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                          APA格式的research proposal怎么寫?

                          時間:2019-03-08 10:50來源:未知 作者:anne 點擊:
                          導讀:我們知道一篇完整的research proposal需要有Title and abstract、introduction、summary of literature 、The hypothesis and the objectives Methodology、Summary and conclusions、參考文獻。以下是一篇用于申請Doctor Appli
                          導讀:我們知道一篇完整的research proposal需要有Title and abstract、introduction、summary of literature 、The hypothesis and the objectives Methodology、Summary and conclusions、參考文獻。以下是一篇用于申請Doctor Application的research proposal提綱,大致的research proposal機構同學們可以參照以下內容:
                           
                          Abject detection based on deep learning
                          Abstract摘要
                           
                          Introduction介紹
                          本文首先分析了國內外對目標檢測算法的研究現狀,著重介紹了基于目標特征訓練分類器對目標進行分類的廣泛應用方法。由于訓練后的分類器對目標分類具有較高的誤報率,本文提出了一種基于卷積神經網絡的行人目標檢測算法。
                          This thesis first analyzes the domestic and foreign research status of object detection algorithm, emphatically introduces the application method which are widely used is based on the object feature trained classifier to classify object. Because of the existing feature of the trained classifier to classify object has high false positives rate, this thesis present a pedestrian object detection algorithm based on convolution neural network on the basis of deep learning.
                          該算法采用卷積神經網絡解決滑動窗效率低的問題,包括兩個步驟:(1)可疑行人窗的確認;(2)行人檢測。在現有的可疑行人視窗確認中,本文采用融合特征作為行人訓練分類器的描述,并以近似于建立分類器金字塔的尺度特征為理想。在檢測到的圖像上,本文利用不同尺度的滑動窗進行滑動橫移,以確定是否存在可疑的行人窗口。在行人檢測中,本文利用大量的正負樣本進行訓練,得到卷積神經網絡。為了更好地適應The algorithm consists of two steps in order to solve the low efficiency of sliding window with convolution neural network, (1) the suspected pedestrian window confirmation; (2) the pedestrian detection. In suspected existing pedestrian window confirmation, this thesis use the fusion feature as the description of the pedestrian training classifier and the ideal of nearby scale feature similar to build classifier pyramid. On the inspected images, this thesis use different scales of sliding window to slide traversal to confirm suspected exist pedestrian window. In the pedestrian detection, this thesis rely a large number of positive and negative samples to train and get a convolution neural network. In order to better adept the 
                          行人檢測,本論文改進了傳統卷積網絡的拓撲結構。將可疑行人窗口輸入改進的卷積神經網絡中,對行人進行檢測。pedestrian detection, this thesis improve the topology of traditional convolution network. Input suspected existence of pedestrian’s window into the improved convolution neural network to detect the pedestrian.
                           
                          Summary of Literature文獻綜述
                          Detection algorithm based on template matching基于模板匹配的檢測算法
                          Pedestrian detection algorithm based on classification基于分類的行人檢測算法
                          The hypothesis and the objectives Methodology假設與目標方法論
                          Suspected pedestrians based on fusion feature window confirmation基于融合特征窗口確認的可疑行人
                          Pedestrian detection based on Convolutional neural networks基于卷積神經網絡的行人檢測
                          Summary and conclusions總結和結論
                           
                           


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