Types Of Imagery Research Articles

P-Wave Detection Using a Fully Convolutional Neural Network in Electrocardiogram Images

Electrocardiogram (ECG) signal analysis is a critical task in diagnosing the presence of any cardiac disorder. There are limited studies on detecting P-waves in various atrial arrhythmias, such as atrial fibrillation (AFIB), atrial flutter, junctional rhythm, and other arrhythmias due to P-wave variability and absence in various cases. Thus, there is a growing need to develop an efficient automated algorithm that annotates a 2D printed version of P-waves in the well-known ECG signal databases for validation purposes. To our knowledge, no one has annotated P-waves in the MIT-BIH atrial fibrillation database. Therefore, it is a challenge to manually annotate P-waves in the MIT-BIH AF database and to develop an automated algorithm to detect the absence and presence of different shapes of P-waves. In this paper, we present the manual annotation of P-waves in the well-known MIT-BIH AF database with the aid of a cardiologist. In addition, we provide an automatic P-wave segmentation for the same database using a fully convolutional neural network model (U-Net). This algorithm works on 2D imagery of printed ECG signals, as this type of imagery is the most commonly used in developing countries. The proposed automatic P-wave detection method obtained an accuracy and sensitivity of 98.56% and 98.78%, respectively, over the first 5 min of the second lead of the MIT-BIH AF database (a total of 8280 beats). Moreover, the proposed method is validated using the well-known automatically and manually annotated QT database (a total of 11,201 and 3194 automatically and manually annotated beats, respectively). This results in accuracies of 98.98 and 98.9%, and sensitivities of 98.97 and 97.24% for the automatically and manually annotated QT databases, respectively. Thus, these results indicate that the proposed automatic method can be used for analyzing long-printed ECG signals on mobile battery-driven devices using only images of the ECG signals, without the need for a cardiologist.

THE RELATIONSHIP BETWEEN PASSION AND IMAGERY USE IN COMPETITIVE YOUTH GYMNASTICS

The purpose of the present study was to examine the relationship between passion and imagery use in competitive youth gymnastics. The participants included 245 (male, n = 10; female, n = 235) gymnasts between the ages of 7-16 years participating in women’s artistic gymnastics (n = 221), men’s artistic gymnastics (n = 7), as well as trampoline and tumbling (n = 17). Athletes completed questionnaires measuring the frequency of imagery use and their passion towards the sport of competitive gymnastics. A series of multiple regression analyses indicated that both harmonious and obsessive passion were significantly related to all five types of imagery. More specifically obsessive passion was most strongly associated with four of the five types of imagery (CS, CG, MS, and MG-A), and harmonious passion was most strongly associated with one of the five types of imagery (MG-M). Results and implications of passion and imagery use in competitive youth sport are discussed.

Initial development of the Dance Imagery Questionnaire for Children (DIQ-C): establishing content validity

Abstract Given the differences between young dancers’ and adult dancers’ use of imagery, a valid and reliable questionnaire specific to young dancers was necessary. The current study is the first phase of a multi-phase study in the development of the Dance Imagery Questionnaire for Children (DIQ-C). Specifically, the purpose of this study was to establish content validity of the DIQ-C. This was achieved through the following three stages: (1) definition, item, and scale development, (2) assessment of item clarity and appropriateness via cognitive interviews, and (3) assessment of item-content relevance via an expert rating panel. Guided by previous qualitative research with young dancers, 46 items representing seven subscales (i.e., imagery types) were developed. The initial item pool was then implemented during cognitive interviews with 16 dancers (15 females; M age=10.63, SD=1.82), which led to the removal of 13 items and the modification of 21 items. Consequently, the revised 33-item pool was then administered to an expert panel of four imagery researchers and four dance instructors to measure item-content relevance. This resulted in the removal of eight items, the revision of four items, and the merging of two subscales. Overall, the current study provides content validity evidence for a 25-item pool (representing five subscales) to be used in further development of the DIQ-C (i.e., identifying and establishing factor structure).

‘And part not with my revelations for a trifling price’: Reconceptualizing Islam’s Aniconism through the lenses of reification and representation as meaning-making

This article contends that Islamic prohibitions on certain types of figural imagery serve a dual function. In addition to fulfilling their traditional Islamic role related to preserving Allah’s sovereignty, such prohibitions also facilitate in preventing Islam’s essential message from being transformed via crass and sentimentalist reproductions of its most sacred symbols and figures. The article first defines the term ‘icon’ and then engages with the more recent discourse surrounding representation as meaning-making. It then connects Islamic aniconism to Marxist and post-structuralist concerns related to reification, commodification, and the transformation of sacred symbols into forms of personal property. The final part of this article engages with the rulings of Yusuf al-Qaradawi and Taha Jabir al-Alwani regarding figural imagery to show that these prohibitions are not absolute and that there are varying degrees of acceptability based on form, content, and purpose.

A novel remote sensing detection method for buildings damaged by earthquake based on multiscale adaptive multiple feature fusion

The rapid and accurate detection of damaged buildings after an earthquake are critical for emergency response. Given the difference in the textures of damaged parts and those of the original buildings, damaged buildings can be accurately detected through textural heterogeneity. However, quantitatively detecting damaged buildings using such heterogeneity from post-earthquake images is difficult. Therefore, we propose a method of automatically extracting house damage information from post-quake high-resolution optical remote sensing imagery through the multiscale fusion of spectral and textural features, which can be achieved in three steps. Firstly, the textural and spectral features of the images are enhanced at the pixel level. Secondly, the resulting feature images are fused at the feature level and the fused feature images are segmented using superpixels. Lastly, a post-quake house damage index model is constructed. Results show an overall accuracy of 76.75%, 75.35% and 83.25% for three different types of imagery. This finding indicates that the proposed algorithm can be used to extract damage information from multisource remote sensing data and provide useful guidance for post-disaster rescue and assessment based on regional house damage conditions.

Nonlinear Endmember Identification for Hyperspectral Imagery via Hyperpath-Based Simplex Growing and Fuzzy Assessment

Nonlinear geometric manifold of hyperspectral data usually makes great trouble for accurate endmember extraction in literature. To address this issue, we propose a novel nonlinear endmember extraction algorithm by building a hypergraph and a fuzzy assessment strategy. The global change of nonlinear data manifold is first measured in a hypergraph whose hyperedges correspond to different local pixel subgroups. In contrast to edges in a simple graph, every hyperpath connected by multiple hyperedges instead of individual pixels effectively facilitates the determination of the simplex spanned by endmembers on complex data manifolds interfered by noises and outliers. Furthermore, in the hypergraph-based manifold system, a reliable fuzzy assessment mechanism for extracting the final endmembers is established by combining the classic simplex volume maximization rule with the inherent properties of hyperspectral data and hypergraph. In this procedure, the impact of outliers and the complex geometric structures of data can be further effectively reduced, leading to better unmixing results. Experimental results of various types of simulated data and real hyperspectral imagery demonstrate that the proposed algorithm (hypergraph and fuzzy-assessment-based nonlinear endmember extraction) can extract more accurate endmembers from complex data manifolds, and it is more robust to noises and outliers compared with state-of-the-art algorithms.

Oil-Spill-Response-Oriented Information Products Derived From a Rapid-Repeat Time Series of SAR Images

New quantitative and semiautomated methods for analyzing oil slick evolution using a time series of $L$ -band synthetic aperture radar (SAR) images with short repeat time are developed and explored. In this study, two methods that are complementary in terms of identifying temporal changes within an oil slick are presented. The two methods reflect two ways of evaluating the oil slicks. The first method identifies regions within the slick that show persistently high damping ratio (the contrast between clean sea and oil intensity), using higher damping values as a proxy for increasing oil thickness. This method also weights the age of the scenes as the algorithm incorporates new images. The second method outputs the short-term drift pattern and the changes in the damping ratios and copolarization ratios between two scenes, proxies for thickness, and emulsification. Both methods can aid in identifying regions of high priority for oil recovery. Due to the simplicity of the methods, they can be adapted to time-series data from different types of sensors, e.g., optical and SAR imagery. The methods are demonstrated on three $L$ -band uninhabited aerial vehicle SAR UAVSAR time series acquired in November 2016 over a persistent seep in the Mississippi Canyon Block 20 of the Gulf of Mexico. The results of the two methods clearly show the movement and the weathering of the oil as a function of both time and location.

Brain-computer interface for post-stroke rehabilitation

In the paper we proposed the approach for increasing of quality of neurorehabilitation of post-stroke patients based on wavelet analysis of EEG signals recoded during motor imagery. Also we proposed brain-computer interface based on the method. We determined all necessary procedures required to find motor imagery type (kinesthetic or visual) for each individual patient and described subsequent rehabilitation process. We tested developed brain-computer interface on 20 participants with post-stroke motor impairment. We believe that developed system can be used not only in laboratory experimental conditions, but also in clinical ones.

Sky pixel detection in outdoor imagery using an adaptive algorithm and machine learning

Computer vision techniques enable automated detection of sky pixels in outdoor imagery. In urban climate, sky detection is an important first step in gathering information about urban morphology and sky view factors. However, obtaining accurate results remains challenging and becomes even more complex using imagery captured under a variety of lighting and weather conditions.To address this problem, we present a new sky pixel detection system demonstrated to produce accurate results using a wide range of outdoor imagery types. Images are processed using a selection of mean-shift segmentation, K-means clustering, and Sobel filters to mark sky pixels in the scene. The algorithm for a specific image is chosen by a convolutional neural network, trained with 25,000 images from the Skyfinder data set, reaching 82% accuracy for the top three classes. This selection step allows the sky marking to follow an adaptive process and to use different techniques and parameters to best suit a particular image. An evaluation of fourteen different techniques and parameter sets shows that no single technique can perform with high accuracy across varied Skyfinder and Google Street View data sets. However, by using our adaptive process, large increases in accuracy are observed. The resulting system is shown to perform better than other published techniques.

Optimization of multi-scale segmentation of satellite imagery using fractal geometry

ABSTRACTAfter decades of research, optimal scale selection for image segmentation remains a key scientific problem in image analysis. In order to contribute to a solution, a new method was developed in this research, based on the use of fractal dimension as an indicator of optimality. First, the image is partitioned according to the rank-size rule (stemming from the Zipf’s law), to detect a set of scales corresponding to constant fractal dimension over image rescaling; these scales are defined as ‘optimal’. Then, the detected scales are transferred to the segmentation process through the projection of every partition head group to the entire image; this can be seen as a topological transformation. The Fractal Net Evolution Assessment (FNEA) is applied as a segmentation algorithm. The new method was structured as a mathematical proposition, then proved and finally was experimented with three types of satellite imagery (namely, Sentinel-2, RapidEye, and WorldView2) in four study areas with diverse land uses. In all cases, the method achieved to indicate those scales at which fractal dimension remains constant, therefore, the optimal scales. The results were verified visually and showed to be successful. Also, they were compared to pre-existing classification data, revealing high correlation between fractal dimension and classification accuracy. The new method is considered to be a generic, fully quantitative, straightforward, objective, rapid, robust, and easy to apply image segmentation tool.

An Exploratory Study of Imagining Sounds and \u201cHearing\u201d Music in Autism

Individuals with autism spectrum disorder (ASD) reportedly possess preserved or superior music-processing skills compared to their typically developing counterparts. We examined auditory imagery and earworms (tunes that get “stuck” in the head) in adults with ASD and controls. Both groups completed a short earworm questionnaire together with the Bucknell Auditory Imagery Scale. Results showed poorer auditory imagery in the ASD group for all types of auditory imagery. However, the ASD group did not report fewer earworms than matched controls. These data suggest a possible basis in poor auditory imagery for poor prosody in ASD, but also highlight a separability between auditory imagery and control of musical memories. The separability is present in the ASD group but not in typically developing individuals.

Mental Imagery in Volleyball Settings: A Scoping Review

Abstract This article presents a scoping review of the published literature on mental imagery in the context of volleyball. Four online databases and one peer-reviewed journals were searched, based on the inclusion criteria. Publication information, procedures and research methods, sample characteristics, sport type and type of mental imagery were analyzed. The selection process found 52 studies, and results showed that from 1987 to 2018 the mental images, along with other psychological skills, have gained popularity in volleyball settings. The examined studies used cross-sectional and experimental approaches, and the analysis was assessed by quantitative data and/ or mental imagery training programs (with relaxation and scripts), mainly focusing on the isolated analysis of the mental imagery construct, and evaluating its use. The studies included both male and female participants, but only 11% were volleyball players. Additionally, the focus was on adolescents and young adult athletes, mostly from high school/ university or international/ elite levels. However, just two studies examined athletes under the age of 16. Considering the present study’s results, limitations, and its implications for practice (e. g. to provide the coach certification programs with specific mental imagery contents), future research directions were highlighted, such as improve mental imagery training programs for children and youth volleyball players.

ANALYSIS OF IMAGERY IN FIVE SELECTED POEMS BY MAYA ANGELOU

This research is qualitative descriptive research that analyzes five selected poems of Maya Angelou “I Know Why the Caged Bird Sing, Still I Rise, Phenomenal Woman, When I Think about My Self and the last is Alone”. The researcher used qualitative method and structural approach to know what kinds of imagery that used in five selected poems by Maya Angelou “I Know Why the Caged Bird Sing, Still I Rise, Phenomenal Woman, When I Think about My Self and the last is Alone”. Then, the data were collected from five poems selected by Maya Angelou. In the process analyzed, the researcher read the poem “I Know Why The Caged Bird Sing, Still I Rise, Phenomenal Woman, When I Think About My Self and the last is Alone” and using five steps based on the technique of analyzing the data by using structural approach to find what kinds of imagery that used in poem “I Know Why the Caged Bird Sing, Still I Rise, Phenomenal Woman, When I Think About My Self and the last is Alone” by Maya Angelou. Then based on the result of this analyzed, the researcher found there are four types of imagery that Maya Angelou used in his poem “I Know Why the Caged Bird Sing, Still I Rise, Phenomenal Woman, When I Think About My Self and the last is Alone” Those are visual imagery, auditory imagery, tactile imagery, gustatory imagery and last is kinesthetic imagery, but the kinds of imagery that very dominant used is auditory imagery. Keywords: Poetry, Imagery, Method and Approach

Kelp-bed dynamics across scales: Enhancing mapping capability with remote sensing and GIS

Kelp are important drivers of productivity and biodiversity patterns in cold-water and nutrient-rich rocky reefs. Scuba- and boat-based methods are routinely used to study submerged kelp beds. However, these time-consuming and labor-intensive methods enable monitoring of beds or the factors and processes that control their distribution over only small spatial (few 100s of m2) and temporal (<5 years) scales. Remote sensing and geographic information system (GIS) technologies are increasingly used to compare marine species distribution over multiple spatiotemporal scales. However, there is currently no clear framework and limited demonstration of their potential for studies of broad-scale changes in completely submerged kelp beds. The present study aims to establish the foundation of a simple, accessible, and robust set of remote sensing and GIS-based methods to address key questions about the stability of subtidal kelp beds across multiple spatial and temporal scales. It tests the suitability of conventional image classification methods for mapping kelp from digital aerial (acquired on board a helicopter) and satellite (SPOT 7) imagery of ~250 ha of seabed around four islands in the Mingan Archipelago (northern Gulf of St. Lawrence, Canada). Three classification methods are compared: 1) a software-led unsupervised classification in which pixels are grouped into clusters based on similarity in spectral signature among pixels; 2) a software-led supervised classification in which pixels are assigned to categories based on similarity in the spectral signature of pixels and that of reference data from each category; and 3) a visual classification carried out by a trained observer. Supervised classification of satellite imagery and visual classification of aerial imagery were the top methods to map kelp, with overall accuracies of 89% and 90%, respectively. Unsupervised classification of both types of imagery showed poor discrimination between kelp and non-kelp benthic classes. Kelp bed edges were more difficult to identify on satellite than aerial imagery because the former presented poorer contrasts and a lower spatial resolution. Kelp bed edges identified with visual classification appeared artificially jagged for both types of imagery, mainly because of the coarse (225-m2) spatial units used for this classification. Kelp bed edges were smoother on maps created with the unsupervised and supervised classifications, which used 1-m-pixel images. The present study demonstrates that conventional remote sensing and GIS methods can accurately map submerged kelp beds over large spatial domains in the Mingan Archipelago or in other benthic systems with similar oceanic conditions and a largely dichotomous (kelp-barrens) biological makeup.

Kosovo Crucified—Narratives in the Contemporary Serbian Orthodox Perception of Kosovo

In contemporary Serbian Orthodox texts, Kosovo is often referred to as the Serbian “Jerusalem”: a city calling for a Christian defense. All Serbs are bound to heed the call in keeping with the Kosovo “covenant” or “pledge” dating back to the Battle of Kosovo Polje in 1389, when Serbian troops, led by Prince Lazar, were defeated by the invading Muslim Ottoman army. The battle and Kosovo in general have since then assumed a central symbolic role in Serbian nationalism and the Serbian Orthodox Church. Furthermore, it has been claimed that the imagery and narratives of Kosovo were the ideological backdrop for the wars in the Balkans in the 1990s. This article investigates the development of the Serbian narratives and imagery pertaining to Kosovo and their modern form in the Serbian Orthodox Church in order to trace what type of imagery is dominant. The main focus will be on whether and to what extent the narratives of Christian defense and holy Serbian warriors fighting in the name of Christ are dominant. This investigation seeks to discuss whether the Kosovo imagery and narratives are formed upon and influenced by a broader Christian European antemurale myth.

An integrated methodology for the study of Atlantic Rock Art

During the Neolithic and Early Bronze Age periods, a specific type of abstract and geometric imagery was being carved on natural outcrops and boulders in the landscapes of western Europe. Often known as Atlantic Rock Art, this prehistoric carving tradition is renowned for its cup-and-ring motifs, cup-marks, wavy grooves and other types of circular imagery. These can be found in the modern countries of Spain, Portugal, Britain and Ireland, where the tradition shares striking similarities. Although these resemblances had been noted in the mid-1950s, they were never fully understood and seldomly studied systematically.This paper offers a discussion on the multi-scalar interdisciplinary methodology used on a large-scale project which studied the phenomenon of Atlantic Rock Art, assessing its similarities and differences between study areas in the aforementioned countries. This approach focused on a number of features of the rock art, extending the investigation beyond the motifs which are obviously similar. The different scales of analysis focused on small details, such as the iconography and carving techniques, followed by a larger scale related to the rock medium, and a final wider scale, that of the landscape in which the carvings are located. The investigation was carried out with a multi-faceted perspective and a combination of methods which included computer modelling for spatial analysis and digital recording techniques, contrasted with more traditional and sensorial/experiential approaches. Results revealed a carving tradition which encompassed many resemblances but was still capable of maintaining unique characters. The study demonstrated the importance of rock art in the narrative of prehistoric Atlantic Europe, contributing to a clearer insight into the connectivity and cultural transmission taking place on the Atlantic seaboard during the Neolithic and Early Bronze Age.

Ensemble-Based Systems to Monitor Algal Bloom With Remote Sensing

In this paper, an ensemble-based system (EBS) for estimating chlorophyll-a concentrations (Chl-a) in inland water bodies using downscaled MODIS images was developed. Seeking additional opinions before making a decision is part of human nature, particularly for decisions involving health issues. The general concept behind EBS algorithms is based on this principle. Forty-six Chl-a measurements, collected over four water bodies between 2000 and 2008, were used to calibrate the EBS. Measurements ranged from 2.7 (oligotrophic waters) to 91 000 mg Chl-a $\text{m}^{{-3}}$ (hypertrophic waters). The EBS performance was evaluated by cross validation, and also using an independent database (Chl-a ranging from 1 to 14 mg $\text{m}^{{-3}}$ ). Cross-validation results were satisfactory both under high-blooming conditions ( $\text{R}^{2} = 0.98$ , relative RMSE (RMSEr) = 15%, relative Bias (BIASr) = −2%, and relative Nash (NASHr) = 0.95) and at the initialization of blooms ( $\text{R}^{2} = 0.77$ , RMSEr = 37%, BIASr = −8%, and NASHr = 0.70). The EBS also performed well on the independent database ( $\text{R}^{2} =0.93$ , RMSEr = 50%, BIASr = −27%, and NASHr = 0.70). A visual comparison mapping Chl-a on the Missisquoi Bay of Lake Champlain additionally illustrates the potential of the EBS to detect early phases of algal growth contrary to other models (adaptive model, Kharu, and APProach by Elimination). This approach is a proof of concept for a more efficient way to quantify algal biomass from remote sensing and could be exported to any types of satellite imagery in a context of water quality monitoring.

Rapid estimation of bathymetry from multispectral imagery without in situ bathymetry data.

Optimization-based semi-analytical methods (OSMs) and empirical methods (EMs) have been developed to derive bathymetry maps from satellite-based multispectral data of coral reefs, allowing for the management, monitoring, and protection of coral reefs. However, OSMs are often criticized due to the time-consuming requirements of iterative computations, yet they are praised for working without the need for in situ bathymetry data. EMs are praised for their time-saving characteristics and criticized for their need for in situ measurements. To estimate the water depth from multispectral data quickly without in situ bathymetry data, we provide a new EM that combines our previously developed OSM called the unmixing-based multispectral optimization process exemplar method (UMOPE) and an EM called Stumpf's ratio method (SRM). In the new method, reflectance values from a small number of sampled pixels and the corresponding water depths estimated by UMOPE are used to determine the regression parameters for SRM. Thus, SRM determines the upper limit of accuracy for the new method, and UMOPE determines the possibility of reaching the upper limit. The new method was evaluated using three types of imagery of Xisha Islands, namely, WorldView-2 imagery with three traditional visible bands (WV-2a), Landsat 8 imagery with four visible bands, and WV-2 imagery with six visible bands (WV-2b). The results show that the new method can perform as well as SRM for Landsat 8 data and WV-2b data with similar root mean square error values at different depths. The lack of a coastal band in WV-2a imagery may cause large errors for the new method in deep water regions, especially when the water-leaving reflectance is noise perturbed. We found that even though the depths estimated by UMOPE are not error free at different ranges of water depth, if the regression line between the depths estimated by UMOPE and the measured depths is near the 1:1 line, the new method can perform as well as SRM. The new method may facilitate the rapid estimation of bathymetry from free Landsat 8 data of optically shallow waters around the world without in situ bathymetry data.

Distinct Mechanisms of Imagery Differentially Influence Speech Perception.

Neural representation can be induced without external stimulation, such as in mental imagery. Our previous study found that imagined speaking and imagined hearing modulated perceptual neural responses in opposite directions, suggesting motor-to-sensory transformation and memory retrieval as two separate routes that induce auditory representation (Tian and Poeppel, 2013). We hypothesized that the precision of representation induced from different types of speech imagery led to different modulation effects. Specifically, we predicted that the one-to-one mapping between motor and sensory domains established during speech production would evoke a more precise auditory representation in imagined speaking than retrieving the same sounds from memory in imagined hearing. To test this hypothesis, we built the function of representational precision as the modulation of connection strength in a neural network model. The model fitted the magnetoencephalography (MEG) imagery repetition effects, and the best-fitting parameters showed sharper tuning after imagined speaking than imagined hearing, consistent with the representational precision hypothesis. Moreover, this model predicted that different types of speech imagery would affect perception differently. In an imagery-adaptation experiment, the categorization of /ba/-/da/ continuum from male and female human participants showed more positive shifts towards the preceding imagined syllable after imagined speaking than imagined hearing. These consistent simulation and behavioral results support our hypothesis that distinct mechanisms of speech imagery construct auditory representation with varying degrees of precision and differentially influence auditory perception. This study provides a mechanistic connection between neural-level activity and psychophysics that reveals the neural computation of mental imagery.

Imagined Interactions and Inner Speech

Similarities and differences between inner speech and imagined interactions (IIs) are discussed. Selected studies in both areas are reviewed. Inner speech originally was conceptualized as a stage in language acquisition and the process of thought. It reflects speaking to oneself in the form of monologue. It has been referred to as verbal thinking, inner speaking, covert self-talk, internal monologue, and internal dialogue as people talk to themselves in silence. IIs are similar, yet different in that imaginary dialogue reflects talking to another person while monologue is self-talk. IIs are a type of daydreaming, social cognition, and mental imagery in which people experience cognitive representations of conversation with accompanying verbal and nonverbal features. We highlight relevant studies in the areas of daydreaming, public speaking, rumination, cardiovascular arousal, and road rage. We conclude with two suggestions for future research.