We present a segmentation algorithm to detect low-level structure present in images. The algorithm is designed to partition a given image into regions, corresponding to image structures, regardless of their shapes, sizes, and levels of interior homogeneity. We model a region as a connected set of pixels that is surrounded by ramp edge discontinuities where the magnitude of these discontinuities is large compared to the variation inside the region. Read More “Low-Level Multiscale Image Segmentation and a Benchmark for its Evaluation”
We propose a new object representation, called connected segmentation tree (CST), which captures canonical characteristics of the object in terms of the photometric, geometric, and spatial adjacency and containment properties of its constituent image regions. CST is obtained by augmenting the object’s segmentation tree (ST) with inter-region neighbor links, in addition to their recursive embedding structure already present in ST. Read More “Connected Segmentation Tree For Object Modeling”
This project involved the design and implementation of a biologically inspired robot. This interdisciplinary approach was provided by an interchange of information between the fields of engineering, entomology and neuroscience to develop a walking robot based on an insect.