Nov 3, 2024 · In this paper, we present an end-to-end variational image segmentation network framework based on the unfolding of the Potts model named PottsNN. It is designed under the guidance of the iterative process of traditional Potts models, which provide the mathematical interpretation of the network.

Jul 18, 2023 · In this paper, we study the encoder-decoder-based network architecture from the algorithmic perspective and provide a mathematical explanation. We use the two-phase Potts model for image segmentation as an example for our explanations.

In this paper, we present an end-to-end variational image segmentation network framework based on the unfolding of the Potts model named PottsNN. It is designed under the guidance of the itera-tive process of traditional Potts models, which provide the mathematical interpretation of …

In this paper, we study the encoder-decoder-based network architecture from the algorithmic perspective and pro- vide a mathematical explanation. We use the two-phase Potts model for image segmentation as an example for our explanations. We associate the segmentation problem with a control problem in the continuous setting.

Optimal Architecture: Identify key architectural elements and parameters for each Deep Learning model version to guide the development of generalized and sector-specific image...

To achieve more precise multi-target classification in a short time, a method for combining discrete non-local theory with traditional variable fraction Potts models is presented in this paper. The nonlocal operator makes better use of the information in a certain region centered on …

“In the absence of a more intelligent strategy, any global image classification approach can be converted into a localization approach by using a sliding-window search.” Map each grid cell in the input window to a histogram counting the gradients per orientation. How to compute a decision for each subwindow? Classifier Construction: Many Choices...

In this review, a detailed analysis of the various deep learning models, comparative analysis and their frameworks, as well as model descriptions have been presented. Convolutional Neural Networks, have been found as the standard method for object recognition, computer vision, image classification, and other applications.

By surveying the current landscape of deep learning for image classification, this essay aims to provide readers with a comprehensive understanding of the state-of-the-art methodologies, challenges, and potential breakthroughs in this dynamic and rapidly evolving field.

Jul 3, 2024 · Pre-trained models have revolutionised image classification by providing powerful, ready-to-use solutions that save time and resources. Models like VGG, ResNet, and Inception have set benchmarks in accuracy and efficiency, finding applications in diverse fields.