Remote Sensing Image Ship Matching Utilising Line Features for Resource-Limited Satellites.

The existing image matching methods for remote sensing scenes are usually based on local features. The most common local features like SIFT can be used to extract point features. However, this kind of methods may extract too many keypoints on the background, resulting in low attention to the main object in a single image, increasing resource consumption and limiting their performance. To address this issue, we propose a method that could be implemented well on resource-limited satellites for remote sensing images ship matching by leveraging line features. A keypoint extraction strategy called line feature based keypoint detection (LFKD) is designed using line features to choose and filter keypoints. It can strengthen the features at corners and edges of objects and also can significantly reduce the number of keypoints that cause false matches. We also present an end-to-end matching process dependent on a new crop patching function, which helps to reduce complexity. The matching accuracy achieved by the proposed method reaches 0.972 with only 313 M memory and 138 ms testing time. Compared to the state-of-the-art methods in remote sensing scenes in extensive experiments, our keypoint extraction method can be combined with all existing CNN models that can obtain descriptors, and also improve the matching accuracy. The results show that our method can achieve ∼50% test speed boost and ∼30% memory saving in our created dataset and public datasets.

Data Augmentation in Classification and Segmentation: A Survey and New Strategies.

In the past decade, deep neural networks, particularly convolutional neural networks, have revolutionised computer vision. However, all deep learning models may require a large amount of data so as to achieve satisfying results. Unfortunately, the availability of sufficient amounts of data for real-world problems is not always possible, and it is well recognised that a paucity of data easily results in overfitting. This issue may be addressed through several approaches, one of which is data augmentation. In this paper, we survey the existing data augmentation techniques in computer vision tasks, including segmentation and classification, and suggest new strategies. In particular, we introduce a way of implementing data augmentation by using local information in images. We propose a parameter-free and easy to implement strategy, the random local rotation strategy, which involves randomly selecting the location and size of circular regions in the image and rotating them with random angles. It can be used as an alternative to the traditional rotation strategy, which generally suffers from irregular image boundaries. It can also complement other techniques in data augmentation. Extensive experimental results and comparisons demonstrated that the new strategy consistently outperformed its traditional counterparts in, for example, image classification.

Electron tomography of prolamellar bodies and their transformation into grana thylakoids in cryofixed Arabidopsis cotyledons.

The para-crystalline structures of prolamellar bodies (PLBs) and light-induced etioplast-to-chloroplast transformation have been investigated via electron microscopy. However, such studies suffer from chemical fixation artifacts and limited volumes of 3D reconstruction. Here, we examined Arabidopsis thaliana cotyledon cells by electron tomography (ET) to visualize etioplasts and their conversion into chloroplasts. We employed scanning transmission ET to image large volumes and high-pressure freezing to improve sample preservation. PLB tubules were arranged in a zinc blende-type lattice-like carbon atoms in diamonds. Within 2 h after illumination, the lattice collapsed from the PLB exterior and the disorganized tubules merged to form thylakoid sheets (pre-granal thylakoids), which folded and overlapped with each other to create grana stacks. Since the nascent pre-granal thylakoids contained curved membranes in their tips, we examined the expression and localization of CURT1 (CURVATURE THYLAKOID1) proteins. CURT1A transcripts were most abundant in de-etiolating cotyledon samples, and CURT1A was concentrated at the PLB periphery. In curt1a etioplasts, PLB-associated thylakoids were swollen and failed to form grana stacks. In contrast, PLBs had cracks in their lattices in curt1c etioplasts. Our data provide evidence that CURT1A is required for pre-granal thylakoid assembly from PLB tubules during de-etiolation, while CURT1C contributes to cubic crystal growth in the dark.

Electron Tomography Analysis of Thylakoid Assembly and Fission in Chloroplasts of a Single-Cell C4 plant, Bienertia sinuspersici.

Bienertia sinuspersici is a single-cell C4 plant species of which chlorenchyma cells have two distinct groups of chloroplasts spatially segregated in the cytoplasm. The central vacuole encloses most chloroplasts at the cell center and confines the rest of the chloroplasts near the plasma membrane. Young chlorenchyma cells, however, do not have large vacuoles and their chloroplasts are homogenous. Therefore, maturing Bienertia chlorenchyma cells provide a unique opportunity to investigate chloroplast proliferation in the central cluster and the remodeling of chloroplasts that have been displaced by the vacuole to the cell periphery. Chloroplast numbers and sizes increased, more notably, during later stages of maturation than the early stages. Electron tomography analyses indicated that chloroplast enlargement is sustained by thylakoid growth and that invaginations from the inner envelope membrane contributed to thylakoid assembly. Grana stacks acquired more layers, differentiating them from stroma thylakoids as central chloroplasts matured. In peripheral chloroplasts, however, grana stacks stretched out to a degree that the distinction between grana stacks and stroma thylakoids was obscured. In central chloroplasts undergoing division, thylakoids inside the cleavage furrow were kinked and severed. Grana stacks in the division zone were disrupted, and large complexes in their membranes were dislocated, suggesting the existence of a thylakoid fission machinery.

Applying physical science techniques and CERN technology to an unsolved problem in radiation treatment for cancer: the multidisciplinary 'VoxTox' research programme.

The VoxTox research programme has applied expertise from the physical sciences to the problem of radiotherapy toxicity, bringing together expertise from engineering, mathematics, high energy physics (including the Large Hadron Collider), medical physics and radiation oncology. In our initial cohort of 109 men treated with curative radiotherapy for prostate cancer, daily image guidance computed tomography (CT) scans have been used to calculate delivered dose to the rectum, as distinct from planned dose, using an automated approach. Clinical toxicity data have been collected, allowing us to address the hypothesis that delivered dose provides a better predictor of toxicity than planned dose.