Tracking Color marker using projective transformation for augmented reality application

Belghit, Hayet; Zenati-Henda, Nadia; Bellabi, Abdelkader.; Benbelkacem, Samir; Belhocine, Mahmoud

doi:10.1109/icmcs.2012.6320245

Cited by 7 publications

(8 citation statements)

References 13 publications

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“…For both methods, the image matching algorithms need to detect distinct features between live video frames that are captured from the environment, and a reference image that is already available. In the marker-based approaches, since the algorithms need to detect the features of a label (e.g., Quick Response Code/QR code), the results are very robust [1] in contrast with markerless AR, which needs to use the natural features of the environment that can vary [23,24] (more information regarding AR is presented in Appendix A).…”

Section: Image Matching Applications In the Construction Industrymentioning

confidence: 99%

“…Markerless AR (feature-based, natural features) This type of AR system uses the natural features of the environment as references [24]. Depending on the algorithm used for this system, these features could be edges, corners, segments, or points [23]. In this online approach, features extracted from current video frames taken from the scene are compared with features extracted from an initial key frame.…”

Section: Conflicts Of Interestmentioning

confidence: 99%

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Development and Assessment of a Sensor-Based Orientation and Positioning Approach for Decreasing Variation in Camera Viewpoints and Image Transformations at Construction Sites

Sabzevar

Gheisari

2020

Applied Sciences

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Image matching techniques offer valuable opportunities for the construction industry. Image matching, a fundamental process in computer vision, is required for different purposes such as object and scene recognition, video data mining, reconstruction of three-dimensional (3D) objects, etc. During the image matching process, two images that are randomly (i.e., from different position and orientation) captured from a scene are compared using image matching algorithms in order to identify their similarity. However, this process is very complex and error prone, because pictures that are randomly captured from a scene vary in viewpoints. Therefore, some main features in images such as position, orientation, and scale of objects are transformed. Sometimes, these image matching algorithms cannot correctly identify the similarity between these images. Logically, if these features remain unchanged during the picture capturing process, then image transformations are reduced, similarity increases, and consequently, the chances of algorithms successfully conducting the image matching process increase. One way to improve these chances is to hold the camera at a fixed viewpoint. However, in messy, dusty, and temporary locations such as construction sites, holding the camera at a fixed viewpoint is not always feasible. Is there any way to repeat and retrieve the camera’s viewpoints during different captures at locations such as construction sites? This study developed and evaluated an orientation and positioning approach that decreased the variation in camera viewpoints and image transformation on construction sites. The results showed that images captured while using this approach had less image transformation in contrast to images not captured using this approach.

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Section: Image Matching Applications In the Construction Industrymentioning

confidence: 99%

Section: Conflicts Of Interestmentioning

confidence: 99%

Development and Assessment of a Sensor-Based Orientation and Positioning Approach for Decreasing Variation in Camera Viewpoints and Image Transformations at Construction Sites

Sabzevar

Gheisari

2020

Applied Sciences

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“…The information conveyed by the virtual objects helps a user perform real-world tasks [2]. In order for AR to be effective, the real and computer-generated objects in 3D must be accurately positioned relative to each other [3] [4]. In order to make a virtual object into the real world, a virtual camera has to be placed in the same position and orientation as the real camera, which requires a robust real-time tracking strategy one of the bottlenecks of AR applications.…”

Section: Introductionmentioning

confidence: 99%

Markerless tracking using interest window for augmented reality applications

Hamidia

Zenati-Henda

Belghit

et al. 2014

2014 International Conference on Multimedia Computing and Systems (ICMCS)

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This paper deals with the problem of object tracking in Augmented Reality (AR) applications. In the recent years AR has been of increasing interest. It consists of inserting a virtual object into a real scene. The temporal coherence between real and virtual objects is one of the hardest challenges of the AR system realization. A markerless tracking based on the interest points matching using SURF (Speed Up Robust Features) is mostly used, but it suffers from high computational complexity. For this reason, we propose a method using an interest window around of the object to be tracked. Moreover, a relationship between the object size and the metric threshold of SURF is investigated. The experimental results show an improvement of the proposed object tracking method performance compared to a standard method based on SURF interest points matching.

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“…For the people who are unfamiliar with this operation have another problem that they do not feel the operation intuitively. In addition, there are object recognition methods using special devices [4] or using color marker [5]. These methods have problems that the recognition performance depends on the illumination changes and the shape features of the device should be prepared in advance.…”

Section: Introductionmentioning

confidence: 99%

A simple pen-type device based interaction for the low cost calculation power

Iwamoto

Lee

2013

Proceedings of the 2013 IEEE/SICE International Symposium on System Integration

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This paper proposes a method of recognition blinking LED from images for a robust and simple interaction. The proposed method uses time-series changes of the brightness values. A switch and LEDs are attached to the pen-type device. The LEDs blink while the switch is pressed and at constant intervals. Brightness image converted to low-resolution image. The features of the brightness values change in the time axis direction for each pixel are analyzed. Finally the pixel, which shows similar aspect of the features of the LED brightness change, is recognized as the tip of the pen-type device. Since the proposed method does not use color information, it is robust against illumination variation. Small calculation power is required for whole recognition procedures. In this paper, the proposed method using the time-series changes of the brightness values is described. The quantitative evaluation on recognition rate is performed and the effectiveness of the propose method is shown in this paper.

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Tracking Color marker using projective transformation for augmented reality application

Cited by 7 publications

References 13 publications

Development and Assessment of a Sensor-Based Orientation and Positioning Approach for Decreasing Variation in Camera Viewpoints and Image Transformations at Construction Sites

Development and Assessment of a Sensor-Based Orientation and Positioning Approach for Decreasing Variation in Camera Viewpoints and Image Transformations at Construction Sites

Markerless tracking using interest window for augmented reality applications

A simple pen-type device based interaction for the low cost calculation power

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