Enhanced imaging colonoscopy facilitates dense motion-based 3D reconstruction.

We propose a novel approach for estimating a dense 3D model of neoplasia in colonoscopy using enhanced imaging endoscopy modalities. Estimating a dense 3D model of neoplasia is important to make 3D measurements and to classify the superficial lesions in standard frameworks such as the Paris classification. However, it is challenging to obtain decent dense 3D models using computer vision techniques such as Structure-from-Motion due to the lack of texture in conventional (white light) colonoscopy.

Assisting the visually impaired: obstacle detection and warning system by acoustic feedback.

The aim of this article is focused on the design of an obstacle detection system for assisting visually impaired people. A dense disparity map is computed from the images of a stereo camera carried by the user. By using the dense disparity map, potential obstacles can be detected in 3D in indoor and outdoor scenarios.

Non-linearity analysis of depth and angular indexes for optimal stereo SLAM.

In this article, we present a real-time 6DoF egomotion estimation system for indoor environments using a wide-angle stereo camera as the only sensor. The stereo camera is carried in hand by a person walking at normal walking speeds 3-5 km/h. We present the basis for a vision-based system that would assist the navigation of the visually impaired by either providing information about their current position and orientation or guiding them to their destination through different sensing modalities.