Multi-modality is widely used in medical imaging, because it can provide multiinformation about a target (tumor, organ or tissue). Segmentation using multimodality consists of fusing multi-information to improve
While GP directly works on the optimization of syntactic graphs, CGP considers a specific encoding of this graph within an integer-based genotype, also called genome (Fig. 1a, see Material and Methods). In this paper, we developed a modular framework called Kartezio to expand CGP into IS ...
Ultraviolet colouration is thought to be an important form of signalling in many bird species, yet broad insights regarding the prevalence of ultraviolet plumage colouration and the factors promoting its evolution are currently lacking. In this paper, we develop a image segmentation pipeline based on ...
Michael Cox and David Ellsworth first coined the term “big data” in 1997 to describe such a scenario.24 Afterward, big data gets more complex and challenging due not only to its volume but also to field-specific attributes. In this context, Harfouche et al.25 expanded the dimensions of ...
Noe that the shape prior p(x) is assumed to be a mixture of Gaussian functions given in eq. 1. We will formulate the pixel likelihood estimation problem by us- ing a graphical model framework, and derive the energy function for the image segmentaion. 3.1 Graphical Model We first describe...
Additionally, they did not fully utilize pixel and channel information, which usually resulted in problems such as residual rain streaks and inadequate recovery of texture details. To address these issues, we propose a novel single-image deraining network called Residual Contextual Hourglass Network (...
Implementation of the paperDeepLSD: Line Segment Detection and Refinement with Deep Image Gradients, accepted at CVPR 2023.DeepLSD is a generic line detector that combines the robustness of deep learning with the accuracy of handcrafted detectors. It can be used to extractgeneric line segments from...
It makes most of existing image segmentation algorithms, such as Mean Shift, very time-consuming and hard to be used in practice. Mean Shift is a non-parametric clustering approach which has no assumptions on the shape of the distribution and the number of clusters. So Mean Shift may achieve...
Fig. 2. Formation of text homogeneity pattern in a local neighborhood of a CC: (a) heterogeneous neighborhood for a non-text CCi, (b) homogeneous neighborhood for a text CCi. Let G(V, E) be an undirected graph where the vertices v∈ V are indexed by the CCs in a document image and...
forSemantic Segmentation: Both multi-task learning and auxiliary learning methods are explored inmedical image segmentation. The subtle difference between these approaches lies in the presence orabsenceof a secondary task during inference time. However, these two terms are often used interchangeably in ...