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Register a new userColor-Phase Analysis for Sinusoidal Structured Light in Rapid Range Imaging
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Added by
Changsoo Je
Publication date
2015
fields
Informatics Engineering
and research's language is
English
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Active range sensing using structured-light is the most accurate and reliable method for obtaining 3D information. However, most of the work has been limited to range sensing of static objects, and range sensing of dynamic (moving or deforming) objects has been investigated recently only by a few researchers. Sinusoidal structured-light is one of the well-known optical methods for 3D measurement. In this paper, we present a novel method for rapid high-resolution range imaging using color sinusoidal pattern. We consider the real-world problem of nonlinearity and color-band crosstalk in the color light projector and color camera, and present methods for accurate recovery of color-phase. For high-resolution ranging, we use high-frequency patterns and describe new unwrapping algorithms for reliable range recovery. The experimental results demonstrate the effectiveness of our methods.
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For structured-light range imaging, color stripes can be used for increasing the number of distinguishable light patterns compared to binary BW stripes. Therefore, an appropriate use of color patterns can reduce the number of light projections and range imaging is achievable in single video frame or in one shot. On the other hand, the reliability and range resolution attainable from color stripes is generally lower than those from multiply projected binary BW patterns since color contrast is affected by object color reflectance and ambient light. This paper presents new methods for selecting stripe colors and designing multiple-stripe patterns for one-shot and two-shot imaging. We show that maximizing color contrast between the stripes in one-shot imaging reduces the ambiguities resulting from colored object surfaces and limitations in sensor/projector resolution. Two-shot imaging adds an extra video frame and maximizes the color contrast between the first and second video frames to diminish the ambiguities even further. Experimental results demonstrate the effectiveness of the presented one-shot and two-shot color-stripe imaging schemes.
Research interest in rapid structured-light imaging has grown increasingly for the modeling of moving objects, and a number of methods have been suggested for the range capture in a single video frame. The imaging area of a 3D object using a single projector is restricted since the structured light is projected only onto a limited area of the object surface. Employing additional projectors to broaden the imaging area is a challenging problem since simultaneous projection of multiple patterns results in their superposition in the light-intersected areas and the recognition of original patterns is by no means trivial. This paper presents a novel method of multi-projector color structured-light vision based on projector-camera triangulation. By analyzing the behavior of superposed-light colors in a chromaticity domain, we show that the original light colors cannot be properly extracted by the conventional direct estimation. We disambiguate multiple projectors by multiplexing the orientations of projector patterns so that the superposed patterns can be separated by explicit derivative computations. Experimental studies are carried out to demonstrate the validity of the presented method. The proposed method increases the efficiency of range acquisition compared to conventional active stereo using multiple projectors.
Multiple color stripes have been employed for structured light-based rapid range imaging to increase the number of uniquely identifiable stripes. The use of multiple color stripes poses two problems: (1) object surface color may disturb the stripe color and (2) the number of adjacent stripes required for identifying a stripe may not be maintained near surface discontinuities such as occluding boundaries. In this paper, we present methods to alleviate those problems. Log-gradient filters are employed to reduce the influence of object colors, and color stripes in two and three directions are used to increase the chance of identifying correct stripes near surface discontinuities. Experimental results demonstrate the effectiveness of our methods.
We present a technically simple implementation of quantitative phase imaging in confocal microscopy based on synthetic optical holography with sinusoidal-phase reference waves. Using a Mirau interference objective and low-amplitude vertical sample vibration with a piezo-controlled stage, we record synthetic holograms on commercial confocal microscopes (Nikon, model: A1R; Zeiss: model: LSM-880), from which quantitative phase images are reconstructed. We demonstrate our technique by stain-free imaging of cervical (HeLa) and ovarian (ES-2) cancer cells and stem cell (mHAT9a) samples. Our technique has the potential to extend fluorescence imaging applications in confocal microscopy by providing label-free cell finding, monitoring cell morphology, as well as non-perturbing long-time observation of live cells based on quantitative phase contrast.
Recent works on interactive video object cutout mainly focus on designing dynamic foreground-background (FB) classifiers for segmentation propagation. However, the research on optimally removing errors from the FB classification is sparse, and the errors often accumulate rapidly, causing significant errors in the propagated frames. In this work, we take the initial steps to addressing this problem, and we call this new task emph{segmentation rectification}. Our key observation is that the possibly asymmetrically distributed false positive and false negative errors were handled equally in the conventional methods. We, alternatively, propose to optimally remove these two types of errors. To this effect, we propose a novel bilayer Markov Random Field (MRF) model for this new task. We also adopt the well-established structured learning framework to learn the optimal model from data. Additionally, we propose a novel one-class structured SVM (OSSVM) which greatly speeds up the structured learning process. Our method naturally extends to RGB-D videos as well. Comprehensive experiments on both RGB and RGB-D data demonstrate that our simple and effective method significantly outperforms the segmentation propagation methods adopted in the state-of-the-art video cutout systems, and the results also suggest the potential usefulness of our method in image cutout system.
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