Huisi Wu

Bio: Huisi Wu is an academic researcher from Shenzhen University. The author has contributed to research in topics: Computer science & Segmentation. The author has an hindex of 10, co-authored 52 publications receiving 327 citations. Previous affiliations of Huisi Wu include The Chinese University of Hong Kong.

Resizing by symmetry-summarization

Abstract: Image resizing can be achieved more effectively if we have a better understanding of the image semantics. In this paper, we analyze the translational symmetry, which exists in many real-world images. By detecting the symmetric lattice in an image, we can summarize, instead of only distorting or cropping, the image content. This opens a new space for image resizing that allows us to manipulate, not only image pixels, but also the semantic cells in the lattice. As a general image contains both symmetry & non-symmetry regions and their natures are different, we propose to resize symmetry regions by summarization and non-symmetry region by warping. The difference in resizing strategy induces discontinuity at their shared boundary. We demonstrate how to reduce the artifact. To achieve practical resizing applications for general images, we developed a fast symmetry detection method that can detect multiple disjoint symmetry regions, even when the lattices are curved and perspectively viewed. Comparisons to state-of-the-art resizing techniques and a user study were conducted to validate the proposed method. Convincing visual results are shown to demonstrate its effectiveness.

Tensor Voting Guided Mesh Denoising

Abstract: Mesh denoising is imperative for improving imperfect surfaces acquired by scanning devices The main challenge is to faithfully retain geometric features and avoid introducing additional artifacts when removing noise Unlike the existing mesh denoising techniques that focus only on either the first-order features or high-order differential properties, our approach exploits the synergy when facet normals and quadric surfaces are integrated to recover a piecewise smooth surface In specific, we vote on surface normal tensors from robust statistics to guide the creation of consistent subneighborhoods subsequently used by moving least squares (MLS) This voting naturally leads to a conceptually simple way that gives a unified mesh-denoising framework for not only handling noise but also enabling the recovering of surfaces with both sharp and small-scale features The effectiveness of our framework stems from: 1) the multiscale tensor voting that avoids the influence from noise; 2) the effective energy minimization strategy to searching the consistent subneighborhoods; and 3) the piecewise MLS that fully prevents the side effects from different subneighborhoods during surface fitting Our framework is direct, practical, and easy to understand Comparisons with the state-of-the-art methods demonstrate its outstanding performance on feature preservation and artifact suppression

Automated Skin Lesion Segmentation Via an Adaptive Dual Attention Module

Abstract: We present a convolutional neural network (CNN) equipped with a novel and efficient adaptive dual attention module (ADAM) for automated skin lesion segmentation from dermoscopic images, which is an essential yet challenging step for the development of a computer-assisted skin disease diagnosis system. The proposed ADAM has three compelling characteristics. First, we integrate two global context modeling mechanisms into the ADAM, one aiming at capturing the boundary continuity of skin lesion by global average pooling while the other dealing with the shape irregularity by pixel-wise correlation. In this regard, our network, thanks to the proposed ADAM, is capable of extracting more comprehensive and discriminative features for recognizing the boundary of skin lesions. Second, the proposed ADAM supports multi-scale resolution fusion, and hence can capture multi-scale features to further improve the segmentation accuracy. Third, as we harness a spatial information weighting method in the proposed network, our method can reduce a lot of redundancies compared with traditional CNNs. The proposed network is implemented based on a dual encoder architecture, which is able to enlarge the receptive field without greatly increasing the network parameters. In addition, we assign different dilation rates to different ADAMs so that it can adaptively capture distinguishing features according to the size of a lesion. We extensively evaluate the proposed method on both ISBI2017 and ISIC2018 datasets and the experimental results demonstrate that, without using network ensemble schemes, our method is capable of achieving better segmentation performance than state-of-the-art deep learning models, particularly those equipped with attention mechanisms.

PolypSeg: An Efficient Context-Aware Network for Polyp Segmentation from Colonoscopy Videos

Abstract: Polyp segmentation from colonoscopy videos is of great importance for improving the quantitative analysis of colon cancer. However, it remains a challenging task due to (1) the large size and shape variation of polyps, (2) the low contrast between polyps and background, and (3) the inherent real-time requirement of this application, where the segmentation results should be immediately presented to the doctors during the colonoscopy procedures for their prompt decision and action. It is difficult to develop a model with powerful representation capability, yielding satisfactory segmentation results in a real-time manner. We propose a novel and efficient context-aware network, named PolypSeg, in order to comprehensively address these challenges. The proposed PolypSeg consists of two key components: adaptive scale context module (ASCM) and semantic global context module (SGCM). The ASCM aggregates the multi-scale context information and takes advantage of an improved attention mechanism to make the network focus on the target regions and hence improve the feature representation. The SGCM enriches the semantic information and excludes the background noise in the low-level features, which enhances the feature fusion between high-level and low-level features. In addition, we introduce the deep separable convolution into our PolypSeg to replace the traditional convolution operations in order to reduce parameters and computational costs to make the PolypSeg run in a real-time manner. We conducted extensive experiments on a famous public available dataset for polyp segmentation task. Experimental results demonstrate that the proposed PolypSeg achieves much better segmentation results than state-of-the-art methods with a much faster speed.

Global contrast based salient region detection

Abstract: Automatic estimation of salient object regions across images, without any prior assumption or knowledge of the contents of the corresponding scenes, enhances many computer vision and computer graphics applications. We introduce a regional contrast based salient object detection algorithm, which simultaneously evaluates global contrast differences and spatial weighted coherence scores. The proposed algorithm is simple, efficient, naturally multi-scale, and produces full-resolution, high-quality saliency maps. These saliency maps are further used to initialize a novel iterative version of GrabCut, namely SaliencyCut, for high quality unsupervised salient object segmentation. We extensively evaluated our algorithm using traditional salient object detection datasets, as well as a more challenging Internet image dataset. Our experimental results demonstrate that our algorithm consistently outperforms 15 existing salient object detection and segmentation methods, yielding higher precision and better recall rates. We also show that our algorithm can be used to efficiently extract salient object masks from Internet images, enabling effective sketch-based image retrieval (SBIR) via simple shape comparisons. Despite such noisy internet images, where the saliency regions are ambiguous, our saliency guided image retrieval achieves a superior retrieval rate compared with state-of-the-art SBIR methods, and additionally provides important target object region information.

Regularization Of Inverse Problems

Abstract: Thank you for downloading regularization of inverse problems. Maybe you have knowledge that, people have search hundreds times for their favorite novels like this regularization of inverse problems, but end up in malicious downloads. Rather than reading a good book with a cup of tea in the afternoon, instead they juggled with some infectious bugs inside their computer. regularization of inverse problems is available in our book collection an online access to it is set as public so you can download it instantly. Our book servers spans in multiple locations, allowing you to get the most less latency time to download any of our books like this one. Kindly say, the regularization of inverse problems is universally compatible with any devices to read.

Design Of Analog Cmos Integrated Circuits

Abstract: Thank you very much for downloading design of analog cmos integrated circuits. Maybe you have knowledge that, people have look hundreds times for their favorite novels like this design of analog cmos integrated circuits, but end up in malicious downloads. Rather than reading a good book with a cup of coffee in the afternoon, instead they cope with some malicious virus inside their laptop. design of analog cmos integrated circuits is available in our book collection an online access to it is set as public so you can download it instantly. Our digital library saves in multiple countries, allowing you to get the most less latency time to download any of our books like this one. Merely said, the design of analog cmos integrated circuits is universally compatible with any devices to read.

U-Net and Its Variants for Medical Image Segmentation: A Review of Theory and Applications

Abstract: U-net is an image segmentation technique developed primarily for image segmentation tasks. These traits provide U-net with a high utility within the medical imaging community and have resulted in extensive adoption of U-net as the primary tool for segmentation tasks in medical imaging. The success of U-net is evident in its widespread use in nearly all major image modalities, from CT scans and MRI to X-rays and microscopy. Furthermore, while U-net is largely a segmentation tool, there have been instances of the use of U-net in other applications. Given that U-net’s potential is still increasing, this narrative literature review examines the numerous developments and breakthroughs in the U-net architecture and provides observations on recent trends. We also discuss the many innovations that have advanced in deep learning and discuss how these tools facilitate U-net. In addition, we review the different image modalities and application areas that have been enhanced by U-net.