• Title/Summary/Keyword: Feature descriptor

Search Result 206, Processing Time 0.027 seconds

A Database Creation and Retrival Method of Feature Descriptors for Markerless Tracking (마커리스 트래킹을 위한 특징 서술자의 데이터베이스 생성 및 검색방법)

  • Yun, Yo-Seop;Kim, Tae-Young
    • Journal of Korea Game Society
    • /
    • v.11 no.3
    • /
    • pp.63-72
    • /
    • 2011
  • In this paper, we propose a novel database creation and retrieval method of feature descriptors to support real-time marker-less tracking in the augmented reality environments. Each feature descriptor is encoded by integer and multi-level database is created in order to retrieve a feature descriptor efficiently. The retrieval of a feature descriptor is performed as follows: Firstly, candidate feature descriptors are searched by traversing the multi-level database. Secondly, the euclidean distance between input feature descriptor and each candidate one is compared. The shortest one is retrieved. The proposed method is 16 ms faster than previous KD-Tree method for each feature descriptor.

Blur-Invariant Feature Descriptor Using Multidirectional Integral Projection

  • Lee, Man Hee;Park, In Kyu
    • ETRI Journal
    • /
    • v.38 no.3
    • /
    • pp.502-509
    • /
    • 2016
  • Feature detection and description are key ingredients of common image processing and computer vision applications. Most existing algorithms focus on robust feature matching under challenging conditions, such as inplane rotations and scale changes. Consequently, they usually fail when the scene is blurred by camera shake or an object's motion. To solve this problem, we propose a new feature description algorithm that is robust to image blur and significantly improves the feature matching performance. The proposed algorithm builds a feature descriptor by considering the integral projection along four angular directions ($0^{\circ}$, $45^{\circ}$, $90^{\circ}$, and $135^{\circ}$) and by combining four projection vectors into a single highdimensional vector. Intensive experiment shows that the proposed descriptor outperforms existing descriptors for different types of blur caused by linear motion, nonlinear motion, and defocus. Furthermore, the proposed descriptor is robust to intensity changes and image rotation.

Efficient Image Stitching Using Fast Feature Descriptor Extraction and Matching (빠른 특징점 기술자 추출 및 정합을 이용한 효율적인 이미지 스티칭 기법)

  • Rhee, Sang-Burm
    • KIPS Transactions on Software and Data Engineering
    • /
    • v.2 no.1
    • /
    • pp.65-70
    • /
    • 2013
  • Recently, the field of computer vision has been actively researched through digital image which can be easily generated as the development and expansion of digital camera technology. Especially, research that extracts and utilizes the feature in image has been actively carried out. The image stitching is a method that creates the high resolution image using features extract and match. Image stitching can be widely used in military and medical purposes as well as in variety fields of real life. In this paper, we have proposed efficient image stitching method using fast feature descriptor extraction and matching based on SURF algorithm. It can be accurately, and quickly found matching point by reduction of dimension of feature descriptor. The feature descriptor is generated by classifying of unnecessary minutiae in extracted features. To reduce the computational time and efficient match feature, we have reduced dimension of the descriptor and expanded orientation window. In our results, the processing time of feature matching and image stitching are faster than previous algorithms, and also that method can make natural-looking stitched image.

Vector space based augmented structural kinematic feature descriptor for human activity recognition in videos

  • Dharmalingam, Sowmiya;Palanisamy, Anandhakumar
    • ETRI Journal
    • /
    • v.40 no.4
    • /
    • pp.499-510
    • /
    • 2018
  • A vector space based augmented structural kinematic (VSASK) feature descriptor is proposed for human activity recognition. An action descriptor is built by integrating the structural and kinematic properties of the actor using vector space based augmented matrix representation. Using the local or global information separately may not provide sufficient action characteristics. The proposed action descriptor combines both the local (pose) and global (position and velocity) features using augmented matrix schema and thereby increases the robustness of the descriptor. A multiclass support vector machine (SVM) is used to learn each action descriptor for the corresponding activity classification and understanding. The performance of the proposed descriptor is experimentally analyzed using the Weizmann and KTH datasets. The average recognition rate for the Weizmann and KTH datasets is 100% and 99.89%, respectively. The computational time for the proposed descriptor learning is 0.003 seconds, which is an improvement of approximately 1.4% over the existing methods.

Real-Time Feature Point Matching Using Local Descriptor Derived by Zernike Moments (저니키 모멘트 기반 지역 서술자를 이용한 실시간 특징점 정합)

  • Hwang, Sun-Kyoo;Kim, Whoi-Yul
    • Journal of the Institute of Electronics Engineers of Korea SP
    • /
    • v.46 no.4
    • /
    • pp.116-123
    • /
    • 2009
  • Feature point matching, which is finding the corresponding points from two images with different viewpoint, has been used in various vision-based applications and the demand for the real-time operation of the matching is increasing these days. This paper presents a real-time feature point matching method by using a local descriptor derived by Zernike moments. From an input image, we find a set of feature points by using an existing fast corner detection algorithm and compute a local descriptor derived by Zernike moments at each feature point. The local descriptor based on Zernike moments represents the properties of the image patch around the feature points efficiently and is robust to rotation and illumination changes. In order to speed up the computation of Zernike moments, we compute the Zernike basis functions with fixed size in advance and store them in lookup tables. The initial matching results are acquired by an Approximate Nearest Neighbor (ANN) method and false matchings are eliminated by a RANSAC algorithm. In the experiments we confirmed that the proposed method matches the feature points in images with various transformations in real-time and outperforms existing methods.

Content-Based Retrieval using MPEG-7 Visual Descriptor and Hippocampal Neural Network (MPEG-7 시각 기술자와 해마 신경망을 이용한 내용기반 검색)

  • Kim Young Ho;Kang Dae-Seong
    • Journal of Institute of Control, Robotics and Systems
    • /
    • v.11 no.12
    • /
    • pp.1083-1087
    • /
    • 2005
  • As development of digital technology, many kinds of multimedia data are used variously and requirements for effective use by user are increasing. In order to transfer information fast and precisely what user wants, effective retrieval method is required. As existing multimedia data are impossible to apply the MPEG-1, MPEG-2 and MPEG-4 technologies which are aimed at compression, store and transmission. So MPEG-7 is introduced as a new technology for effective management and retrieval of multimedia data. In this paper, we extract content-based features using color descriptor among the MPEG-7 standardization visual descriptor, and reduce feature data applying PCA(Principal Components Analysis) technique. We model the cerebral cortex and hippocampal neural network in engineering domain, and team content-based feature vectors fast and apply the hippocampal neural network algorithm to compose of optimized feature. And then we present fast and precise retrieval effect when indexing and retrieving.

A Multiple Features Video Copy Detection Algorithm Based on a SURF Descriptor

  • Hou, Yanyan;Wang, Xiuzhen;Liu, Sanrong
    • Journal of Information Processing Systems
    • /
    • v.12 no.3
    • /
    • pp.502-510
    • /
    • 2016
  • Considering video copy transform diversity, a multi-feature video copy detection algorithm based on a Speeded-Up Robust Features (SURF) local descriptor is proposed in this paper. Video copy coarse detection is done by an ordinal measure (OM) algorithm after the video is preprocessed. If the matching result is greater than the specified threshold, the video copy fine detection is done based on a SURF descriptor and a box filter is used to extract integral video. In order to improve video copy detection speed, the Hessian matrix trace of the SURF descriptor is used to pre-match, and dimension reduction is done to the traditional SURF feature vector for video matching. Our experimental results indicate that video copy detection precision and recall are greatly improved compared with traditional algorithms, and that our proposed multiple features algorithm has good robustness and discrimination accuracy, as it demonstrated that video detection speed was also improved.

Experimental Optimal Choice Of Initial Candidate Inliers Of The Feature Pairs With Well-Ordering Property For The Sample Consensus Method In The Stitching Of Drone-based Aerial Images

  • Shin, Byeong-Chun;Seo, Jeong-Kweon
    • KSII Transactions on Internet and Information Systems (TIIS)
    • /
    • v.14 no.4
    • /
    • pp.1648-1672
    • /
    • 2020
  • There are several types of image registration in the sense of stitching separated images that overlap each other. One of these is feature-based registration by a common feature descriptor. In this study, we generate a mosaic of images using feature-based registration for drone aerial images. As a feature descriptor, we apply the scale-invariant feature transform descriptor. In order to investigate the authenticity of the feature points and to have the mapping function, we employ the sample consensus method; we consider the sensed image's inherent characteristic such as the geometric congruence between the feature points of the images to propose a novel hypothesis estimation of the mapping function of the stitching via some optimally chosen initial candidate inliers in the sample consensus method. Based on the experimental results, we show the efficiency of the proposed method compared with benchmark methodologies of random sampling consensus method (RANSAC); the well-ordering property defined in the context and the extensive stitching examples have supported the utility. Moreover, the sample consensus scheme proposed in this study is uncomplicated and robust, and some fatal miss stitching by RANSAC is remarkably reduced in the measure of the pixel difference.

Implementation of sin/cos Processor for Descriptor on SIFT (SIFT의 descriptor를 위한 sin/cos 프로세서의 구현)

  • Kim, Young-Jin;Lee, Hyon Soo
    • The Journal of the Korea Contents Association
    • /
    • v.13 no.4
    • /
    • pp.44-52
    • /
    • 2013
  • The SIFT algorithm is being actively researched for various image processing applications including video surveillance and autonomous vehicle navigation. The computation of sin/cos function is the most cost part needed in whole computational complexity and time for SIFT descriptor. In this paper, we implement a hardware to sin/cos function of descriptor on sift feature detection algorithm. The proposed Sin/Cosine processor is coded in Verilog and synthesized and simulated using Xilinx ISE 9.2i. The processor is mapped onto the device Spartan 2E (XC2S200E-PQ208-6). It consumes 149 slices, 233 LUTs and attains a maximum operation frequency of 60.01 MHz. As compared with the software realization, our FPGA circuit can achieve the speed improvement by 40 times in average.

Facial Expression Recognition Using SIFT Descriptor (SIFT 기술자를 이용한 얼굴 표정인식)

  • Kim, Dong-Ju;Lee, Sang-Heon;Sohn, Myoung-Kyu
    • KIPS Transactions on Software and Data Engineering
    • /
    • v.5 no.2
    • /
    • pp.89-94
    • /
    • 2016
  • This paper proposed a facial expression recognition approach using SIFT feature and SVM classifier. The SIFT was generally employed as feature descriptor at key-points in object recognition fields. However, this paper applied the SIFT descriptor as feature vector for facial expression recognition. In this paper, the facial feature was extracted by applying SIFT descriptor at each sub-block image without key-point detection procedure, and the facial expression recognition was performed using SVM classifier. The performance evaluation was carried out through comparison with binary pattern feature-based approaches such as LBP and LDP, and the CK facial expression database and the JAFFE facial expression database were used in the experiments. From the experimental results, the proposed method using SIFT descriptor showed performance improvements of 6.06% and 3.87% compared to previous approaches for CK database and JAFFE database, respectively.