In this work, we propose a step towards a more accurate prediction of the environment light given a single picture of a known object. To achieve this, we develop a deep learning method that is able to encode the latent space of indoor lighting using few parameters that is trained on a database of environment maps. This latent space is then used to generate predictions of the light that are both more realistic and accurate than previous methods. Our first contribution is a deep autoencoder which is capable of learning the feature space that compactly models lighting. Our second contribution is a convolutional neural network that predicts the light from a single image of a known object. To train these networks, our third contribution is a novel dataset that contains 21,000 HDR indoor environment maps. Finally, we evaluate our method on a dataset of synthetic objects and find it to outperform the state-of-the-art techniques across a variety of materials and poses.

This paper presents a new approach for automatic license plate detection using an embedded camera inside a moving vehicle, implemented in a low-cost prototype. The key idea of the proposed method is to initially identify shadowed regions under the rear of visible vehicles, and then explore information from a calibrated camera to reduce the search space for license plates. Within the search space, any “baseline” detector can be used, and the results are validated exploring the expected size of a license plate in world coordinates and the known camera parameters. Our experimental results show that we can increase the speed of a “baseline” detector by 93%, and at the same time improving the f-score (increase in precision without significant loss in recall).

We introduce a new approach for hand and object segmentation using RGB-D cameras suitable for gesture-based Human-Computer Interfaces (HCIs) that involve an interaction plane. The technique consists of detecting the interaction plane using a temporally coherent version of RANSAC, followed by segmenting off-plane objects using a markers-based watershed transform with an energy function that combines depth and chromaticity gradients. Experimental results show that our approach can segment the hand even when it is very close to the interaction plane, unlike traditional approaches based on distance thresholds.