Trouble Is a Friend
歌詞：
Verse 1：
I'm standing on a mountain top
我在山頂站著
Looking for a little bit of something
尋找著一些小小的東西
But I can't see anything
但我什麼也看不到
But I know that there's something there
但我明知道那裡有東西
Chorus：
Trouble is a friend of mine
困難是我一個朋友
It's always there to help me out of my misery
它總是在我需要幫助的時候出現
It doesn't come and go away
它不來也不走
It's there to stay with me forever
它與我相伴永遠不離
Verse 2：
I'm always going to have a friend like this
我總是會有這樣的朋友相伴
Cause they don't ever leave me alone
因為他們永遠不會離我而去
But they never let me down
他們永遠不會讓我失望
They just come and go away with me forevermore
他們只是隨我一起相伴永遠不離
Chorus：
Trouble is a friend of mine
困難是我一個朋友
It's always there to help me out of my misery
它總是在我需要幫助的時候出現
I thought you'd be the end of my misery, yeah yeah yeah! ...... but I know, you won't, no, no! (2x)...... Oh, no! ...... Yeah, oh, oh! (x2) yeah! (x2) (the last two repeats with more passion)......Markov random field (MRF) is a powerful tool for modeling spatial dependencies in computer vision and graphics. It has been widely used in various tasks such as image segmentation, object detection, and 3D reconstruction. In this paper, we propose a novel Markov random field (MRF) model for multi-view image segmentation. The proposed model is based on the observation that different views of the same object may provide complementary information to improve segmentation accuracy. To address this issue, we introduce an extended pairwise potential function that captures dependencies between pixels from different views. The proposed potential function can be easily integrated with existing MRF models by adding an additional pairwise term. We demonstrate the effectiveness of our method using the benchmark data from multiple publicly available datasets. Experimental results show that our method achieves superior performance compared to state-of-the-art methods in terms of both accuracy and efficiency. Our code is available at
This is an Accepted Manuscript of an article published by Taylor & Francis in their journal "Pattern Recognition". The final version is available online at: