rgb to hsv 中文意思是什麼
rgb to hsv
解釋
（三原色到色彩模型）工具-
Reference to mpeg - 7 standards, mainly researches feature extracting schemes of video visual information. it includes histogram refinement in rgb color space, histogram in hsv color space, color coherence vectors algorithm and homogeneous texture descriptor extraction algorithm
論文中主要研究的視覺特徵提取方法有： rgb顏色空間的改進直方圖法、 hsv顏色空間的直方圖統計、顏色相關向量法和提取同類紋理描述符演算法。 -
Two parts are included in the software : first, by adjusting the parameters of the rgb cmky and hsv color space to change colors. it is convenient for designers to rectify the color deviation and optimize colors matching. second, by adjusting the parameters of one color space in turns, the image can shade in colors, so that the images with ideal color effect can be caught, which can help the designers " originality
這一軟體包括兩大部分：一是通過分別調節rgb 、 cmyk 、 hsv等顏色空間內的參數值，達到色彩調整的目的，便於包裝產品設計人員糾正色彩偏差，優化色彩搭配；二是通過依次改變某一顏色空間內的各參數值，產生顏色漸變效果，並捕捉到具有理想色彩效果的圖像，有助於包裝產品設計人員的設計創意與圖像實現。 -
According to human vision character rgb color space is converted to hsv color space. instead of the gray information, the hue information is used as the model parameter
首先從人眼視覺特徵出發，將彩色視頻序列從rgb顏色模型轉換到hsv顏色模型，選擇色調屬性作為基本的識別參數，用色調信息取代主動形狀模型中原本使用的灰度信息。 -
In view of the above mentioned problem, the author adopts information technology such as image processing and pattern recognition to research into the method of automatic analysis and classification. in accordance with the difficulty in medical image analysis ( for example, the background of microimage of section is complicated and is difficult to be segmented. ), the paper puts forward two kinds of segmentation methods based on standardized colorful space and rgb and hsv colorful model
本文針對上述問題，用計算機圖像處理及模式識別等信息技術對顯微細胞圖像的自動分析和分類的方法進行了研究，並針對醫學圖像分析中的難點（例如，顯微切片圖像背景復雜，分割困難） ，提出了基於歸一化彩色空間和rgb ， hsv彩色模型的兩類分割方法：利用模式識別技術中關于特徵向量空間聚類的方法實施真彩色分割。 -
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。 -
The thesis has implemented adaptive background model to detect moving objects. we propose to operate in the hue - saturation - value ( hsv ) color space, instead of the traditional rgb space, and show that it provides a better use of the color information, and naturally incorporates gray - level only processing. at each instant, the system constructs three gauss distribution for a pixel and maintains an updated background model, and a list of occluding regions that can then be tracked
此外，自適應的背景模板，採用圖像上的每個象素點的h 、 s 、 v的分佈為高斯分佈建立的三維模型，該模型與實際情況比較接近；在背景、前景的判斷上，充分利用了顏色信息；而且在更新演算法上也考慮了學習率的問題。
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