even if good AI technology is secured at the construction site, it is reluctant to introduce it because there is no advantage in terms of time and cost compared to the existing method to apply it only to some processes. Currently, an AI technique with a faster development process and accurate recognition has been developed to cope with a fluid situation, so it will be important to understand and introduce the rapidly changing AI development method.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.42 no.5 s.305
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• pp.29-40
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• 2005

According as base of digital hologram has been magnified, discussion of compression technology is expected as a international standard which defines the compression technique of 3D image and video has been progressed in form of 3DAV which is a part of MPEG. As we can identify in case of 3DAV, the coding technique has high possibility to be formed into the hybrid type which is a merged, refined, or mixid with the various previous technique. Therefore, we wish to present the relationship between various image/video coding techniques and digital hologram In this paper, we propose an efficient coding method of digital hologram using standard compression tools for video and image. At first, we convert fringe patterns into video data using a principle of CGH(Computer Generated Hologram), and then encode it. In this research, we propose a compression algorithm is made up of various method such as pre-processing for transform, local segmentation with global information of object image, frequency transform for coding, scanning to make fringe to video stream, classification of coefficients, and hybrid video coding. Finally the proposed hybrid compression algorithm is all of these methods. The tool for still image coding is JPEG2000, and the toots for video coding include various international compression algorithm such as MPEG-2, MPEG-4, and H.264 and various lossless compression algorithm. The proposed algorithm illustrated that it have better properties for reconstruction than the previous researches on far greater compression rate above from four times to eight times as much. Therefore we expect that the proposed technique for digital hologram coding is to be a good preceding research.

• Journal of Korean Society for Geospatial Information Science
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• v.20 no.1
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• pp.83-91
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• 2012

Crown density, which is defined as the proportion of the forest floor concealed by tree crown, is important and useful information in various fields. Previous methods of measuring crown density have estimated crown density by interpreting aerial photographs or through a ground survey. These are time-consuming, labor-intensive, expensive and inconsistent approaches, as they involve a great deal of subjectivity and rely on the experience of the interpreter. In this study, the crown density of a forest in Korea was estimated using KOMPSAT-2 high-resolution satellite images. Using the image segmentation technique and stand information of the digital forest map, the forest area was divided into zones. The crown density for each segment was determined using the discriminant analysis method and the forest ratio method. The results showed that the accuracy of the discriminant analysis method was about 60%, while the accuracy of the forest ratio method was about 85%. The probability of extraction of candidate to update was verified by comparing the result with the digital forest map.