SVDD: Single-view Video Dense Deformation Variation Based on Histogram and Line Filtering


SVDD: Single-view Video Dense Deformation Variation Based on Histogram and Line Filtering – Learning effective feature representations is one of the primary challenges in this field of learning visual feature representations for medical domains. In this paper, we propose a new deep learning approach for image classification in the context of feature learning. Our deep learning based approach works on the CNN network to classify images based on the features extracted from the images and then use these features for classification. To train CNNs, we use a fully convolutional-coherent architecture. We use the ConvNet architecture to perform the classification in three different settings: for the first setting we use a single ConvNet or a new convolutional-coherent architecture. In order to increase classification accuracy, we use three different kinds of convolutional-coherent architecture, the Fully Convolutional, Normalized and Normalized and propose a semi-supervised approach for classifying images using the CNNs. Experimental evaluation on four ImageNet benchmark datasets shows that our approach has superior performance compared to traditional method for classification accuracy and classification speed.

In this paper, a novel deep learning based image segmentation method is proposed that relies on a hierarchical classification scheme which is implemented in Convolutional Neural Network (CNN). Extensive studies were conducted using different datasets, including one with a small number of segmentations, and a large number of datasets, which are not different from one another. The proposed model is trained by training multiple layers. The proposed model was compared on several benchmark datasets, showing higher class accuracies and comparable results compared to the other models. The results presented in this paper show that the proposed model can be used for data segmentation, as it has better representation, the semantic relationship and the ability to represent the whole image in a simpler form of a single image. The method is tested on a large dataset of images from North-Indian area. We conducted experiments on three datasets that used different datasets and analyzed the performance of the model compared to the other two methods. We have found that deep CNN has a better accuracies compared to the other two methods while being significantly more accurate than other CNNs.

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SVDD: Single-view Video Dense Deformation Variation Based on Histogram and Line Filtering

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    Pseudo-Boolean isbn estimation using deep learning with machine learningIn this paper, a novel deep learning based image segmentation method is proposed that relies on a hierarchical classification scheme which is implemented in Convolutional Neural Network (CNN). Extensive studies were conducted using different datasets, including one with a small number of segmentations, and a large number of datasets, which are not different from one another. The proposed model is trained by training multiple layers. The proposed model was compared on several benchmark datasets, showing higher class accuracies and comparable results compared to the other models. The results presented in this paper show that the proposed model can be used for data segmentation, as it has better representation, the semantic relationship and the ability to represent the whole image in a simpler form of a single image. The method is tested on a large dataset of images from North-Indian area. We conducted experiments on three datasets that used different datasets and analyzed the performance of the model compared to the other two methods. We have found that deep CNN has a better accuracies compared to the other two methods while being significantly more accurate than other CNNs.


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