是哪個區塊；此外，由於使用邊緣圖像來分割物體，導致如果物體過於複雜，分割的區 塊數可能過多，影響到辨識的速度，未來可以針對如何將相鄰的過小區塊合併，使之成 為一個大區塊來減少區塊數目，進而改善原本辨識的速度。
最後在於如何將三維辨識與二維辨識結合，並且真正應用在機器人視覺中，因為二 維辨識容易受到外在光源的影響，導致辨識率下降，但好處是藉由強度影像與二維辨識，
可有效且快速地分割物體與背景，並且找出物體的輪廓去進行辨識，相對於，深度資訊 而言，在某些情況下，只利用深度資訊是無法將物體跟地面或是背景分離的，由其是在 某些視角下，目標物體與背景的深度變化幾乎是連續的，故無法有效地分離；然而，三 維辨識的優點是，只利用二維影像來獲得物體的輪廓以進行辨識時，會明明表面形狀不 同的物體，但可能投影在二維影像上後，有相同的輪廓，導致利用輪廓辨識的二維辨識 之失敗。
未來可以將本論文應用到使用大型的雷射測距儀所量測到複雜深度資訊，如圖 5-1 所示，來辨識出複雜場景中的大型物體。
圖 5-1 雷射測距儀所測得的大型複雜深度影像 資料來源：中科院
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