Image-based Modeling by Minimizing Projection Error of Primitive Edges
نویسندگان
چکیده
منابع مشابه
Scalable Image Coding with Projection-Based Context Modeling
Many state-of-the-art wavelet image coders use nonorthog-onal transforms for both lossy and lossless wavelet image coding 1, 2, 3, 4]. In this paper, a projection prediction is described that capitalizes on the non-orthogonality of wavelet transform basis vectors to improve the prediction of high-frequency coeecients. For lossy wavelet coders, the prediction yields improved context modeling and...
متن کاملColor image denoising by chromatic edges based vector valued diffusion
In this letter we propose to denoise digital color images via an improved geometric diffusion scheme. By introducing edges detected from all three color channels into the diffusion the proposed scheme avoids color smearing artifacts. Vector valued diffusion is used to control the smoothing and the geometry of color images are taken into consideration. Color edge strength function computed from ...
متن کاملorder reduction by minimizing integral square error and h∞ norm of error
in this paper, a new alternative method for order reduction of high order systems is presented based on optimization of multi objective fitness function by using harmony search algorithm. at first, step response of full order system is obtained as a vector, then, a suitable fixed structure considered for model order reduction which order of original system is bigger than fixed structure model. ...
متن کاملmodeling loss data by phase-type distribution
بیمه گران همیشه بابت خسارات بیمه نامه های تحت پوشش خود نگران بوده و روش هایی را جستجو می کنند که بتوانند داده های خسارات گذشته را با هدف اتخاذ یک تصمیم بهینه مدل بندی نمایند. در این پژوهش توزیع های فیزتایپ در مدل بندی داده های خسارات معرفی شده که شامل استنباط آماری مربوطه و استفاده از الگوریتم em در برآورد پارامترهای توزیع است. در پایان امکان استفاده از این توزیع در مدل بندی داده های گروه بندی ...
Automatic Parameter Selection by Minimizing Estimated Error
We address the problem of nding the parameter settings that will result in optimal performance of a given learning algorithm using a particular dataset as training data. We describe a \wrapper" method, considering determination of the best parameters as a discrete function optimization problem. The method uses bestrst search and crossvalidation to wrap around the basic induction algorithm: the ...
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: The KIPS Transactions:PartB
سال: 2005
ISSN: 1598-284X
DOI: 10.3745/kipstb.2005.12b.5.567