Wuhan University

About: Wuhan University is a education organization based out in Wuhan, China. It is known for research contribution in the topics: Computer science & Population. The organization has 92849 authors who have published 92882 publications receiving 1691049 citations. The organization is also known as: WHU & Wuhan College.

Morphological Building/Shadow Index for Building Extraction From High-Resolution Imagery Over Urban Areas

Abstract: Morphological building index (MBI) is a recently developed approach for automatic indication of buildings in high-resolution imagery. However, MBI is subject to commission errors due to the similar characteristics between buildings, bare soil and roads. Furthermore, omission errors occur in dark and heterogeneous roofs. In this study, a systematic framework for building extraction from high-resolution imagery is proposed, aiming to alleviate both commission and omission errors for the original MBI algorithm. The improvements include three aspects: 1) a morphological shadow index (MSI) is proposed to detect shadows that are used as a spatial constraint of buildings; 2) a dual-threshold filtering is proposed to integrate the information of MBI and MSI; 3) the proposed framework is implemented in an object-based environment, where a geometrical index and a vegetation index are then used to remove noise from narrow roads and bright vegetation. The proposed framework was validated on an Ikonos image of Washington DC Mall with 1-m resolution and an 8-channel WorldView-2 image of Hangzhou, east of China, with 2-m resolution. By comparison with the ground truth references, it was shown that our method achieved over 90% overall accuracy for discrimination between buildings and backgrounds for both datasets. In the comparative study, it was revealed that the proposed method improved the original MBI significantly. Furthermore, the proposed method was more accurate than the support vector machine interpretation with the differential morphological profiles (DMP) and multiscale urban complexity index (MUCI).

PraNet: Parallel Reverse Attention Network for Polyp Segmentation

Abstract: Colonoscopy is an effective technique for detecting colorectal polyps, which are highly related to colorectal cancer. In clinical practice, segmenting polyps from colonoscopy images is of great importance since it provides valuable information for diagnosis and surgery. However, accurate polyp segmentation is a challenging task, for two major reasons: (i) the same type of polyps has a diversity of size, color and texture; and (ii) the boundary between a polyp and its surrounding mucosa is not sharp. To address these challenges, we propose a parallel reverse attention network (PraNet) for accurate polyp segmentation in colonoscopy images. Specifically, we first aggregate the features in high-level layers using a parallel partial decoder (PPD). Based on the combined feature, we then generate a global map as the initial guidance area for the following components. In addition, we mine the boundary cues using a reverse attention (RA) module, which is able to establish the relationship between areas and boundary cues. Thanks to the recurrent cooperation mechanism between areas and boundaries, our PraNet is capable of calibrating any misaligned predictions, improving the segmentation accuracy. Quantitative and qualitative evaluations on five challenging datasets across six metrics show that our PraNet improves the segmentation accuracy significantly, and presents a number of advantages in terms of generalizability, and real-time segmentation efficiency.

Cellulose Aerogels from Aqueous Alkali Hydroxide–Urea Solution

Abstract: Highly porous and strong cellulose aerogels were prepared by gelation of cellulose from aqueous alkali hydroxide/urea solution, followed by drying with supercritical CO2. Their morphology, pore structure, and physical properties were characterized by scanning and transmission electron microscopy, X-ray diffraction, nitrogen adsorption measurements, UV/Vis spectrometry, and tensile tests. The cellulose hydrogel was composed of interconnected about 20 nm wide. By using supercritical CO2 drying, the network structure in the hydrogel was well preserved in the aerogel. The results are preliminary but demonstrate the ability of this method to give cellulose aerogels of large surface areas (400-500 m2 g(-1)) which may be useful as adsorbents, heat/sound insulators, filters, catalyst supports, or carbon aerogel precursors.