An integrated novel method of recognizing huge target is described that combines some relatively mature image processing techniques such as edge detection, thresholding, morphology, image segmentation and so forth. Af...An integrated novel method of recognizing huge target is described that combines some relatively mature image processing techniques such as edge detection, thresholding, morphology, image segmentation and so forth. After thresholding the edge image obtained by using Sobel operator, erosion is firstly used to reduce noise and extrusive pixels; then dilation is used to expand some separated pixels into various regions, after that the image segmentation technique is utilized to distinguish the target region with a criterion. The location of the target is also offered. Each technique adopted herein seems not complicated at all, the experimental results demonstrate the efficiency of the combination of these techniques. It is its high computational speed and remarkable robustness resulting from its simplicity that make the method promise to be applied in practical problems requiring real time processing.展开更多
Objective: To propose a method to segment tongue- images efficiently, and extract tongue- body accurately and quickly. Methods: Firstly, a kind of color- images' pre- processing technique was used to solve tongue-...Objective: To propose a method to segment tongue- images efficiently, and extract tongue- body accurately and quickly. Methods: Firstly, a kind of color- images' pre- processing technique was used to solve tongue- surface reflection problem. Neighbouring and similar region's information was used to restore the region with tongue- surface reflection problem by replacement. Secondly, the restored image was transformed into a gray one, and then processed by mathematical morphological operation- dilation to get a closed- loop edge. The third technique used was watershed algorithm, which is an usual tool in image segmentation. 'Watershed' function of matlab software was used to complete this algorithm. After that, region- combination technique was used. Through measuring neighbourship and similarity of regions, a non- objective and non- background region was merged into one of its neighbouring regions. This step was repeated until only two regions, objective and background regions, were left in the image. At last, corresponding to the merged image, tongue- body image was got from the original image. Results: 316 images were randomly taken from the image library for experiments, and 299 images were correctly segmented, so, the successful ratio is 94.62%. On the other hand, average time of running this method was about 50 s under whole sampling environment. Conclusion: The method presented in this paper can segment a tongue- body image from its original one effectively, and thus laying a good foundation for the following analysis work.展开更多
Extracting the cell objects of red tide algae is the most important step in the construction of an automatic microscopic image recognition system for harmful algal blooms.This paper describes a set of composite method...Extracting the cell objects of red tide algae is the most important step in the construction of an automatic microscopic image recognition system for harmful algal blooms.This paper describes a set of composite methods for the automatic segmentation of cells of red tide algae from microscopic images.Depending on the existence of setae,we classify the common marine red tide algae into non-setae algae species and Chaetoceros,and design segmentation strategies for these two categories according to their morphological characteristics.In view of the varied forms and fuzzy edges of non-setae algae,we propose a new multi-scale detection algorithm for algal cell regions based on border-correlation,and further combine this with morphological operations and an improved GrabCut algorithm to segment single-cell and multicell objects.In this process,similarity detection is introduced to eliminate the pseudo cellular regions.For Chaetoceros,owing to the weak grayscale information of their setae and the low contrast between the setae and background,we propose a cell extraction method based on a gray surface orientation angle model.This method constructs a gray surface vector model,and executes the gray mapping of the orientation angles.The obtained gray values are then reconstructed and linearly stretched.Finally,appropriate morphological processing is conducted to preserve the orientation information and tiny features of the setae.Experimental results demonstrate that the proposed methods can effectively remove noise and accurately extract both categories of algae cell objects possessing a complete shape,regular contour,and clear edge.Compared with other advanced segmentation techniques,our methods are more robust when considering images with different appearances and achieve more satisfactory segmentation effects.展开更多
文摘An integrated novel method of recognizing huge target is described that combines some relatively mature image processing techniques such as edge detection, thresholding, morphology, image segmentation and so forth. After thresholding the edge image obtained by using Sobel operator, erosion is firstly used to reduce noise and extrusive pixels; then dilation is used to expand some separated pixels into various regions, after that the image segmentation technique is utilized to distinguish the target region with a criterion. The location of the target is also offered. Each technique adopted herein seems not complicated at all, the experimental results demonstrate the efficiency of the combination of these techniques. It is its high computational speed and remarkable robustness resulting from its simplicity that make the method promise to be applied in practical problems requiring real time processing.
基金National Natural Science Foundation of China grant number: 30371717
文摘Objective: To propose a method to segment tongue- images efficiently, and extract tongue- body accurately and quickly. Methods: Firstly, a kind of color- images' pre- processing technique was used to solve tongue- surface reflection problem. Neighbouring and similar region's information was used to restore the region with tongue- surface reflection problem by replacement. Secondly, the restored image was transformed into a gray one, and then processed by mathematical morphological operation- dilation to get a closed- loop edge. The third technique used was watershed algorithm, which is an usual tool in image segmentation. 'Watershed' function of matlab software was used to complete this algorithm. After that, region- combination technique was used. Through measuring neighbourship and similarity of regions, a non- objective and non- background region was merged into one of its neighbouring regions. This step was repeated until only two regions, objective and background regions, were left in the image. At last, corresponding to the merged image, tongue- body image was got from the original image. Results: 316 images were randomly taken from the image library for experiments, and 299 images were correctly segmented, so, the successful ratio is 94.62%. On the other hand, average time of running this method was about 50 s under whole sampling environment. Conclusion: The method presented in this paper can segment a tongue- body image from its original one effectively, and thus laying a good foundation for the following analysis work.
基金Supported by the National Natural Science Foundation of China(Nos.61301240,61271406)
文摘Extracting the cell objects of red tide algae is the most important step in the construction of an automatic microscopic image recognition system for harmful algal blooms.This paper describes a set of composite methods for the automatic segmentation of cells of red tide algae from microscopic images.Depending on the existence of setae,we classify the common marine red tide algae into non-setae algae species and Chaetoceros,and design segmentation strategies for these two categories according to their morphological characteristics.In view of the varied forms and fuzzy edges of non-setae algae,we propose a new multi-scale detection algorithm for algal cell regions based on border-correlation,and further combine this with morphological operations and an improved GrabCut algorithm to segment single-cell and multicell objects.In this process,similarity detection is introduced to eliminate the pseudo cellular regions.For Chaetoceros,owing to the weak grayscale information of their setae and the low contrast between the setae and background,we propose a cell extraction method based on a gray surface orientation angle model.This method constructs a gray surface vector model,and executes the gray mapping of the orientation angles.The obtained gray values are then reconstructed and linearly stretched.Finally,appropriate morphological processing is conducted to preserve the orientation information and tiny features of the setae.Experimental results demonstrate that the proposed methods can effectively remove noise and accurately extract both categories of algae cell objects possessing a complete shape,regular contour,and clear edge.Compared with other advanced segmentation techniques,our methods are more robust when considering images with different appearances and achieve more satisfactory segmentation effects.
