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This paper research the use of computer visual and image information collected preprocessing based on pattern recognition of soybean Nitrogen element detect. When Nitrogen elements of the soybean plant changes, color and texture will be characteristic. By nurturing and collecting samples, This work analyzed the characteristics determine preprocessing, established a preprocessing system model. The...
Automatic classification of cancer lesions in tissues observed using gastroenterology imaging is a non-trivial pattern recognition task involving filtering, segmentation, feature extraction and classification. In this paper we measure the impact of a variety of segmentation algorithms (mean shift, normalized cuts, level-sets) on the automatic classification performance of gastric tissue into three...
In this paper we propose an efficient unsupervised texture segmentation method. We introduce a texture extension of a state-of-the-art color segmentation algorithm. We show how to use covariance matrices of low level features for texture description. These features are efficiently calculated using integral images. Furthermore, a multi-scale extension allows to provide accurate texture segmentation...
We describe a method to automatically extract video objects, which are important for object-based indexing of videos in an MPEG-7 compliant video database system. Most of the existing salient object detection approaches detect visually conspicuous image structures, while our method aims to find regions that may be important for indexing in a video database system. Our method works on a shot basis...
This paper presents a Windows based system for image analysis and computer vision of mineral froth. To make the system work efficiently both in laboratory and in the plant, the main content of the system consists of image acquisition, image processing, froth (bubble) delineation, froth analysis and modeling, and interface between different computers in use. Hundreds of functions for froth analysis...
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