Superpixel-based class-semantic texton occurrences for natural roadside vegetation segmentation

Abstract

Vegetation segmentation from roadside data is a field that has received relatively little attention in present studies, but can be of great potentials in a wide range of real-world applications, such as road safety assessment and vegetation condition monitoring. In this paper, we present a novel approach that generates class-semantic color-texture textons and aggregates superpixel based texton occurrences for vegetation segmentation in natural roadside images. Pixel-level class-semantic textons are first learnt by generating two individual sets of bag-of-word visual dictionaries from color and filter-bank texture features separately for each object class using manually cropped training data. For a testing image, it is first oversegmented into a set of homogeneous superpixels. The color and texture features of all pixels in each superpixel are extracted and further mapped to one of the learnt textons using the nearest distance metric, resulting in a color and a texture texton occurrence matrix. The color and texture texton occurrences are aggregated using a linear mixing method over each superpixel and the segmentation is finally achieved using a simple yet effective majority voting strategy. Evaluations on two public image datasets from videos collected by the Department of Transport and Main Roads (DTMR), Queensland, Australia, and a public roadside grass dataset show high accuracy of the proposed approach. We also demonstrate the effectiveness of the approach for vegetation segmentation in real-world scenarios.

Color image segmentation using saturated RGB colors and decoupling the intensity from the hue

“…This aspect has changed the last years because of the technology advances, but also due to the necessity to develop algorithms for color image processing applied in different areas where color analysis is important. For instance, for food analysis the color is employed to determine the ripeness of fruits [2,7] or illness detection of fruits [10]; in medicine the color is used to recognize ulcer tissue [12], detection of breast tumors [20], white blood cell counting [34] or study of human eye [16]; among others areas [4,8,19,29,53,54].…”

A map-based normalized cross correlation algorithm using dynamic template for vision-guided telerobot

“…It can replace operator to finish the massive tasks, such as grasping and classification, and release the workload of the operator. (2) SB-GrabCut method, which combines with superpixels and GrabCut algorithm, [29][30][31][32][33][34] is applied for the creation of template. It can separate the object from texture-rich background with less iteration times and time consumption compared with the traditional GrabCut algorithm.…”