Multiscale Digital Porous Rock Reconstruction Using Template Matching
نویسندگان
چکیده
منابع مشابه
Abnormalities Detection in Digital Mammography Using Template Matching
Breast cancer affects 1 in 8 women in the United States. Early detection and diagnosis is key to recovery. ComputerAided Detection (CAD) of breast cancer helps decrease morbidity and mortality rates. In this study we apply Template Matching as a method for breast cancer detection to a novel data set comprised of mammograms annotated according to ground truth. Performance is evaluated in terms o...
متن کاملAffine Resistant Digital Audio Watermarking Using Template Matching
In this paper, we present a novel approach for embedding a digital watermark inaudibly into an audio clip, in the time domain, according to the difference between two half blocks of each block. The proposed scheme does not require any host-related information for watermark extraction. The embedded watermark is robust to common audio signal manipulations, such as MP3 compression, time shifting, ...
متن کاملE cient Multiscale Template Matching with Orthogonal Wavelet Decompositions
I develop a method for e cient, multiscale template matching using orthogonal wavelet representations. I also develop an e cient approximate mechanism for initially nding the \correct" values for the basis coe cients ajk at the nest scale (as opposed to using the sample values as coe cients). I then present applications of these techniques to two important image coding/computer vision tasks: mo...
متن کاملEvaluation of Similarity Measures for Template Matching
Image matching is a critical process in various photogrammetry, computer vision and remote sensing applications such as image registration, 3D model reconstruction, change detection, image fusion, pattern recognition, autonomous navigation, and digital elevation model (DEM) generation and orientation. The primary goal of the image matching process is to establish the correspondence between two ...
متن کاملObject Recognition using Template Matching
Object Recognition is inherently a hard problem in computer vision. Current standard object recognition techniques require small training data sets of images and apply sophisticated algorithms. These methods tend to perform poorly because the small data set does not reflect the true distribution (selection bias). Recently, Torralba et al [1] have proposed to develop a large data set of images (...
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: Water Resources Research
سال: 2019
ISSN: 0043-1397,1944-7973
DOI: 10.1029/2019wr025219