• 한국HCI학회:학술대회논문집
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• 2008.02a
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• pp.504-509
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• 2008

Support Vector Machine (SVM) is one of powerful learning machine and has been applied to varying task with generally acceptable performance. The success of SVM for classification tasks in one domain is affected by features which represent the instance of specific class. Given the representative and discriminative features, SVM learning will give good generalization and consequently we can obtain good classifier. In this paper, we will assess the problem of feature choices for human detection tasks and measure the performance of each feature. Here we will consider HOG-family feature. As a natural extension of SVM, we combine SVM with Principal Component Analysis (PCA) to reduce dimension of features while retaining most of discriminative feature vectors.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.64 no.3
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• pp.187-192
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• 2015

Detecting human in images is a challenging task owing to their variable appearance and the wide range of poses the they can adopt. The first need is a robust feature set that allows the human form to be discriminated cleanly, even in cluttered background under difficult illumination. A large number of vision application rely on matching keypoints across images. These days, the deployment of vision algorithms on smart phones and embedded device with low memory and computation complexity has even upped the ante: the goal is to make descriptors faster compute, more compact while remaining robust scale, rotation and noise. In this paper we focus on improving the speed of pedestrian(walking person) detection using Histogram of Oriented Gradient(HOG) descriptors provide excellent performance and tracking using kalman filter.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2014.11a
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• pp.164-166
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• 2014

본 논문에서는 HOG 기반 보행자 검출 및 추적에서, HOG feature의 슬라이딩 윈도우의 수와 피라미드 층 수가 알고리즘의 수행속도와 직접적인 관계가 있다는 것을 확인한다. 그리고 이 결과를 바탕으로 윈도우의 수와 피라마드 층 수를 줄이는 방법을 제안하여 전체적인 보행자 검출 및 추적 속도를 증가시키고자 한다. 구체적으로, 제안하는 알고리즘은 검출 단계에서 색상의 선명도를 이용하여 관심 영역을 프레임 내에 지정함으로써 슬라이딩 윈도우의 수를 줄이고, 부가적으로 피라미드 층 수 또한 줄어들어서 보행자 검출 속도를 향상시킨다. 그리고 추적 단계에서는 보행자로 검출된 윈도우의 색상 정보를 이용하여 검출된 보행자를 빠르고 정확하게 추적하는 하는 방법을 제시한다.

• Proceedings of the KIEE Conference
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• 2015.07a
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• pp.1365-1366
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• 2015

본 논문에서는 HOG 특징을 이용한 다항식 방사형 기저함수 신경회로망 기반 숫자 인식 시스템의 설계를 제안한다. 제안한 숫자 인식 시스템은 HOG 특징을 이용하여 숫자를 입력 데이터로 사용하기 위해 특징을 계산한다. 다항식 방사형 기저 함수 신경회로망은 고차원 데이터의 입-출력 형태를 갖는 클래스를 분류하는데 용이하며, 활성함수의 중심점 및 분포상수는 Fuzzy C-Means(FCM) 알고리즘에 의해 초기 값을 설정한다. 또한 제안한 분류기의 최적화를 위해 Particle Swarm Optimization(PSO)를 사용하여 최적화된 분류기의 성능을 비교한다. 숫자 인식을 위하여 공인 데이터베이스인 MNIST handwritten digit database를 사용하여 분류기의 성능을 평가하고 분석한다.

• International Journal of Contents
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• v.9 no.3
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• pp.1-9
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• 2013

In this paper, we propose a fast and accurate system for detecting pedestrians from a static image. Histogram of Oriented Gradients (HOG) is a well-known feature for pedestrian detection systems but extracting HOG is expensive due to its high dimensional vector. It will cause long processing time and large memory consumption in case of making a pedestrian detection system on high resolution image or video. In order to deal with this problem, we use Principal Components Analysis (PCA) technique to reduce the dimensionality of HOG. The output of PCA will be input for a linear SVM classifier for learning and testing. The experiment results showed that our proposed method reduces processing time but still maintains the similar detection rate. We got twenty five times faster than original HOG feature.

• Proceedings of the Korea Information Processing Society Conference
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• 2014.11a
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• pp.921-923
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• 2014

보행자 검출과정은 특징추출, 추출된 특징을 기반으로 한 학습과정, 그리고 학습된 데이터를 기반으로 한 분류과정으로 나눌 수 있다. 이들 중 연산시간이 가장 오래 걸리는 특징추출과정이다. 기존의 HOG 특징 추출은 하나의 학습 샘플 이미지에 대하여 많은 픽셀 연산이 필요하기 때문에 많은 시간이 소요되었다. 본 논문에서는 실시간 스트리밍 환경에서 이전 프레임의 HOG 특징 검출정보를 분석하여 다음 프레임에서 보행자가 존재 할 가능성이 높은 부분에 대해서만 특징을 추출한다. 이 방법으로 기존의 연구와 비교하여 인식성능에는 거의 영향을 주지 않고 인식 속도를 향상할 수 있다.

• Proceedings of the Korean Society of Computer Information Conference
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• 2019.07a
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• pp.271-272
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• 2019

현재 4차 산업혁명으로 인해 많은 이들의 삶의 질이 이전보다 개선되었음에도 불구하고, 소외된 계층을 위한 개발은 타 분야에 비해서 더뎌지고 있는 실정이다. 현대의 청각 장애인과 언어 장애인들은 시각 언어인 수화를 이용하여 의사소통을 한다. 그러나 수화는 진입 장벽이 높기 때문에, 이를 사용하지 않는 사람들은 청각 장애인 및 언어 장애인과 의사소통을 하는데 어려움을 겪는다. 본 논문은 이러한 불편함을 줄이기 위해 서포트 벡터 머신(Support Vector Machine, SVM) 기반의 HOG(Histogram of Oriented Gradients) 특징 벡터를 이용하여 수화의 기본인 숫자를 분류할 수 있는 시스템을 구현하여 수화를 번역할 수 있는 가능성을 제안한다.

• International Journal of Computer Science & Network Security
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• v.23 no.3
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• pp.187-192
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• 2023

With development of information technology and necessity for high security, using different identification methods has become very important. Each biometric feature has its own advantages and disadvantages and choosing each of them depends on our usage. Retinal scanning is a bio scale method for identification. The retina is composed of vessels and optical disk. The vessels distribution pattern is one the remarkable retinal identification methods. In this paper, a new approach is presented for identification via retinal images using LBP and hog methods. In the proposed method, it will be tried to separate the retinal vessels accurately via machine vision techniques which will have good sustainability in rotation and size change. HOG-based or LBP-based methods or their combination can be used for separation and also HSV color space can be used too. Having extracted the features, the similarity criteria can be used for identification. The implementation of proposed method and its comparison with one of the newly-presented methods in this area shows better performance of the proposed method.

• Journal of the Korea Institute of Military Science and Technology
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• v.20 no.1
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• pp.33-39
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• 2017

In this paper, an effective feature which is capable of classifying targets among the detections obtained from 2D range-bearing maps generated in active sonar environments is proposed. Most conventional approaches for target classification with the 2D maps have considered magnitude of peak and statistical features of the area surrounding the peak. To improve the classification performance, HOG(Histogram of Gradient) feature, which is popular for their robustness in the image textures analysis is applied. In order to classify the target signal, SVM(Support Vector Machine) method with reduced HOG feature by the PCA(Principal Component Analysis) algorithm is incorporated. The various simulations are conducted with the real clutter signal data and the synthesized target signal data. According to the simulated results, the proposed method considering HOG feature is claimed to be effective when classifying the active sonar target compared to the conventional methods.

• Journal of Broadcast Engineering
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• v.17 no.2
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• pp.316-324
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• 2012

In this paper, a novel descreening method using HOG(histogram of gradient)-based adaptive smoothing filter is proposed. Conventional edge-oriented smoothing methods does not provide enough smoothing to the halftone image due to the edge-like characteristic of the halftone noise. Moreover, clustered-dot halftoning method, which is commonly used in printing tends to create Moire pattern because of the intereference in color channels. Therefore, the proposed method uses HOG to distinguish edges and the amount of smoothing to be performed on the halftone image is then calculated according to the magnitude of the HOG in the edge and edge normal orientation. The proposed method was tested on various scanned halftone materials, and the results show that it effectively removes halftone noises as well as Moire pattern while preserving image details.