Two-dimensional Scene Research Articles

FBSA-Net: A novel model based on attention mechanisms for emotion recognition in VR and 2D scenes

Recent studies have found that electroencephalographic (EEG) features from different frequency bands and different brain regions contribute differently to emotion recognition (ER) in virtual reality (VR) and two-dimensional (2D) scenes. Despite achievements in the task of ER in both VR and 2D environments, there has not been much prior effort to develop a unified ER model that can be applied to both VR and 2D environments. We propose a novel Frequency-Band-Spatial-based Attention Network, named FBSA-Net, for ER in VR and 2D scenes. Specifically, FBSA-Net adaptively captures the frequency-band-spatial relationship of EEG signals through cascade fusion of frequency-band attention (FBA) module and spatial attention (SA) module. The FBA module can automatically assign FBA weights to each band feature. The SA module then applies the ProbeParse attention mechanism to adaptively explore channel relationships both within and across brain regions, thus facilitating a comprehensive understanding of the global features of the EEG signals. The model's efficacy was validated through the VRSDEED dataset, created from emotion induction experiments using VR and 2D induction programs. Multiple comparative experiments have shown that FBSA-Net achieved state-of-the-art recognition results on VRSDEED datasets, achieving average recognition accuracies of 96.63% and 96.49% in VR and 2D scenes, respectively. The research results indicate that the significant contribution of FBSA-Net is not only reflected in the ability to provide ER using the same model in VR and 2D environments, but also in its excellent ability to distinguish between the emotional states in these two scenarios, providing a valuable reference for using the same model for ER.

Multiscale hypothesis testing theory and methods for aerosol and cloud layer detection of lidar

Lidar is a unique instrument for profiling aerosol and cloud layers. Detecting the boundaries of these layers is crucial because a missing layer will not be retrieved later. Many layer detection methods, which can be divided into slope-type and threshold-type methods, have been proposed and applied to a one-dimensional (1D) profile or a two-dimensional (2D) scene using numerous empirical settings. However, many eye-visible missing layers remain, indicating the need for a more effective detector. Herein, we demonstrated that layer detection is essentially a hypothesis testing issue. Theoretically, after providing a possible distribution of the background air signal, the task of layer detection is to test whether a given signal conforms to the provided hypothesis of a background air distribution under a particular significance. Consequently, we propose multiscale hypothesis testing methods (MHTs) for cloud and aerosol layer detection, which can work on 1D profiles and 2D scenes and are named 1D-MHT and 2D-MHT, respectively. The 2D-MHT detected 1.67 times layer area as the CALIOP Version 4.2 Level 2 product totally. Particularly, the results show that the 2D-MHT at 5 km resolution can detect 16.3% and 0.5% more layer area than the CALIOP official product at 5–80 km resolutions at daytime and nighttime, respectively. Evaluation using the extinction retrievals from aircraft lidar and the depolarization ratio of ice clouds shows the reliability of the additional detected layers by 2D-MHT. Thus, the hypothesis testing methods have high potential to effectively improve the accuracy and resolution for retrieving lidar products in the future.

Weighted Centroid Localization Algorithm Based on MEA-BP Neural Network and DBSCAN Clustering

In order to overcome RSSI ranging error and improve the accuracy of positioning results, a weighted localization algorithm based on MEA-BP Neural Network and DBSCAN clustering is proposed in this paper. This algorithm uses MEA-BP Neural Network (MEA-BP NN) model to optimize ranging information firstly, then it uses trilateral measurement method to get multiple initial localization results about unknown node and form a set. After clustering the results by DBSCAN and eliminating noise points, the estimated coordinate of unknown node in each cluster is obtained by using the weighted centroid localization algorithm based on collinearity. Next the number of core points in each cluster is regarded as weight value, the weighted centroid localization algorithm is used again, thus the final coordinates of unknown node can be got. Simulation results show that the localization accuracy of wireless sensor network can be improved significantly by using this algorithm in two-dimensional scene.

Developmental changes in colour constancy in a naturalistic object selection task.

When the illumination falling on a surface change, so does the reflected light. Despite this, adult observers are good at perceiving surfaces as relatively unchanging-an ability termed colour constancy. Very few studies have investigated colour constancy in infants, and even fewer in children. Here we asked whether there is a difference in colour constancy between children and adults; what the developmental trajectory is between six and 11 years; and whether the pattern of constancy across illuminations and reflectances differs between adults and children. To this end, we developed a novel, child-friendly computer-based object selection task. In this, observers saw a dragon's favourite sweet under a neutral illumination and picked the matching sweet from an array of eight seen under a different illumination (blue, yellow, red, or green). This set contained a reflectance match (colour constant; perfect performance) and a tristimulus match (colour inconstant). We ran two experiments, with two-dimensional scenes in one and three-dimensional renderings in the other. Twenty-six adults and 33 children took part in the first experiment; 26 adults and 40 children took part in the second. Children performed better than adults on this task, and their performance decreased with age in both experiments. We found differences across illuminations and sweets, but a similar pattern across both age groups. This unexpected finding might reflect a real decrease in colour constancy from childhood to adulthood, explained by developmental changes in the perceptual and cognitive mechanisms underpinning colour constancy, or differences in task strategies between children and adults. HIGHLIGHTS: Six- to 11-year-old children demonstrated better performance than adults on a colour constancy object selection task. Performance decreased with age over childhood. These findings may indicate development of cognitive strategies used to overcome automatic colour constancy mechanisms.

Curved Alleyway Understanding Based on Monocular Vision in Street Scenes

Delivery using autonomous vehicles for medical and emergency supplies is a potential way to avoid unsafe and unpredictable factors. However, its implementation is hindered due to several key issues. A major dilemma is understanding curved alleyways in street scenes. These can be seen as compositions of non-Manhattan structures, which can help us estimate their original posture in three-dimensional scenes. We propose a new methodology to understand curved alleyways, and to bridge the gap between two-dimensional scene understanding and three-dimensional environment reconstruction from a monocular camera. Angle projections are assigned to clusters. Coplanar surfaces, which can compose fold structures, are estimated. Curved alley scenes are approximately represented by Manhattan and non-Manhattan fold structures, and approximated in the reconstruction of alley scenes. With geometric features, the algorithm requires no prior training or knowledge of the camera’s internal parameters. We compared the estimated layout to the ground truth and measured the percentage of incorrectly classified pixels. The results showed that the algorithm can successfully understand alley scenes including both Manhattan and curved non-Manhattan structures.

Depth and Perspective Perception of Flat Images in Static and Dynamic Visual Scenes

The paper shows that a sense of depth can arise from two-dimensional (2D) scenes without the presence of a stereoscopic depth signal. Experimental information was obtained on three-dimensional (3D) visual perception of 2D static and dynamic scenes. The technique is based on fixing the conditions of eye movement during the perception of two-dimensional stimulus scenes. To obtain registration of the depth perception effects, they used volume and spatial perspective of 2D images (3D phenomenon), and a binocular eye tracker. The 3D phenomenon is identified using 3D raster images. It is assumed that the comparison of eye movements during a 3D raster image viewing allows you to identify uniquely the effects of the 3D phenomenon of stimulus planar scenes displayed on the monitor screen. The first part of the work shows the conditions for the emergence of a 3D phenomenon on two plots of dynamic and static scenes. The second part demonstrates the three-dimensional attributes of dynamic scenes with the highlighting of various video components. We emphasize that dynamic and static scenes are obtained directly from TV programs. The proposed graphical and mathematical method of analysis made it possible to show qualitatively the perception of the 3D phenomenon by KFU students and revealed the features of volume observation for planar images without the occurrence of binocular disparity.

Bistatic Radar Positioning of a Moving Target, Using Only Range and Range-Rate Measurements

Of the many applications of bistatic (BS) radars, this paper addresses the possibility to replace a monostatic radar with its highly directive antenna, by a coherent BS radar using a single short baseline between the transmitter and the receiver, and no direction measurements. The method applies to a moving target and requires both <i>range</i> and <i>range-rate</i> BS measurements. Theory and field-test results demonstrate, on a two-dimensional scene, how several consecutive pairs of BS <i>range</i> <inline-formula><tex-math notation="LaTeX">$( r )$</tex-math></inline-formula> and <i>range-rate</i> <inline-formula><tex-math notation="LaTeX">$( {\dot{r}} )$</tex-math></inline-formula> measurements of a moving target can provide the target&#x0027;s position, velocity and heading (<i>x, y, v_x, v_y</i>). In a three-dimensional scene, with 5 estimated target parameters (<i>x, y, h, v_x, v_y</i>), a second receiver is needed, not in-line with the original baseline. The <inline-formula><tex-math notation="LaTeX">$( {r,\dot{r}} )$</tex-math></inline-formula> measurements distinguish the suggested concept from most other multistatic radars as well as the well-known multilateration positioning or interferometric angle-of-arrival estimation.

Three-Dimensional Image Reconstruction for Virtual Talent Training Scene

In real training, the training conditions are often undesirable, and the use of equipment is severely limited. These problems can be solved by virtual practical training, which breaks the limit of space, lowers the training cost, while ensuring the training quality. However, the existing methods work poorly in image reconstruction, because they fail to consider the fact that the environmental perception of actual scene is strongly regular by nature. Therefore, this paper investigates the three-dimensional (3D) image reconstruction for virtual talent training scene. Specifically, a fusion network model was deigned, and the deep-seated correlation between target detection and semantic segmentation was discussed for images shot in two-dimensional (2D) scenes, in order to enhance the extraction effect of image features. Next, the vertical and horizontal parallaxes of the scene were solved, and the depth-based virtual talent training scene was reconstructed three dimensionally, based on the continuity of scene depth. Finally, the proposed algorithm was proved effective through experiments.

Sources of variation in search and foraging: A theoretical perspective.

Search-the problem of exploring a space of alternatives to identify target goals-is a fundamental behaviour for many species. Although its foundation lies in foraging, most studies of human search behaviour have been directed towards understanding the attentional mechanisms that underlie the efficient visual exploration of two-dimensional (2D) scenes. With this review, we aim to characterise how search behaviour can be explained across a wide range of contexts, environments, spatial scales, and populations, both typical and atypical. We first consider the generality of search processes across psychological domains. We then review studies of interspecies differences in search. Finally, we explore in detail the individual and contextual variables that affect visual search and related behaviours in established experimental psychology paradigms. Despite the heterogeneity of the findings discussed, we identify that variations in control processes, along with the ability to regulate behaviour as a function of the structure of search space and the sampling processes adopted, to be central to explanations of variations in search behaviour. We propose a tentative theoretical model aimed at integrating these notions and close by exploring questions that remain unaddressed.

Snapshot hyperspectral imaging polarimetry with full spectropolarimetric resolution

By integrating spectrally-modulated polarimetry into the optically replicating and remapping imaging spectrometer (ORRIS), a snapshot optically replicating and remapping imaging spectropolarimeter (ORRISp) is presented and analyzed. While spectrally-modulated polarimetry uses cascaded polarimetric modulation elements to encode the spectral dependence of Stokes parameters into a single spectrum, the ORRIS combines a focal-plane continuous variable filter with a staircase-grid lenslet array to directly acquire the encoded spectra from a two-dimensional (2D) scene. An iterative loop of two-step least-squares algorithm, with the capability of maintaining the intrinsic spectral resolution of the ORRIS and implementing auto-calibration, is employed to recover the spectrally-resolved Stokes parameters from the encoded spectra of the 2D scene. The build of a compact proof-of-principle prototype of the ORRISp and its operation are presented in addition to the image processing, system calibration, and reconstruction techniques. The ORRISp prototype's spectral and spectropolarimetric imaging performances are demonstrated from the laboratory and outdoor tests. The prototype can cover the spectral range of 450 - 750 nm with 56 spectral and spectropolarimetric channels, and operates over a ± 5.8° field of view with a spatial resolution of 400 × 400 pixels. It has the capability of capturing spatially- and spectrally-resolved Stokes parameters over the 2D scene in a single shot and operating at video rate.

Effect of Metamer Mismatching on Color Constancy in Two-Dimensional Simulated Scenes

Effect of Metamer Mismatching on Color Constancy in Two-Dimensional Simulated Scenes

A Study of Modified Infotaxis Algorithms in 2D and 3D Turbulent Environments.

Emergency response to hazardous gases in the environment is an important research field in environmental monitoring. In recent years, with the rapid development of sensor technology and mobile device technology, more autonomous search algorithms for hazardous gas emission sources are proposed in uncertain environment, which can avoid emergency personnel from contacting hazardous gas in a short distance. Infotaxis is an autonomous search strategy without a concentration gradient, which uses scattered sensor data to track the location of the release source in turbulent environment. This paper optimizes the imbalance of exploitation and exploration in the reward function of Infotaxis algorithm and proposes a mobile strategy for the three-dimensional scene. In two-dimensional and three-dimensional scenes, the average steps of search tasks are used as the evaluation criteria to analyze the information trend algorithm combined with different reward functions and mobile strategies. The results show that the balance between the exploitation item and exploration item of the reward function proposed in this paper is better than that of the reward function in the Infotaxis algorithm, no matter in the two-dimensional scenes or in the three-dimensional scenes.

Embedding and transmitting multi-dimensional optical information through noisy environments using a single-pixel detector

Fourier single-pixel imaging (FSPI) technique can encode a real two-dimensional scene to one-dimensional time-varying light intensity sequences traveling in the free space. In this work, FSPI technique with weighted light source is regarded as a carrier of multi-dimensional information while capturing the object scene using a lens-free photodetector, and can carry visible and invisible optical information simultaneously utilizing a novel spectrum division multiplexing technique. A wavelength division multiplexing technique is adopted to improve the channel capacity as well, in which a total of six watermarks are embedded once. More importantly, our scheme successfully demonstrates that triple information (host scene, visible, and invisible optical information) can be transmitted and retrieved with high fidelity in four noisy environments including external attack, environmental interference light, thick and dynamic scattering.

Elemental Image Generation Algorithm Using Reverse Iteration Along Optical Path

We propose a fast elemental image generation algorithm for integral imaging display based on depth information and a two-dimensional scene image. Existing elemental image generation algorithms establish many-to-one mappings from the scene to the elemental image, thereby including redundant calculations. The improved algorithm entails establishing a single mapping from elemental image to the scene. Each pixel in the elemental image matches a unique scene pixel by inverse iteration along the optical path. This establishes a high precision matching and eliminates hole caused by depth discontinuities. In this paper, we obtain a unique mapping solution by inverse iteration along the optical path using a one-pixel step. The time complexity of this algorithm is mainly driven by the pixel sum of the elemental image array. We generate the elemental image seven times faster than the original algorithm. The larger total quantity of the scene pixels further demonstrates the advantages of our algorithm. The experiments show that the update rate can reach more than 30 fps according to the VCD standard (352×240 resolution) using four parallel calculation threads with a four-core Intel® Core(TM) i7-7500U 2.70 GHz CPU despite various delay factors. The results verify the correctness of the theoretical analysis.

Three-dimensional landscape modeling of Quancheng Square in Jinan city

Nowadays, with the booming of tourism industry and the rapid development of modern urban construction, two-dimensional scene expression is quite far from satisfying people’s requirements for travelling, sightseeing, shopping and other aspects. Compared with the two-dimensional scene expression, three-dimensional scene expression can provide people with more realistic scenes display and more quality services. Therefore, we carried out three-dimensional landscape belt modeling in Quancheng Square for delivering people more vivid scenery and more details of the sights of Quancheng Square. The modeling procedure consists of three major parts, including the iconic objects modeling by SketchUp, the entire three-dimensional scene modeling and three-dimensional spatial analysis by Skyline. People can clearly learn about the landscape of Quancheng Square through the 3D models and obtain more travelling information of the landscape. Thus, it has been widely applied in many fields, such as virtual tourism, tourism resources management, urban planning and the study of dynamic changes in urban environment.

Applying constrained virtual environments to serious games for heritage

Virtual environments are an important aspect of serious games for heritage. However navigable three-dimensional (3D) environments can be costly and resource-intensive to create and for users to run. In this paper, we propose an alternative approach using “constrained virtual environments”, which present an environment through a series of reduced fidelity two-dimensional (2D) scenes without exhaustive detail. We describe the development of a constrained virtual environment to replicate a 3D environment from a serious game concerning ancient Mesopotamian history. An exploratory experiment discovered that participants experienced a similar sense of presence in the constrained environment to that of the 3D environment and rated the two games to be of similar quality. Participants were equally likely to pursue further information on the subject matter afterwards and collected more information tokens from within the constrained environment. A subsequent interview with a museum expert explored opportunities for such games to be implemented in museum displays, and based on the experiences and issues encountered, a preliminary set of guidelines was compiled for implementing future constrained virtual environments within serious games for heritage.

Fundamental visual features for aesthetic classification of photographs across datasets

The recent and exponential increase of online photographs is catalysing the development of artificial intelligence systems that evaluate images on their aesthetics in order to filter out photos and provide users with more pleasing content. This paper proposes a new approach inspired by findings in psychophysics and neuroscience, to build a cross-dataset aesthetic classifier which learns by extracting an efficient set of features from images. Inspired from low-level features present in the human early visual process, the artificial intelligent system extracts percentage distributions for orientation, curvature, colour and global reflectional symmetry. Knowing only people's aesthetic judgments on images, the features are then fed to a deep neural network under the form of only 114 inputs. Once trained, the proposed system was successful in classifying unseen images depending on their aesthetics to state-of-the-art level, even on datasets different from the initial training dataset. Analysis of differences in extracted features between aesthetically good and poor images highlights previously observed human aesthetic preferences in static two-dimensional scenes, such as preference for the colour blue or horizontal lines. By learning from brain-inspired features, it is hoped to allow a knowledge transfer of aesthetic expertise in photographs towards other types of visual media (paintings, movies, etc.).

Color Constancy in Two-Dimensional and Three-Dimensional Scenes: Effects of Viewing Methods and Surface Texture.

There has been debate about how and why color constancy may be better in three-dimensional (3-D) scenes than in two-dimensional (2-D) scenes. Although some studies have shown better color constancy for 3-D conditions, the role of specific cues remains unclear. In this study, we compared color constancy for a 3-D miniature room (a real scene consisting of actual objects) and 2-D still images of that room presented on a monitor using three viewing methods: binocular viewing, monocular viewing, and head movement. We found that color constancy was better for the 3-D room; however, color constancy for the 2-D image improved when the viewing method caused the scene to be perceived more like a 3-D scene. Separate measurements of the perceptual 3-D effect of each viewing method also supported these results. An additional experiment comparing a miniature room and its image with and without texture suggested that surface texture of scene objects contributes to color constancy.

Single-frequency microwave imaging with dynamic metasurface apertures

Conventional microwave imaging schemes, enabled by the ubiquity of coherent sources and detectors, have traditionally relied on frequency bandwidth to retrieve range information, while using mechanical or electronic beamsteering to obtain cross-range information. This approach has resulted in complex and expensive hardware when extended to large-scale systems requiring ultrawide bandwidth. Relying on bandwidth can create difficulties in calibration, alignment, and imaging of dispersive objects. We present an alternative approach using an electrically-large, dynamically reconfigurable, metasurface antenna that generates spatially-distinct radiation patterns as a function of tuning state. The metasurface antenna comprises a waveguide feeding an array of metamaterial radiators, each of whose properties can be modified by applying a voltage to a diode integrated into the element. By deploying two of these apertures, one as the transmitter and one as the receiver, we realize sufficient spatial diversity to alleviate the dependence on frequency bandwidth and obtain both range and cross-range information using measurements at a single frequency. We demonstrate the method experimentally, using one-dimensional dynamic metasurface apertures and reconstructing various two-dimensional scenes (range and cross-range). Furthermore, we modify a conventional microwave imaging technique---the range migration algorithm---to be compatible with such configurations, resulting in an imaging system that is fast and simple in both software and hardware. The imaging scheme presented in this paper has broad application to radio frequency imaging, including security screening, through-wall imaging, biomedical diagnostics, and synthetic aperture radar.

Low photon count based digital holography for quadratic phase cryptography.

Recently, the vulnerability of the linear canonical transform-based double random phase encryption system to attack has been demonstrated. To alleviate this, we present for the first time, to the best of our knowledge, a method for securing a two-dimensional scene using a quadratic phase encoding system operating in the photon-counted imaging (PCI) regime. Position-phase-shifting digital holography is applied to record the photon-limited encrypted complex samples. The reconstruction of the complex wavefront involves four sparse (undersampled) dataset intensity measurements (interferograms) at two different positions. Computer simulations validate that the photon-limited sparse-encrypted data has adequate information to authenticate the original data set. Finally, security analysis, employing iterative phase retrieval attacks, has been performed.