High-resolution remote-sensing image change detection plays an important role in areas such as land and resources investigation, natural disaster prediction, and military strategy research. Current change detection methods often focus on extracting more discriminative features, while ignoring the information loss and imbalance problems in the process of feature fusion, which results in weakness in small change objects and edge pixels of change objects. In this letter, a simple but efficient network architecture, extraction, comparison and fusion network (ECFNet), for change detection in remote-sensing images is proposed. By constraining the number of feature channels in the fusion process by the feature comparison module (FCM), ECFNet can better utilize the fine-grained information in the multiscale feature map for result prediction, which not only improves the detection performance of small objects, but also reduces the false detection around the edge pixels of change objects. Experiments on the deeply supervised image fusion network for change detection (DSIFN-CD) test set show that ECFNet achieves state-of-the-art results with a small amount of computation.

Using deep-learing based object detection technology to detect contraband is to locate and classify it in X-RAY luggage images. It can circumvent the problem of missed and false detection caused by traditional manual security inspection. This paper proposes a novel anchor-free detector named Scale Adaptive CenterNet (SA-CenterNet), which models the contraband as its center point at multiple scales. Our method uses keypoint estimation to find center points and regresses to object’s sizes to obtain bounding boxes. SA-CenterNet introduces a scale adaptive module which uses multi-level feature maps to predict occluded contraband with the different sizes and the same center point. In additIon, we propose a feature enhancement module (FEM) to better extract the feature of small and obscure contraband. We use lightweight backbone and group convolution in detection head to reduce the amount of calculation and speed up the inference speed. Experiments demonstrate that our method achieves 95.2%AP50at 105 FPS on our dataset named UNICOMP, which surpasses the state-of-the-art methods. Our method can robustly detect contraband passing the security inspection machine in real time, saving manpower and material resources.

Virtual marine scene authoring plays an important role in generating large-scale 3D scenes and it has a wide range of applications in computer animation and simulation. Existing marine scene authoring methods either produce periodic patterns or generate unnatural group distributions when tiling marine entities such as schools of fish and groups of reefs. To this end, we propose a new large-scale marine scene authoring method based on real examples in order to create more natural and realistic results. Our method first extracts the distribution of multiple marine entities from real images to create Octahedral Blocks, and then we use a modified Wang Cubes algorithm to quickly tile the 3D marine scene. As a result, our method is able to generate aperiodic tiling results with diverse distributions of density and orientation of entities. We validate the effectiveness of our method through intensive comparative experiments. User study results show that our method can generate satisfactory results which are in accord with human preferences.

Bas-relief is a special art-form, which can be used as decorations. Deformable 3D models are usually used in image segmentation and 3D reconstruction, aiming at the reconstruction of certain category especially. To quickly reconstruct different objects of the same category and take the psychological process and visual characteristics of human observation images into account, in this paper, we propose a bas-reliefs generation method for flowers based on deformable 3D models which is a type of parametric model that can deform shapes by changing related parameters. First, we create a dataset by labelling images with keypoints. Then, we use Non Rigid Structure from Motion (NRSfM) algorithm to estimate viewpoints. A deformable 3D model is reconstructed by Visual Hull. Next, we deform the 3D model to a variant of the object according to an input 2D image. After that, we enhance and smoothen the image by applying gamma correction on the gray scale image with gradient. Finally, we calculate height values as details using illumination model and add them to the variant. The experimental results demonstrate that our method can reconstruct bas-reliefs for flowers in different shapes with complex background, which is also proven to be able to extend to other categories, such as birds and fishes.

Autonomous perception requires high-quality environment sensing in the form of 3D bounding boxes of dynamic objects. The primary sensors used in automotive systems are light-based cameras and LiDARs. However, they are known to fail in adverse weather conditions. Radars can potentially solve this problem as they are barely affected by adverse weather conditions. However, specular reflections of wireless signals cause poor performance of radar point clouds. We introduce Pointillism, a system that combines data from multiple spatially separated radars with an optimal separation to mitigate these problems. We introduce a novel concept of Cross Potential Point Clouds, which uses the spatial diversity induced by multiple radars and solves the problem of noise and sparsity in radar point clouds. Furthermore, we present the design of RP-net, a novel deep learning architecture, designed explicitly for radar's sparse data distribution, to enable accurate 3D bounding box estimation. The spatial techniques designed and proposed in this paper are fundamental to radars point cloud distribution and would benefit other radar sensing applications