本論文主要提出了階層式估算方法於影像處理上應用，在單張影像方面主要應用於影像壓縮傳輸系統，透過階層式錯誤恢復(error concealment),達到更好錯誤容忍能力。在多張影像應用方面，從multi-view camera視訊影像中，進行階層式資料融合(data fusion)並進行物體追蹤。
針對階層式估算應用於JPEG2000影像壓縮，提出應用方法為Hierarchical Uneven Block-sized Information Included Marker，簡稱為(HUBIIM)。 可應用於JPEG2000壓縮後資料流(bitstream)傳輸錯誤時作為錯誤恢復之用。在這方法中，我們在每一個區塊(code-block)之後緊接著插入了一個附加資訊的標記(marker)，作為保護資料流之用。此標記不但具同步標記的功能，並且附加了VS索引和TR索引兩個資訊；其中VS索引是從其對應區塊的鄰近區塊中找到一個和其最為接近的區塊，而TR索引是由其區塊相對位置處之高一階層(level)中找到一個pattern相近的參考區塊。當發生錯誤時，再利用這兩個索引找出另一個區塊來代替錯誤的區塊，以達到有效的錯誤恢復(Error recovery)。最後顯示實驗結果比JPEG2000之錯誤容錯方法及其它階層式方法效果好。
階層式估算於多張影像方面，提出一個稱為closed-loop local-global integrated hierarchical estimator (CLGIHE)方法來進行多攝影機物體追蹤。這個階層式架構共分為local 及 global 兩層且各別獨立運作，其中local層所做的工作包括去除背景、物體偵測、量測值(measurement)之決定、估計物體下一步移動位置等。之後所有local層的資訊傳至global層進行整合式的資料融合，以更精確的判斷出物體的位置。所提出的方法與其他階層式資料融合進行比較，得到更佳的實驗結果。

A closed-loop local-global integrated hierarchical estimator (CLGIHE) approach for object tracking using multiple cameras is proposed. The Kalman filter is used in both the local and global estimates. In contrast to existing approaches where the local and global estimations are performed independently, the proposed approach combines local and global estimates into one for mutual compensation. Consequently, the Kalman-filter-based data fusion optimally adjusts the fusion gain based on environment conditions derived from each local estimator. The global estimation outputs are included in the local estimation process. Closed-loop mutual compensation between the local and global estimations is thus achieved to obtain higher tracking accuracy. A set of image sequences from multiple views are applied to evaluate performance. Computer simulation and experimental results indicate that the proposed approach successfully tracks objects.
This dissertation also proposes the hierarchical uneven block-sized information included marker (HUBIIM) for JPEG 2000 to achieve both error resilience and error concealment capability. Included in the HUBIIM approach is the marker adopted in JPEG 2000 devised to incorporate both error detection and correction capabilities. The HUBIIM consists of VS Index and TR Index. VS Index is used to indicate a value support block, a block which neighbors an erroneous block and whose pixel values are most similar to those of the erroneous block. TR Index is used to indicate a texture reference block, the block in the corresponding position of the erroneous block in the same subband of one-higher level of which the pixel value distribution is similar as the erroneous block. Thus, HUBIIM protects other code blocks when a code block is affected by errors, and provides error concealment via VS Index and TR Index. When errors occur, a new block is formed to replace the erroneous block using the value support block and the texture reference block to achieve error concealment. The sizes of the blocks in HUBIIM are determined based on the importance factor of various levels of subbands; that is, different levels have different block sizes. The proposed approach greatly reduces the overhead caused by the addition of markers between every two blocks and significantly improved error resilience capability.

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