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Segmenting human faces automatically is very important for face recognition and verification, security system, and computer vision. In this paper, we present an accurate segmentation system for cutting human faces out from video sequences in real-time. First, a learning based face detector is developed to rapidly find human faces. To speed up the detection process, a face rejection cascade is constructed to remove most of negative samples while retaining all the face samples. Then, we develop a coarse-to-fine segmentation approach to extract the faces based on a min-cut optimization. Finally, a new matting algorithm is proposed to estimate the alpha-matte based on an adaptive trimap generation method. Experimental results demonstrate the effectiveness and robustness of our proposed method that can compete with the well-known interactive methods in real-time. 相似文献
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免疫功能受损或免疫系统紊乱会导致机体疾病发生,影响人身体健康,基于调节机体免疫治疗疾病已成为临床应用热点。红芪作为甘肃大宗药材,临床应用广泛,具有抗肿瘤、抗炎、免疫调节等多种药理作用。红芪活性成分主要有红芪多糖、黄酮类、萜类、皂苷类等,其中红芪多糖为红芪发挥免疫调节作用主要的物质基础。红芪增强机体免疫力主要体现在增强免疫器官功能,诱导免疫细胞分化、促进免疫因子分泌,提高单核巨噬细胞吞噬功能、调节炎性因子水平以及激活相关通路等方面来达到其抗肿瘤、抗炎作用。本文主要对红芪活性物质免疫调节作用进行阐述,为红芪的进一步临床开发应用提供思路。 相似文献
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Clinical information about a variety of disorders is available through blood cell counting, which is usually done by manual methods. However, manual methods are complex, time-consuming and susceptible to the subjective experience of inspectors. Although many efforts have been made to develop automated blood cell counting algorithms, the complexity of blood cell distribution and the highly overlapping nature of some red blood cells (RBCs) remain significant challenges that limit the improvement of analytical accuracy. Here, we proposed an end-to-end method for blood cell counting based on deep learning. Firstly, U-Net++ was used to segment the whole blood cell image into several regions of interest (ROI), and each ROI contains only one single cell or multiple overlapping cells. Subsequently, YOLOv5 was used to detect blood cells in each ROI. Specifically, we proposed several strategies, including fine classification of RBCs, adaptive adjustment for non-maximal suppression (NMS) threshold and blood cell morphology constraints to improve the accuracy of detection. Finally, the detection outcomes for each ROI were combined and superimposed. The results show that our method can effectively address the issue of high overlap and precisely segment and detect blood cells, with a 98.18% accuracy rate for blood cell counting. 相似文献
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Applied Intelligence - In recent years, person re-identification (re-ID) has become a widespread research topic that focuses on retrieving target pedestrians from a set of images, typically taken... 相似文献
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