Relating vanishing points to catadioptric camera calibration

This paper presents the analysis and derivation of the geometric relation between vanishing points and camera parameters of central catadioptric camera systems. These vanishing points correspond to the three mutually orthogonal directions of 3D real world coordinate system (i.e. X, Y and Z axes). Compared to vanishing points (VPs) in the perspective projection, the advantages of VPs under central catadioptric projection are that there are normally two vanishing points for each set of parallel lines, since lines are projected to conics in the catadioptric image plane. Also, their vanishing points are usually located inside the image frame. We show that knowledge of the VPs corresponding to XYZ axes from a single image can lead to simple derivation of both intrinsic and extrinsic parameters of the central catadioptric system. This derived novel theory is demonstrated and tested on both synthetic and real data with respect to noise sensitivity

DualAttNet: Synergistic fusion of image-level and fine-grained disease attention for multi-label lesion detection in chest X-rays

Chest radiographs are the most commonly performed radiological examinations for lesion detection. Recent advances in deep learning have led to encouraging results in various thoracic disease detection tasks. Particularly, the architecture with feature pyramid network performs the ability to recognise targets with different sizes. However, such networks are difficult to focus on lesion regions in chest X-rays due to their high resemblance in vision. In this paper, we propose a dual attention supervised module for multi-label lesion detection in chest radiographs, named DualAttNet. It efficiently fuses global and local lesion classification information based on an image-level attention block and a fine-grained disease attention algorithm. A binary cross entropy loss function is used to calculate the difference between the attention map and ground truth at image level. The generated gradient flow is leveraged to refine pyramid representations and highlight lesionrelated features. We evaluate the proposed model on VinDr-CXR, ChestX-ray8 and COVID-19 datasets. The experimental results show that DualAttNet surpasses baselines by 0.6% to 2.7% mAP and 1.4% to 4.7% AP50 with different detection architectures. The code for our work and more technical details can be found at https://github.com/xq141839/DualAttNet

An Automatic Nuclei Image Segmentation Based on Multi-Scale Split-Attention U-Net

Nuclei segmentation is an important step in the task of medical image analysis. Nowadays, deep learning techniques based on Convolutional Neural Networks (CNNs) have become prevalent methods in nuclei segmentation. In this paper, we propose a network called Multi-scale Split-Attention U-Net (MSAU-Net) for further improving the performance of cell segmentation. MSAU-Net is based on U-Net architecture and the original blocks used to down-sampling and up-sampling paths are replaced with Multi-scale Split-Attention blocks for capturing independent semantic information of nuclei images. A public microscopy image dataset from 2018 Data Science Bowl grand challenge is selected to train and evaluate MSAU-Net. By running trained models on the test set, our model reaches average Intersection over Union (IoU) of 0.851, which is better than other prominent models, especially 4.8 percent higher than the original U-Net. For other evaluation metrics including accuracy, precision, recall and F1-score, MSAU-Net shows better performance in the most of indicators. The outstanding result reveals that our proposed model presents a promising nuclei segmentation method for the microscopy image analysis

DCSAU-Net: A deeper and more compact split-attention U-Net for medical image segmentation

Deep learning architecture with convolutional neural network achieves outstanding success in the field of computer vision. Where U-Net has made a great breakthrough in biomedical image segmentation and has been widely applied in a wide range of practical scenarios. However, the equal design of every downsampling layer in the encoder part and simply stacked convolutions do not allow U-Net to extract sufficient information of features from different depths. The increasing complexity of medical images brings new challenges to the existing methods. In this paper, we propose a deeper and more compact split-attention u-shape network, which efficiently utilises low-level and high-level semantic information based on two frameworks: primary feature conservation and compact split-attention block. We evaluate the proposed model on CVC-ClinicDB, 2018 Data Science Bowl, ISIC-2018, SegPC-2021 and BraTS-2021 datasets. As a result, our proposed model displays better performance than other state-of-the-art methods in terms of the mean intersection over union and dice coefficient. More significantly, the proposed model demonstrates excellent segmentation performance on challenging images. The code for our work and more technical details can be found at https://github.com/xq141839/DCSAU-Net

Investigating Refractoriness in Collision Perception Neuronal Model

Currently, collision detection methods based on visual cues are still challenged by several factors including ultrafast approaching velocity and noisy signal. Taking inspiration from nature, though the computational models of lobula giant movement detectors (LGMDs) in locust’s visual pathways have demonstrated positive impacts on addressing these problems, there remains potential for improvement. In this paper, we propose a novel method mimicking neuronal refractoriness, i.e. the refractory period (RP), and further investigate its functionality and efficacy in the classic LGMD neural network model for collision perception. Compared with previous works, the two phases constructing RP, namely the absolute refractory period (ARP) and relative refractory period (RRP) are computationally implemented through a ‘link (L) layer’ located between the photoreceptor and the excitation layers to realise the dynamic characteristic of RP in discrete time domain. The L layer, consisting of local time-varying thresholds, represents a sort of mechanism that allows photoreceptors to be activated individually and selectively by comparing the intensity of each photoreceptor to its corresponding local threshold established by its last output. More specifically, while the local threshold can merely be augmented by larger output, it shrinks exponentially over time. Our experimental outcomes show that, to some extent, the investigated mechanism not only enhances the LGMD model in terms of reliability and stability when faced with ultra-fast approaching objects, but also improves its performance against visual stimuli polluted by Gaussian or Salt-Pepper noise. This research demonstrates the modelling of refractoriness is effective in collision perception neuronal models, and promising to address the aforementioned collision detection challenges

UAV photogrammetry in intertidal mudflats: accuracy, efficiency, and potential for integration with satellite imagery

The rapid, up-to-date, cost-effective acquisition and tracking of intertidal topography are the fundamental basis for timely, high-priority protection and restoration of the intertidal zone. The low cost, ease of use, and flexible UAV-based photogrammetry have revolutionized the monitoring of intertidal zones. However, the capability of the RTK-assisted UAV photogrammetry without ground control points, the impact of flight configuration difference, the presence of surface water in low-lying intertidal areas on the photogrammetric accuracy, and the potential of UAV/satellite Synergy remain unknown. In this paper, we used an RTK-assisted UAV to assess the impact of the above-mentioned considerations quantitatively on photogrammetric results in the context of annual monitoring of the Chongming Dongtan Nature Reserve, China based on an optimal flight combination. The results suggested that (1) RTK-assisted UAVs can obtain high-accuracy topographic data with a vertical RMSE of 3.1 cm, without the need for ground control points. (2) The effect of flight altitude on topographic accuracy was most significant and also nonlinear. (3) The elevation obtained by UAV photogrammetry was overestimated by approximately 2.4 cm in the low-lying water-bearing regions. (4) The integration of UAV and satellite observations can increase the accuracy of satellite-based waterline methods by 51%. These quantitative results not only provide scientific insights and guidelines for the balance between accuracy and efficiency in utilizing UAV-based intertidal monitoring, but also demonstrate the great potential of combined UAV and satellite observations in identifying coastal erosion hotspots. This establishes high-priority protection mechanisms and promotes coastal restoration

A Novel Camera Based Approach for Automatic Expiry Date Detection and Recognition on Food Packages

There is abundant of information on food packages, which include the food name, the expiry date and the ingredients. These information, especially the expiry date needs to be coded correctly before the products can be released into the market/supply chains. Failure of printing the correct expiry date can lead to both the health issues to the public and financial issues for recalling product back and even reimbursement. In this paper, we develop an automatic system that can achieve the expiry date region detection and recognition in an efficient and effective way. A deep neural network (DNN) based approach is firstly applied to find the expiry date region on the food package. The date characters are then extracted and recognized through the image processing and machine learning methods from the expiry date region. The system is the first camera based automatic system for recognizing expiry date on food packages. And the results tested on different types of food packages show that the system can achieve good performance on both detection and recognition of the expiry date

Superconducting Zirconium Polyhydrides at Moderate Pressures.

Highly compressed hydrides have been at the forefront of the search for high-Tc superconductivity. The recent discovery of record-high Tc's in H3S and LaH10±x under high pressure fuels the enthusiasm for finding good superconductors in similar hydride groups. Guided by first-principles structure prediction, we successfully synthesized ZrH3 and Zr4H15 at modest pressures (30-50 GPa) in diamond anvil cells by two different reaction routes: ZrH2 + H2 at room temperature and Zr + H2 at ∼1500 K by laser heating. From the synchrotron X-ray diffraction patterns, ZrH3 is found to have a Pm3̅n structure corresponding to the familiar A15 structure, and Zr4H15 has an I4̅3d structure isostructural to Th4H15. Electrical resistance measurement and the dependence of Tc on the applied magnetic field of the sample showed the emergence of two superconducting transitions at 6.4 and 4.0 K at 40 GPa, which correspond to the two Tc's for ZrH3 and Zr4H15.This work was supported by the National Key R&D Program of China (No. 2018YFA0305900), National Natural Science Foundation of China (Nos. 51632002, 11674122, 51572108, and 11504127), Program for Changjiang Scholars and Innovative Research Team in University (No. IRT_15R23), the 111 Project (No. B12011), and the Natural Sciences and Engineering Research Council of Canada (NSERC). C.J.P. acknowledges financial support from the Engineering and Physical Sciences Research Council (Grant EP/P022596/1) and a Royal Society Wolfson Research Merit award

A supervised texton based approach for automatic segmentation and measurement of the fetal head and femur in 2D ultrasound images

This paper presents a supervised texton based approach for the accurate segmentation and measurement of ultrasound fetal head (BPD, OFD, HC) and femur (FL). The method consists of several steps. First, a non-linear diffusion technique is utilized to reduce the speckle noise. Then, based on the assumption that cross sectional intensity profiles of skull and femur can be approximated by Gaussian-like curves, a multi-scale and multi-orientation filter bank is designed to extract texton features specific to ultrasound fetal anatomic structure. The extracted texton cues, together with multi-scale local brightness, are then built into a unified framework for boundary detection of ultrasound fetal head and femur. Finally, for fetal head, a direct least square ellipse fitting method is used to construct a closed head contour, whilst, for fetal femur a closed contour is produced by connecting the detected femur boundaries. The presented method is demonstrated to be promising for clinical applications. Overall the evaluation results of fetal head segmentation and measurement from our method are comparable with the inter-observer difference of experts, with the best average precision of 96.85%, the maximum symmetric contour distance (MSD) of 1.46 mm, average symmetric contour distance (ASD) of 0.53 mm; while for fetal femur, the overall performance of our method is better than the inter-observer difference of experts, with the average precision of 84.37%, MSD of 2.72 mm and ASD of 0.31 mm