That is, we show that the unique polarization pattern encoded in the polarimetric appearance of an object captured under the sky can be decoded to reveal the surface normal at each pixel.
The costly process of obtaining semantic segmentation labels has driven research towards weakly supervised semantic segmentation (WSSS) methods, using only image-level, point, or box labels.
Toward this goal, we propose two main deep learning-based modules for: 1) cranberry fruit segmentation to delineate the exact fruit regions in the cranberry field image that are exposed to sun, 2) prediction of cloud coverage conditions and sun irradiance to estimate the inner temperature of exposed cranberries.
We demonstrate that H2O-Net outperforms the state-of-the-art semantic segmentation methods on satellite imagery by 10% and 12% pixel accuracy and mIoU respectively for the task of flood segmentation.
We demonstrate the increased performance of AngLNet over prior state-of-the-art in material segmentation from satellite imagery.
The steering angle at a particular time instance is the worker network output which is regulated by the manager's high level task.
The connection between visual input and tactile sensing is critical for object manipulation tasks such as grasping and pushing.
Precision agriculture has become a key factor for increasing crop yields by providing essential information to decision makers.
The residuals are computed by differencing the sparse-sampled reflectance function with a dictionary of pre-defined dense-sampled reflectance functions.
In this paper, we explore the impact of global contextual information in semantic segmentation by introducing the Context Encoding Module, which captures the semantic context of scenes and selectively highlights class-dependent featuremaps.
Ranked #15 on Semantic Segmentation on PASCAL VOC 2012 test (using extra training data)
Despite the rapid progress in style transfer, existing approaches using feed-forward generative network for multi-style or arbitrary-style transfer are usually compromised of image quality and model flexibility.
We realize this by developing a framework for differential angular imaging, where small angular variations in image capture provide an enhanced appearance representation and significant recognition improvement.
We sample from the resulting metamer sets to find color steps for each base color to embed a binary message into an arbitrary image with reduced visible artifacts.
In this work, we address the question of what reflectance can reveal about materials in an efficient manner.
Reflectance offers a unique signature of the material but is challenging to measure and use for recognizing materials due to its high-dimensionality.