Completely automated public Turing test to tell humans apart (CAPTCHA) is an effective mechanism to protect websites and online applications from malicious bots programs. Image-based CAPTCHA is one of the most widely used schemes. However, deep learning techniques have significantly weakened the security of some image-based CAPTCHA schemes. Mooney images (MIs) are important research materials in the field of cognitive science. Compared to natural images, MI exhibits fewer visual cues, fragmented content, and greater ambiguity, leading to the perception of MI relying more on the iterative process between feedforward and feedback mechanisms. In this paper, we raise an intriguing question: can MIs be used to enhance the security of CAPTCHA? Before this study, we first propose a novel framework HiMI that generates the high-quality MIs from natural images and also allows flexible adjustment of the perceived difficulty. Based on MI, we design two MI-CAPTCHA schemes related to object detection and instance segmentation tasks, respectively. We experimentally demonstrate that HiMI performs better than other baseline methods in terms of both image quality and application potential in two MI-CAPTCHA schemes. Additionally, we conduct experiments to explore the solving performance of humans and CAPTCHA solvers under different parameter settings of schemes, providing valuable reference for the practical application.

Perceptual edge grouping is a technique for organizing the cluttered edge pixels into meaningful structures and further serves high-level vision tasks, which has long been a basic and critical task in computer vision. Existing methods usually have a poor performance when coping with the junctions caused by occlusion and noise in natural images. In this paper, we present GPGrouper, a perceptual edge grouping model based on gestalt theory and the primary visual cortex (V1). Different from the existing methods, GPGrouper leverages the edge representation and grouping matrix (ERGM), a functional structure inspired by V1 mechanisms, to represent edges in a way that can effectively reduce grouping errors caused by occlusion between objects. ERGM is trained with natural image contours and further provides a priori guidance for the construction of the edge connection graph (ECG) that is useful to minimize the impact of noise on grouping. In the experiment, we compared GPGrouper and the state-of-the-art (SOTA) method of perceptual grouping on the visual psychology pathfinder challenge. The results demonstrate that GPGrouper outperforms the SOTA method in grouping performance. Furthermore, in the grouping experiments involving line segments with varying lengths detected by the Line Segment Detector (LSD), as well as those involving superpixel segmentation results with significant levels of interfering noise using the SLIC algorithm, GPGrouper was superior to the existing methods in terms of grouping effect and robustness. Moreover, the results of applying the grouping results to the vision tasks objectness demonstrate that GPGrouper can contribute significantly to high-level visual tasks.

The edges of an image contains rich visual cognitive cues. However, the edge information of a natural scene usually is only a set of disorganized unorganized pixels for a computer. In psychology, the phenomenon of quickly perceiving global information from a complex pattern is called the global precedence effect (GPE). For example, when one observes the edge map of an image, some contours seem to automatically “pop out” from the complex background. This is a manifestation of GPE on edge information and is called global contour precedence (GCP). The primary visual cortex (V1) is closely related to the processing of edges. In this article, a neural computational model to simulate GCP based on the mechanisms of V1 is presented. There are three layers in the proposed model: the representation of line segments, organization of edges, and perception of global contours. In experiments, the ability to group edges is tested on the public dataset BSDS500. The results show that the grouping performance, robustness, and time cost of the proposed model are superior to those of other methods. In addition, the outputs of the proposed model can also be applied to the generation of object proposals, which indicates that the proposed model can contribute significantly to high-level visual tasks.