• Title/Summary/Keyword: Feature Extracting

Search Result 592, Processing Time 0.023 seconds

Image Feature Extracting Operators Using DBAH/DBAG and its Implementation (이미지 특징 추출연산자 DBAH/DBAG 와 하드웨어 실현)

  • Cho, Sung-Mok
    • Journal of the Korea Society of Computer and Information
    • /
    • v.6 no.1
    • /
    • pp.31-37
    • /
    • 2001
  • Human psychovisual phenomena involved in extracting features is more sensitive in dark regions than in bright regions Therefore, feature extracting operators should be considered local intensities in order to perceive objects analogous to human vision system. Generally, conventional feature extracting operators have some handicaps like an computational complexity or multivariable needs. In this paper a novel feature extracting operator is proposed to overcome these demerits. This operator could be implemented very simply and be proved good performances through experiments applied to synthetic and real images.

  • PDF

Panoramic Image Stitching Using Feature Extracting and Matching on Embedded System

  • Lee, June-Hwan
    • Transactions on Electrical and Electronic Materials
    • /
    • v.18 no.5
    • /
    • pp.273-278
    • /
    • 2017
  • Recently, one of the areas where research is being actively conducted is the Internet of Things (IoT). The field of using the Internet of Things system is increasing, coupled with a remarkable increase of the use of the camera. However, general cameras used in the Internet of Things have limited viewing angles as compared to those available to the human eye. Also, cameras restrict observation of objects and the performance of observation. Therefore, in this paper, we propose a panoramic image stitching method using feature extraction and matching based on an embedded system. After extracting the feature of the image, the speed of image stitching is improved by reducing the amount of computation using the necessary information so that it can be used in the embedded system. Experimental results show that it is possible to improve the speed of feature matching and panoramic image stitching while generating a smooth image.

Object Recognition by Invariant Feature Extraction in FLIR (적외선 영상에서의 불변 특징 정보를 이용한 목표물 인식)

  • 권재환;이광연;김성대
    • Proceedings of the IEEK Conference
    • /
    • 2000.11d
    • /
    • pp.65-68
    • /
    • 2000
  • This paper describes an approach for extracting invariant features using a view-based representation and recognizing an object with a high speed search method in FLIR. In this paper, we use a reformulated eigenspace technique based on robust estimation for extracting features which are robust for outlier such as noise and clutter. After extracting feature, we recognize an object using a partial distance search method for calculating Euclidean distance. The experimental results show that the proposed method achieves the improvement of recognition rate compared with standard PCA.

  • PDF

A Study on Machining data Extraction using Feature Recognition Rules (특정형상인식을 이용한 가공테이터 추출에 관한 연구)

  • 이석희;정구섭
    • Proceedings of the Korean Society of Precision Engineering Conference
    • /
    • 1996.04a
    • /
    • pp.581-586
    • /
    • 1996
  • This paper presents a feature recognition system for recognizing and extracting feature information needed for machining from design data contained in the CAD database of AutoCAD system. The developed system carries out feature recognition from an orthographic view of a press mold containing not only atomic features such as holes, pockets, and slots, but also compound features. Based on the result of feature recognition, it generates a 3-D modeling of the press mold. Especially, The feature recognition part is designed for detecting feature styles according to feature definition and classification, extracting parameters for various atomic features, and constructing necessary data structures for the recognized features.

  • PDF

Comparative Analysis of the Performance of SIFT and SURF (SIFT 와 SURF 알고리즘의 성능적 비교 분석)

  • Lee, Yong-Hwan;Park, Je-Ho;Kim, Youngseop
    • Journal of the Semiconductor & Display Technology
    • /
    • v.12 no.3
    • /
    • pp.59-64
    • /
    • 2013
  • Accurate and robust image registration is important task in many applications such as image retrieval and computer vision. To perform the image registration, essential required steps are needed in the process: feature detection, extraction, matching, and reconstruction of image. In the process of these function, feature extraction not only plays a key role, but also have a big effect on its performance. There are two representative algorithms for extracting image features, which are scale invariant feature transform (SIFT) and speeded up robust feature (SURF). In this paper, we present and evaluate two methods, focusing on comparative analysis of the performance. Experiments for accurate and robust feature detection are shown on various environments such like scale changes, rotation and affine transformation. Experimental trials revealed that SURF algorithm exhibited a significant result in both extracting feature points and matching time, compared to SIFT method.

MSFM: Multi-view Semantic Feature Fusion Model for Chinese Named Entity Recognition

  • Liu, Jingxin;Cheng, Jieren;Peng, Xin;Zhao, Zeli;Tang, Xiangyan;Sheng, Victor S.
    • KSII Transactions on Internet and Information Systems (TIIS)
    • /
    • v.16 no.6
    • /
    • pp.1833-1848
    • /
    • 2022
  • Named entity recognition (NER) is an important basic task in the field of Natural Language Processing (NLP). Recently deep learning approaches by extracting word segmentation or character features have been proved to be effective for Chinese Named Entity Recognition (CNER). However, since this method of extracting features only focuses on extracting some of the features, it lacks textual information mining from multiple perspectives and dimensions, resulting in the model not being able to fully capture semantic features. To tackle this problem, we propose a novel Multi-view Semantic Feature Fusion Model (MSFM). The proposed model mainly consists of two core components, that is, Multi-view Semantic Feature Fusion Embedding Module (MFEM) and Multi-head Self-Attention Mechanism Module (MSAM). Specifically, the MFEM extracts character features, word boundary features, radical features, and pinyin features of Chinese characters. The acquired font shape, font sound, and font meaning features are fused to enhance the semantic information of Chinese characters with different granularities. Moreover, the MSAM is used to capture the dependencies between characters in a multi-dimensional subspace to better understand the semantic features of the context. Extensive experimental results on four benchmark datasets show that our method improves the overall performance of the CNER model.

Workpart and Setup Planning for NC Machining of Prismatic Model:Feature-Based Approach (형상인식에 의한 다면체모델의 NC 가공을 위한 소개 및 셋업계획)

  • 지우석;서석환;강재관
    • 제어로봇시스템학회:학술대회논문집
    • /
    • 1992.10a
    • /
    • pp.1078-1083
    • /
    • 1992
  • Extracting the process planning information from the CAD data is the key issue in integrated CAD/CAM system. In this paper, we develop algorithms for extracting the shape and setup configuration for NC machining of prismatic parts. In determining the workpart shape, the minimum-enclosing condept is applied so that the material waste is minimized. To minimize the number of setups, feature based algorithm is developed considrint the part shape, tool shape, and tool approach direction. The validity and effectiveness of the developed algorithms were tested by computer simulations.

  • PDF

Some Worthy Signal Processing Techniques for Mechanical Fault Diagnosis

  • Chan, Jin
    • Proceedings of the Korean Society for Noise and Vibration Engineering Conference
    • /
    • 2002.05a
    • /
    • pp.39-52
    • /
    • 2002
  • Research Direction The significant research direction in mechanical fault diagnosis area: Theorles and approaches for fault feature extracting and fault classification. Identification Complicated fault generating mechanism and its model Intelligent fault diagnosis system (including the expert system and network based remote diagnosis system) One of the Key Points: Fault feature extracting techniques based on (modern) signal processing(omitted)

  • PDF

A Study on the Fractal Attractor Creation and Analysis of the Printed Korean Characters

  • Shon, Young-Woo
    • Journal of information and communication convergence engineering
    • /
    • v.1 no.1
    • /
    • pp.53-57
    • /
    • 2003
  • Chaos theory is a study researching the irregular, unpredictable behavior of deterministic and non-linear dynamical system. The interpretation using Chaos makes us evaluate characteristic existing in status space of system by tine series, so that the extraction of Chaos characteristic understanding and those characteristics enables us to do high precision interpretation. Therefore, This paper propose the new method which is adopted in extracting character features and recognizing characters using the Chaos Theory. Firstly, it gets features of mesh feature, projection feature and cross distance feature from input character images. And their feature is converted into time series data. Then using the modified Henon system suggested in this paper, it gets last features of character image after calculating Box-counting dimension, Natural Measure, information bit and information dimension which are meant fractal dimension. Finally, character recognition is performed by statistically finding out the each information bit showing the minimum difference against the normalized pattern database. An experimental result shows 99% character classification rates for 2,350 Korean characters (Hangul) using proposed method in this paper.