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Adaptive color calibration based one-shot structured light system.

Zhou Y, Zhao D, Yu Y, Yuan J, Du S - Sensors (Basel) (2012)

Bottom Line: In one-shot color structured light systems, the color of stripe patterns are typically distorted with respect to color crosstalk, ambient light and the albedo of the scanned objects, leading to mismatch in the correspondence of color stripes between the projected and captured images.The adaptive color calibration, according to the relative albedo in RGB channels, can improve the accuracy of labeling stripe by alleviating the effect of albedo and ambient light while decoding the color.Furthermore, the Discrete Trend Transform in the M channel makes the color calibration an effective method for detecting weak stripes due to the uneven surfaces or reflectance characteristics of the scanned objects.

View Article: PubMed Central - PubMed

Affiliation: School of Electronic Science and Engineering, Nanjing University, Nanjing 210093, Jiangsu, China. nackzhou@nju.edu.cn

ABSTRACT
In one-shot color structured light systems, the color of stripe patterns are typically distorted with respect to color crosstalk, ambient light and the albedo of the scanned objects, leading to mismatch in the correspondence of color stripes between the projected and captured images. In this paper, an adaptive color calibration and Discrete Trend Transform algorithm are presented to achieve high-resolution 3D reconstructions. The adaptive color calibration, according to the relative albedo in RGB channels, can improve the accuracy of labeling stripe by alleviating the effect of albedo and ambient light while decoding the color. Furthermore, the Discrete Trend Transform in the M channel makes the color calibration an effective method for detecting weak stripes due to the uneven surfaces or reflectance characteristics of the scanned objects. With this approach, the presented system is suitable for scanning moving objects and generating high-resolution 3D reconstructions without the need of dark laboratory environments.

No MeSH data available.


Related in: MedlinePlus

A comparison of stripe segmentation result. (a) captured source image; (b) stripe segmentation result using local adaptive thresholding method; (c) stripe segmentation result using DTT.
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f5-sensors-12-10947: A comparison of stripe segmentation result. (a) captured source image; (b) stripe segmentation result using local adaptive thresholding method; (c) stripe segmentation result using DTT.

Mentions: Figure 4 clarifies how DTT works. For example, f(n) is a scan-line in the M channel. If f(n) is locally monotonically decreasing in the window, then T achieves its maximum value . In contrast, T reaches its minimum value at f(n)'s rising edge. The values between the maximum and minimum denote that f(n) is neither monotonically increasing nor decreasing. The transition from the maximum to minimum values in T indicates a peak in the M channel. Thus, the max-to-min transition in T is searched for to locate the area of stripes. The rising edge in the M channel is marked as the start of a stripe area, and the falling edge in the M channel is marked as the end of a stripe area. The result of peak detection using DTT is illustrated in Figure 5. We can determine that DTT is more robust than the local adaptive threshold method [19] for weak stripes.


Adaptive color calibration based one-shot structured light system.

Zhou Y, Zhao D, Yu Y, Yuan J, Du S - Sensors (Basel) (2012)

A comparison of stripe segmentation result. (a) captured source image; (b) stripe segmentation result using local adaptive thresholding method; (c) stripe segmentation result using DTT.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3472868&req=5

f5-sensors-12-10947: A comparison of stripe segmentation result. (a) captured source image; (b) stripe segmentation result using local adaptive thresholding method; (c) stripe segmentation result using DTT.
Mentions: Figure 4 clarifies how DTT works. For example, f(n) is a scan-line in the M channel. If f(n) is locally monotonically decreasing in the window, then T achieves its maximum value . In contrast, T reaches its minimum value at f(n)'s rising edge. The values between the maximum and minimum denote that f(n) is neither monotonically increasing nor decreasing. The transition from the maximum to minimum values in T indicates a peak in the M channel. Thus, the max-to-min transition in T is searched for to locate the area of stripes. The rising edge in the M channel is marked as the start of a stripe area, and the falling edge in the M channel is marked as the end of a stripe area. The result of peak detection using DTT is illustrated in Figure 5. We can determine that DTT is more robust than the local adaptive threshold method [19] for weak stripes.

Bottom Line: In one-shot color structured light systems, the color of stripe patterns are typically distorted with respect to color crosstalk, ambient light and the albedo of the scanned objects, leading to mismatch in the correspondence of color stripes between the projected and captured images.The adaptive color calibration, according to the relative albedo in RGB channels, can improve the accuracy of labeling stripe by alleviating the effect of albedo and ambient light while decoding the color.Furthermore, the Discrete Trend Transform in the M channel makes the color calibration an effective method for detecting weak stripes due to the uneven surfaces or reflectance characteristics of the scanned objects.

View Article: PubMed Central - PubMed

Affiliation: School of Electronic Science and Engineering, Nanjing University, Nanjing 210093, Jiangsu, China. nackzhou@nju.edu.cn

ABSTRACT
In one-shot color structured light systems, the color of stripe patterns are typically distorted with respect to color crosstalk, ambient light and the albedo of the scanned objects, leading to mismatch in the correspondence of color stripes between the projected and captured images. In this paper, an adaptive color calibration and Discrete Trend Transform algorithm are presented to achieve high-resolution 3D reconstructions. The adaptive color calibration, according to the relative albedo in RGB channels, can improve the accuracy of labeling stripe by alleviating the effect of albedo and ambient light while decoding the color. Furthermore, the Discrete Trend Transform in the M channel makes the color calibration an effective method for detecting weak stripes due to the uneven surfaces or reflectance characteristics of the scanned objects. With this approach, the presented system is suitable for scanning moving objects and generating high-resolution 3D reconstructions without the need of dark laboratory environments.

No MeSH data available.


Related in: MedlinePlus