搜索结果: 1-7 共查到“计算机图象处理 Segmentation”相关记录7条 . 查询时间(0.112 秒)
LESION DETECTION IN CT IMAGES USING DEEP LEARNING SEMANTIC SEGMENTATION TECHNIQUE
Deep Neural Networks Convolutional Neural Networks Semantic Segmentation Chest Radiography Tuberculosis
2017/6/19
In this paper, the problem of automatic detection of tuberculosis lesion on 3D lung CT images is considered as a benchmark for testing out algorithms based on a modern concept of Deep Learning. For tr...
MILCut: A Sweeping Line Multiple Instance Learning Paradigm for Interactive Image Segmentation
Sweeping Line Multiple Instance Learning Paradigm Interactive Image Segmentation
2016/1/22
Interactive segmentation, in which a user provides a bounding box to an object of interest for image segmenta-tion, has been applied to a variety of applications in im-age editing, crowdsourcing, comp...
Performance analysis of EM-MPM and K-means clustering in 3D ultrasound breast image segmentation
segmentation EM-MPM ultrasound
2015/1/20
Mammographic density is an important risk factor for breast cancer, detecting and screening at an early stage could help save lives. To analyze breast density distribution, a good segmentation algorit...
3D EM/MPM MEDICAL IMAGE SEGMENTATION USING AN FPGA EMBEDDED DESIGN IMPLEMENTATION
FPGA 3D image
2015/1/20
This thesis presents a Field Programmable Gate Array (FPGA) based embedded system which is used to achieve high speed segmentation of 3D images. Segmenta- tion is performed using Expectation-Maximizat...
3D Image Segmentation Implementation on FPGA Using EM/MPM Algorithm
3D Image FPGA Hardware Implementation EM/MPM Algorithm
2015/1/20
In this thesis, 3D image segmentation is targeted to a Xilinx Field Programmable Gate Array (FPGA), and verified with extensive simulation. Segmentation is performed using the Expectation-Maximization...
基于PSO与K-均值算法的农业超绿图像分割方法(Agriculture Extra-green Image Segmentation Based on Particle Swarm Optimization and K-means Clustering)
图像分割 微粒群算法 K均值算法
2009/9/11
为了解决K-均值算法对农业图像中常用的超绿特征2G—R—B图像分割效果不佳的缺点,提出一种基于微粒群与K均值算法的图像分割方法。先用K均值算法对图像进行快速分类,然后将分类结果作为其中一个微粒的结果,利用微粒群算法计算,最后用K-均值算法在新的分类基础上计算新的聚类中心,更新当前的位置,以得到最优的图像分割阈值。试验结果表明,改进算法对超绿特征2G—R—B图像能够准确分割目标,且对不同类型的农业超...