object classification造句
例句與造句
- Object classification mainly used for activity understanding in scene
目標(biāo)分類研究主要應(yīng)用于場(chǎng)景中的行為理解。 - 3 . some means which improve the performance of object classification are discussed
3 .研究了提高目標(biāo)分類性能的一些方法。 - Object classification and subject definition of state ownership : a way of thinking of economic analysis
我國(guó)物權(quán)立法所有權(quán)類型模式探析 - A series of experiments show the effectiveness of the presented 3 - d object classification method
大量的實(shí)驗(yàn)結(jié)果證明了所提出的部件級(jí)三維物體分類方法的有效性。 - Therefore studying multi - class nonlinear object classification is significant for the development of automatic video comprehension
解決多目標(biāo)非線性分類問題,對(duì)于自動(dòng)視頻理解技術(shù)的發(fā)展有重要意義。 - It's difficult to find object classification in a sentence. 用object classification造句挺難的
- These parameters are closely related to the materials detection , target detection , object classification and velocity measurement in nondestructive evaluation
這些參數(shù)對(duì)于超聲無損檢測(cè)中材料檢測(cè)、目標(biāo)探測(cè)、目標(biāo)分類、及速率測(cè)量等密切相關(guān)。 - Then a new method for object classification based on multi - feature is proposed , in this method basic probability assignment and combination rule is resolved
然后提中文摘要出了一種基于多特征的目標(biāo)識(shí)別方法，其中包括初始概率賦值的確定方法和一種新的合成規(guī)則。 - For classifying unknown 3 - d objects into a set of predetermined object classes , a part - level object classification method based on the improved interpretation tree is presented
摘要為了實(shí)現(xiàn)對(duì)未知物體的分類，提出了一種基于改進(jìn)解釋樹的部件級(jí)三維物體分類方法。 - Multi - class svm classifier based on small sample is built . labeled samples are used to train the classifier . the problem of multi - class non - linear object classification can be solved better
2 .構(gòu)建了基于小樣本學(xué)習(xí)理論的多類支持向量機(jī)分類器,用已標(biāo)記樣本對(duì)分類器進(jìn)行訓(xùn)練,較好地解決了多類別非線性目標(biāo)分類問題。 - The advanced user interfaces will involve widely and include the fields such as motion detection , object classification , people ’ s tracking , gait recognition , voice recognition and behavior understanding
基于視覺的高級(jí)用戶接口所涉及的研究領(lǐng)域非常廣泛,包括運(yùn)動(dòng)檢測(cè)、目標(biāo)分類、人的跟蹤、語音識(shí)別、步態(tài)識(shí)別等等以及行為理解等。 - Objects classification is an important aspect of video - based motion analysis whose research content is to classify moving objects into semantically meaningful categories , associating the correct object class label with the region of interest
其中,目標(biāo)分類是基于視頻的運(yùn)動(dòng)分析課題中的一個(gè)重要方面,其研究?jī)?nèi)容是對(duì)提取的運(yùn)動(dòng)目標(biāo)進(jìn)行語義上的分類,將不同的目標(biāo)對(duì)應(yīng)于不同的類別。 - As the core technology in the intelligence surveillance , moving objects analysis based on video consists of the moving objects detection and retrieval , the object classification , incident detection , action identification and analysis , and so on , the detection and retrieval of moving objects is the foundation and key of it
運(yùn)動(dòng)物體視覺分析作為智能監(jiān)控中的一項(xiàng)核心技術(shù),它包括運(yùn)動(dòng)物體檢測(cè)與提取、物體分類、事件檢測(cè)、行為識(shí)別和分析等,而運(yùn)動(dòng)物體檢測(cè)與提取又是其中的基礎(chǔ)和關(guān)鍵。 - The moving object classification of normal outdoor scenes based on static odd - camera is studied in this paper . on the basis of summarizing and analyzing actuality research and algorithms both here and aboard , an object classification algorithm based on shape features and support vector machines is proposed . it classes the objects detected in video into four familiar sorts : person , crowd , vehicle , bicycles
本文研究基于靜止單攝像機(jī)的普通戶外場(chǎng)景下的運(yùn)動(dòng)目標(biāo)分類技術(shù),在總結(jié)分析目標(biāo)分類研究現(xiàn)狀和當(dāng)前國(guó)內(nèi)外已有算法的基礎(chǔ)上,提出了一種基于形狀特征和支持向量機(jī)的目標(biāo)分類算法,將視頻中檢測(cè)到的目標(biāo)分類為幾種常見的目標(biāo)類別:人、人群、車、自行車。 - This paper ' s method gives a new approach that groups the pixels by larger - scale neighborhood and gets the parameters of the neighborhood by k - l transform . these parameters marked as x , y , a , 0 , specifying the center coordinate , length and direction of the neighborhood separately , are the basic data for following process . then some traditional algorithms of edge track and object classification are used to accomplish the task of gaining last objects
該方法的新穎之處在于不直接用象素點(diǎn)作為處理單元，而是引入了一個(gè)鄰域替代的概念，就是說將鄰域內(nèi)所有的數(shù)據(jù)點(diǎn)作為一個(gè)整體的處理單元看待，利用鄰域內(nèi)的所有數(shù)據(jù)點(diǎn)，計(jì)算一些可以表示鄰域的參數(shù)x ， y ， ， (鄰域的位置、方向、長(zhǎng)度) ，這些參數(shù)完全根據(jù)鄰域內(nèi)數(shù)據(jù)點(diǎn)的位置信息獲得。