Stereo matching is a fundamental and crucial problem in computer vision. In the last decades, many researchers have been working on it and made great progress. Generally stereo algorithms can be classified into local ...Stereo matching is a fundamental and crucial problem in computer vision. In the last decades, many researchers have been working on it and made great progress. Generally stereo algorithms can be classified into local methods and global methods. In this paper, the challenges of stereo matching are first introduced, and then we focus on local approaches which have simpler structures and higher efficiency than global ones. Local algorithms generally perform four steps： cost computation, cost aggregation, disparity computation and disparity refinement. Every step is deeply investigated, and most work focuses on cost aggregation. We studied most of the classical local methods and divide them into several classes. The classification well illustrates the development history of local stereo correspondence and shows the essence of local matching along with its important and difficult points. At the end we give the future development trend of local methods.展开更多
By considering higher order approximation to the interaural phase difference, a more general localization equation for stereo sound image with interchannel phase difference is derived. At very low frequency or low int...By considering higher order approximation to the interaural phase difference, a more general localization equation for stereo sound image with interchannel phase difference is derived. At very low frequency or low interchannel phase difference, the equation can be simplified to Makita theory. In general, image position is obviously affected by frequency.It is shown that image position varying with freqllency is the main reason for image width broadening in stereo reproduction with interchannel phase difference. And an extra interaural sound level difference caused by interchannel phase difference is the main reason for image naturalness degrading. In practice, it is necessary to reduce the interchannel phase difference,at least, to less than 60°.展开更多
文摘Stereo matching is a fundamental and crucial problem in computer vision. In the last decades, many researchers have been working on it and made great progress. Generally stereo algorithms can be classified into local methods and global methods. In this paper, the challenges of stereo matching are first introduced, and then we focus on local approaches which have simpler structures and higher efficiency than global ones. Local algorithms generally perform four steps： cost computation, cost aggregation, disparity computation and disparity refinement. Every step is deeply investigated, and most work focuses on cost aggregation. We studied most of the classical local methods and divide them into several classes. The classification well illustrates the development history of local stereo correspondence and shows the essence of local matching along with its important and difficult points. At the end we give the future development trend of local methods.
文摘By considering higher order approximation to the interaural phase difference, a more general localization equation for stereo sound image with interchannel phase difference is derived. At very low frequency or low interchannel phase difference, the equation can be simplified to Makita theory. In general, image position is obviously affected by frequency.It is shown that image position varying with freqllency is the main reason for image width broadening in stereo reproduction with interchannel phase difference. And an extra interaural sound level difference caused by interchannel phase difference is the main reason for image naturalness degrading. In practice, it is necessary to reduce the interchannel phase difference,at least, to less than 60°.
