Search Results (4)

Camouflage evaluation is traditionally evaluated through human visual search and detection experiments, which are time-consuming and resource intensive. To address this, we explored whether a pre-trained convolutional neural network (YOLOv4-tiny) can provide an automated, image-based measure of camouflage effectiveness that aligns with human perception. We conducted behavioral experiments to obtain human detection performance metrics—such as search time and target conspicuity—and compared these to the classification probabilities output by the YOLO model when detecting camouflaged individuals in rural and urban scenes. YOLO’s classification probability was adopted as a proxy for detectability, allowing direct comparison with human observer performance. We found a strong overall correspondence between YOLO-predicted camouflage effectiveness and human detection results. However, discrepancies emerged at close distances, where YOLO’s performance was particularly sensitive to high-contrast, shape-breaking elements of the camouflage pattern. CNNs such as YOLO have significant potential for assessing camouflage effectiveness for a wide range of applications, such as evaluating or optimizing one’s signature and predicting optimal hiding locations in each environment. Still, further research is required to fully establish YOLO’s limitations and applicability for this purpose in real time. Full article

Camouflage is a natural or artificial process that prevents an object from being detected, while camouflage breaking is a countering process for the identification of the concealed object. We report that a perfectly camouflaged object can be retrieved from the background and detected with stereo-vision-assisted three-dimensional (3D) imaging. The analysis is based on a binocular neuron energy model applied to general 3D settings. We show that a perfectly concealed object with background interference can be retrieved with vision stereoacuity to resolve the hidden structures. The theoretical analysis is further tested and demonstrated with distant natural images taken by a drone camera, processed with a computer and displayed using autostereoscopy. The recovered imaging is presented with the removal of background interference to demonstrate the general applicability for camouflage breaking with stereo imaging and sensing. Full article

Creatures in nature make extensive use of structural color adaptive camouflage to survive. Cholesteric liquid crystals, with nanostructures similar to those of natural organisms, can be combined with actuators to produce bright structural colors in response to a wide range of stimuli. Structural colors modulated by nano-helical structures can continuously and selectively reflect specific wavelengths of light, breaking the limit of colors recognizable by the human eye. In this review, the current state of research on cholesteric liquid crystal photonic actuators and their technological applications is presented. First, the basic concepts of cholesteric liquid crystals and their nanostructural modulation are outlined. Then, the cholesteric liquid crystal photonic actuators responding to different stimuli (mechanical, thermal, electrical, light, humidity, magnetic, pneumatic) are presented. This review describes the practical applications of cholesteric liquid crystal photonic actuators and summarizes the prospects for the development of these advanced structures as well as the challenges and their promising applications. Full article

When searching a visual image that contains multiple target objects of interest, human subjects often show a satisfaction of search (SOS) effect, whereby if the subjects find one target, they are less likely to find additional targets in the image. Reducing SOS or, equivalently, subsequent search miss (SSM), is of great significance in many real-world situations where it is of paramount importance to find all targets in a given image, not just one. However, studies have shown that even highly trained and experienced subjects, such as expert radiologists, are subject to SOS. Here, using the detection of camouflaged objects (or camouflage-breaking) as an illustrative case, we demonstrate that when naïve subjects are trained to detect camouflaged objects more effectively, it has the side effect of reducing subjects’ SOS. We tested subjects in the SOS task before and after they were trained in camouflage-breaking. During SOS testing, subjects viewed naturalistic scenes that contained zero, one, or two targets, depending on the image. As expected, before camouflage-training, subjects showed a strong SOS effect, whereby if they had found a target with relatively high visual saliency in a given image, they were less likely to have also found a lower-saliency target when one existed in the image. Subjects were then trained in the camouflage-breaking task to criterion using non-SOS images, i.e., camouflage images that contained zero or one target. Surprisingly, the trained subjects no longer showed significant levels of SOS. This reduction was specific to the particular background texture in which the subjects received camouflage training; subjects continued to show significant SOS when tested using a different background texture in which they did not receive camouflage training. A separate experiment showed that the reduction in SOS was not attributable to non-specific exposure or practice effects. Together, our results demonstrate that perceptual expertise can, in principle, reduce SOS, even when the perceptual training does not specifically target SOS reduction. Full article