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基於點群排序關係的特徵描述子建構 / Feature descriptor based on local intensity order relations of pixel group吳家禎, Wu,Chia Chen Unknown Date (has links)
隨著科技的進步以及網際網路的普及,影像資訊的傳遞已經漸漸取代文字的表達,人們對於影像的需求也越來越多元,使得影像處理技術以及影像資訊分析也就越來越重要。然而,影像中其中一項重要的資訊為特徵描述子,強而有力的描述子能使得影像在辨識、分類等應用上有較佳的回饋,描述子的建構方式根據編碼原則分為:基於區域梯度統計、基於點對關係以及基於點群關係。其中,基於點群關係的編碼方式因為點群的選取及排序過程中,可能會產生過多的關係表示方法數,以至於不利於計算,因此過去較少有利用點群關係的編碼方式所建構而成的特徵描述子。
本論文提出描述子建構方式-LIOR,是以點群排序關係為基礎的編碼方式,相較於LIOP方法隨著點群內的點數增加,元素關係數大幅度的成長,造成描述子維度過大,計算時間和空間皆可能需要大量的消耗,而本研究方法足以改善計算維度的問題,重新定義點群關係的排名機制,並以像素值為基準加入權重分配,以區別加權排序之間不同大小差值所造成的影響程度。
實驗結果顯示本研究方法對於不同影像劣化效果的資料集,不僅能提升選取多點為一群的影像比對評估效能,同時也能改善點群內元素關係過多的排名表示法,降低以多點為群集的特徵描述子維度,節省了影像比對的計算時間以及空間,仍可維持整體影像配對之效能。
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基於點群排序關係的動態設定特徵描述子建構及優化 / Construction and optimization of feature descriptor based on dynamic local intensity order relations of pixel group游佳霖, Yu, Carolyn Unknown Date (has links)
隨著智慧型手機的普及,在移動裝置上直接處理圖像的需求也大幅增加,故對於影像特徵描述子的要求,除了要表現出區域特徵的穩健性,同時也要維持良好的特徵比對效率與合理的儲存空間。過去所提出的區域影像特徵描述子建構方法之中,LIOP方法具有相當不錯的表現力,但其特徵描述子維度會隨著點群取樣數量的提高而以倍數增加,因此本研究提出Dynamic Local Intensity Order Relations (DLIOR)特徵描述子建構方法,利用LIOR方法探討點群中點與點之間的關係,減緩其維度增長幅度;透過動態設定像素差距門檻值,處理影像間像素差距分佈不均的問題,並使用線性轉換、點對歐幾里德距離等方式,重新定義描述子欄位的權重設定。經過實驗證實,DLIOR方法能夠使用比LIOP方法更少的維度空間,描述更多點群數的特徵資訊,並且具有更高的特徵比對能力。 / With the popularity of smart phones, the amounts of images being captured and processed on mobile devices have grown significantly in recent years. Image feature descriptors, which play crucial roles in recognition tasks, are expected to exhibit robust matching performance while at the same time maintain reasonable storage requirement. Among the local feature descriptors that have been proposed previously, local intensity order patterns (LIOP) demonstrated superior performance in many benchmark studies. As LIOP encodes the ranking relation in a point set (with N elements), however, its feature dimension increases drastically (N!) with the number of the neighboring sampling points around a pixel. To alleviate the dimensionality issue, this thesis presents a local feature descriptor by considering pairwise intensity relation in a pixel group, thereby reducing feature dimension to the order of C^N_2. In the proposed method, the threshold for assigning order relation is set dynamically according to local intensity distribution. Different weighting schemes, including linear transformation and Euclidean distance, have also been investigated to adjust the contribution of each pairing relation. Ultimately, the dynamic local intensity order relations (DLIOR) is devised to effectively encode intensity order relation of each pixel group. Experimental results indicate that DLIOR consumes less storage space than LIOP but achieves better feature matching performance using benchmark dataset.
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