Deep Residual Texture Network for Terrain Recognition
نویسندگان
چکیده
منابع مشابه
Deep Texture Manifold for Ground Terrain Recognition
We present a texture network called Deep Encoding Pooling Network (DEP) for the task of ground terrain recognition. Recognition of ground terrain is an important task in establishing robot or vehicular control parameters, as well as for localization within an outdoor environment. The architecture of DEP integrates orderless texture details and local spatial information and the performance of DE...
متن کاملTexture Virtualization for Terrain Rendering
Virtual texturing is a technique that allows the use of arbitrarily large textures within the limited physical video memory. Through a paging and streaming system, only the currently visible parts of a mipmap chain are stored in the video memory while the rest of the data may reside in any other memory or storage device. Not only does this enable the use of unique and very detailed textures, bu...
متن کاملDeep convolutional filter banks for texture recognition and segmentation
Research in texture recognition often concentrates on the problem of material recognition in uncluttered conditions, an assumption rarely met by applications. In this work we conduct a first study of material and describable texture attributes recognition in clutter, using a new dataset derived from the OpenSurface texture repository. Motivated by the challenge posed by this problem, we propose...
متن کاملDARN: a Deep Adversial Residual Network for Intrinsic Image Decomposition
We present a new deep supervised learning method for intrinsic decomposition of a single image into its albedo and shading components. Our contributions are based on a new fully convolutional neural network that estimates absolute albedo and shading jointly. As opposed to classical intrinsic image decomposition work, it is fully data-driven, hence does not require any physical priors like shadi...
متن کاملDeep Predictive Coding Network for Object Recognition
Inspired by predictive coding in neuroscience, we designed a bi-directional and recurrent neural net, namely deep predictive coding networks (PCN). It uses convolutional layers in both feedforward and feedback networks, and recurrent connections within each layer. Feedback connections from a higher layer carry the prediction of its lower-layer representation; feedforward connections carry the p...
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: IEEE Access
سال: 2019
ISSN: 2169-3536
DOI: 10.1109/access.2019.2926994