Multi-Channel Multi-Resolution RGB-D Light Field Video with Convolutional Neural Networks – Constraint-based image segmentation is a key challenge for many computer vision problems. Most existing methods either use an RGB-D image as a pre-processing step, or directly feed the RGB image into a convolutional neural network (CNN). Previous work has explored the idea of adapting CNN’s structure to make use of the features of the input image. This work is based on learning a CNN model of the input image. In this paper, to overcome these two shortcomings, we propose a novel deep learning-based method to segment the input image with a CNN. Using the deep CNN model, we extend the existing CNN segmentation approach to the task of fine-tuning the image features. Results demonstrate that our proposed CNN model achieves a better performance on our segmentation task than the existing CNN model with respect to the performance of other existing deep learning-based CNN models.
The human visual system is equipped with a plethora of visual features, which we will focus on. To facilitate the analysis of features we will take a large series of videos of different visual categories, which will be represented by different spatial coordinates and are used as a classification task. In this work, we present a new visual classification task based on spatial information from the videos and the classification is performed using multi-view clustering method. The resulting classification model has been trained by using the image and video data sets with the aim of training the discriminative model. The results demonstrate that the proposed training method is very effective.
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Multi-Channel Multi-Resolution RGB-D Light Field Video with Convolutional Neural Networks
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Mining Social Views on PinterestThe human visual system is equipped with a plethora of visual features, which we will focus on. To facilitate the analysis of features we will take a large series of videos of different visual categories, which will be represented by different spatial coordinates and are used as a classification task. In this work, we present a new visual classification task based on spatial information from the videos and the classification is performed using multi-view clustering method. The resulting classification model has been trained by using the image and video data sets with the aim of training the discriminative model. The results demonstrate that the proposed training method is very effective.