進到最後一幀 的英文怎麼說
中文拼音 [jìndàozuìhòuyīzhèng]
進到最後一幀
英文
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An algorithm for detecting moving ir point target in complex background is proposed, which is based on the reverse phase feature of neighborhood ( rpfn ) of target in difference between neighbor frame images that two positions of the target in the difference image are near and the gray values of them are close to in absolute value but with inverse sign. firstly, pairs of points with rpfn are detected in the difference image between neighbor frame images, with which a virtual vector graph is made, and then the moving point target can be detected by the vectors ' sequence cumulated in vector graphs. in addition, a theorem for the convergence of detection of target contrail by this algorithm is given and proved so as to afford a solid guarantee for practical applications of the algorithm proposed in this paper. finally, some simulation results with 1000 frames from 10 typical images in complex background show that moving point targets with snr not lower than 1. 5 can be detected effectively
基於運動點目標在鄰幀差分圖像中所具有的近鄰反相特徵，即運動點目標的兩個位置相鄰近、灰度值一正一負，提出一種在復雜背景下，基於紅外序列圖像的運動點目標檢測演算法.本演算法利用該特徵在鄰幀差分圖像中檢測反相點對，進而構造反相點對矢量圖，最後依據累積反相點對矢量圖中多矢量首位相接的連續性檢測出運動的點目標.文中給出並證明應用本演算法能以概率1檢測到運動點目標的收斂性定理.對典型復雜背景下10幅1000幀圖像的模擬結果表明，當信噪比大於或等於1 . 5時，可以有效檢測出運動點目標Also an algorithm which combines both model matching and feature matching is put forward. the algorithm uses the object contour in previous frame as the reference template of current frame. based on the fact that object has a continuous track in movement, object ’ s current position can be predicted based on previous position and then match the reference template around the predicted position
該演算法將前一幀目標輪廓作為當前幀的參考模板，根據目標在運動過程中具有軌跡連續性的特點，利用目標過去的跟蹤點位置信息得到當前的預測位置點，然後在預測位置點周圍一定范圍內進行模型匹配，以與參考模板匹配值最大的輪廓作為當前幀的目標輪廓，並且把它更新作為下一幀的參考模板。However, most of the current wireless networks still work in low bandwidths, and mobile devices still suffer from weak computational power, short battery lifetime and limited display capability. therefore, this thesis deals with developing a very low bit - rate bi - level video coding technique, which can be used in video communications almost anywhere, anytime on any device as follows : ( 1 ) convert true color video sequences from ccd and video card to grayscale ones, ( 2 ) detect static region of successive frames by the sum of absolute differences ( sad ) and update current frame using static region of previous frame, to decrease flicker. ( 3 ) threshold the images to bi - level video sequences using ridler ' s iterative selection
為此，本論文在傳統h . 26x和mpeg - x等視頻編解碼技術的基礎上提出了一種新的基於輪廓的視頻編解碼方案，步驟如下： （ 1 ）先把從ccd攝像頭捕捉到的視頻幀序列轉換成灰度幀序列， （ 2 ）在灰度幀序列的基礎上通過sad演算法找出相鄰幀的靜態區域，用前一幀的靜態區域更新當前幀的相應區域，這樣可以降低畫面閃爍， （ 3 ）然後用灰度直方圖迭代所產生的閾值二值化圖像，生成基於輪廓的視頻幀序列， （ 4 ）最後運用基於上下文的算術編碼技術對由第三步生成的二值化視頻幀序列進行算術編碼。Users plot a coarse outline of video objects in the graphic user interface ( gui ) using the mouse at the first step, then fill the outline to obtain a binary model, using seed growing and wavelet edge correct the outline. in tracking video objects, we obtain an initial segmentation uses motion information and the model of previous frame, and correct by the information of space. finally, we obtain an accurate segmentation
利用視覺系統的周邊抑制機制對模板外的象素進行屏蔽，消除背景影響，由自動閾值選取的小波邊緣提取獲得視頻對象的邊界，利用種子生長法進行輪廓擬合，由最短路徑法校正模板，在進行視頻對象的跟蹤時，利用運動信息和上一幀的模板，得到一個初始分割，利用空間信息對邊界象素調整，最後得到精確分割的視頻對象。The article will put forward a kind of new denoising method of digital image in frequency domain : start with a series of fourier transforms on hand and wrist x - ray image ; then carry on single frame mean value to deal with ; finally, carry out the single frame smooth handling of frame frequency. one that is through steps the above treated, get better test result, improve the artificial accuracy that read
文章提出了一種新的圖像頻域祛噪方法：首先對採集到的手腕部骨圖像進行傅立葉變換；然後進行單幀均值化處理；最後進行單幀頻域平滑處理。通過以上步驟的處理后，得到了較好的試驗效果，大大提高了人工判讀的準確性。分享友人