RGB-D Scene Representations for Prosthetic Vision
Creators: name: Feng, David
Publisher: name: The Australian National University
Year: 2017
Subjects: rgbd saliency
depth saliency
rgbd salient object detection
depth salient object detection
local background enclosure
lbe
depth structural descriptor
dsd
histogram of surface orientation
hoso
prosthetic vision
rgbd edge detection
surface irregularities
salient structure
deep edge detection

description: This thesis presents a new approach to scene representation for prosthetic vision. Structurally salient information from the scene is conveyed through the prosthetic vision display. Given the low resolution and dynamic range of the display, this enables robust identification and reliable interpretation of key structural features that are missed when using standard appearance-based scene representations. Specifically, two different types of salient structure are investigated: salient edge structure, for depiction of scene shape to the user; and salient object structure, for emulation of biological attention deployment when viewing a scene. This thesis proposes and evaluates novel computer vision algorithms for extracting salient edge and salient object structure from RGB-D input. Extraction of salient edge structure from the scene is first investigated through low-level analysis of surface shape. Our approach is based on the observation that regions of irregular surface shape, such as the boundary between the wall and the floor, tend to be more informative of scene structure than uniformly shaped regions. We detect these surface irregularities through multi-scale analysis of iso-disparity contour orientations, providing a real time method that robustly identifies important scene structure. This approach is then extended by using a deep CNN to learn high level information for distinguishing salient edges from structural texture. A novel depth input encoding called the depth surface descriptor (DSD) is presented, which better captures scene geometry that corresponds to salient edges, improving the learned model. These methods provide robust detection of salient edge structure in the scene. The detection of salient object structure is first achieved by noting that salient objects often have contrasting shape from their surroundings. Contrasting shape in the depth image is captured through the proposed histogram of surface orientations (HOSO) feature. This feature is used to modulate depth and colour contrast in a saliency detection framework, improving the precision of saliency seed regions and through this the accuracy of the final detection. After this, a novel formulation of structural saliency is introduced based on the angular measure of local background enclosure (LBE). This formulation addresses fundamental limitations of depth contrast methods and is not reliant on foreground depth contrast in the scene. Saliency is instead measured through the degree to which a candidate patch exhibits foreground structure. The effectiveness of the proposed approach is evaluated through both standard datasets as well as user studies that measure the contribution of structure-based representations. Our methods are found to more effectively measure salient structure in the scene than existing methods. Our approach results in improved performance compared to standard methods during practical use of an implant display.