Objects are used in our everyday *** is full of *** are classified based on their shapes such as spherical,rectangular,square,*** classify objects with the help of our senses but it is very difficult for a computer to...
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ISBN:
(纸本)9789380544199
Objects are used in our everyday *** is full of *** are classified based on their shapes such as spherical,rectangular,square,*** classify objects with the help of our senses but it is very difficult for a computer to classify as they don't have any *** we have to program them in such a way so that they can classify or recognize different objects. In this paper, we will classify the shape of object that whether they belong to any geometric shape or not. We will recognize the shape of the objects from the black and white image, which was earlier a colored *** will represent the image of an object with the help of Canny Edge Detection Technique.
To reduce the influence of the eyelid for the iris recognition rate, an eyelid detection algorithm for the iris recognition is proposed. The grayscale morphological operations are employed to remove the interference o...
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To reduce the influence of the eyelid for the iris recognition rate, an eyelid detection algorithm for the iris recognition is proposed. The grayscale morphological operations are employed to remove the interference of the eyelash and the light spot to the eyelid region. The points of the minimum grayscale value of each column in the eyelid region are extracted as edge points. The least squares parabolic fitting eyelids is carried out for the edge points of the eyelid. The eyelid parabolic moving up and down in neighborhood region is used to localizing the eyelid precisely, when the grayscale mutation happens. The experimental results show that the eyelid localization algorithm can detect the eyelid effectively and quickly. Compared with the hough transform algorithm, the accurate rate is the same, but the speed is improved about 4 seconds.
Image-based, high throughput genome-wide RNA interference (RNAi) experiments are increasingly carried out to facilitate the understanding of gene functions in intricate biological processes. Automated screening of suc...
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Image-based, high throughput genome-wide RNA interference (RNAi) experiments are increasingly carried out to facilitate the understanding of gene functions in intricate biological processes. Automated screening of such experiments generates a large number of images with great variations in image quality, which makes manual analysis unreasonably time-consuming. Therefore, effective techniques for automatic image analysis are urgently needed, in which segmentation is one of the most important steps. This paper proposes a fully automatic method for cells segmentation in genome-wide RNAi screening images. The method consists of two steps: nuclei and cytoplasm segmentation. Nuclei are extracted and labelled to initialize cytoplasm segmentation. Since the quality of RNAi image is rather poor, a novel scale-adaptive steerable filter is designed to enhance the image in order to extract long and thin protrusions on the spiky cells. Then, constraint factor GCBAC method and morphological algorithms are combined to be an integrated method to segment tight clustered cells. Compared with the results obtained by using seeded watershed and the ground truth, that is, manual labelling results by experts in RNAi screening data, our method achieves higher accuracy. Compared with active contour methods, our method consumes much less time. The positive results indicate that the proposed method can be applied in automatic image analysis of multi-channel image screening data.
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