• Title/Summary/Keyword: object-based approach

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A Salient Based Bag of Visual Word Model (SBBoVW): Improvements toward Difficult Object Recognition and Object Location in Image Retrieval

  • Mansourian, Leila;Abdullah, Muhamad Taufik;Abdullah, Lilli Nurliyana;Azman, Azreen;Mustaffa, Mas Rina
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.10 no.2
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    • pp.769-786
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    • 2016
  • Object recognition and object location have always drawn much interest. Also, recently various computational models have been designed. One of the big issues in this domain is the lack of an appropriate model for extracting important part of the picture and estimating the object place in the same environments that caused low accuracy. To solve this problem, a new Salient Based Bag of Visual Word (SBBoVW) model for object recognition and object location estimation is presented. Contributions lied in the present study are two-fold. One is to introduce a new approach, which is a Salient Based Bag of Visual Word model (SBBoVW) to recognize difficult objects that have had low accuracy in previous methods. This method integrates SIFT features of the original and salient parts of pictures and fuses them together to generate better codebooks using bag of visual word method. The second contribution is to introduce a new algorithm for finding object place based on the salient map automatically. The performance evaluation on several data sets proves that the new approach outperforms other state-of-the-arts.

Object Detection Using Deep Learning Algorithm CNN

  • S. Sumahasan;Udaya Kumar Addanki;Navya Irlapati;Amulya Jonnala
    • International Journal of Computer Science & Network Security
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    • v.24 no.5
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    • pp.129-134
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    • 2024
  • Object Detection is an emerging technology in the field of Computer Vision and Image Processing that deals with detecting objects of a particular class in digital images. It has considered being one of the complicated and challenging tasks in computer vision. Earlier several machine learning-based approaches like SIFT (Scale-invariant feature transform) and HOG (Histogram of oriented gradients) are widely used to classify objects in an image. These approaches use the Support vector machine for classification. The biggest challenges with these approaches are that they are computationally intensive for use in real-time applications, and these methods do not work well with massive datasets. To overcome these challenges, we implemented a Deep Learning based approach Convolutional Neural Network (CNN) in this paper. The Proposed approach provides accurate results in detecting objects in an image by the area of object highlighted in a Bounding Box along with its accuracy.

A Derivation of the Accuracy Relationship between the Reconstruction of 3D Object Coordinates and the Number of Closed Curves (폐곡선의 수에 따른 3차원 물체의 좌표 복원 정확도 관계 도출)

  • Lee, Deokwoo
    • Journal of Korea Multimedia Society
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    • v.20 no.7
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    • pp.1004-1013
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    • 2017
  • This paper presents a relationship between the number of curves and geometric parameters of a 3D object. Once the relationship is established, the number of closed curves that can reliably represent 3D object is derived. Inspired by Shannon-Nyquist Sampling Theorem, in this paper, approach for sampling rate (defined as the minimum number of curves) for 3D reconstruction is proposed. The relationship is straightforward, is suitable for application to 3D object overlaid with closed-continuous curves, and can achieve efficient 3D reconstruction system in practice. To substantiate the proposed approach, simulation results are provided and the results show that the number of curves can be decreased without loss of generality of characteristics of a target 3D object.

Object Tracking with Radical Change of Color Distribution Using EM algorithm

  • Whoang In-Teck;Choi Kwang-Nam
    • Proceedings of the Korean Information Science Society Conference
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    • 2006.06b
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    • pp.388-390
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    • 2006
  • This paper presents an object tracking with radical change of color. Conventional Mean Shift do not provide appropriate result when major color distribution disappear. Our tracking approach is based on Mean Shift as basic tracking method. However we propose tracking algorithm that shows good results for an object of radical variation. The key idea is iterative update previous color information of an object that shows different color by using EM algorithm. As experiment results, we show that our proposed algorithm is an effective approach in tracking for a real object include an object having radical change of color.

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Combining an Edge-Based Method and a Direct Method for Robust 3D Object Tracking

  • Lomaliza, Jean-Pierre;Park, Hanhoon
    • Journal of Korea Multimedia Society
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    • v.24 no.2
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    • pp.167-177
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    • 2021
  • In the field of augmented reality, edge-based methods have been popularly used in tracking textureless 3D objects. However, edge-based methods are inherently vulnerable to cluttered backgrounds. Another way to track textureless or poorly-textured 3D objects is to directly align image intensity of 3D object between consecutive frames. Although the direct methods enable more reliable and stable tracking compared to using local features such as edges, they are more sensitive to occlusion and less accurate than the edge-based methods. Therefore, we propose a method that combines an edge-based method and a direct method to leverage the advantages from each approach. Experimental results show that the proposed method is much robust to both fast camera (or object) movements and occlusion while still working in real time at a frame rate of 18 Hz. The tracking success rate and tracking accuracy were improved by up to 84% and 1.4 pixels, respectively, compared to using the edge-based method or the direct method solely.

Circular Object Detection by the Hough Transform using an Area of Cumulated Points (Hough 변환에 의해 나타나는 누적분포 면적을 이용한 원형물체의 검출)

  • 전호민;최우영
    • Proceedings of the IEEK Conference
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    • 2000.11d
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    • pp.5-8
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    • 2000
  • In this paper, a technique to estimate the circular object's center and radius under noisy condition is described. The technique is based on Davies'Hough transform approach to circular object location but more robust to noise and faster to estimate the circle by using an area of cumulated points.

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A COMPARISON OF OBJECTED-ORIENTED AND PIXELBASED CLASSIFICATION METHODS FOR FUEL TYPE MAP USING HYPERION IMAGERY

  • Yoon, Yeo-Sang;Kim, Yong-Seung
    • Proceedings of the KSRS Conference
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    • v.1
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    • pp.297-300
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    • 2006
  • The knowledge of fuel load and composition is important for planning and managing the fire hazard and risk. However, fuel mapping is extremely difficult because fuel properties vary at spatial scales, change depending on the seasonal situations and are affected by the surrounding environment. Remote sensing has potential of reduction the uncertainty in mapping fuels and offers the best approach for improving our abilities. This paper compared the results of object-oriented classification to a pixel-based classification for fuel type map derived from Hyperion hyperspectral data that could be enable to provide this information and allow a differentiation of material due to their typical spectra. Our methodological approach for fuel type map is characterized by the result of the spectral mixture analysis (SMA) that can used to model the spectral variability in multi- or hyperspectral images and to relate the results to the physical abundance of surface constitutes represented by the spectral endmembers. Object-oriented approach was based on segment based endmember selection, while pixel-based method used standard SMA. To validate and compare, we used true-color high resolution orthoimagery

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Object Recognition using Smart Tag and Stereo Vision System on Pan-Tilt Mechanism

  • Kim, Jin-Young;Im, Chang-Jun;Lee, Sang-Won;Lee, Ho-Gil
    • 제어로봇시스템학회:학술대회논문집
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    • 2005.06a
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    • pp.2379-2384
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    • 2005
  • We propose a novel method for object recognition using the smart tag system with a stereo vision on a pan-tilt mechanism. We developed a smart tag which included IRED device. The smart tag is attached onto the object. We also developed a stereo vision system which pans and tilts for the object image to be the centered on each whole image view. A Stereo vision system on the pan-tilt mechanism can map the position of IRED to the robot coordinate system by using pan-tilt angles. And then, to map the size and pose of the object for the robot to coordinate the system, we used a simple model-based vision algorithm. To increase the possibility of tag-based object recognition, we implemented our approach by using as easy and simple techniques as possible.

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Object Recognition for Mobile Robot using Context-based Bi-directional Reasoning (상황 정보 기반 양방향 추론 방법을 이용한 이동 로봇의 물체 인식)

  • Lim, G.H.;Ryu, G.G.;Suh, I.H.;Kim, J.B.;Zhang, G.X.;Kang, J.H.;Park, M.K.
    • Proceedings of the KIEE Conference
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    • 2007.04a
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    • pp.6-8
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    • 2007
  • In this paper, We propose reasoning system for object recognition and space classification using not only visual features but also contextual information. It is necessary to perceive object and classify space in real environments for mobile robot. especially vision based. Several visual features such as texture, SIFT. color are used for object recognition. Because of sensor uncertainty and object occlusion. there are many difficulties in vision-based perception. To show the validities of our reasoning system. experimental results will be illustrated. where object and space are inferred by bi -directional rules even with partial and uncertain information. And the system is combined with top-down and bottom-up approach.

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A Meta-Model for the Storage of XML Schema using Model-Mapping Approach (모델 매핑 접근법을 이용한 XML 스키마 저장 메타모델에 대한 연구)

  • Lim, Hoon-Tae;Lim, Tae-Soo;Hong, Keun-Hee;Kang, Suk-Ho
    • IE interfaces
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    • v.17 no.3
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    • pp.330-337
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    • 2004
  • Since XML (eXtensible Markup Language) was highlighted as an information interchange format, there is an increasing demand for incorporating XML with databases. Most of the approaches are focused on RDB (Relational Databases) because of legacy systems. But these approaches depend on the database system. Countless researches are being focused on DTD (Document Type Definition). However XML Schema is more comprehensive and efficient in many perspectives. We propose a meta-model for XML Schema that is independent of the database. There are three processes to build our meta-model: DOM (Document Object Model) tree analysis, object modeling and storing object into a fixed DB schema using model mapping approach. We propose four mapping rules for object modeling, which conform to the ODMG (Object Data Management Group) 3.0 standard. We expect that the model will be especially useful in building XML-based e-business applications.