For a sequence (cn) of complex numbers we consider the quadratic polynomials fcn(z) := z 2 + cn and the sequence (Fn) of iterates Fn := fcn ◦ · · · ◦ fc1 . The Fatou set F(cn) is by definition the set of all z ∈ Ĉ such that (Fn) is normal in some neighbourhood of z, while the complement of F(cn) is called the Julia set J(cn). The aim of this article is to study geometric properties, Lebesgue me...

Low-shot learning methods for image classification support learning from sparse data. We extend these techniques to support dense semantic image segmentation. Specifically, we train a network that, given a small set of annotated images, produces parameters for a Fully Convolutional Network (FCN). We use this FCN to perform dense pixel-level prediction on a test image for the new semantic class....

DIFFUSE (Distributed Firewall and Flowshaper Using Statistical Evidence) is a network prioritisation system which implements Machine-Learning (ML) based techniques to classify and subsequently prioritise flows. Inefficient testing methodologies impacts on future development, making it difficult to upgrade and expand. We have developed a Fake Classifier Node (FCN) that enables manual creation of...

Artificial intelligence is making great changes in academy and industry with the fast development of deep learning, which is a branch of machine learning and statistical learning. Fully convolutional network [1] is the standard model for semantic segmentation. Conditional random fields coded as CNN [2] or RNN [3] and connected with FCN has been successfully applied in object detection [4]. In t...

In civil engineering, image recognition technology in artificial intelligence is widely used structural damage detection. Traditional crack monitoring based on concrete images uses processing, which requires high preprocessing techniques, and the results of detection are vulnerable to factors, such as lighting noise. this study, full convolutional neural networks FCN-8s, FCN-16s, FCN-32s applie...

Co-saliency detection aims to detect common salient objects from a group of relevant images. Some attempts have been made with the Fully Convolutional Network (FCN) framework and achieve satisfactory results. However, due stacking convolution layers pooling operation, boundary details tend be lost. In addition, existing models often utilize extracted features without discrimination, leading red...

Abstract Colonoscopy is widely recognised as the gold standard procedure for early detection of colorectal cancer (CRC). Segmentation valuable two significant clinical applications, namely lesion and classification, providing means to improve accuracy robustness. The manual segmentation polyps in colonoscopy images time-consuming. As a result, use deep learning (DL) automation polyp has become ...

Estimating virtual CT(vCT) image from MRI data is in crucial need for medical application due to the relatively high dose of radiation exposure in CT scan and redundant workflow of both MR and CT. Among the existing work, the fully convolutional neural network(FCN) shows its superiority in generating vCT of high fidelity which merits further investigation. However, the most widely used evaluati...

We make use of a fully convolutional network (FCN) [4] as a baseline model for parsing the scene images. We follow Chen et al. [1] and use the vanilla ResNet-101 [3] to initialize the FCN model. Preserving high spatial resolution of feature maps is very important for accurately segmenting small objects. Therefore, we disable the last down-sampling layer by setting its stride as 1. This increase...