Exact order based feature descriptor for illumination robust image matching

Kim, Bongjoe; Yoo, Hunjae; Sohn, Kwanghoon

doi:10.1016/j.patcog.2013.04.015

Cited by 21 publications

(12 citation statements)

References 14 publications

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“…Illumination changes are usually handled at the beginning of the VBL pipeline, during the data description step. Local features robust to illumination, like SIFT or SURF, consider gradient quantity in order to be invariant to pixel intensity variation caused by different illumination conditions [81]. However, Valgren and Lilienthal [205] have shown that these representations are not well suited for similarity association across season cycles.…”

Section: Appearance Changesmentioning

confidence: 99%

A survey on Visual-Based Localization: On the benefit of heterogeneous data

Piasco

Sidibé

Demonceaux

et al. 2018

Pattern Recognition

222

112

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We are surrounded by plenty of information about our environment. From these multiple sources, numerous data could be extracted: set of images, 3D model, coloured points cloud... When classical localization devices failed (e.g. GPS sensor in cluttered environments), aforementioned data could be used within a localization framework. This is called Visual Based Localization (VBL). Due to numerous data types that can be collected from a scene, VBL encompasses a large amount of different methods. This paper presents a survey about recent methods that localize a visual acquisition system according to a known environment. We start by categorizing VBL methods into two distinct families: indirect and direct localization systems. As the localization environment is almost always dynamic, we pay special attention to methods designed to handle appearances changes occurring in a scene. Thereafter, we highlight methods exploiting heterogeneous types of data. Finally, we conclude the paper with a discussion on promising trends that could permit to a localization system to reach high precision pose estimation within an area as large as possible.

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Section: Appearance Changesmentioning

confidence: 99%

A survey on Visual-Based Localization: On the benefit of heterogeneous data

Piasco

Sidibé

Demonceaux

et al. 2018

Pattern Recognition

222

112

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show abstract

“…It offers image classification and retrieval [3][4][5][6], object recognition and matching [7][8][9], 3D scene reconstruction [10], robot localization [11], object detection and tracking and video processing. All of these processing systems rely on the presence of stable and meaningful features in the image.…”

Section: Computer Vision Systemmentioning

confidence: 99%

Machine Learning framework for image classification

Loussaief¹,

Abdelkrim²

2018

Adv. sci. technol. eng. syst. j.

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Hereby in this paper, we are going to refer image classification. The main issue in image classification is features extraction and image vector representation. We expose the Bag of Features method used to find image representation. Class prediction accuracy of varying classifiers algorithms is measured on Caltech 101 images. For feature extraction functions we evaluate the use of the classical Speed Up Robust Features technique against global color feature extraction. The purpose of our work is to guess the best machine learning framework techniques to recognize the stop sign images.The trained model will be integrated into a robotic system in a future work.

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“…However, ordering pixels by discrete intensity yields significantly different order distributions when the illumination changes, thereby leading to changes in the nonlinear intensity. To address this problem, the exact order based descriptor (EOD) employed by Kim [18] used an exact order method.…”

Section: Introductionmentioning

confidence: 99%

Random Binary Local Patch Clustering Transforms Based Image Matching for Nonlinear Intensity Changes

Wang

2018

Mathematical Problems in Engineering

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This paper presents a new feature descriptor that is suitable for image matching under nonlinear intensity changes. The proposed approach consists of the following three steps. First, a binary local patch clustering transform response is employed as the transform space. The value of the new space exhibits a high similarity after changes in intensity. Then, a random binary pattern coding method extracts raw feature histograms from the new space. Finally, the discrimination of the proposed feature descriptor is enhanced by using a multiple spatial support region-based binning method. Experimental results show that the proposed method is able to provide a more robust image matching performance under nonlinear intensity changes.

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Exact order based feature descriptor for illumination robust image matching

Cited by 21 publications

References 14 publications

A survey on Visual-Based Localization: On the benefit of heterogeneous data

A survey on Visual-Based Localization: On the benefit of heterogeneous data

Machine Learning framework for image classification

Random Binary Local Patch Clustering Transforms Based Image Matching for Nonlinear Intensity Changes

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