• Journal of Space Technology and Applications
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• v.3 no.2
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• pp.154-164
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• 2023

In space exploration, spectroscopic observation is useful for understanding objects' composition and physical properties. There are various methods for analyzing spectral data, and there are differences depending on the object and the wavelength. This paper introduces a method for analyzing visible & nearinfrared (VNIR) spectral data, which is mainly applied in lunar exploration. The main analysis methods include false color ratio image processing, reflectance pattern analysis, integrated band depth (IBD) processing, and continuum removal as preprocessing before analysis. These spectroscopic analysis methods help to understand the mineral properties of the lunar surface in the VNIR region and can be applied to other celestial bodies such as Mars.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.37 no.2A
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• pp.105-112
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• 2012

Face detection at a distance faces is very challenging since images are often degraded by blurring and noise as well as low resolution. This paper proposes an improved face detection method with AdaBoost filtering and sequential testing stages with color and shape information. The conventional AdaBoost filter detects face regions but often generates false alarms. The face detection method is improved by adopting sequential testing stages in order to remove false alarms. The testing stages comprise skin-color test and variable edge-mask filtering. The skin-color filtering is composed of two steps, which involve rectangular window regions and individual pixels to generate binary face clusters. The size of the variable edge-mask is determined by the ellipse which is estimated from the face cluster. The validation of the horizontal and vertical ratio of the mask is also investigated. In the experiments, the efficacy of the proposed algorithm is proved by images captured by a CCTV and a smart-phone

• 한국HCI학회:학술대회논문집
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• 2008.02a
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• pp.215-217
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• 2008

The most widely used method for searching contents of mot ion picture compares contents by extracted cuts. The cut extract ion methods, such as CHD(Color Histogram Difference) or ECR(Edge Change Ratio), are very weak at modifications such as cropping, resizing and low bit rate. The suggested method uses audio contents for indexing and searching to make search be robust against these modification. Scenes of audio contents are extracted for modification-robust search. And based on these scenes, make spectral powers binary on each frequency bin. in the time-frequency domain. The suggested method shows failure rate less than 1% on the false positive error and the true negative error to the modified(using cropping, clipping, row bit rate, addtive frame) contents.

• The Journal of the Korea Contents Association
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• v.9 no.4
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• pp.1-11
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• 2009

To classifying and filtering of adult images, in recent the computer vision techniques are actively investigated because rapidly increase for the amount of adult images accessible on the Internet. In this paper, we investigate and develop the tool filtering of adult images using skin color model. The tool is consisting of two steps. In the first step, we use a skin color classifier to extract skin color regions from an image. In the nest step, we use a region feature classifier to determine whether an image is an adult image or not an adult image depending on extracted skin color regions. Using histogram color model, a skin color classifier is trained for RGB color values of adult images and not adult images. Using SVM, a region feature classifier is trained for skin color ratio on 29 regions of adult images. Experimental results show that suggested classifier achieve a detection rate of 92.80% with 6.73% false positives.

• 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.

• Proceedings of the IEEK Conference
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• 2000.06d
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• pp.7-10
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• 2000

There are many Problems such as low detection ratio, velocity and increase of false hit ratio on the detection of gradual scene changes with the previous shot transition detection algorithms. In this paper, we Propose an improved dissolve detection method using color information on low-frequency subband and edge elements on high-frequency subband. The Possible dissolve transition are found by analyzing the edge change ratio in the high-frequency subband with edge elements of each direction. Using the double chromatic difference on the lowest frequency subband, we have the improvement of the dissolve detection ratio. The simulation results show that the performance of the proposed algorithm is better than the conventional one for dissolve detection on a diverse set of uncompressed video sequences.

• Journal of Korea Multimedia Society
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• v.20 no.1
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• pp.17-31
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• 2017

The aim of the paper is to present an object recognition method toward augmented reality system that utilizes existing education instruments that was designed without any consideration on image processing and recognition. The light reflection, sizes, shapes, and color range of the existing target education instruments are major hurdles to our object recognition. In addition, the real-time performance requirements on embedded devices and user experience constraints for children users are quite challenging issues to be solved for our image processing and object recognition approach. In order to meet these requirements we employed a method cascading light-weight weak classification methods that are complimentary each other to make a resultant complicated and highly accurate object classifier toward practically reasonable precision ratio. We implemented the proposed method and tested the performance by video with more than 11,700 frames of actual playing scenario. The experimental result showed 0.54% miss ratio and 1.35% false hit ratio.

• Journal of Korea Multimedia Society
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• v.17 no.3
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• pp.287-293
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• 2014

Early fire detection is very important to avoid loss of lives and material damage. The conventional smoke detector sensors have difficulties in detecting smoke in large outdoor areas. The video-based smoke detection can overcome these drawbacks. This paper proposes a new smoke detection method in video sequences. It uses the ratio of variation rate of subband energy in the wavelet transform domain. In order to reduce the false alarm, candidate smoke blocks are detected by using motion, decrease of chromaticity and the average intensity of block in the YUV color space. Finally, it decides whether the candidate smoke blocks are smokes or not by using their temporal changes of subband energies in the wavelet transform domain. Experimental results show that the proposed method noticeably increases the accuracy of smoke detection and reduces false alarm compared with the conventional smoke detection methods using wavelets.

• Journal of Korea Multimedia Society
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• v.19 no.12
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• pp.1909-1918
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• 2016

This paper presents a unified framework for joint Convolutional Neural Network (CNN) based vehicle detection by leveraging multi-spectral image pairs. With the observation that under challenging environments such as night vision and limited light source, vehicle detection in a single color image can be more tractable by using additional far-infrared (FIR) image, we design joint CNN architecture for both RGB and FIR image pairs. We assume that a score map from joint CNN applied to overall image can be considered as confidence of vehicle existence. To deal with various scale ratios of vehicle candidates, multi-scale images are first generated scaling an image according to possible scale ratio of vehicles. The vehicle candidates are then detected on local maximal on each score maps. The generation of overlapped candidates is prevented with non-maximal suppression on multi-scale score maps. The experimental results show that our framework have superior performance than conventional methods with a joint framework of multi-spectral image pairs reducing false positive generated by conventional vehicle detection framework using only single color image.

• International Journal of Advanced Culture Technology
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• v.8 no.4
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• pp.229-234
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• 2020

Dehazing significantly improves image quality by restoring the loss of contrast and color saturation for images taken in the presence. However, when applied to images not taken according to the prior information, dehazing can cause unintended degradation of image quality. To avoid unintended degradations, we present a hazy scene detection algorithm using a single image based on the distributions of hue, saturation, and dark channel. Through a heuristic approach, we find out statistical characteristics of the distribution of hue, saturation, and dark channels in the hazy scene and make a detection model using them. The proposed method can precede the dehazing to prevent unintended degradation. The detection performance evaluated with a set of test images shows a high hit rate with a low false alarm ratio. Ultimately the proposed method can be used to control the effect of dehazing so that the dehazing can be applied to wide variety of images without unintended degradation of image quality.