Scale-invariant template matching using histogram of dominant gradients

Yoo, Jung Ho; Hwang, Sung Soo; Kim, Seong Dae; Ki, Myung Seok; Cha, Jaehyuk

doi:10.1016/j.patcog.2014.02.016

Cited by 40 publications

(18 citation statements)

References 12 publications

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“…Template matching is a main task in various computer vision applications. It is extensively applied in various areas, such as object detection, video compression, and automatic inspection (Yoo et al 2014; Brunelli 2009). Template matching is the process of determining the existence and position of a sub-image or an object inside a larger scene image (Choi and Kim 2002; Goshtasby et al 1984).…”

Section: Vomentioning

confidence: 99%

“…It computes the degree of similarity between the template and search area by shifting the template over the search area and calculating the degree of similarity in each location based on various similarity measures. The shift position that has the largest similarity degree is the likely position of the template found in the search area (Yoo et al 2014; Jurie and Dhome 2002; Goshtasby et al 1984; Choi and Kim 2002). …”

Section: Vomentioning

confidence: 99%

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Review of visual odometry: types, approaches, challenges, and applications

et al. 2016

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Accurate localization of a vehicle is a fundamental challenge and one of the most important tasks of mobile robots. For autonomous navigation, motion tracking, and obstacle detection and avoidance, a robot must maintain knowledge of its position over time. Vision-based odometry is a robust technique utilized for this purpose. It allows a vehicle to localize itself robustly by using only a stream of images captured by a camera attached to the vehicle. This paper presents a review of state-of-the-art visual odometry (VO) and its types, approaches, applications, and challenges. VO is compared with the most common localization sensors and techniques, such as inertial navigation systems, global positioning systems, and laser sensors. Several areas for future research are also highlighted.

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Section: Vomentioning

confidence: 99%

Section: Vomentioning

confidence: 99%

Review of visual odometry: types, approaches, challenges, and applications

et al. 2016

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“…Wu et al also proposed a method to speed-up template matching and decrease the computational costs of conventional methods [25]. Yoo et al presented a histogram-based template matching method for the situation of large-scale differences between target and template images [26]. To deal with heavy appearance variations, Sun et al proposed a multiple template method to track fast motion by generating virtual templates that are affinely transformed from the original one [27].…”

Section: Introductionmentioning

confidence: 99%

Template Matching for Wide-Baseline Panoramic Images from a Vehicle-Borne Multi-Camera Rig

Hong³

et al. 2018

IJGI

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Automatic detection and locating of objects such as poles, traffic signs, and building corners in street scenes captured from a mobile mapping system has many applications. Template matching is a technique that could automatically recognise the counterparts or correspondents of an object from multi-view images. In this study, we aim at finding correspondents of an object from wide baseline panoramic images with large geometric deformations from sphere projection and significant systematic errors from multi-camera rig geometry. Firstly, we deduce the camera model and epipolar model of a multi-camera rig system. Then, epipolar errors are analysed to determine the search area for pixelwise matching. A low-cost laser scanner is optionally used to constrain the depth of an object. Lastly, several classic feature descriptors are introduced to template matching and evaluated on the multi-view panoramic image dataset. We propose a template matching method combining a fast variation of a scale-invariant feature transform (SIFT) descriptor. Our method experimentally achieved the best performance in terms of accuracy and efficiency comparing to other feature descriptors and the most recent robust template matching methods.

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“…Tian et al [9] extract the target regions from the raw data by using an adaptive thresholding based image segmentation algorithm. Template matching methods include improved template matching [10] and scale invariant template matching [11]. These matching methods fail to detect the shape of the pedestrian and are sensitive to noise.…”

Section: Introductionmentioning

confidence: 99%