Two-level joint local laplacian texture filtering
نویسندگان
چکیده
منابع مشابه
Laplacian Texture Synthesis and Mixing on Surfaces
In neighborhood-based texture synthesis, adjacent local regions need to satisfy color continuity constraints in order to avoid visible seams. Such continuity constraints seriously restrict the variability of synthesized textures, making it impossible to generate new textures by mixing multiple input textures with very different base colors. In this paper, we propose to relax such restrictions a...
متن کاملTexture segmentation through adaptive filtering
Segmentation of images based in texture is a fundamental task in many computer vision environments. There are many objects in the real word whose main characteristic is not their mean gray level but other features related with their texture (like grain size, orientation, etc.) The existing methods for texture segmentation differ in the definition of the texture descriptors, and they range from ...
متن کاملDifferent Levels of Detail Display for Exposure Fusion Using Local Laplacian Filtering
Exposure fusion is an efficient method for directly fusing multi-exposure images into a high-quality low dynamic range image, without the high dynamic range (HDR) production and tone mapping process. The previous exposure fusion methods only produced an image that contains a fixed amount of details, which can not satisfy further demands for more detail information. We introduce Local Laplacian ...
متن کاملTexture classification by a two-level hybrid scheme
In this paper we propose a novel feature extraction scheme for texture classi cation, in which the texture features are extracted by a two-level hybrid scheme by integrating two statistical techniques of texture analysis. In the rst step, the low level features are extracted by the Gabor lters, and they are encoded with the feature map indices using the Kohonen's SOFM algorithm. In the next ste...
متن کاملLocal Parametrizations via Laplacian Eigenfunctions
Eigenfunction methods for mapping high dimensional data sets into lower dimensional spaces are useful in a broad range of applications. In particular, a recent paper by Jones et al [2] shows that eigenfunctions of the Laplacian operator give a good local coordinate system under very general conditions. Here I outline the proof of the theorem and explore its use in cryo-electron microscopy.
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 Visual Computer
سال: 2015
ISSN: 0178-2789,1432-2315
DOI: 10.1007/s00371-015-1138-3