Rapid Reconstruction of 3D Structural Model Based on Interactive Graph Cuts

The image-based 3D reconstruction technique has been applied in many scenarios of civil engineering, such as earthquake prevention and disaster reduction, construction monitoring, and intelligent city construction. However, the traditional technique is time-consuming, and the modeling efficiency has become a bottleneck limiting its application in emergency scenarios. In this paper, a rapid reconstruction method is proposed which combines the traditional image-based 3D reconstruction technique and an interactive graph cuts algorithm. Firstly, a sequence of images is collected around the target structure. Then, the images are preprocessed using the interactive iterative graph cuts algorithm to extract the target from each image. Finally, the resulting sequence of images is used to perform the 3D reconstruction. During the preprocessing, only a few images require manual intervention while the rest can be processed automatically. To verify the modeling accuracy of the proposed method, a column that has been destroyed is selected as a target for 3D reconstruction. The results show that compared with the traditional method, the modeling efficiency of the fast reconstruction method is doubled. In addition, the modeling accuracy is 97.65%, which is comparable to the modeling accuracy of the traditional method (97.73%); as well, by comparing the point clouds, the alignment between the two models is tremendously close, with tiny difference. The proposed rapid reconstruction method can be applied in emergency scenarios, such as rapid assessment in post-disaster situations.

Multiway p-spectral graph cuts on Grassmann manifolds

Nonlinear reformulations of the spectral clustering method have gained a lot of recent attention due to their increased numerical benefits and their solid mathematical background. We present a novel direct multiway spectral clustering algorithm in the p-norm, for $$p\in (1,2]$$ p ∈ ( 1 , 2 ] . The problem of computing multiple eigenvectors of the graph p-Laplacian, a nonlinear generalization of the standard graph Laplacian, is recasted as an unconstrained minimization problem on a Grassmann manifold. The value of p is reduced in a pseudocontinuous manner, promoting sparser solution vectors that correspond to optimal graph cuts as p approaches one. Monitoring the monotonic decrease of the balanced graph cuts guarantees that we obtain the best available solution from the p-levels considered. We demonstrate the effectiveness and accuracy of our algorithm in various artificial test-cases. Our numerical examples and comparative results with various state-of-the-art clustering methods indicate that the proposed method obtains high quality clusters both in terms of balanced graph cut metrics and in terms of the accuracy of the labelling assignment. Furthermore, we conduct studies for the classification of facial images and handwritten characters to demonstrate the applicability in real-world datasets.

Improving U-Net architecture and graph cuts optimization to classify arterioles and venules in retina fundus images

The central retinal artery and its branches supply blood to the inner retina. Vascular manifestations in the retina indirectly reflect the vascular changes and damage in organs such as the heart, kidneys, and brain because of the similar vascular structure of these organs. The diabetic retinopathy and risk of stroke are caused by increased venular caliber. The degrees of these diseases depend on the changes of arterioles and venules. The ratio between the calibers of arterioles and venules (AVR) is various. AVR is considered as the useful diagnostic indicator of different associated health problems. However, the task is not easy because of the lack of information of the features being used to classify the retinal vessels as arterioles and venules. This paper proposed a method to classify the retinal vessels into the arterioles and venules based on improving U-Net architecture and graph cuts. The accuracy of the proposed method is about 97.6%. The results of the proposed method are better than the other methods in RITE dataset and AVRDB dataset.

Identifying functional modules using energy minimization with graph cuts

Aims: The aim of this article was to find functional (or disease-relevant) modules using gene expression data. Background: Biotechnological developments are leading to a rapid increase in the volume of transcriptome data and thus driving the growth of interactome data. This has made it possible to perform transcriptomic analysis by integrating interactome data. Considering that genes do not exist nor operate in isolation, and instead participate in biological networks, interactomics is equally important to expression profiles. Objective: We constructed a network-based method based on gene expression data in order to identify functional (or disease-relevant) modules. Method: We used the energy minimization with graph cuts method by integrating gene interaction networks under the assumption of the ‘guilt by association’ principle. Result: Our method performs well in an independent simulation experiment and has the ability to identify strongly disease-relevant modules in real experiments. Our method is able to find important functional modules associated with two subtypes of lymphoma in a lymphoma microarray dataset. Moreover, the method can identify the biological subnetworks and most of the genes associated with Duchenne muscular dystrophy. Conclusion: We successfully adapted the energy minimization with the graph cuts method to identify functionally important genes from genomic data by integrating gene interaction networks.