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This article presents an efficient visual landmark extraction and recognition method that can autonomously and rapidly detect visual features such as objects or groups of small objects, and that can be applied to visual object recognition based SLAM and navigation in indoor/large environments using a monocular/omnidirection vision system. Our method consists of two-stage: (1) we autonomously extract object regions with modified fuzzy object segmentation. We generate a saliency map of the scene based on Modified Phase spectrum of Fourier Transform (mPFT) and extract the final salient object landmark with weighted combination of candidate of objects and saliency map. (2) Using these result, we register current objects as visual landmark and then recognize the current image the scale invariant feature transform (SIFT) - based recognition with probabilistic voting. In experiments results in real indoor and large hall environments, the proposed method was simpler and 10~15% better performance in computation efficiency and successfully extracted salient object landmark in complex environments with high recognition rates. The proposed algorithm can be easily implemented in real-time by reducing the number of objects considered.