With rapid advancements in remote sensing image registration algorithms, comprehensive imaging applications are no longer limited to single-modal remote sensing images. Instead, multi-modal remote sensing (MMRS) image registration has become a research focus in recent years. However, considering multi-source, multi-temporal, and multi-spectrum input introduces significant nonlinear radiation differences in MMRS images for which researchers need to develop novel solutions. At present, comprehensive reviews and analyses of MMRS image registration methods are inadequate in related fields. Thus, this paper introduces three theoretical frameworks: namely, area-based, feature-based and deep learning-based methods. We present a brief review of traditional methods and focus on more advanced methods for MMRS image registration proposed in recent years. Our review or comprehensive analysis is intended to provide researchers in related fields with advanced understanding to achieve further breakthroughs and innovations.

Pedestrian trajectory prediction in crowd scenes plays a significant role in intelligent transportation systems. The main challenges are manifested in learning motion patterns and addressing future uncertainty. Typically, trajectory prediction is considered in two dimensions, including temporal dynamics modeling and social interactions capturing. For temporal dependencies, although existing models based on recurrent neural networks (RNNs) or convolutional neural networks (CNNs) achieve high performance on short-term prediction, they still suffer from limited scalability for long sequences. For social interactions, previous graph-based methods only consider fixed features but ignore dynamic interactions between pedestrians. Considering that the transformer network has a strong capability of capturing spatial and long-term temporal dynamics, we propose Long-Short Term Spatio-Temporal Aggregation (LSSTA) network for human trajectory prediction. First, a modern variant of graph neural networks, named spatial encoder, is presented to characterize spatial interactions between pedestrians. Second, LSSTA utilizes a transformer network to handle long-term temporal dependencies and aggregates the spatial and temporal features with a temporal convolution network (TCN). Thus, TCN is combined with the transformer to form a long-short term temporal dependency encoder. Additionally, multi-modal prediction is an efficient way to address future uncertainty. Existing auto-encoder modules are extended with static scene information and future ground truth for multi-modal trajectory prediction. Experimental results on complex scenes demonstrate the superior performance of our method in comparison to existing approaches.

“Seeing through” occluders is one of the most important effects that can be achieved with synthetic aperture imaging. As well, the occlusion problem, a challenging task for many computer vision applications, can be easily handled. Synthetic aperture imaging takes advantage of the property that only objects on the focal plane are sharp. The resulting image that is obtained by averaging images from different views consists of blurry objects away from the focal plane and sharp objects on the focal plane. Removing the blurriness caused by defocusing in synthetic aperture images to achieve an all-in-focus “seeing through” image is a challenging research problem. In this paper, we propose a novel method to improve the image quality of synthetic aperture imaging using image matting via energy minimization by estimating the foreground and the background. In particular, we first estimate the out-of-focus region by focusing on the background objects in each camera view using energy minimization. Next, we utilize a labeling method to create a sharp “see through” synthetic aperture image of the hidden objects. Then, image matting is used to extract the alpha matte of the hidden objects. Finally, by compositing the hidden objects with the estimated background regions, a sharp “see through” synthetic aperture image is created. The experimental results show that the proposed method outperforms the traditional synthetic aperture imaging method [1] as well as its improved versions [2]-[4], which simply dim and blur the area in the image that is out of focus, and a recent all-in-focus method [5]. We show that both the occluded objects and the background can be combined using our method to create a sharp synthetic aperture image.

Class attendance is an important means in the management of university students. Using face recognition is one of the most effective techniques for taking daily class attendance. Recently, many face recognition algorithms via deep learning have achieved promising results with large-scale labeled samples. However, due to the difficulties of collecting samples, face recognition using convolutional neural networks (CNNs) for daily attendance taking remains a challenging problem. Data augmentation can enlarge the samples and has been applied to the small sample learning. In this paper, we address this problem using data augmentation through geometric transformation, image brightness changes, and the application of different filter operations. In addition, we determine the best data augmentation method based on orthogonal experiments. Finally, the performance of our attendance method is demonstrated in a real class. Compared with PCA and LBPH methods with data augmentation and VGG-16 network, the accuracy of our proposed method can achieve 86.3%. Additionally, after a period of collecting more data, the accuracy improves to 98.1%.

Mobile crowdsensing in intelligent transportation systems is a new data acquisition mode that utilizes vehicles to sense a changing traffic environment, where incentive mechanisms play a vital role in motivating vehicles to participate. Nevertheless, existing incentive mechanisms do not simultaneously consider enhancing the efficiency of task allocation, maximizing the benefits of the entire system, and balancing various costs. Therefore, a two-stage incentive mechanism, considering task aggregation, is designed to maximize the benefits of vehicles and the platform. In the first stage, each vehicle, utilizing the multiagent deep reinforcement learning algorithm, dynamically adjusts its path selection strategy to maximize its net income and then uploads the selected path information to the platform. In the second stage, this platform solves an NP-hard problem of maximizing satisfaction by combining the bid price, time consumption, and task coverage to assign tasks to the appropriate vehicles. In particular, vehicle satisfaction is measured by the analytic hierarchy process, and rewards are allocated accordingly. The proposed mechanism is compared with the state-of-the-art method, the greedy, random, and optimal algorithms. Experiments on the real trajectory dataset manifest that the proposed mechanism can fully use the platform budget to achieve the best net income, platform satisfaction, and task completion rate.

With the three-dimensional (3D) coordinates of objects captured by a sequence of images taken in different views, object reconstruction is a technique which aims to recover the shape and appearance information of objects. Although great progress in object reconstruction has been made over the past few years, object reconstruction in occlusion situations remains a challenging problem. In this paper, we propose a novel method to reconstruct occluded objects based on synthetic aperture imaging. Unlike most existing methods, which either assume that there is no occlusion in the scene or remove the occlusion from the reconstructed result, our method uses the characteristics of synthetic aperture imaging that can effectively reduce the influence of occlusion to reconstruct the scene with occlusion. The proposed method labels occlusion pixels according to variance and reconstructs the 3D point cloud based on synthetic aperture imaging. Accuracies of the point cloud are tested by calculating the spatial difference between occlusion and non-occlusion conditions. The experiment results show that the proposed method can handle the occluded situation well and demonstrates a promising performance.