Salient Region Detection in outdoor scene using Imagery and 3-D Data
This project develops an algorithm for detecting and segmenting out the salient regions of a scene using both 3-D laser scan and imagery information. Fig.1 shows our problem statement. Given an image and its extrinsically calibrated 3-D scan data, our algorithm delineates top k-most salient regions in a bottom-up manner without any high-level priors, models, or learning.
The detection of salient clusters is formulated into finding the set of clusters that best fit some pre-defined families of probability density functions in 3-D scan data. Here we use two pdfs, which are Gaussian and Uniform. As a fundamental tool for this approach, we use the Robust Information-theoretic Clustering (RIC) method. After clustering, we calculate saliency value of each cluster by summing four different saliency features, which capture local, regional, and global properties of 3-D data and RGB color information.
Fig.2 shows some examples using the dataset taken in parking lots around CMU.