Building vector extraction from aerial images is a challenge in many applications, especially location-based services. In recent years, different deep-learning techniques have improved the accuracy of building extraction. We propose a framework (JuliMa-Net) for building vector extraction from aerial images without boundary regularization or vertex sampling. The wireframe generated by our framework shows more detail about the building’s structure than its footprints. We initially selected three pretrained networks of Mask R-convolutional neural network, line, and junction detection. To improve the performance of the primary junction detection network, unique sets of decoders were designed. The smaller training set was used to fine-tune all three networks simultaneously. In addition, after applying proper processing on the obtained building masks, the outputs of the other two networks—the detected lines and junctions—were precisely selected and combined. The result of this process is a significant reduction in false detections and an increase in precision of 96%. Additionally, the final processing adds precise lines to the wireframes after combining junctions and lines. This improves the recall value.
To understand the visual world, a device knows not only the instances but also how they interact. Humans are at the center of such interactions. Detection of human–object interaction (HOI) is one of the growing research fields in computer vision. However, identifying HOIs due to the large label space of verbs and their interaction with various object types still needs much research. We focus on HOIs in images, which is necessary for a deeper understanding of the scene. In addition to two-dimensional (2D) information, such as the appearance of humans and objects and their spatial location, three-dimensional (3D) status, especially in the configuration of the human body and object as well as their location and spatial, can play an important role in learning HOI. The mapping of 2D to 3D world adds depth information to the problem. These issues led us to collect 3D information along with the 2D features of the images to provide more accurate results. We show 3D attributes, such as face transformation, the viewing angle, the position of an object, and its related location to the human face, can improve HOI learning. The results of experiments on large-scale data show that our method has been able to improve the outcome of interactions.
3D object recognition from point clouds is a fast-growing field of research in computer vision. 3D object recognition methods can be classified into two categories: global feature-based and local feature-based methods. The local feature-based methods are more popular than global ones. Because the global feature-based methods need a prior segmentation of the scene, they are not suitable for real-world scenes. Many previous local descriptor methods limit their performance by introducing a local reference frame or axis (LRF/A). Estimating the LRF/A for each keypoint leads to extra computational time and error. We use the fundamental theorem of surface theory to introduce a simple and efficient local feature descriptor based on the coefficients of discrete first and second fundamental forms. The proposed method overrides the necessity of an LRF/A, and it required a small feature dimension of seven, which means it is a low-complexity and fast procedure. To assess the proposed method, we have compared it with eight state-of-the-art descriptors and applied it to the three popular datasets to extract features and recognize the correspondences. Experimental results demonstrate the superiority of the proposed approach to the compared methods in terms of pairwise registration measurements, recall versus 1-precision curve, and the computational time.
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