Mechanisms and Effectiveness of Imagery Strategies in Reducing Disgust in Contamination-Related Obsessive–Compulsive Disorder: Comparing Imagery Rescripting, Imagery Self-compassion and Mood-Focused Imagery

Background Strong feelings of disgust and anxiety are maintaining factors in contamination-related obsessive–compulsive disorder (C-OCD). To this day there are not many studies that investigated strategies for changing pathological disgust. In a previous study, it was shown that imagery rescripting could successfully change disgust. However, whether imagery rescripting or more general imagery processing, helps to reduce pathological disgust, remains unclear. Therefore, the aim of the present study was to investigate how successful imagery rescripting is in comparison to imagery self-compassion and a passive positive imagery condition in reducing disgust. Methods For this, the three strategies were compared to each other on 2 days (within-subject) in a laboratory experiment. The study included 24 subjects with diagnosed C-OCD, and 24 matched, healthy controls (between-subject). Results The results show that all three strategies changed disgust, they do not differ from each other and that different traits appear to influence the strategies’ success or failure. The theoretically derived underlying mechanisms of the strategies were found in an elaborate content analysis. Conclusions The present study provides first indications that imagery in general can help to change pathological disgust experience.

Does Vivid Imagination Deter Visitation? The Role of Mental Imagery Processing in Virtual Tourism on Tourists’ Behavior

The digital transformation of the tourism industry influences tourists’ behavior. Grounded in dual-processing theory, this study developed a holistic framework to explain the underlying psychological mechanisms of virtual tourism. The study’s overarching objectives were to (1) examine how mental imagery processing (MIP) of sensory stimuli in virtual tourist attractions influence cognition (learning) and emotion and (2) contribute to prior research that focused on the positive effect of MIP. This study aims to explore the potential negative impacts of MIP on future behavioral intention to visit actual tourist attractions. Two rounds of surveys in China show that MIP influenced cognition and emotion, which together may affect future visitation. MIP inspired a desire to visit through learning, although it also decreased interest because of negative emotions. The current study contributes to the virtual tourism literature and MIP theory and suggests implications for the use of virtual technologies in tourism marketing.

Optimizing aerial imagery collection and processing parameters for drone-based individual tree mapping in structurally complex conifer forests

Recent advances in remotely piloted aerial system (“drone”) and imagery processing technologies have enabled individual tree mapping in forest stands across broad areas with low-cost equipment and minimal ground-based data collection. One such method, “structure from motion” (SfM), involves collecting many partially overlapping aerial photos over a focal area and using photogrammetric analysis to infer 3D structure and detect individual trees. SfM-based forest mapping involves myriad decisions surrounding the selection of methods and parameters for imagery acquisition and processing, but no studies have comprehensively and quantitatively evaluated the influence of these parameters on the accuracy of the resulting tree maps.We collected and processed drone imagery of a moderate-density, structurally complex mixed-conifer stand. We tested 22 imagery collection methods (altering flight altitude, camera pitch, and image overlap), 12 imagery processing parameterizations, and 286 tree detection methods (algorithms and their parameterizations) to create 7,568 tree maps. We compared these maps to a 3.23-ha ground-truth map of 1,916 trees > 5 m tall that we created using traditional field survey methods.We found that the accuracy of individual tree detection (ITD) and the resulting tree maps was generally maximized by collecting imagery at high altitude (120 m) with at least 90% image-to-image overlap, photogrammetrically processing images into a canopy height model (CHM) with a 2-fold upscaling (coarsening) step, and detecting trees from the CHM using a variable window filter after first applying a moving-window mean smooth to the CHM. Using this combination of methods, we mapped trees with an accuracy that exceeds expectations for our structurally complex forest based on other recent results (for overstory trees > 10 m tall, sensitivity = 0.69 and precision = 0.90). Remotely-measured tree heights corresponded to ground-measured heights with R2 = 0.95. Accuracy was higher for taller trees and lower for understory trees, and it is likely to be higher in lower density and less structurally-complex stands.Our results may guide others wishing to efficiently produce individual-tree maps of conifer forests over broad extents without investing substantial time tailoring imagery acquisition and processing parameters. The resulting tree maps create opportunities for addressing previously intractable ecological questions and increasing the efficiency of forest management.

Multi-scale CNN Approach for Accurate Detection of Underwater Static Fish Image

Recently, the identification and naming of fish species in underwater imagery processing has been in high demand. This is an essential activity for everyone, from biologists to scientists to fisherman. Humans' interests have recently expanded from the earth to the sky and the sea. Robots could be utilized to send mankind to explore the ocean and outer space, as well as for some dangerous professions that human beings are unlikely to perform. Humans have recently shifted their focus from land-based exploration to celestial exploration and the sea. Robots are used for the activities that pose a risk to mankind, like exploration of the seas and outer space. This research article provides a solution to underwater image detection techniques by using an appended transmission map, refinement method and deep learning approach. The features are deeply extracted by multi-scale CNN for attaining higher accuracy in detecting fish features from the input images with the help of segmentation process. Object recognition errors are minimized and it has been compared with other traditional processes. The overall performance metrics graph has been plotted for the proposed algorithm in the results and discussion section.

How Image Acquisition Geometry of UAV Campaigns Affects the Derived Products and Their Accuracy in Areas with Complex Geomorphology

The detailed and accurate mapping of landscapes and their geomorphological characteristics is a key issue in hazard management. The current study examines whether the image acquisition geometry of unmanned aerial vehicle (UAV) campaigns affects the accuracy of the derived products, i.e., orthophotos, digital surface models (DSMs) and photogrammetric point clouds, while performing a detailed geomorphological mapping of a landslide area. UAV flights were executed and the collected imagery was organized into three subcategories based on the viewing angle of the UAV camera. The first subcategory consists of the nadir imagery, the second is composed of the oblique imagery and the third category blends both nadir and oblique imagery. UAV imagery processing was carried out using structure-from-motion photogrammetry (SfM). High-resolution products were generated, consisting of orthophotos, DSMs and photogrammetric-based point clouds. Their accuracy was evaluated utilizing statistical approaches such as the estimation of the root mean square error (RMSE), calculation of the geometric mean of a feature, length measurement, calculation of cloud-to-cloud distances as well as qualitive criteria. All the quantitative and qualitative results were taken into account for the impact assessment. It was demonstrated that the oblique-viewing geometry as well as the combination of nadir and oblique imagery could be used effectively for geomorphological mapping in areas with complex topography and steep slopes that overpass 60 degrees. Moreover, the accuracy assessment revealed that those acquisition geometries contribute to the creation of significantly better products compared to the corresponding one arising from nadir-viewing imagery.

ANALYSIS OF CREATING BENCHMARK IMAGES METHODS TO TEST THE ACCURACY OF PHOTOGRAMMETRIC SOFTWARE

Digital photogrammetry is based on the use of specialized photogrammetric software (or digital photogrammetric systems) to solve problems related to the aerospace imagery processing. A wide range of programs and high price motivate consumers to choose the right software that responds to requirements of processing accuracy, amount of work, time of execution, etc. The main goal of this study is to analyze the existing methods of benchmark images creating to test photogrammetric pro-grams. The article carries out the analysis of existing techniques of creating benchmark images, classi-fication, selection of benchmark images types suitable for testing of photogrammetric software, and substantiates the necessity for checking of aerial survey results quality in specialized software.

Assessment of vegetation state based on modified fuzzy algorithm

Assessment of forest vegetation is based on multispectral space imagery processing. The issues of the formation of initial data and selection of test sites for automated identification of landscape elements are considered. The aim of the work is to present an assessment technology for natural objects that applies the mathematical apparatus of fuzzy logic to automated processing of multispectral aerospace imagery. An example of assessing the forest state and determining the consequences of a forest fi re is presented.