• 대한원격탐사학회지
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• 제23권3호
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• pp.153-160
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• 2007

Forest cover density studies using high resolution satellite data and object oriented classification are limited in India. This article focuses on the potential use of QuickBird satellite data and object oriented classification in forest density mapping. In this study, the high-resolution satellite data was classified based on NDVI/pixel based and object oriented classification methods and results were compared. The QuickBird satellite data was found to be suitable in forest density mapping. Object oriented classification was superior than the NDVI/pixel based classification. The Object oriented classification method classified all the density classes of forest (dense, open, degraded and bare soil) with higher producer and user accuracies and with more kappa statistics value compared to pixel based method. The overall classification accuracy and Kappa statistics values of the object oriented classification were 83.33% and 0.77 respectively, which were higher than the pixel based classification (68%, 0.56 respectively). According to the Z statistics, the results of these two classifications were significantly different at 95% confidence level.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제10권1호
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• pp.364-380
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• 2016

The most popular approach in object classification is based on the bag of visual-words model, which has several fundamental problems that restricting the performance of this method, such as low time efficiency, the synonym and polysemy of visual words, and the lack of spatial information between visual words. In view of this, an object classification based on weakly supervised E2LSH and saliency map weighting is proposed. Firstly, E2LSH (Exact Euclidean Locality Sensitive Hashing) is employed to generate a group of weakly randomized visual dictionary by clustering SIFT features of the training dataset, and the selecting process of hash functions is effectively supervised inspired by the random forest ideas to reduce the randomcity of E2LSH. Secondly, graph-based visual saliency (GBVS) algorithm is applied to detect the saliency map of different images and weight the visual words according to the saliency prior. Finally, saliency map weighted visual language model is carried out to accomplish object classification. Experimental results datasets of Pascal 2007 and Caltech-256 indicate that the distinguishability of objects is effectively improved and our method is superior to the state-of-the-art object classification methods.

• 항공우주기술
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• 제6권1호
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• pp.213-221
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• 2007

본 연구에서는 2002년 4월에 획득된 Hyperion 초분광 원격탐사 자료를 이용하여 산불연료지도 제작을 위한 객체기반 분류 기법을 제시하였으며, 또한 객체기반 분석결과와 화소기반 분석결과를 비교해 보았다. 이를 위해 우선적으로 Hyperion 위성영상에 있는 잡음 화소 보정과 잡음 밴드를 제거하였으며, 또한 정확한 자료 처리를 위해 대기보정을 수행하였다. 산불 연료 지도 제작을 위한 방법은 분광혼합분석(SMA) 처리 결과를 재구성하여 얻었다. 객체 기반 접근 방법은 세그먼트 기반의 endmember 선택방법을 활용하였으며, 화소기반 분석은 표준 분광혼합분석기법을 적용하였다. 검증 및 비교를 위해서는 고해상도 칼라 항공정사영상이 활용되었다.

• 한국멀티미디어학회논문지
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• 제7권12호
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• pp.1657-1664
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• 2004

We propose a method that classifies images into two object types man-made and natural objects. A central object is extracted from each image by using central object extraction method[1] before classification. A central object in an images defined as a set of regions that lies around center of the image and has significant color distribution against its surrounding. We define three measures to classify the object images. The first measure is energy of edge direction histogram. The energy is calculated based on the direction of only non-circular edges. The second measure is an energy difference along directions in Gabor filter dictionary. Maximum and minimum energy along directions in Gabor filter dictionary are selected and the energy difference is computed as the ratio of the maximum to the minimum value. The last one is a shape of an object, which is also represented by Gabor filter dictionary. Gabor filter dictionary for the shape of an object differs from the one for the texture in an object in which the former is computed from a binarized object image. Each measure is combined by using majority rule tin which decisions are made by the majority. A test with 600 images shows a classification accuracy of 86%.

• 한국정보전자통신기술학회논문지
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• 제1권3호
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• pp.83-89
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• 2008

This paper introduces a visual object classification algorithm based on statistical information. Objects are characterized through the Histogram of Oriented Gradients (HOG) method and classification is performed using Multiclass AdaBoost. Salient features of an object's appearance are detected by HOG blocks Blocks of different sizes are tested to define the most suitable configuration. To select the most informative blocks for classification a multiclass AdaBoostSVM algorithm is applied. The proposed method has a high speed processing and classification rate. Results of the evaluation based on example of hand gesture recognition are presented.

• Journal of Forest and Environmental Science
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• 제35권3호
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• pp.181-188
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• 2019

This study was performed to construct tree species classification map according to three information types (spectral information, texture information, and spectral and texture information) by altitude (30 m, 60 m, 90 m) using the unmanned aerial vehicle images and the object-based classification method, and to evaluate the concordance rate through field survey data. The object-based, optimal weighted values by altitude were 176 for 30 m images, 111 for 60 m images, and 108 for 90 m images in the case of Scale while 0.4/0.6, 0.5/0.5, in the case of the shape/color and compactness/smoothness respectively regardless of the altitude. The overall accuracy according to the type of information by altitude, the information on spectral and texture information was about 88% in the case of 30 m and the spectral information was about 98% and about 86% in the case of 60 m and 90 m respectively showing the highest rates. The concordance rate with the field survey data per tree species was the highest with about 92% in the case of Pinus densiflora at 30 m, about 100% in the case of Prunus sargentii Rehder tree at 60 m, and about 89% in the case of Robinia pseudoacacia L. at 90 m.

• 전기학회논문지
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• 제57권4호
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• pp.700-705
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• 2008

In this paper, we propose object classification algorithm for real-time surveillance system. We have approached this problem using silhouette-based template matching. The silhouette of the object is extracted, and then it is compared with representative template models. Template models are previously stored in the database. Our algorithm is similar to previous pixel-based template matching scheme like Hausdorff Distance, but we use 1D image array rather than 2D regions inspired by Hilbert Path. Transformation of images could reduce computational burden to compute similarity between the detected image and the template images. Experimental results show robustness and real-time performance in object classification, even in low resolution images.

• 한국지리정보학회지
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• 제20권1호
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• pp.1-14
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• 2017

토지피복도는 지역의 현황을 파악하는 기초적 자료이지만 시간적 공간적 해상도의 한계로 인하여 생태 연구 분야에서의 활용성은 떨어지는 측면이 있다. 이에 본 연구에서는 UAV으로 취득된 고해상도 영상을 기반으로 토지피복도 제작과 자료의 활용가능성을 알아보고자 하였다. UAV를 이용하여 연구대상지 $2.5km^2$ 범위에서 10.5cm 정사영상을 취득하였으며 객체기반(Object-based)과 화소기반(pixel-based) 분류를 통해 얻어진 토지피복도를 비교 분석하였다. 정확도 검증 결과 화소기반 분류는 Kappa 0.77, 객체기반 분류는 Kappa 0.82로 분류정확도가 높았으며, 전반적인 면적비율은 유사하지만 초지, 습지 지역에서 양호한 분류 결과가 나타났다. 객체기반 분류를 위한 최적의 영상분할 가중치는 Scale150, Shape 0.5, Compactness 0.5, Color 1로 선정하였으며 가중치 선정과정에서 Scale이 가장 큰 영향을 주었다. 화소기반 분류 결과와 비교해 객체간의 명확한 경계를 가지므로 결과물 판독이 용이한 것으로 나타났으며, 환경부 토지피복도(세분류)와 비교하여 개발지역(도로, 건물 등)을 제외한 자연지역(산림, 초지, 습지 등)의 분류에 효과적이었다. UAV 영상을 활용한 토지피복 분류방법으로서 객체기반 분류기법의 적용은 자료의 최신성, 정확성, 경제성 등의 장점으로 생태 연구 분야에 기여할 수 있을 것으로 판단된다.

• ETRI Journal
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• 제45권5호
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• pp.847-861
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• 2023

Dynamic object detection is essential for ensuring safe and reliable autonomous driving. Recently, light detection and ranging (LiDAR)-based object detection has been introduced and shown excellent performance on various benchmarks. Although LiDAR sensors have excellent accuracy in estimating distance, they lack texture or color information and have a lower resolution than conventional cameras. In addition, performance degradation occurs when a LiDAR-based object detection model is applied to different driving environments or when sensors from different LiDAR manufacturers are utilized owing to the domain gap phenomenon. To address these issues, a sensor-fusion-based object detection and classification method is proposed. The proposed method operates in real time, making it suitable for integration into autonomous vehicles. It performs well on our custom dataset and on publicly available datasets, demonstrating its effectiveness in real-world road environments. In addition, we will make available a novel three-dimensional moving object detection dataset called ETRI 3D MOD.

• 반도체디스플레이기술학회지
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• 제21권1호
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• pp.108-113
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• 2022

The deep-learning-based measurement method with the through-focus scanning optical microscopy (TSOM) estimated the size of the object using the classification. However, the measurement performance of the method depends on the number of subdivided classes, and it is practically difficult to prepare data at regular intervals for training each class. We propose an approach to measure the size of an object in the TSOM image using the deep-learning regression model instead of using classification. We attempted our proposed method to estimate the top critical dimension (TCD) of through silicon via (TSV) holes with 2461 TSOM images and the results were compared with the existing method. As a result of our experiment, the average measurement error of our method was within 30 nm (1σ) which is 1/13.5 of the sampling distance of the applied microscope. Measurement errors decreased by 31% compared to the classification result. This result proves that the proposed method is more effective and practical than the classification method.