detecting threshold 中文意思是什麼
detecting threshold
解釋
檢測閾-
Secondly, programmed the image processing arithmetic code which include the bottom arithmetic for the general condition comprises threshold division, region combination and informate and the middle level arithmetic for the given task comprises detecting the line dation creirection according to the hough transform in order to fix on the hole ’ s azimuth angle, detecting the aiguille tip position according to the image movement according to the environment and the image format
然後，根據目標環境要求和攝像機採集圖像格式，開發了圖像處理演算法程序。圖像處理演算法包括底層演算法和中層演算法兩部分，底層演算法針對通用情況，包括閾值分割、區域合併和信息生成。中層演算法針對具體任務設計，包括利用hough變換檢測棱線的方向，從而確定圓孔的方位角和利用基於圖像運動檢測鉆頭尖端位置。 -
The motion detecting is achieved using temporal differencing with a threshold
移動檢測是採用時域差值移動物體檢測法配合門檻值的設置。 -
At the same time, an automatic gain control and floating threshold setting intelligent detecting and processing method based on linear ccd is proposed, and the disposal and transform of ccd video signal is carried out by hardware and software
提出了一種基於線陣ccd的自動增益控制和浮動閾值的智能信號檢測與處理方法，用硬體和軟體實現了對ccd視頻信號的進行處理和變換。 -
So such kind of diseases can be forecasted through qst. first, the physiological basis of vpt is introduced. then the thesis discusses in detail the psychophysical knowledge of the threshold detecting method, especially the transformed - rule up and down method and forced - choice method
本文首先介紹了定量振動感覺測試系統的生理學基礎，接著對閾值檢出演算法的心理物理學基礎進行了詳細的敘述，著重介紹了系統採用的變形階梯法同強迫選擇法相結合的演算法。 -
In first time, the relationship between probability of detection and probability of fault to snr, contrast and detecting threshold is derived in the assessment of detecting result
在檢測效果評估方面首次推導了檢測概率、虛警概率與信噪比、對比度和檢測門限之間的關系。 -
The ids works by two way, misuse detection and anomaly detection, misuse detection flags an intrusion on intrusion signature, this kind of detecting technic can be realized much more easily, and much more accurate, but it can not find some intrusiones that have been disguised or new kinds of intrusion. the anomaly detection can detect in more wide field, anomaly detection can compare new statistic data with average record, then anomaly record will be found, but it ' s more difficult to set a threshold, if the threshold is too big, some intrusion may be put through, if the threshold is too small, the ids will give more false positive alarm, and the threshold will be different with different people or different period, so the ids just simply show us their suspicious record, the administrator or expert will be in duty to analyze this record and give conclusion, the ids give more alarm than it should, leave us more detection record to analyze, and this is a hard work, we can not distinguish an intrusion or not if we analyze only one record, but we can judge if we find the relation among mass detection evidence. in this article, we try distinguish an intrusion using d - s theory ( proof theory ) instead using manual work, the ids will be more helpful and efficient
濫用檢測採用的是特徵檢測的方法，實現較為簡單，判斷的準確性較高，但是不能判斷一些經過偽裝的入侵或特徵庫中尚未包含的入侵，異常檢測能夠根據以往記錄的特徵平均值，判斷出異常情況，但是對于異常到什麼程度才視為入侵，這個閥值非常難以確定，閥值設定的太高，有可能漏過真正的入侵，如果設定的閥值太低，又會產生較高的誤警率，而且這個閥值因人而異，因時而異，因此現在的入侵檢測系統把這部分異常記錄以一定的形式顯示出來或通知管理人員，交給管理人員去判斷，而這些ids系統難以判斷的記錄，如果對每個證據單獨地進行觀察，可能是難以判斷是否是入侵，而把許多先後證據關聯起來，專家或管理人員根據經驗能夠判斷訪問的合法性，本文試圖引入人工智慧中證據理論的推理策略和示例學習方法，代替人工檢查分析，可以提高效率，降低誤警率，並可以對一個正在進行得可疑訪問實現實時檢測，通過搜索及時判斷，及時阻斷非法訪問，比事後得人工處理更有意義。 -
An example for detecting the eyelid closure over the pupil over time is given. the combinations of mathematical model and symmetry analysis increases the robustness of the performance while the target is deformed the scheme is suitable for human face location in intelligent human - machine interface and provides a groundwork to practical application efface recognition techniques. the laboratory experiments are conducted to verify the feasibility of the detecting and evaluating techniques of motor driver fatigue mentioned above and to determine the threshold of motor driver fatigue
在駕駛過程中，連續測試駕駛員的perclos值和眼睛持續閉合時間，一日駕駛員的眼睛perclos值人於40 、眼睛持續閉合時間大於3s ，系統就判定該駕駛員的疲勞程度己超過駕駛疲勞程度的閾值，並且立即發出「哺哺哺」的警告聲，如果駕駛員5秒內還繼續駕駛車輛，系統會自動切斷山路和油路，使車輛自動停車，以避免發生交通事故。 -
But to this algorithm, it is important to select initial value and the amount of computation is large ; ( 3 ) an algorithm is presented to estimate the prfs based on stochastic dynamic - linear models. ( 4 ) a new algorithm for selecting detecting threshold based on the wavelet theory is presented to the environment when pulse sequences distribute unevenly in the whole sampling time
該演算法的不足是對初始狀態的選取非常重要且運算量較大； （ 3 ）提出基於動態線性模型利用prf進行重頻分選的演算法； （ 4 ）將小波理論應用到重頻分選中，提出了一種新的檢測門限，適用於脈沖列分佈不均勻的信號環境。 -
The paper recounts implementing thoughts of this system and advances some improved algorithm in pretreating image, for example image enforce, segmentation of image and so on. we get rather satisfying effect by using these algorithms. in initial phase of the system, in order to get the information of field and automobile " s edge, the paper introduces detecting algorithm to confirm end - points of field, improved hough algorithm, and worm - following algorithm to pick up edge. in real - time checking phase, the most importance aim is to identify sign circles, so we advance a qiuck searching algorithm based on threshold
文中詳敘了系統的實現思路，對于預處理階段採用的圖像處理技術，如圖像增強、圖像分割等，提出了一些相應的改進演算法，取得了較滿意的處理效果；在系統初始化階段，為了獲取場地的端點信息和汽車邊緣信息，分別介紹了自動確定場地端點位置的檢測演算法、改進的hough變換演算法和提取區域邊緣的「蟲隨法」 ；在系統的實時檢測階段，最主要的目的是識別檢測標志圓，為此，文中提出了基於閾值的快速搜索演算法，有效的提高了目標物體區域的提取和識別速度。 -
Later on, after elaborating the disadvantages of the old methods in detecting and recognizing moving objects, a series of corresponding approaches are proposed, such as grid scan, local tracking bug and dynamic window in object tracing to reduce the huge data needed to be processed, maximum and minimum for selecting a proper segmentation threshold and improved conversion from rgb model to hsv and so on to decrease the influence of inhomogeneous lighting and the color noise, a bilinear interpolation in each quadrant to eliminate the bad effect on the recognition precise because of the distortions of the camera. after that, much emphasis is given on application study in pattern recognition with a feed - forward neural network. both the basic bp algorithm and improved bp algorithm in the study process are described in detail, and the later is used to quicken convergence speed and improve validity of the network
然後，分析和闡明了傳統的運動目標檢測方法的不足，並在此基礎上結合研究中的實際實驗環境，提出了一系列解決方法，包括針對降低龐大數據量而提出的網格掃描、局部「跟蟲」追蹤和動態窗口掃描等目標檢測方法，針對實驗環境中光照不均和顏色干擾提出基於人機交互的最大最小值閾值選取方法和引入改進的rgb模型到hsv模型的轉換方法，為消除圖像畸變對識別精度的惡劣影響而採用的通過控制點進行雙線性插值進行畸變校正的方法；緊接著，概述了神經網路的發展歷史和幾種常用神經網路模型的特點，重點研究了前饋型神經網路在模式識別中的應用問題，詳細闡述了基本的bp演算法和學習過程中bp演算法的改進，從而使網路收斂速度更快，解決問題更有效，並在此基礎上，設計了一個基於bp神經網路的運動目標識別系統，給出了實驗結果。 -
Then the de - lay differential method of detecting the milling cutter breakage is put forward according to the above research work and the changing characteristics of power signal and vibration displacement signal when the tool breakage occurs, the monitoring stategy of sensors fusion based on the artificial neural network is put forward. the monitoring system of milling cutter breakage based on the information fusion of the power signal and vibration displace - ment signal has been developed. the diff culty in giving a dectecting threshold due to the changeable character of character of cutting condition has been solved, and a good reliability is ensured over a wide range of conditions
在上述研究工作的基礎上，提出了基於人工神經網路用於多傳感器信息融合的監測策略，開發研製了以機床主電機功率信號和主軸振動位移信號特徵參量融合的銑刀破損監測實驗系統，解決了銑削過程中切削條件多變導致監測閾值難以設置的難題，保證了在較寬的工作范圍內具有較高的識別準確率。 -
Automatic threshold choice is difficult and important to color images edge detection, first of all, on the base of human visual properties, threshold versus intensity function of human visual system are researched and analyzed, by using method of mathematic modeling and the result of saturation research, function of least color difference that can be perceptible by visual systems in detecting edge is given
摘要自動閥值選取是彩色圖像邊緣檢測的難點和關鍵問題，首先從人類的視覺特性出發，分析了人類視覺系統的亮度感知門限函數，利用數學建模方法和色度學方面的研究成果，給出人類視覺系統對彩色圖像邊緣所能識別的最小彩色差函數。 -
Compare a lot of face image characteristic vector with face image sets characteristic matrix in order to get their similarity, and find the least value of similarity as threshold. in the detecting phase, compute the similarity between characteristic vector of testing region in gray image and face image sets characteristic matrix, if the similarity bigger or equal to threshold then the testing region is a human face, otherwise is not
然後，用大量的人臉圖像的特徵向量與人臉圖像集特徵矩陣比較它們的相似程度，找出值小相似度，並把這個最小相似度作為閾值；在檢測階段，求出灰度圖像的待測區域的特徵向量與人臉特徵矩陣的相似度，若該相似度大於等於閾值，則是人臉，否則不是人臉。
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