Weather-dependent road conditions are a major factor in many automobile incidents;computervision algorithms for automatic classification of road conditions can thus be of great benefit. This paper presents a system f...
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ISBN:
(纸本)9781467399616
Weather-dependent road conditions are a major factor in many automobile incidents;computervision algorithms for automatic classification of road conditions can thus be of great benefit. This paper presents a system for classification of road conditions using still-frames taken from an uncalibrated dashboard camera. The problem is challenging due to variability in camera placement, road layout, weather and illumination conditions. The system uses a prior distribution of road pixel locations learned from training data then fuses normalized luminance and texture features probabilistically to categorize the segmented road surface. We attain an accuracy of 80% for binary classification (bare vs. snow/ice-covered) and 68% for 3 classes (dry vs. wet vs. snow/ice-covered) on a challenging dataset, suggesting that a useful system may be viable.
— This paper proposes a lossless compression scheme for greyscale images using Zipper Transformation (ZT) and Inverse Zipper Transformation (iZT). The proposed transformation exploits the conjugate symmetry property ...
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image segmentation plays a crucial role in effective understanding of digital images, planar or volumetric images. The current research in graph based methods is oriented towards producing approximate solution (or sub...
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–If a car windshield with distortion flaws will make object deformation and motion blur from the driver's sight easily, the drivers can cause visual misjudgment and have safety concerns on the road. This study pr...
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K-nn is one of the popular techniques in the field of pattern recognition. Here it has been applied for the prediction of severe thunderstorms. Three types of weather parameters i. e., moisture difference, adiabatic l...
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Deep convolutional neural networks (DCNNs) have been employed in many computervision tasks with great success due to their robustness in feature learning. One of the advantages of DCNNs is their representation robust...
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ISBN:
(纸本)9781467399616
Deep convolutional neural networks (DCNNs) have been employed in many computervision tasks with great success due to their robustness in feature learning. One of the advantages of DCNNs is their representation robustness to object locations, which is useful for object recognition tasks. However, this also discards spatial information, which is useful when dealing with topological information of the image (e.g. scene labeling, face recognition). In this paper, we propose a deeper and wider network architecture to tackle the scene labeling task. The depth is achieved by incorporating predictions from multiple early layers of the DCNN. The width is achieved by combining multiple outputs of the network. We then further refine the parsing task by adopting graphical models (GMs) as a post-processing step to incorporate spatial and contextual information into the network. The new strategy for a deeper, wider convolutional network coupled with graphical models has shown promising results on the PASCAL-Context dataset.
This paper proposed a multimodal complex emotion recognition system using image, voice, and brainwave based on multidimensional affective model. Based on face image, voice, and brain wave of users, features correspond...
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This paper presents a new approach on 2D to 3D video automatic conversion based on Kinect. There are mainly two contributions in our approach. First, the depth maps captured by Kinect are usually of poor quality. Ther...
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ISBN:
(纸本)9789898533524
This paper presents a new approach on 2D to 3D video automatic conversion based on Kinect. There are mainly two contributions in our approach. First, the depth maps captured by Kinect are usually of poor quality. There are a lot of depth information missing regions. An improved energy function based Inpainting algorithm is proposed, which can help to recover the missed depth information smoothly. Then a new hole filling approach in depth image based rendering (DIBR) is proposed to synthesize the 3D images by using the corresponding color images and the repaired depth maps. Experiments are conducted on videos in different scenes. The results show the effectiveness in both the quality and the running time.
We propose a robust image matching method using statistical modeling and clustering of geometric similarity between matching-pairs. Local feature matching is an uncertain process which may provide incorrect matches du...
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RGB-D camera like Kinect make available RGB images along with per-pixel depth information in real time. This paper uses the Kinect Fusion developed by Microsoft Research for the 3D reconstruction of the scene in real ...
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RGB-D camera like Kinect make available RGB images along with per-pixel depth information in real time. This paper uses the Kinect Fusion developed by Microsoft Research for the 3D reconstruction of the scene in real time using the MicroKinect Camera and applies it as an aid for Visual Odometry of a Robotic Vehicle where no external reference like GPS is available. (C) 2016 The Authors. Published by Elsevier B.V.
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