Sequential Scene Matching Using Edge Features

Wong, Robert Y.

doi:10.1109/taes.1978.308586

Cited by 30 publications

(13 citation statements)

References 13 publications

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“…The larger the number of such regions contained in the image, the higher the probability that the image is a good candidate for scene matching. In this paper, we consider the edge information [7,24] as the most salient information for determining regions of interest for scene matching. This choice enables a fast implementation-the edge clustering method has also been shown to be an efficient method for fast object detection [4].…”

Section: Compactness Of Edgesmentioning

confidence: 99%

An efficient framework for location-based scene matching in image databases

Das

Loui

2012

Int J Multimed Info Retr

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SIFT-based methods have been widely used for scene matching of photos taken at particular locations or places of interest. These methods are typically very time consuming due to the large number and high dimensionality of features used, making them unfeasible for use in consumer image collections containing a large number of images where computational power is limited and a fast response is desired. Considerable computational savings can be realized if images containing signature elements of particular locations can be automatically identified from the large number of images and only these representative images used for scene matching. We propose an efficient framework incorporating a set of discriminative image features that effectively enables us to select representative images for fast location-based scene matching. These image features are used for classifying images into good or bad candidates for scene matching, using different classification approaches. Furthermore, the image features created from our framework can facilitate the process of using sub-images for location-based scene matching with SIFT features. The experimental results demonstrate the effectiveness of our approach compared with the traditional SIFT-, PCA-SIFT-, and SURF-based approaches by reducing the computational time by an order of magnitude.

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Section: Compactness Of Edgesmentioning

confidence: 99%

An efficient framework for location-based scene matching in image databases

Das

Loui

2012

Int J Multimed Info Retr

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“…The larger the number of unique objects contained in the image, the higher the probability that the image could be a good candidate image for scene matching. The edge information is the most salient information for determination of specific objects [10][11]. The edge-clustering method has shown to be an efficient method for fast object detection [12].…”

Section: Compactness Of Edgesmentioning

confidence: 99%

A novel framework for fast scene matching in consumer image collections

Chen

Das

Loui

2010

2010 IEEE International Conference on Multimedia and Expo

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“…A radar scene matching technique has been widely adopted in various aeronautic applications to navigate current positions by mapping a pre-stored digital elevation map (DEM) with a realtime terrain image produced by a radar [1,2]. As the radar transmits electromagnetic (EM) waves to measure the elevation profile under its flight path, the accuracy of this technique is easily affected by the antenna characteristics of the radar and EM properties of the terrain [3,4].…”

Section: Introductionmentioning

confidence: 99%

“…The estimation error is defined as the root mean square (RMS) difference between the real value (h real ) and the estimated heights (h est. ) obtained from all steering angles and distance, as shown in (2).…”

Section: Introductionmentioning

confidence: 99%

A Modeling Process of Equivalent Terrains for Reduced Simulation Complexity in Radar Scene Matching Applications

Byun¹,

Hwang²,

Park³

et al. 2017

Journal of electromagnetic engineering and science

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51 I. INTRODUCTIONA radar scene matching technique has been widely adopted in various aeronautic applications to navigate current positions by mapping a pre-stored digital elevation map (DEM) with a realtime terrain image produced by a radar [1,2]. As the radar transmits electromagnetic (EM) waves to measure the elevation profile under its flight path, the accuracy of this technique is easily affected by the antenna characteristics of the radar and EM properties of the terrain [3,4]. However, previous studies are limited to using the ray tracing method without any indepth consideration of antenna characteristics, such as the halfpower beam width (HPBW), side-lobe level, and polarization [5]. Although some papers present the effect of terrain properties using EM simulations, the huge electrical size of terrains has been obstructed to perform a full-wave EM analysis because of the tremendous computational load and time [6].In this study, we propose a modeling process of equivalent terrains that significantly reduces the computational load and time for a full-wave EM simulation. The proposed process is employed for a sample geometry with a size of 3 m  3 m, and the terrain is equivalently quantized based on the minimum range resolution of the radar. The quantized model is then imported as piecewise mesh triangles into the EM simulation, and the antenna characteristics are taken into account by including the far-field radiation pattern of a transmit antenna as Key Words: Antenna Beamforming, Antenna Characteristics, EM Simulation Complexity, Radar Scene Matching. Manuscript received January 3, 2017 ; Revised February 13, 2017 ; Accepted February 22, 2017. (ID No. 20170103-001J) This is an Open-Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. ⓒ

show abstract

Sequential Scene Matching Using Edge Features

Cited by 30 publications

References 13 publications

An efficient framework for location-based scene matching in image databases

An efficient framework for location-based scene matching in image databases

A novel framework for fast scene matching in consumer image collections

A Modeling Process of Equivalent Terrains for Reduced Simulation Complexity in Radar Scene Matching Applications

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