• 한국통신학회논문지
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• 제34권3C호
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• pp.268-273
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• 2009

홍채 인식은 홍채 패턴 정보를 이용하여 사람의 신원을 확인하는 생체 인식 기술이다. 이러한 홍채 인식 시스템에 있어 조명의 영향이나 동공의 크기, 머리의 기울어짐 등으로 인해 발생될 수 있는 홍채 패턴의 변화에 대해 무관한 특징을 추출하는 것은 중요한 과제이다. 본 논문에서는 국부적 방향 히스토그램을 이용해 조명의 변화나 홍채의 회전에 강인한 홍채인식 방법을 제안하였다. 제안된 방법은 특징 추출 및 특징 비교 시 회전에 대해 별도의 처리가 필요하지 않아 고속의 특징 추출 및 특징 비교가 가능하며 성능도 기존의 방법과 대등함을 실험을 통하여 확인하였다.

• 전자공학회논문지
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• 제51권11호
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• pp.66-72
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• 2014

Histogram of oriented gradient (HOG) 특징은 영상 기반 보행자 인식에서 널리 사용되고 있다. HOG 특징을 이용한 보행자 인식의 인식률을 높이는데 가장 중요한 역할을 하는 것은 보간 기술이다. HOG 특징 연산에 보간 기술을 적용하기 위해서는 각 픽셀의 기울기 방향에 가장 근접한 두 개의 기울기 방향 bin과 가중치를 계산해야 한다. 따라서 본 논문에서는 HOG 특징 연산에 적용하기 위한 효율적인 기울기 방향 bin 및 가중치 연산 회로를 제안한다. 제안하는 회로는 탄젠트 함수와 나눗셈 연산을 피하기 위해 미리 계산된 값을 테이블로 지정하여 사용하였으며, 탄젠트 함수와 가중치 값의 특성을 이용함으로써 회로 내 테이블의 크기를 최소화하였다. 또한 처리 속도 향상을 위해 파이프라인 구조를 적용하였으며, 효율적인 coarse 및 fine 탐색 방법을 적용하여 각 픽셀에 대한 기울기 방향 bin과 가중치를 두 클락 사이클 내에 계산한다. 본 논문에서 제안하는 회로는 $1^{\circ}$ 단위로 기울기 방향을 계산하여 기울기 방향 bin과 가중치를 모두 결정하기 때문에 HOG 특징을 위한 보간 기술에 적용되어 높은 인식률을 제공하기 위해 사용될 수 있다.

• 정보학연구
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• 제12권4호
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• pp.25-32
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• 2009

Iris recognition is a biometric technology which can identify a person using the iris pattern. It is important for the iris recognition system to extract the feature which is invariant to changes in iris patterns. Those changes can be occurred by the influence of lights, changes in the size of the pupil, and head tilting. This paper is appropriate for the embedded environment using local gradient histogram embedded system using iris feature extraction methods have implement. The proposed method enables high-speed feature extraction and feature comparison because it requires no additional processing to obtain the rotation invariance, and shows comparable performance to the well-known previous methods.

• Journal of Information Processing Systems
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• 제10권3호
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• pp.443-458
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• 2014

An extreme learning machine (ELM) is a recently proposed learning algorithm for a single-layer feed forward neural network. In this paper we studied the ensemble of ELM by using a bagging algorithm for facial expression recognition (FER). Facial expression analysis is widely used in the behavior interpretation of emotions, for cognitive science, and social interactions. This paper presents a method for FER based on the histogram of orientation gradient (HOG) features using an ELM ensemble. First, the HOG features were extracted from the face image by dividing it into a number of small cells. A bagging algorithm was then used to construct many different bags of training data and each of them was trained by using separate ELMs. To recognize the expression of the input face image, HOG features were fed to each trained ELM and the results were combined by using a majority voting scheme. The ELM ensemble using bagging improves the generalized capability of the network significantly. The two available datasets (JAFFE and CK+) of facial expressions were used to evaluate the performance of the proposed classification system. Even the performance of individual ELM was smaller and the ELM ensemble using a bagging algorithm improved the recognition performance significantly.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제9권11호
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• pp.4534-4555
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• 2015

We propose a robust object cataloging method using multiple locally distinct heterogeneous features for aiding image retrieval. Due to challenges such as variations in object size, orientation, illumination etc. object recognition is extraordinarily challenging problem. In these circumstances, we adapt local interest point detection method which locates prototypical local components in object imageries. In each local component, we exploit heterogeneous features such as gradient-weighted orientation histogram, sum of wavelet responses, histograms using different color spaces etc. and combine these features together to describe each component divergently. A global signature is formed by adapting the concept of bag of feature model which counts frequencies of its local components with respect to words in a dictionary. The proposed method demonstrates its excellence in classifying objects in various complex backgrounds. Our proposed local feature shows classification accuracy of 98% while SURF,SIFT, BRISK and FREAK get 81%, 88%, 84% and 87% respectively.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제4권3호
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• pp.305-323
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• 2010

This paper proposes an efficient and yet powerful local descriptor called phase-space partition based descriptor (PPD). This descriptor is designed for the mobile image matching and retrieval. PPD, which is inspired from SIFT, also encodes the salient aspects of the image gradient in the neighborhood around an interest point. However, without employing SIFT's smoothed gradient orientation histogram, we apply the region based gradient statistics in phase space to the construction of a feature representation, which allows to reduce much computation requirements. The feature matching experiments demonstrate that PPD achieves favorable performance close to that of SIFT and faster building and matching. We also present results showing that the use of PPD descriptors in a mobile image retrieval application results in a comparable performance to SIFT.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제12권12호
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• pp.6000-6017
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• 2018

This paper presents a new descriptor, named Histograms of Prominent Edge Directions (HPED), for the recognition of facial expressions in a person-independent environment. In this paper, we raise the issue of sampling error in generating the code-histogram from spatial regions of the face image, as observed in the existing descriptors. HPED describes facial appearance changes based on the statistical distribution of the top two prominent edge directions (i.e., primary and secondary direction) captured over small spatial regions of the face. Compared to existing descriptors, HPED uses a smaller number of code-bins to describe the spatial regions, which helps avoid sampling error despite having fewer samples while preserving the valuable spatial information. In contrast to the existing Histogram of Oriented Gradients (HOG) that uses the histogram of the primary edge direction (i.e., gradient orientation) only, we additionally consider the histogram of the secondary edge direction, which provides more meaningful shape information related to the local texture. Experiments on popular facial expression datasets demonstrate the superior performance of the proposed HPED against existing descriptors in a person-independent environment.

• 정보학연구
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• 제12권4호
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• pp.1-10
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• 2009

The robustness of communication service should be guaranteed to validate its security of the whole service not just high performance. One kind of practical test-beds is the chinese communication service based on SIM Card and GSM. In paper, we try to experiment the possibility of SIM cards clone in various mobile communications using 2G in china, and hence discovered the security vulnerabilities such as the incoming outgoing, SMS service and additional services on the mobile phones using clone SIM cards. The experiments show that chinese communication service should be prepared the Fraud Management System against the cloning SIM card. and furthermore, regulations related to the communication service should be tuned the realistic security environments.

• 제어로봇시스템학회논문지
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• 제20권9호
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• pp.930-935
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• 2014

A Gabor filter is a linear filter used for edge detectionas frequency and orientation representations of Gabor filters are similar to those of the human visual system. In this thesis, we propose a pedestrian detection algorithm using a Gabor filter bank. In order to extract the features of the pedestrian, we use various image processing algorithms and data structure algorithms. First, color image segmentation is performed to consider the information of the RGB color space. Second, histogram equalization is performed to enhance the brightness of the input images. Third, convolution is performed between a Gabor filter bank and the enhanced images. Fourth, statistical values are calculated by using the integral image (summed area table) method. The calculated statistical values are used for the feature matrix of the pedestrian area. To evaluate the proposed algorithm, the INRIA pedestrian database and SVM (Support Vector Machine) are used, and we compare the proposed algorithm and the HOG (Histogram of Oriented Gradient) pedestrian detector, presentlyreferred to as the methodology of pedestrian detection algorithm. The experimental results show that the proposed algorithm is more accurate compared to the HOG pedestrian detector.

• 한국멀티미디어학회논문지
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• 제17권3호
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• pp.300-311
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• 2014

환경의 변화에 따라 급속도로 변화하는 생태계에 대한 체계적인 연구를 위해 식물의 정보를 수집 분석하기 위한 연구가 활발하게 진행되고 있다. 특히, 스마트 기기의 카메라를 이용하여 언제 어디서나 사용자가 원하는 식물의 종류를 검색할 수 있는 기술에 대한 관심이 증가하고 있다. 본 논문은 식물 인식 및 생태계 분석을 위해 다양한 식물의 잎을 종류별로 분석할 수 있는 방법에 대해 제안한다. 이를 위해, 카메라부터 입력된 식물 잎 사진의 관심 영역을 GrabCut을 통해 배경과 분리한 후, 형태 기술자 추출 방법인 SIFT(Scale-Invariant Feature Transform), HOG(Histogram of Oriented Gradient)를 이용하여 형태 기술자를 추출하고, 이것을 부호화 기법 및 공간 피라미드 방법을 이용한 분류 특징 벡터를 만든다. SVM(Support Vector Machine)을 통한 식물 잎 분류 및 인식한다. 다양한 식물 잎에 대한 실험 결과를 통해 비슷한 색상이나 형태를 가지고 있더라도 방향성 특징 기술자를 활용한 식물 잎 분류 방법이 매우 효율적임을 알 수 있다.