We systematically study the design of image recognition CAPTCHAs (IRCs) in this paper. We first review and examine all existing IRCs schemes and evaluate each scheme against the practical requirements in CAPTCHA applications, particularly in large-scale real-life applications such as Gmail and Hotmail. Then we present a security analysis of the representative schemes we have identified. For the schemes that remain unbroken, we present our novel attacks. For the schemes for which known attacks are available, we propose a theoretical explanation why those schemes have failed. Next, we provide a simple but novel framework for guiding the design of robust IRCs. Then we propose an innovative IRC called Cortcha that is scalable to meet the requirements of large-scale applications. It relies on recognizing objects by exploiting the surrounding context, a task that humans can perform well but computers cannot. An infinite number of types of objects can be used to generate challenges, which can effectively disable the learning process in machine learning attacks. Cortcha does not require the images in its image database to be labeled. Image collection and CAPTCHA generation can be fully automated. Our usability studies indicate that, compared with Google's text CAPTCHA, Cortcha allows a slightly higher human accuracy rate but on average takes more time to solve a challenge.

Mobile laser scanning (MLS) measurement technology is well-suited for individual-tree-based urban forest inventory because it can capture a dense three-dimensional (3D) point cloud from vertical structures of roadside trees in a rapid and cost-effective way. Street tree segmentation from MLS data is a preliminary and essential step for further extraction and analysis of tree attributes and is also a challenging problem because of the complex street environment. The accuracy of current methods is limited by information loss introduced by voxelization and artificially designed detection and segmentation rules based on prior knowledge that more or less lack ability to distinguish between street trees and other urban objects. To address the above issues, a two-step strategy for street tree segmentation method from MLS data was proposed, which worked on points directly instead of a set of voxels generated by spatial partition and made use of tree crown and trunk detectors trained by supervised learning algorithm. First, 16 local statistical features, including width, depth, elevation, dimensionality, and density features, were extracted from the sphere domain of each point using the grid index. Then these features were fused and tree crown and trunk detectors were trained from labelled MLS data of street through Discrete AdaBoost algorithm. Second, based on frame projection of point-wise prediction results of tree crown and trunk detectors, individual trees were located and segmented by exploiting non-connectivity between adjacent trees and adjacency within single trees. Experimental results have demonstrated that the proposed method can obtain point-wise street tree segmentation from MLS data with high accuracy tree crown and trunk detection without tedious detection or segmentation rules. The error rates of crown detector on test set were less than 0.3% accompanied by over 99.33% recall rates and over 98.59% precision rates and the error rates of trunk detector on test set were less than 0.06% accompanied by over 92.44% recall rates and over 94.14% precision rates.

With the development of light detection and ranging (LiDAR) technology, mobile laser scanning (MLS) systems, which deploy one or multiple LiDARs on a ground-based vehicle,¹ can quickly collect a high-resolution and high-precision point cloud of the surroundings of the vehicle and have gained increasing attention in three-dimensional (3D) urban scene analysis,² including urban 3D modeling³ and automated urban driving.⁴