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Citations since 2016
6 Research Items
Deep learning-based image classification model learns from the fixed and specific training dataset. For the generalization and adaptation of human learning behaviour, some of these models adapted incremental learning to enhance the learning and knowledge from updated and incremented dataset. An incremented dataset can be in form of increment of exa...
Low resolution images contain less visual information, so classifications of these images are difficult. For overcoming this problem, cascade CNN framework for low resolution image classification is proposed. In this framework, super resolution CNN (SRCNN) enlarges low resolution image into super resolution image. The convolutional features of thes...
Several fusion methods are used to integrate different trained classification models to generalize classification over the distributed dataset. This paper proposes a Meta fusion approach for integrating trained heterogeneous image classifier using classifier selection. In this approach, classifier selector selects a trained classifier from the set...
Image quality is affected by different types of quality factors (such as resolution, noise, contrast, blur, compression). Resolution of the image affects the visual information of the image. Image with higher resolution contains more visual information details while an image with lower resolution contains less visual details. Convolutional neural n...
To leverage feature representation of CNN and fast classification learning of ELM, Ensemble of Hybrid CNN-ELM model is proposed for image classification. In this model, image representation features are learned by Convolutional Neural Network (CNN) and fed to Extreme Learning Machine (ELM) for classification. Three hybrid CNN-ELMs are ensemble in p...
For early diagnosis of retinal disease, Blood vessel segmentation of retinal images play important role. For blood vessel segmentation of retinal images, we propose a supervised neighbouring pixel based ELM approach. In NP based ELM approach, maximum vessel enhanced image is obtained by contrast enhance and edge detection operation on green channel...
Suppose a time series data is generated with random noise. Due to random behavior of noise, it is very complex to model these noisy data. Which and how DL denoising methods handle this problem?