A new hierarchical method for image segmentation and inpainting using Mumford-Shah model

Image segmentation is a popular topic in computer vision and image processing. As a region-based (global) approach, the Mumford and Shah (MS) model is a powerful and robust segmentation technique as compared to edge-based (local) methods. However, there are some difficulties with the MS model. One difficulty is the detection of roof edges. In this thesis, we first modify the MS model to include second order derivative term and use linear approximation to implement the solution. In this way, we can detect not only step edges but also creases and roof edges. The most important difficulty of MS model is that the segmentation results depend on the initial curves. To overcome this problem, we present in this thesis a hierarchical strategy that takes into account both the local information at the pixel level and the global information of the MS model. With this hierarchical segmentation scheme, we can segment an image into regions until each region is smooth enough and need no additional segmentation. Compared with previous works, our approach can automatically detect both main structure and details in an image with multi-level-set functions, and it can stop automatically when the boundaries are detected. In our approach, the final segmentation does not depend on the initial condition. Many experimental results indicate that our approach is effective in many applications. Especially, we apply the new approach to the image inpainting problem. Compared with previous work, because the new approach can detect all the edges in an image, it can preserve more edges and details in the inpainting process

Image Segmentation and Its Applications Based on the Mumford-Shah Model

Image segmentation is an important topic in computer vision and image processing. As a region-based (global) approach, the Mumford and Shah (MS) model is a powerful and robust segmentation technique as compared to edge-based (local) methods. In this thesis we apply the MS model to two interesting problems: image inpainting and text line detection. We further extend it by proposing a new image segmentation model to overcome some of the difficulties of the original model. As a demonstration of the new model, we apply it to the segmentation of retinal images. The results are better than the state-of-the-art approaches. In image inpainting, the MS model is used to detect and estimate the object boundaries inside the inpainting areas. These boundaries are preserved in the inpainting results. We present a hierarchical segmentation method to detect boundaries of both the main structure and the details. The inpainting result can preserve detailed edges. In text line detection, we use a combination of Gaussian blurring, the MS model, and morphing method. Different from other general text image detection approaches, our method segments text documents without any knowledge of the written texts, so it can detect handwriting text lines of different languages. It can also handle different gaps and overlaps among the text lines. Although the MS model has been used successfully in many applications, its implementation has always been based on some forms of approximation. These approximations are either inefficient computationally or applicable only to some special cases. Our new model consists of only one variable, the segmentation curve, therefore the computation is very efficient. Furthermore, no approximation is required, hence the method can segment objects with complicated intensity distribution. The new model can detect both step and roof edges, and can use different filters to detect objects of different levels of intensity. To show the advantages of the new model, we use a combination of the new model and Gabor filter to detect blood vessels in retinal images. This new model can detect objects with complicated image intensity distribution, and can handle non-uniform illumination cases effectively

ContraNorm: A Contrastive Learning Perspective on Oversmoothing and Beyond

Oversmoothing is a common phenomenon in a wide range of Graph Neural Networks (GNNs) and Transformers, where performance worsens as the number of layers increases. Instead of characterizing oversmoothing from the view of complete collapse in which representations converge to a single point, we dive into a more general perspective of dimensional collapse in which representations lie in a narrow cone. Accordingly, inspired by the effectiveness of contrastive learning in preventing dimensional collapse, we propose a novel normalization layer called ContraNorm. Intuitively, ContraNorm implicitly shatters representations in the embedding space, leading to a more uniform distribution and a slighter dimensional collapse. On the theoretical analysis, we prove that ContraNorm can alleviate both complete collapse and dimensional collapse under certain conditions. Our proposed normalization layer can be easily integrated into GNNs and Transformers with negligible parameter overhead. Experiments on various real-world datasets demonstrate the effectiveness of our proposed ContraNorm. Our implementation is available at https://github.com/PKU-ML/ContraNorm.Comment: ICLR 202

Collaboration in distributed injection mold design: Process analysis and system implementation

Master'sMASTER OF ENGINEERIN

Perspectives on studies on soil carbon stocks and the carbon sequestration potential of China

Peer reviewedPostprin

Scaling Relationships of Gas-Solid Spouted Beds

An additional parameter, the particle-particle coefficient of restitution was added to the scaling relationship of spouted beds proposed by He et al. (4) by granular kinetic theory analysis of the particle movements in both spout and annulus regions. The experimental verification was conducted in two spouted beds with 80 and 120 mm ID. It has been found that good similarity could not be obtained when the coefficients of restitution were not matched

Vision-based displacement test method for high-rise building shaking table test

The vision-based displacement measurement system was developed, which using digital video camcorder to test the deformation of high-rise structures. It is more economical than contact and contact-less displacement sensors. A series of tests were conducted to investigate the precision, serviceability, and stability of the vision-based displacement method. The results show that, the proposed method can effectively test the dynamic displacement, moreover, the method can be effectively applied to test the displacement caused by vibration which contains various frequency components. Based on the system, the deformation of high-rise building structure was tested. The results show that, the displacement obtained by vision-based can illustrate the free-vibration characteristics of structure well, meanwhile, this method can test bidirectional displacement in shaking table test and practical engineering

The Binding Mechanism Between Inositol Phosphate (InsP) and the Jasmonate Receptor Complex: A Computational Study

Jasmonates are critical plant hormones, mediating stress response in plants and regulating plant growth and development. The jasmonate receptor is a multi-component complex, composed of Arabidopsis SKP-LIKE PROTEIN1 (ASK1), CORONATINE INSENSITIVE 1 (COI1), inositol phosphate (InsP), and jasmonate ZIM-domain protein (JAZ). COI1 acts as multi-component signaling hub that binds with each component. InsP is suggested to play important roles in the hormone perception. How InsP binds with COI1 and the structural changes in COI1 upon binding with InsP, JA-Ile, and JAZ are not well understood. In this study, we integrated multiple computational methods, such as molecular docking, molecular dynamics simulations, residue interaction network analysis and binding free energy calculation, to explore the effect of InsP on the dynamic behavior of COI1 and the recognition mechanism of each component of the jasmonate receptor complex. We found that upon binding with InsP, JA-Ile, and JAZ1, the structure of COI1 becomes more compact. The binding of InsP with COI1 stabilizes the conformation of COI1 and promotes the binding between JA-Ile or JAZ1 and COI1. Analysis of the network parameters led to the identification of some hub nodes in this network, including Met88, His118, Arg120, Arg121, Arg346, Tyr382, Arg409, Trp467, and Lys492. The structural and dynamic details will be helpful for understanding the recognition mechanism of each component and the discovery and design of novel jasmonate signaling pathway modulators