網路偵測影像的技術常用於入侵感測的裝置，如影像處理效率不佳，會導 致錯誤的偵測。過去的研究著重於影像分割與特徵分類，常需複雜的運算。本 研究提出適應性區域成長的方法以有效率地偵測遠距感測影像。所提方法使用 適應性區域成長產生的像素分類各種不同物體，以達成有效率的分類。此創新 的區域成長方法為依據像素分類的結果作適應性演算，此演算可自動偵測 Wi-Fi 所傳來的遠距影像。實驗結果顯示此適應性區域成長的演算可達成智慧 型偵測的目標與百分之九十以上的辨識率。所提出的方法與現有的方法比較， 呈現較佳的結果。

Detection of remote images on the network has rapidly appeared in intrusion- detection devices. Inefficient data processing of remote sensing images can cause poor detection results. Previous studies focus on image segmentation and feature-extraction classification for the detection. This study proposes an adaptive region-growing-based method that implements image segmentation. Unlike other classification methods, which use complex calculations, the proposed method uses the direct calculation of total pixels in the growth regions. To achieve efficient classification, the pixels in the regions produced by the region-growing procedure are used to classify various objects. An adaptive algorithm based on the proposed efficient classification method can automatically detect the remote images through Wi-Fi. Experiments show that the adaptive region-growing algorithm can achieve smart detection and recognition rates higher than 90%. The proposed method achieves better results than existing methods.