Non-parametric Depth Estimation for Images from a Single Reference Depth

We present a non-parametric method for estimating depth of a single still image. We start from a single reference image and its corresponding 3-d depth and use an unsupervised neural network to transform the reference depth to represent the target image. In doing so, we attempt to mimic the human vision capability of perceiving the depth of a given image. Existing depth recovery methods either work for scenes with perpendicular planar surfaces or assume availability of a training database of known images and depths. We propose a method that can recover depth of a target image from a single reference depth. We redesign Self-organizing map (SOM) to learn in an environment with only three input data points and each data point with a different semantic meaning. We combine the proposed Parallel SOM (PSOM) with Gabor wavelets to handle discrepancy between the target and reference images in lighting and orientation. The proposed method gives promising results on images of faces and of daily objects even when using reference image and depth obtained in a poorly lighted setting.

Error Metrics for Learning Reliable Manifolds from Streaming Data

Spectral dimensionality reduction is frequently used to identify low-dimensional structure in high-dimensional data. However, learning manifolds, especially from the streaming data, is computationally and memory expensive. In this paper, we argue that a stable manifold can be learned using only a fraction of the stream, and the remaining stream can be mapped to the manifold in a significantly less costly manner. Identifying the transition point at which the manifold is stable is the key step. We present error metrics that allow us to identify the transition point for a given stream by quantitatively assessing the quality of a manifold learned using Isomap. We further propose an efficient mapping algorithm, called S-Isomap, that can be used to map new samples onto the stable manifold. We describe experiments on a variety of data sets that show that the proposed approach is computationally efficient without sacrificing accuracy