• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2012.07a
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• pp.269-270
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• 2012

Features which exhibit scale and rotation invariance, such as SIFT, are notorious for expensive computation time, and often overlooked for real-time tracking scenarios. This paper proposes a descriptorless matching algorithm based on motion vectors between consecutive frames to find the geometrically closest candidate to each tracked reference feature in the database. Descriptor-less matching forgoes expensive SIFT descriptor extraction without loss of matching accuracy and exhibits dramatic speed-up compared to traditional, naive matching based trackers. Descriptor-less SIFT tracking runs in real-time on an Intel dual core machine at an average of 24 frames per second.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.59 no.2
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• pp.429-435
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• 2010

Three dimensional (3-D) object detection and pose estimation from a single view query image has been an important issue in various fields such as medical applications, robot vision, and manufacturing automation. However, most of the existing methods are not appropriate in a real time environment since object detection and pose estimation requires extensive information and computation. In this paper, we present a fast 3-D object detection and pose estimation scheme based on surrounding camera view-changed images of objects. Our scheme has two parts. First, we detect images similar to the query image from the database based on the shape feature, and calculate candidate poses. Second, we perform accurate pose estimation for the candidate poses using the scale invariant feature transform (SIFT) method. We earned out extensive experiments on our prototype system and achieved excellent performance, and we report some of the results.

• Annual Conference of KIPS
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• 2013.11a
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• pp.1560-1562
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• 2013

정확하고 강인한 영상 등록(Registration)은 영상 검색과 컴퓨터 비전과 같은 여러 응용 분야에서 성능을 좌우하는 매우 중요한 역할을 담당하며, 특징 추출 및 매칭 단계를 통해 수행된다. 영상의 특징을 관심 점으로 지정하여 추출하는 대표적인 알고리즘으로, SIFT (Scale Invariant Feature Transform)와 SURF (Speeded Up Robust Feature)가 있다. 본 논문에서는 2 개의 특징점 추출 알고리즘을 구현하고 예제 데이터를 기반으로 실험을 통해 성능적 비교 분석을 수행한다. 실험 결과, SURF 알고리즘이 특징 추출 및 매칭, 처리시간 측면에서 SIFT 보다 효율적인 성능을 보였다.

• Annual Conference of KIPS
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• 2009.11a
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• pp.347-348
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• 2009

멀티미디어 컨텐츠에 가시적 혹은 비 가시적으로 정보를 삽입하는 기술을 워터마킹이라고 한다. 워터마크를 삽입한 컨텐츠는 일련의 공격에 강인하도록 설계되어야 하는데, 본 기법은 기하학적 공격에 강인하도록 설계하였다. Scale Invariant Feature Transform (SIFT)을 이용하여 특징점을 찾고, 특징점 주변에 원형의 워터마크를 삽입하였다. SIFT에서 구해지는 방향벡터를 사용하여, 워터마크를 삽입할 원형 반구를 설정하였으며, 기존의 원형 기반의 워터마킹 삽입기법보다 강인한 워터마크를 구성할 수 있는 장점을 가진다.

• Annual Conference of KIPS
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• 2023.11a
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• pp.1220-1223
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• 2023

폐기물을 처리하는데 있어 배출과 수거에 대한 프로세스 자동화를 위해 폐기물 객체 유사도 판별이 요구된다. 이를 위해 본 연구에서는 폐기물 데이터셋에서 SIFT(Scale-Invariant Feature Transform)와 HSV(Hue, Saturation, Value)기반으로 두 이미지의 공통된 특징을 추출해 융합하고, 기계학습을 통해 이미지 객체 간의 유사도를 측정하는 모델을 제안한다. 실험을 위해 수집된 폐기물 데이터셋 81,072 장을 활용하여 이미지를 학습시키고, 전통적인 임계치 기반 유사도 측정과 본 논문에서 제시하는 유사도 측정을 비교하여 성능을 확인하였다. 임계치 기반 측정에서 SIFT 와 HSV 는 각각 0.82, 0.89(Acc)가 측정되었고, 본 논문에서 제시한 특징 추출 방법을 사용한 기계학습의 성능은 DT(Decision Tree)와 SVM(Support Vector Machine) 모두 0.93 (Acc)로 4%의 정확도가 향상되었다.

• Journal of the Korean Institute of Intelligent Systems
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• v.20 no.1
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• pp.116-122
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• 2010

This paper proposes a method for classifying targets robust to geometric transformations of targets such as rotation, scale change, translation, and pose change. Targets which have rotation, scale change, and shift is firstly classified based on CM(Confidence Map) which is generated by similarity, scale ratio, and range of orientation for SIFT(Scale-Invariant Feature Transform) feature vectors. On the other hand, DB(DataBase) which is acquired in various angles is used to deal with pose variation of targets. Range of the angle is determined by comparing and analyzing the execution time and performance for sampling intervals. We experiment on various images which is geometrically changed to evaluate performance of proposed target classification method. Experimental results show that the proposed algorithm has a good classification performance.

• International Journal of Contents
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• v.13 no.4
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• pp.70-79
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• 2017

Images are an important element in patents and many experts use images to analyze a patent or to check differences between patents. However, there is little research on image analysis for patents partly because image processing is an advanced technology and typically patent images consist of visual parts as well as of text and numbers. This study suggests two methods for using image processing; the Scale Invariant Feature Transform(SIFT) algorithm and Optical Character Recognition(OCR). The first method which works with SIFT uses image feature points. Through feature matching, it can be applied to calculate the similarity between documents containing these images. And in the second method, OCR is used to extract text from the images. By using numbers which are extracted from an image, it is possible to extract the corresponding related text within the text passages. Subsequently, document similarity can be calculated based on the extracted text. Through comparing the suggested methods and an existing method based only on text for calculating the similarity, the feasibility is achieved. Additionally, the correlation between both the similarity measures is low which shows that they capture different aspects of the patent content.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.4
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• pp.1066-1079
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• 2023

Multi-target tracking based on the detector is a very hot and important research topic in target tracking. It mainly includes two closely related processes, namely target detection and target tracking. Where target detection is responsible for detecting the exact position of the target, while target tracking monitors the temporal and spatial changes of the target. With the improvement of the detector, the tracking performance has reached a new level. The problem that always exists in the research of target tracking is the problem that occurs again after the target is occluded during tracking. Based on this question, this paper proposes a DeepSORT model based on SIFT features to improve ship tracking. Unlike previous feature extraction networks, SIFT algorithm does not require the characteristics of pre-training learning objectives and can be used in ship tracking quickly. At the same time, we improve and test the matching method of our model to find a balance between tracking accuracy and tracking speed. Experiments show that the model can get more ideal results.

• IEIE Transactions on Smart Processing and Computing
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• v.4 no.2
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• pp.110-114
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• 2015

This paper reports a novel object-detection technique in video sequences. The proposed algorithm consists of detection of objects in illuminated and occluded videos by using object features and a neural network technique. It consists of two functional modules: region-based object feature extraction and continuous detection of objects in video sequences with region features. This scheme is proposed as an enhancement of the Lowe's scale-invariant feature transform (SIFT) object detection method. This technique solved the high computation time problem of feature generation in the SIFT method. The improvement is achieved by region-based feature classification in the objects to be detected; optimal neural network-based feature reduction is presented in order to reduce the object region feature dataset with winner pixel estimation between the video frames of the video sequence. Simulation results show that the proposed scheme achieves better overall performance than other object detection techniques, and region-based feature detection is faster in comparison to other recent techniques.

• Annual Conference of KIPS
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• 2014.04a
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• pp.781-784
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• 2014

Face recognition under controlled settings, such as limited viewpoint and illumination change, can achieve good performance nowadays. However, real world application for face recognition is still challenging. In this paper, we use Affine SIFT to detect affine invariant local descriptors for face recognition under large viewpoint change. Affine SIFT is an extension of SIFT algorithm. SIFT algorithm is scale and rotation invariant, which is powerful for small viewpoint changes in face recognition, but it fails when large viewpoint change exists. In our scheme, Affine SIFT is used for both gallery face and probe face, which generates a series of different viewpoints using affine transformation. Therefore, Affine SIFT allows viewpoint difference between gallery face and probe face. Experiment results show our framework achieves better recognition accuracy than SIFT algorithm on FERET database.