Mineral Froth Image Classification and Segmentation

Accurate segmentation of froth images is always a problem in the research of floating modeling based on Machine Vision. Since a froth image is with the characteristic of complexity and diversity, it is a feasible research idea for the workflow of which the froth image is firstly classified and then segmented by the image segmentation algorithm designed for each type of froth images. This study proposes a new froth image classification algorithm. The texture feature is extracted to complete the classification. Meanwhile, an improved method based on the original valley‐edge detection algorithm is also proposed in the study. Firstly, the fractional differential is introduced to design the new valley‐edge detection templates which can extract more information on bubble edges after the enhancement of the weak edges, and finally the close bubble boundaries are obtained by carrying out the improved deburring and gap connection algorithms. Experimental results show that the new classification method can be used to distinguish the types of small, middle and large bubble images. The improved image segmentation algorithm can well reduce the problems of over‐segmentation and under‐segmentation, and it is in higher adaptability

Real-time automated road, lane and car detection for autonomous driving

In this paper, we discuss a vision based system for autonomous guidance of vehicles. An autonomous intelligent vehicle has to perform a number of functionalities. Segmentation of the road, determining the boundaries to drive in and recognizing the vehicles and obstacles around are the main tasks for vision guided vehicle navigation. In this article we propose a set of algorithms which lead to the solution of road and vehicle segmentation using data from a color camera. The algorithms described here combine gray value difference and texture analysis techniques to segment the road from the image, several geometric transformations and contour processing algorithms are used to segment lanes, and moving cars are extracted with the help of background modeling and estimation. The techniques developed have been tested in real road images and the results are presented

Fast Graph-Based Object Segmentation for RGB-D Images

Object segmentation is an important capability for robotic systems, in particular for grasping. We present a graph- based approach for the segmentation of simple objects from RGB-D images. We are interested in segmenting objects with large variety in appearance, from lack of texture to strong textures, for the task of robotic grasping. The algorithm does not rely on image features or machine learning. We propose a modified Canny edge detector for extracting robust edges by using depth information and two simple cost functions for combining color and depth cues. The cost functions are used to build an undirected graph, which is partitioned using the concept of internal and external differences between graph regions. The partitioning is fast with O(NlogN) complexity. We also discuss ways to deal with missing depth information. We test the approach on different publicly available RGB-D object datasets, such as the Rutgers APC RGB-D dataset and the RGB-D Object Dataset, and compare the results with other existing methods