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This paper proposes a solution to the 2-D phase unwrapping problem, inherent to time-of-flight range sensing technology due to the cyclic nature of phase. Our method uses a single frequency capture period to improve frame rate and decrease the presence of motion artifacts encountered in multiple frequency solutions. We present a probabilistic framework that considers intensity image in addition to the phase image. The phase unwrapping problem is cast in terms of global optimization of a carefully chosen objective function. Comparative experimental results confirm the effectiveness of the proposed approach.

This paper proposes a solution to the 2-D phase unwrapping problem, inherent to time-of-flight range sensing technology due to the cyclic nature of phase. Our method uses a single frequency capture period to improve frame rate and decrease the presence of motion artifacts encountered in multiple frequency solutions. We present an illumination model that considers intensity image and estimates of the surface normal in addition to the phase image. Considering the number of phase wrap as the 'label', the likelihood of each label is estimated at each pixel, and support for the labeling is shared between pixels throughout the image by Non-Local Cost Aggregation. Comparative experimental results confirm the effectiveness of the proposed approach.

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Efficient and compact representation of images is a fundamental problem in computer vision. In this paper, we propose methods that use Haar-like binary box functions to represent a single image or a set of images. A desirable property of these box functions is that their inner product operation with an image can be computed very efficiently. We propose two closely related novel subspace methods to model images: the non-orthogonal binary subspace (NBS) method and binary principal component analysis (B-PCA) algorithm. NBS is spanned directly by binary box functions and can be used for image representation, fast template matching and many other vision applications. B-PCA is a structure subspace that inherits the merits of both NBS (fast computation) and PCA (modeling data structure information). B-PCA base vectors are obtained by a novel PCA guided NBS method. We also show that BPCA base vectors are nearly orthogonal to each other. As a result, in the non-orthogonal vector decomposition process, the computationally intensive pseudo-inverse projection operator can be approximated by the direct dot product without causing significant distance distortion. Experiments on real image datasets show promising performance in image matching, reconstruction and recognition tasks with significant speed improvement.