Angular Features Research Articles

EV-LFV: Synthesizing Light Field Event Streams From an Event Camera and Multiple RGB Cameras.

Light field videos captured in RGB frames (RGB-LFV) can provide users with a 6 degree-of-freedom immersive video experience by capturing dense multi-subview video. Despite its potential benefits, the processing of dense multi-subview video is extremely resource-intensive, which currently limits the frame rate of RGB-LFV (i.e., lower than 30 fps) and results in blurred frames when capturing fast motion. To address this issue, we propose leveraging event cameras, which provide high temporal resolution for capturing fast motion. However, the cost of current event camera models makes it prohibitive to use multiple event cameras for RGB-LFV platforms. Therefore, we propose EV-LFV, an event synthesis framework that generates full multi-subview event-based RGB-LFV with only one event camera and multiple traditional RGB cameras. EV-LFV utilizes spatial-angular convolution, ConvLSTM, and Transformer to model RGB-LFV's angular features, temporal features, and long-range dependency, respectively, to effectively synthesize event streams for RGB-LFV. To train EV-LFV, we construct the first event-to-LFV dataset consisting of 200 RGB-LFV sequences with ground-truth event streams. Experimental results demonstrate that EV-LFV outperforms state-of-the-art event synthesis methods for generating event-based RGB-LFV, effectively alleviating motion blur in the reconstructed RGB-LFV.

Hand gesture recognition via deep data optimization and 3D reconstruction

Hand gesture recognition (HGR) are the most significant tasks for communicating with the real-world environment. Recently, gesture recognition has been extensively utilized in diverse domains, including but not limited to virtual reality, augmented reality, health diagnosis, and robot interaction. On the other hand, accurate techniques typically utilize various modalities generated from RGB input sequences, such as optical flow which acquires the motion data in the images and videos. However, this approach impacts real-time performance due to its demand of substantial computational resources. This study aims to introduce a robust and effective approach to hand gesture recognition. We utilize two publicly available benchmark datasets. Initially, we performed preprocessing steps, including denoising, foreground extraction, and hand detection via associated component techniques. Next, hand segmentation is done to detect landmarks. Further, we utilized three multi-fused features, including geometric features, 3D point modeling and reconstruction, and angular point features. Finally, grey wolf optimization served useful features of artificial neural networks for hand gesture recognition. The experimental results have shown that the proposed HGR achieved significant recognition of 89.92% and 89.76% over IPN hand and Jester datasets, respectively.

No-Reference Light Field Image Quality Assessment Exploiting Saliency

In the near future, the broadcasting scenario will be characterized by immersive content. One of the systems for capturing the 3D content of a scene is the Light Field imaging. The huge amount of data and the specific transmission scenario impose strong constraints on services and applications. Among others, the evaluation of the quality of the received media cannot rely on the original signal but should be based only on the received data. In this direction, we propose a no-reference quality metric for light field images which is based on spatial and angular characteristics. In more details, the estimated saliency and cyclopean maps of light field images are exploited to extract the spatial features. The angular consistency features are, instead, measured with the use of the Global Luminance Distribution knowledge and the Weighted Local Binary Patterns operator on Epipolar Plane Images. The effectiveness of the proposed metric is assessed by comparing its performance with state-of-the-art quality metrics using 4 datasets: SMART, Win5-LID, VALID 10-bit, and VALID 8-bit. Furthermore, the performance is analyzed in cross-datasets, with different distortions, and for different saliency maps. The achieved results show that the performance of the proposed model outperforms state-of-the-art approaches and perform well for different distortion types and with various saliency models.

Progressive spatial-angular feature enhancement network for light field image super-resolution

The intensity and direction of the light field (LF) can be recorded simultaneously by using LF cameras. However, since LF cameras sacrifice spatial resolution for higher angular resolution, the images acquired by LF cameras tend to have low spatial resolution. Therefore, LF image super-resolution (SR) has become an integral part of LF studies. Many existing LF image SR methods fail to fully utilize angular and spatial information due to only using partial sub-aperture images (SAIs). In this paper, we propose a progressive spatial-angular feature enhancement network (PSAFENet) to deal with the problem of missing information in LF image SR. Specifically, we first extract the spatial features of SAIs, the spatial and angular features contained in the macro-pixel images (MacPIs) by three different feature extraction modules. Then, these features are fed into a spatial-angular feature enhancement (SAFE) module to perform enhancement of spatial-angular information on the SAIs. To improve the reconstruction accuracy, we also use the information multi-distillation block (IMDB) to remove the redundant information before upsampling. Our network can well merge the angular and spatial information into each SAI, which facilitates the reconstruction of the LF images. Experimental results on five public datasets show that the proposed PSAFENet method outperforms existing methods in both qualitative and quantitative comparisons.

Human Sitting Postures Classification Based on Angular Features with Fuzzy-Logic Labeling

Musculoskeletal disorders (MSDs) are generally associated with sitting postures. Assessing and ensuring healthy sitting posture are indispensable aspects of reducing the occurrence of MSDs. This study aims to develop a system that allows office workers' body postures to be contactless and recognized by different classification methods while sitting on a chair and can be used for health applications. Five different sitting body postures have been determined within the scope of medical and health literature studies and relevant standards. Thirty subjects were asked to sit in these body postures for 30 seconds. While the subjects were sitting, skeleton point position defined as a pose data of the subjects were obtained from the Kinect device simultaneously. Five angles that are thought to distinguish sitting positions according to different joint positions were determined and calculated. The angle values that can take in the standard sitting position in the literature have been determined. According to these values, the angle values in other postures were determined. A rule-based fuzzy inference system was designed using angle values for labeling sitting posture data. Angle values were calculated to classify the labeled depth values, and an artificial neural network classifier was designed according to these angle values. As a result, five different sitting body postures were classified with KNN (K-Nearest Neighbours) and Neural Network (NN), respectively, with 98.9% and 97% overall accuracy values. The study was compared with other studies in the literature. In this context, a high-performance system design that can improve healthy sitting behaviors for office workers that can be used in both health applications and robot vision is presented.

Prediction of brown tide algae using improved Gramian angular field and deep learning based on laser-induced fluorescence spectrum

The frequent occurrence of algal blooms has seriously affected the marine environment and human production activities. Therefore, it is crucial to monitor the phytoplankton concentration in water bodies. In this study, a prediction method for brown tide algae using improved Gramian angular field (IGAF) and deep learning based on the laser-induced fluorescence spectrum was proposed. The method combined one-dimensional (1D) fluorescence spectrum with IGAF for image coding. The internal normalizing approach of the original Gramian angle field algorithm was upgraded from local to global, which can increase the difference between samples with various concentrations. Then, we established a novel technique that fully takes into account the Gramian angular difference field and Gramian angular summation field features, allowing it to control the main and sub-diagonal features and successfully convert 1D sequences into images by adding various weight factors. Using depthwise separable convolutional neural network to extract image features helps reduce model training parameters, paired with long short-term memory network to rapidly predict the concentration of brown tide. To confirm the actual performance of the given approach, ablation and contrast experiments were carried out, and the results showed that the method’s regression accuracy, R 2 was 97.8%, with the lowest mean square error and mean absolute error. This study investigated the transformation of 1D spectra into images using IGAF, which not only explored the application of the fluorescence spectrum image coding method for algal regression but also enabled the introduction of the potent benefits of deep learning image processing into the field of spectral analysis.

Machine-learning-based head impact subtyping based on the spectral densities of the measurable head kinematics

BackgroundTraumatic brain injury can be caused by head impacts, but many brain injury risk estimation models are not equally accurate across the variety of impacts that patients may undergo, and the characteristics of different types of impacts are not well studied. We investigated the spectral characteristics of different head impact types with kinematics classification. MethodsData were analyzed from 3262 head impacts from lab reconstruction, American football, mixed martial arts, and publicly available car crash data. A random forest classifier with spectral densities of linear acceleration and angular velocity was built to classify head impact types (e.g., football, car crash, mixed martial arts). To test the classifier robustness, another 271 lab-reconstructed impacts were obtained from 5 other instrumented mouthguards. Finally, with the classifier, type-specific, nearest-neighbor regression models were built for brain strain. ResultsThe classifier reached a median accuracy of 96% over 1000 random partitions of training and test sets. The most important features in the classification included both low- and high-frequency features, both linear acceleration features and angular velocity features. Different head impact types had different distributions of spectral densities in low- and high-frequency ranges (e.g., the spectral densities of mixed martial arts impacts were higher in the high-frequency range than in the low-frequency range). The type-specific regression showed a generally higher R2 value than baseline models without classification. ConclusionThe machine-learning-based classifier enables a better understanding of the impact kinematics spectral density in different sports, and it can be applied to evaluate the quality of impact-simulation systems and on-field data augmentation.

AngClust: Angle Feature-Based Clustering for Short Time Series Gene Expression Profiles.

When clustering gene expression, it is expected that correlation coefficients of genes in the same clusters are high, and that gene ontology (GO) enrichment analysis of most clusters will be significant. However, existing short-term gene expression clustering algorithms have limitations. To address this problem, we proposed a novel clustering process based on angular features for short-term gene expression. Our method (named AngClust) uses angular features to indicate the change of trend in gene expression levels at two neighboring time points. The changes of angles at multiple time points reflects the change of trend of the overall expression levels. Such changes are used to measure whether the expression trends of different genes are similar. To obtain functionally significant clusters from the clustering results, we evaluated numbers of genes in clusters, average correlation coefficient, fluctuation, and their correlation with GO term enrichment. The efficacy of AngClust outperform two other measures, Euclidean distance (ED) and dynamic time warping of correlation (DTW), on a dataset of yeast gene expression. The ratios of GO and pathway term-enriched of clusters of AngClust is higher than or equal to that of STEM and TMixClust on human, mouse, and yeast time series of gene expression.

Attention-based encoder–decoder network for depth estimation from color-coded light fields

Compressive light field cameras have attracted notable attention over the past few years because they can efficiently determine redundancy from light fields. However, much of the research has only concentrated on reconstructing the entire light field from compressed sampling, which ignores the possibility of directly extracting information such as depth from it. In this paper, we introduce a light field camera configuration with a random color-coded microlens array. Considering the color-coded light fields, we propose a novel attention-based encoder–decoder network. Specifically, the encoder part compresses the coded measurement into a low-dimensional representation that removes most redundancy, and the decoder part constructs the depth map directly from the latent representation. The attention mechanism enables the network to process spatial and angular features dynamically and effectively, thus significantly improving performance. Extensive experiments on synthetic and real-world datasets show that our method outperforms the state-of-the-art light field depth estimation method designed for non-coded light fields. To our knowledge, this is the first study that combines the color-coded light field with the attention-based deep learning approach, which provides a crucial insight into the design of enhanced light field photography systems.

High-Fidelity Light Field Reconstruction Method Using View-Selective Angular Feature Extraction

Deep learning (DL) provides an effective approach for light field (LF) reconstruction that aims to synthesize novel views from sparse-sampled views. However, it is challenging to address domain asymmetry when adopting spatial-angular interaction LF reconstruction methods. To solve this problem, a view-selective angular feature extraction block (VS-LFAFE) is proposed to obtain full-resolution angular features that enumerate whole view directions in a macropixel. By applying the VS-LFAFE, a novel LF reconstruction method is proposed with two subblocks: a spatial-angular feature extraction and fusion block and an angular upsampling block. Experimental results validate that the VS-LFAFE is effective, and the proposed method can achieve superior performance compared with the state-of-the-art methods.

A Novel Computationally Efficient Approach to Identify Visually Interpretable Medical Conditions from 2D Skeletal Data

Timely identification and treatment of medical conditions could facilitate faster recovery and better health. Existing systems address this issue using custom-built sensors, which are invasive and difficult to generalize. A low-complexity scalable process is proposed to detect and identify medical conditions from 2D skeletal movements on video feed data. Minimal set of features relevant to distinguish medical conditions: AMF, PVF and GDF are derived from skeletal data on sampled frames across the entire action. The AMF (angular motion features) are derived to capture the angular motion of limbs during a specific action. The relative position of joints is represented by PVF (positional variation features). GDF (global displacement features) identifies the direction of overall skeletal movement. The discriminative capability of these features is illustrated by their variance across time for different actions. The classification of medical conditions is approached in two stages. In the first stage, a low-complexity binary LSTM classifier is trained to distinguish visual medical conditions from general human actions. As part of stage 2, a multi-class LSTM classifier is trained to identify the exact medical condition from a given set of visually interpretable medical conditions. The proposed features are extracted from the 2D skeletal data of NTU RGB + D and then used to train the binary and multi-class LSTM classifiers. The binary and multi-class classifiers observed average F1 scores of 77% and 73%, respectively, while the overall system produced an average F1 score of 69% and a weighted average F1 score of 80%. The multi-class classifier is found to utilize 10 to 100 times fewer parameters than existing 2D CNN-based models while producing similar levels of accuracy.

Exploiting Angular and Spectral Features for B-Format Speech Separation with Deep Neural Networks

Exploiting Angular and Spectral Features for B-Format Speech Separation with Deep Neural Networks

View position prior-supervised light field angular super-resolution network with asymmetric feature extraction and spatial-angular interaction

Light field imaging can record the intensity and direction information of light rays in space, which attracts extensive attention. However, the trade-off between angular and spatial resolution cannot be avoided due to the limitation of sensors in commercial light field cameras. To mitigate this problem, this paper proposes the view position prior-supervised light field angular super-resolution network with asymmetric feature extraction and spatial-angular interaction. First, there is a severe information asymmetry between the spatial and angular dimensions in light fields with sparse views. The asymmetric feature extraction block is proposed to extract spatial and angular features with different receptive fields in an asymmetric manner. As a result, more light field intrinsic features are extracted, which improves the utilization rate of the limited light field information. Second, the existing methods usually ignore the correlations among the newly synthesized views. The spatial-angular interaction module is proposed to collect the local and global information, build relations between any two points in the feature space, and reconstruct light field consistencies. Thus, the complete view correlations can be established. Last but not least, we investigate the impact of the given views on each viewpoint and propose a loss function based on the view position prior, which reduces the quality difference among the synthesized sub-aperture images and further improves the network performance. Comprehensive experiments demonstrate that our method can perform best on all datasets, and the depth estimation results present a new perspective to show the superiority of the proposed method.

An explanation for hydrogen’s stability

The electron spin angular momentum features are revealed to be identical to those of a uniformly charged ring. The electric field for this configuration can capture the electron being accelerated toward a proton, thus explaining the stability of the hydrogen atom. In addition, the proposed atom structure describes the bonding of two hydrogen atoms, which illuminates the covalent bond formation, the electronic state, bond length, and binding energy. Additionally, important wave properties ascribed to photons are assigned to particles with mass such as wave energy, angular frequency, phase velocity, and quantum spin.

Locomotor posture and swimming-intensity quantification in starvation-stress behavior detection of individual fish

The welfare of fish is gaining importance in aquaculture. The starvation-stress behavior of fish is important for quantifying their welfare. Therefore, to detect the stress behavior of individual fish under starvation stress, this study proposes swimming intensity as a new parameter for quantifying the swimming activity of fish. This parameter considers the angular information of the fish, including the steering angle, tail-bending angle, and turning speed, as key factors. First, angular features are extracted. A multi-scale cascade perceptual network is proposed to generate limb masks of individual fish. The centroid coordinates of the fish limbs are calculated using the limb mask. The angular features of the fish are calculated by establishing a follower coordinate system using centroid coordinates of the limb. Considering the influence of the occlusion of multiple fish on limb-centroid extraction, two methods are proposed to estimate the centroid coordinates of fish limbs in slight and severe occlusions. Entropy and statistics are used to measure the magnitude of change in the motion of individual fish. Finally, the swimming intensity of the fish is quantified by combining the amplitude of the change in motion and turning speed. Results obtained via human observation and the evacuation of the gastrointestinal content-based long experiments of individual darkbarbel catfish (Pelteobagrus vachelli) reveal that the proposed method exhibits good performance in detecting starvation-stress behavior of darkbarbel catfish with an accuracy rate of at least 96.21 ± 1.42 % and a maximum false negative rate of 0.21 ± 0.05 %. The purpose of the study was to establish a potentially effective method for detecting and recognizing the starvation-stress behavior of individual fish to provide reliable theoretical support for the study of aquaculture management, particularly the precision-feeding strategy of fish, to ensure fish welfare.

Road Network Extraction from SAR Images with the Support of Angular Texture Signature and POIs

Urban road network information is an important part of modern spatial information infrastructure and is crucial for high-precision navigation map production and unmanned driving. Synthetic aperture radar (SAR) is a widely used remote-sensing data source, but the complex structure of road networks and the noises in images make it very difficult to extract road information through SAR images. We developed a new method of extracting road network information from SAR images by considering angular (A) and texture (T) features in the sliding windows and points of interest (POIs, or P), and we named this method ATP-ROAD. ATP-ROAD is a sliding window-based semi-automatic approach that uses the grayscale mean, grayscale variance, and binary segmentation information of SAR images as texture features in each sliding window. Since POIs have much-duplicated information, this study also eliminates duplicated POIs considering distance and then selects a combination of POI linkages by discerning the direction of these POIs to initially determine the road direction. The ATP-ROAD method was applied to three experimental areas in Shanghai to extract the road network using China’s Gaofen-3 imagery. The experimental results show that the extracted road network information is relatively complete and matches the actual road conditions, and the result accuracy is high in the three different regions, i.e., 89.57% for Area-I, 96.88% for Area-II, and 92.65% for Area-III. Our method together with our extraction software can be applied to extract information about road networks from SAR images, providing an alternative for enriching the variety of road information.

Robust Image Hashing Combining 3D Space Contour and Vector Angle Features

AbstractIn this paper, an image hashing scheme combining 3D space contour (TDSC) features with vector angle (VA) features is proposed. The proposed algorithm extracts the 3D contours of the local component variation features of the image and the expression changes of the local component of the image in the form of a 3D VA to improve the performance. First, the gray component of the color image is used to construct a 3D space and the contour change features of the local component of the gray image are extracted using multi-perspectives. Then, the opposite color component and the brightness component Y of the YCbCr color space are extracted from the input image. The angular features of several image components are, respectively, extracted in the 3D space. Finally, the TDSC features are combined with the VA features to obtain image hashing. The simulations demonstrate and validate that the proposed image hashing scheme not only has better classification performance compared with the other image hashing techniques but is also equipped with the performance of tamper localization.

Ray‐tracing assisted fingerprinting for localization in IoT Health 4.0

AbstractThe Internet of Things (IoT) can help healthcare companies establish Ambient Assisted Living (AAL). It permits innovative solutions to a well‐known problem, notably the real‐time localization and monitoring of patients and clinicians. Yet, there is no method for indoor localization in the healthcare context that delivers centimeter‐level precision. Although radio‐based localization has intriguing potential, the primary limitations of such a method are non‐line‐of‐sight radio signal transmission and multipath reflection, both of which lead to the lowest accuracy. The advanced 5G communication technology allows for the simultaneous utilization of temporal and angular features of a broadcast signal, as well as its multipath property, to accomplish precise target localization. This article presents a method for multipath channel‐assisted localization in the AAL system by utilizing the temporal and angular measurement of the signal concurrently. We simulated and tested the localization approach in an AAL system and achieved centimeter‐level accuracy over 90% of the time in all four cases.

A Pilot Study Comparing the Effects of Concurrent and Terminal Visual Feedback on Standing Balance in Older Adults.

While balance training with concurrent feedback has been shown to improve real-time balance in older adults, terminal feedback may simplify implementation outside of clinical settings. Similarly, visual feedback is particularly well-suited for use outside the clinic as it is relatively easily understood and accessible via ubiquitous mobile devices (e.g., smartphones) with little additional peripheral equipment. However, differences in the effects of concurrent and terminal visual feedback are not yet well understood. We therefore performed a pilot study that directly compared the immediate effects of concurrent and terminal visual feedback as a first and necessary step in the future design of visual feedback technologies for balance training outside of clinical settings. Nineteen healthy older adults participated in a single balance training session during which they performed 38 trials of a single balance exercise including trials with concurrent, terminal or no visual feedback. Analysis of trunk angular position and velocity features recorded via an inertial measurement unit indicated that sway angles decreased with training regardless of feedback type, but sway velocity increased with concurrent feedback and decreased with terminal feedback. After removing feedback, training with either feedback type yielded decreased mean velocity, but only terminal feedback yielded decreased sway angles. Consequently, this study suggests that, for older adults, terminal visual feedback may be a viable alternative to concurrent visual feedback for short duration single-task balance training. Terminal feedback provided using ubiquitous devices should be further explored for balance training outside of clinical settings.

On angular features of axial channeling radiation in crystals

Channeling of light relativistic particles in crystals is accompanied by intense radiation emission known as channeling radiation. Typically all calculations of channeling radiation aim in getting the total radiation intensity and its dependence on the parameters of particles and crystals. In the same time, since the discovery, the angular behaviours of channeling radiation have been studied just in a few works, in which only a polar dependence of the radiation intensity near the forward direction is estimated. However, simple analysis of the interaction potential predicts very specific features to be observed in the angular distributions of channeling radiation, especially at axial regime. In this work, for the first time, the expressions for angular and spectral distributions of electromagnetic radiation at axial channeling of relativistic charged particles in thin crystals are analytically refined within the QED theory. Obtained results allows predicting complex structures of radiation intensity and polarisation distributions. The results obtained might be of special interests for experimental studies.