OATM: Occlusion Aware Template Matching by Consensus Set Maximization

Korman, Simon; Soatto, Stefano; Milam, Mark B.

doi:10.1109/cvpr.2018.00283

Cited by 18 publications

(12 citation statements)

References 35 publications

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“…Meanwhile, ABC-CF was selected as an improved version of ABC to compare the matching results. In the experiments, different challenges could be tried, such as rotation, deformation, occlusion, scale variation, and illumination variation [34][35][36][37], to prove the merits of ABC and ABC-CF. The results obtained in a scene measuring 360 × 640 are shown in Figure 10.…”

Section: Experimental Comparison With Color Histogram-based Methodsmentioning

confidence: 99%

Robust and Precise Matching Algorithm Combining Absent Color Indexing and Correlation Filter

Tian

Kaneko

et al. 2021

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This paper presents a novel method that absorbs the strong discriminative ability from absent color indexing (ABC) to enhance sensitivity and combines it with a correlation filter (CF) for obtaining a higher precision; this method is named ABC-CF. First, by separating the original color histogram, apparent and absent colors are introduced. Subsequently, an automatic threshold acquisition is proposed using a mean color histogram. Next, a histogram intersection is selected to calculate the similarity. Finally, CF follows them to solve the drift caused by ABC during the matching process. The novel approach proposed in this paper realizes robustness in distortion of target images and higher margins in fundamental matching problems, and then achieves more precise matching in positions. The effectiveness of the proposed approach can be evaluated in the comparative experiments with other representative methods by use of the open data.

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Section: Experimental Comparison With Color Histogram-based Methodsmentioning

confidence: 99%

Robust and Precise Matching Algorithm Combining Absent Color Indexing and Correlation Filter

Tian

Kaneko

et al. 2021

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“…However, it is limited to working with grayscale images and the large search-space of possible affine transformations makes this algorithm slow. A more recent variation on this algorithm, OATM (Korman et al, 2018), has increased speed but remains both slower and less accurate than another approach, DDIS, which is discussed in the following paragraph.…”

Section: Related Workmentioning

confidence: 99%

Explaining away results in accurate and tolerant template matching

Spratling

2020

Pattern Recognition

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Recognising and locating image patches or sets of image features is an important task underlying much work in computer vision. Traditionally this has been accomplished using template matching. However, template matching is notoriously brittle in the face of changes in appearance caused by, for example, variations in viewpoint, partial occlusion, and non-rigid deformations. This article tests a method of template matching that is more tolerant to such changes in appearance and that can, therefore, more accurately identify image patches. In traditional template matching the comparison between a template and the image is independent of the other templates. In contrast, the method advocated here takes into account the evidence provided by the image for the template at each location and the full range of alternative explanations represented by the same template at other locations and by other templates. Specifically, the proposed method of template matching is performed using a form of probabilistic inference known as "explaining away". The algorithm used to implement explaining away has previously been used to simulate several neurobiological mechanisms, and been applied to image contour detection and pattern recognition tasks. Here is is applied for the first time to image patch matching, and is shown to produce superior results in comparison to the current state-of-the-art methods.

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“…Skyline features can model the general geometric characteristic of a street in a geo-tagged image and yield the yaw angle when matched with the skyline extracted from the GIS. To calculate the pitch and role, the system uses a vertical vanishing point technique [118]. The percentage of successful registrations with a rotation error less than 2.0 degrees is 90%, and the average computation time is 671 ms (471 ms for vertical vanishing point detection), which is not sufficient for real-time applications.…”

Section: Edge-based Trackingmentioning

confidence: 99%

A Survey of Marker-Less Tracking and Registration Techniques for Health & Environmental Applications to Augmented Reality and Ubiquitous Geospatial Information Systems

Sadeghi‐Niaraki

Choi

2020

Sensors

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Most existing augmented reality (AR) applications are suitable for cases in which only a small number of real world entities are involved, such as superimposing a character on a single surface. In this case, we only need to calculate pose of the camera relative to that surface. However, when an AR health or environmental application involves a one-to-one relationship between an entity in the real-world and the corresponding object in the computer model (geo-referenced object), we need to estimate the pose of the camera in reference to a common coordinate system for better geo-referenced object registration in the real-world. New innovations in developing cheap sensors, computer vision techniques, machine learning, and computing power have helped to develop applications with more precise matching between a real world and a virtual content. AR Tracking techniques can be divided into two subcategories: marker-based and marker-less approaches. This paper provides a comprehensive overview of marker-less registration and tracking techniques and reviews their most important categories in the context of ubiquitous Geospatial Information Systems (GIS) and AR focusing to health and environmental applications. Basic ideas, advantages, and disadvantages, as well as challenges, are discussed for each subcategory of tracking and registration techniques. We need precise enough virtual models of the environment for both calibrations of tracking and visualization. Ubiquitous GISs can play an important role in developing AR in terms of providing seamless and precise spatial data for outdoor (e.g., environmental applications) and indoor (e.g., health applications) environments.

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OATM: Occlusion Aware Template Matching by Consensus Set Maximization

Cited by 18 publications

References 35 publications

Robust and Precise Matching Algorithm Combining Absent Color Indexing and Correlation Filter

Robust and Precise Matching Algorithm Combining Absent Color Indexing and Correlation Filter

Explaining away results in accurate and tolerant template matching

A Survey of Marker-Less Tracking and Registration Techniques for Health & Environmental Applications to Augmented Reality and Ubiquitous Geospatial Information Systems

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