A robust approach to recovery of shape from shading information is presented. Assuming uniform albedo and Lambertian surface for the imaging model, we first present methods for the estimation of illuminant direction and surface albedo. The illuminant azimuth is estimated by averaging local estimates. The illuminant elevation and surface albedo are estimated from image statistics. Using the estimated reflectance map parameters, we then compute the surface shape using a new procedure, which implements the smoothness constraint by enforcing the gradients of reconstructed intensity to be close to the gradients of the input image. Typical results on real images are given to illustrate the usefulness of our approach.
A robust approach to recovery of shape from shading information is presented. Assuming uniform albedo and Lambertian surface for the imaging model, we first present methods for the estimation of illuminant direction and surface albedo. The illuminant azimuth is estimated by averaging local estimates. The illuminant elevation and surface albedo are estimated from image statistics. Using the estimated reflectance map parameters, we then compute the surface shape using a new procedure, which implements the smoothness constraint by enforcing the gradients of reconstructed intensity to be close to the gradients of the input image. Typical results on real images are given to illustrate the usefulness of our approach.
We introduce the notion of consensus skeletons for non-rigid space-time registration of a deforming shape. Instead of basing the registration on point features, which are local and sensitive to noise, we adopt the curve skeleton of the shape as a global and descriptive feature for the task. Our method uses no template and only assumes that the skeletal structure of the captured shape remains largely consistent over time. Such an assumption is generally weaker than those relying on large overlap of point features between successive frames, allowing for more sparse acquisition across time. Building our registration framework on top of the low-dimensional skeletontime structure avoids heavy processing of dense point or volumetric data, while skeleton consensusization provides robust handling of incompatibilities between per-frame skeletons. To register point clouds from all frames, we deform them by their skeletons, mirroring the skeleton registration process, to jump-start a non-rigid ICP. We present results for non-rigid space-time registration under sparse and noisy spatio-temporal sampling, including cases where data was captured from only a single view.
We describe algorithms for detecting pedestrians in videos acquired by infrared (and color) sensors. Two approaches are proposed based on gait. The first employs computationally efficient periodicity measurements. Unlike other methods, it estimates a periodic motion frequency using two cascading hypothesis testing steps to filter out non-cyclic pixels so that it works well for both radial and lateral walking directions. The extraction of the period is efficient and robust with respect to sensor noise and cluttered background. In order to integrate shape and motion, we convert the cyclic pattern into a binary sequence by Maximal Principal Gait Angle (MPGA) fitting in the second method. It does not require alignment and continuously estimates the period using a Phase-locked Loop. Both methods are evaluated by experimental results that measure performance as a function of size, movement direction, frame rate and sequence length.
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