Weakly supervised labeling of dominant regions

The capability of associating semantic concepts with available sensory data is an important component of environment understanding. In this work we describe an approach for annotation of dominant image regions of uniform appearance, which are typically encountered indoors, such as doors, walls and floors.

One of the main challenges behind correct classification of these regions requires handling large changes in the appearance as a function of lighting conditions. Instead of using large amount of training data taken under different illumination conditions, we propose an online updating of the model learned from a small number of training examples in the initial frame.

We follow a two stage classification strategy: first we estimate the probabilities of individual regions belonging to each class based on appearance only; in the second stage we use Markov Random Fields (MRF) to exploit spatial layout of the scene and improve classification results. The appearance model learned in the first frame is updated in subsequent frames using the confidences obtained by the two stage classification strategy. We demonstrate our approach on two sequences of indoor environments.

Visual Door Detection

Important component of human-robot interaction is the capability to associate semantic concepts to encountered locations and objects. This functionality is essential for visually guided navigation as well as location and object recognition. In this paper we focus on the problem of door detection using visual information only. Doors are frequently encountered in structured man-made environments and function as transitions between different places. We adopt a probabilistic approach for door detection, by defining the likelihood of various features for generated door hypotheses. Different from the previous approaches the proposed model captures both the shape and appearance of the door. This is learned from a few training examples, exploiting additional assumptions about structure of indoors environments. After the learning stage, we describe a hypothesis generation process and several approaches to evaluate the likelihood of the generated hypotheses. In the following figure on the left the door location hypothesis are shown, and on the right, the likelihood of each of them is represented.