Image Segmentation using a Fuzzy Roughness Measure

Measures of uncertainty are highly useful for determining the information content of a system. In this paper, new measures of information on fuzzy approximation spaces are introduced based on divergence measures of fuzzy sets. The proposed fuzzy rough uncertainty measure is used to develop an algorithm for histogram based foreground background segmentation of a grey level image and it is experimented with twelve standard test images. It is observed that the overlapping of the foreground background pixels in the images segmented using the proposed method is lesser than those produced by OTSU and FCM methods. The segmented images are compared using their root mean square error values.

Shape-Based Analysis on Component-Graphs for Multivalued Image Processing

Connected operators based on hierarchical image models have been increasingly considered for the design of efficient image segmentation and filtering tools in various application fields. Among hierarchical image models, component-trees represent the structure of grey-level images by considering their nested binary level-sets obtained from successive thresholds. Recently, a new notion of component-graph was introduced to extend the component-tree to any grey-level or multivalued images. The notion of shaping was also introduced as a way to improve the anti-extensive filtering by considering a two-layer component-tree for grey-level image processing. In this article, we study how component-graphs (that extend the component-tree from a spectral point of view) and shapings (that extend the component-tree from a conceptual point of view) can be associated for the effective processing of multivalued images. We provide structural and algorithmic developments. Although the contributions of this article are theoretical and methodological, we also provide two illustration examples that qualitatively emphasize the potential use and usefulness of the proposed paradigms for image analysis purposes.

Tea Flushes Identification Based on Machine Vision for High-Quality Tea at Harvest

Tea flushes identification from their natural background is the first key step for the intelligent tea-picking robot. This paper focuses on the algorithms of identifying the tea flushes based on color image analysis. A tea flushes identification system was developed as a means of guidance for a robotic manipulator in the picking of high-quality tea. Firstly, several color indices, including y-c, y-m, (y-c)/(y+c) and (y-m)/(y+m) in CMY color space, S channel in HSI color space, and U channel in YUV color space, were studied and tested. These color indices enhanced and highlighted the tea flushes against their background. Afterwards, grey level image was transformed into binary image using Otsu method and then area filter was employed to eliminate small noise regions. The algorithm and identification system has been tested extensively and proven to be well adapted to the complexity of a natural environment. Experiments show that these indices were particularly effective for tea flushes identification and could be used for future tea-picking robot development.