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.

The same game is often played in real and virtual worlds. We integrate in-person and on-line playing of board games such as Go, bringing the real world into the virtual world. A player may record an in-person game by placing their camera on the table next to the game board, taking photos of the game. After automatically detecting the board and playing pieces, we perform inference on the time series of detections to eliminate errors and accurately estimate long sequences of moves. The game transcript may be studied afterwards, shared with friends and teachers, or added to online compilations, bringing the attendant benefits of online game play to an in-person game. We present a method to transcribe the moves of a board game in the real world, automatically and unobtrusively. A player simply places a camera on the table next to the board, recording a series of photos; our algorithm automatically finds the board and detects moves, creating a complete transcription of the game.

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.

This paper presents a novel approach that represents an image or a set of images using a non-orthogonal binary subspace (NBS) spanned by box-like base vectors. These base vectors possess the property that the inner product operation with them can be computed very efficiently. We investigate the optimized orthogonal matching pursuit method for finding the best NBS base vectors. It is demonstrated in this paper how the NBS based expansion can be applied to speed up several common computer vision algorithms, including normalized cross correlation (NCC), sum of squared difference (SSD) matching, appearance subspace projection and subspace-based object recognition. Promising experimental results on facial and natural images are demonstrated in this paper.