Unsupervised learning of disparity maps from stereo images
نویسندگان
چکیده
منابع مشابه
Wyner-ziv Coding of Stereo Images with Unsupervised Learning of Disparity
Wyner-Ziv coding can exploit the similarity of stereo images without communication among the cameras. For good compression performance, the disparity among the images should be known at the decoder. Since the Wyner-Ziv encoder has access only to one image, the disparity must be inferred from the compressed bitstream. We develop an Expectation Maximization algorithm to perform unsupervised learn...
متن کاملDistributed Compression of Stereoscopic Images with Unsupervised Learning of Disparity
Distributed compression is particularly attractive for stereoscopic images since it avoids communication between cameras. Since compression performance depends on exploiting the redundancy between images, knowing the disparity is important at the decoder. Unfortunately, distributed encoders cannot calculate this disparity and communicate it. In this paper, we propose an Expectation Maximization...
متن کاملCalculating depth maps from digital holograms using stereo disparity.
Depth extraction is an important aspect of three-dimensional (3D) image processing with digital holograms and an essential step in extended focus imaging and metrology. All available depth extraction techniques with macroscopic objects are based on variance; however, the effectiveness of this is object dependent. We propose to use disparity between corresponding points in intensity reconstructi...
متن کاملEstimating uncertainty in dense stereo disparity maps
Dense stereo is a well studied problem in computer vision. Generally dense stereo algorithms provide only a single estimate of disparity, ignoring uncertainty in the disparity map. Here however, we present a new, linear-time, exact method for recovering entire distributions for disparity at all pixels. This is accomplished by using a recent extension, due to Durbin et al., of the well-known for...
متن کاملDisparity from Stereo-segment Silhouettes of Weakly-textured Images
In stereo vision, 3D information is reconstructed from two images of the same scene taken from different viewpoints. Different approaches to stereo disparity estimation have been extensively compared in several studies [1, 3]. Most stereo algorithms perform well in textured image areas, but often fail when there is only weak texture, due to the correspondence problem. Here local matching fails,...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Frontiers in Computational Neuroscience
سال: 2009
ISSN: 1662-5188
DOI: 10.3389/conf.neuro.10.2009.14.126