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The availability of accurate depth cameras have made real-time human pose estimation possible; however, there are still demands for faster algorithms on low power processors. This paper introduces 1000 frames per second pose estimation method on a single core CPU. A large computation gain is achieved by random walk sub-sampling. Instead of training trees(More)
Regression tree used Single regression tree is trained to output the offset to the nearest joint position. How to inference? All joint positions (without labels) estimated using simple K-means clustering. Localization problem: The 15 3D position of each joint must be accurately estimated for human pose. Identification Problem: Joint positions must be(More)
Under the popular Bayesian approach, a stereo problem can be formulated by defining likelihood and prior. Likelihoods are often associated with unary terms and priors are defined by pair-wise or higher order cliques in Markov random field (MRF). In this paper, we propose to use high order likelihood model in stereo. Numerous conventional patch based(More)
3D videos play an important role in adoption of 3DTV display modules for the masses because creating realistic contents for 3DTV is a hard and time-consuming process. In this paper, we consider the problem of generating three-view 3D video depth using a segment-based stereo algorithm and a depth camera (ZCam), and propose a new foreground/background video(More)
In this paper, we present a novel cascaded classification framework for automatic detection of individual and clusters of microcalcifications (μC). Our framework comprises three classification stages: i) a random forest (RF) classifier for simple features capturing the second order local structure of individual μCs, where non-μC pixels in the target(More)
Under the popular Markov random field (MRF) model, low-level vision problems are usually formulated by prior and likelihood models. In recent years, the priors have been formulated from high-order cliques and have demonstrated their robustness in many problems. However, the likelihoods have remained zeroth-order clique potentials. This zeroth-order clique(More)
We propose a scanline energy minimization algorithm for stereo vision. The proposed algorithm differs from conventional energy minimization techniques in that it focuses on the relationship between local match cost solution and the energy minimization solution. The local solution is transformed into energy minimization solution through the optimization of(More)
We present multiple random forest methods for human pose estimation from single depth images that can operate in very high frame rate. We introduce four algorithms: random forest walk, greedy forest walk, random forest jumps, and greedy forest jumps. The proposed approaches can accurately infer the 3D positions of body joints without additional information(More)