Representation Of Imagery Research Articles

A new feature extraction scheme based on support optimization in Enhanced Multivariance Products Representation for Hyperspectral Imagery

A new feature extraction scheme based on support optimization in Enhanced Multivariance Products Representation for Hyperspectral Imagery

Analysis of image representations of suicide prevention on the yellow september website

Introduction: The present essay consists in an analysis of imagery representations of suicide prevention on the Yellow September Campaign website, with the main goal of demosrating how this Campaign is essential for the mental health and well-being improvement of Brazilian individuals. The inquiry had as a methodology the research classified as descriptive and qualitative, focused in Brazil. The enphasized problematic comprehends the usage of the Gestalt approach and its basic concepts. Five images of the website Campaign were analyzed with the purpose of noticing how Gestalt, specially through its concept of background figure, contributes so that the perception of the images are used by individuals and professionals, in the sense that, through awareness and phenomenology, allow the resignification of the individuals’ lives, being perceived and discussed in the data analysis, that the images come to break taboos of debate about the theme, bringing light to the issue, through the individual’s awarness, the caring about the previous diagnosis and with the effective participation of the therapist, as much as with ideas of life appreciation and mainly a care about the human beings, concluding at last, that it is possible an individual awareness of how to do and what to do in the present moment, that is, here and now, through choices that this individual can have a better well-being and mental health as a being in the world, this way bringing more positive results for the Yellow September Campaign, through a reading of Gestalt Therapy.

Revolutionizing Wildfire Detection Through UAV-Driven Fire Monitoring with a Transformer-Based Approach

The rapid detection and accurate localization of wildfires are critical for effective disaster management and response. This study proposes an innovative Unmanned aerial vehicles (UAVs)-based fire detection system leveraging a modified Miti-DETR model tailored to meet the computational constraints of drones. The enhanced architecture incorporates a redesigned AlexNet backbone with residual depthwise separable convolution blocks, significantly reducing computational load while improving feature extraction and accuracy. Furthermore, a novel residual self-attention mechanism addresses convergence issues in transformer networks, ensuring robust feature representation for complex aerial imagery. The model, which was trained on the FLAME dataset encompassing diverse fire scenarios, demonstrates superior performance in terms of Mean Average Precision (mAP) and Intersection over Union (IoU) metrics compared to existing systems. Its capability to detect and localize fires across varied backgrounds highlights its practical application in real-world scenarios. This advancement represents a pivotal step forward in applying deep learning for real-time wildfire detection, with implications for broader emergency management applications.

Ensuring Appropriate Representation in Artificial Intelligence-Generated Medical Imagery: Protocol for a Methodological Approach to Address Skin Tone Bias.

In medical education, particularly in anatomy and dermatology, generative artificial intelligence (AI) can be used to create customized illustrations. However, the underrepresentation of darker skin tones in medical textbooks and elsewhere, which serve as training data for AI, poses a significant challenge in ensuring diverse and inclusive educational materials. This study aims to evaluate the extent of skin tone diversity in AI-generated medical images and to test whether the representation of skin tones can be improved by modifying AI prompts to better reflect the demographic makeup of the US population. In total, 2 standard AI models (Dall-E [OpenAI] and Midjourney [Midjourney Inc]) each generated 100 images of people with psoriasis. In addition, a custom model was developed that incorporated a prompt injection aimed at "forcing" the AI (Dall-E 3) to reflect the skin tone distribution of the US population according to the 2012 American National Election Survey. This custom model generated another set of 100 images. The skin tones in these images were assessed by 3 researchers using the New Immigrant Survey skin tone scale, with the median value representing each image. A chi-square goodness of fit analysis compared the skin tone distributions from each set of images to that of the US population. The standard AI models (Dalle-3 and Midjourney) demonstrated a significant difference between the expected skin tones of the US population and the observed tones in the generated images (P<.001). Both standard AI models overrepresented lighter skin. Conversely, the custom model with the modified prompt yielded a distribution of skin tones that closely matched the expected demographic representation, showing no significant difference (P=.04). This study reveals a notable bias in AI-generated medical images, predominantly underrepresenting darker skin tones. This bias can be effectively addressed by modifying AI prompts to incorporate real-life demographic distributions. The findings emphasize the need for conscious efforts in AI development to ensure diverse and representative outputs, particularly in educational and medical contexts. Users of generative AI tools should be aware that these biases exist, and that similar tendencies may also exist in other types of generative AI (eg, large language models) and in other characteristics (eg, sex, gender, culture, and ethnicity). Injecting demographic data into AI prompts may effectively counteract these biases, ensuring a more accurate representation of the general population.

XIV-LDDMM Toolkit: A Suite of Image-Varifold Based Technologies for Representing and Mapping 3D Imaging and Spatial-omics Data Simultaneously Across Scales.

Advancements in imaging and molecular techniques enable the collection of subcellular-scale data. Diversity in measured features, resolution, and physical scope of capture across technologies and experimental protocols pose numerous challenges to integrating data with reference coordinate systems and across scales. This resource paper describes a collection of technologies that we have developed for cross-modality 3D mapping for the alignment of transcriptomics at the micron scales of genes and cells to the anatomical tissue scales. Our collection of technologies include (i) an explicit censored data representation for the partial matching problem mapping whole brains to subsampled subvolumes, (ii) image-varifold measure norms for supporting nearly universal crossing of modality, (iii) a multi, scale-space optimization technology for generating resampling grids optimized to represent spatial geometry at fixed complexities, and (iv) mutual-information based functional feature selection. Collectively, these methods afford efficient representations of peta-scale imagery providing the algorithms for mapping from the nano to millimeter scales which we term cross-modality image-varifold LDDMM (xIV-LDDMM).

Humans underestimate the movement range of their own hands

Motor planning and motor imagery are assumed to use veridical internal representations of the biomechanical properties of our limbs. Here, we report that people underestimate their hands’ range of motion. We used two tasks probing representations of own motion range, estimation and imagery, in which participants were supposed to judge their rotational hand movement ranges. In both tasks participants’ judgments were underestimated in three out of four cardinal directions. We suggest that this representational bias provides an optimal balance between movement efficiency and safety in face of the inherently stochastic nature of movement execution.

QURANIC IMAGERY OF SABR (PATIENCE)

This study investigates the Quranic representation of both kinetic and abstract imagery to elucidate the significance and essence of sabr as conveyed in the verses of the Quran. Both abstract and kinetic pictures are analyzed to enhance the imagery elaboration of the abstract idea of sabr as presented in the verses of the Quran. The abstract image underwent a first evaluation to ascertain the value and quality of sabr. The study examines diverse methods of practicing sabr through abstract concepts, including confidence, hope, serenity, piety, and obedience, which are classified as faith and piety. The study identified the representation of dynamic visuals in the portrayal of the tangible concept of sabr. The concrete images of sabr are interpreted through two techniques of kinetic imagery, represented by second and third-person pronouns. The second pronoun is employed to engage the reader in vivid imagery, while the third pronoun aims to facilitate readers visualization of the narratives of sabr. This study assessed the kinetic and abstract representations in interpreting abstract qualities and values. Examining the representations of abstract concepts from the Quran is recommended to attain a more profound comprehension of them.

3D printing for teaching and exploration in astronomy for individuals with blindness/visual impairment: textured representations of imagery

ABSTRACT Astronomy, a captivating field that draws upon science, mathematics, and engineering, has traditionally relied on visual representations to convey the wonders of the cosmos. While this approach effectively engages the sighted population, the use of imagery can exclude individuals with blindness or visual impairment (B/VI). Astronomical research is incorporated into press releases, media, outreach efforts, and educational systems aimed at enhancing public interest and often skill in science, but visual materials can hamper a population with B/VI. This paper explores the potential of 3D printing as an assistive technology providing an alternative to imagery. We produced textured 3D prints of astronomical research data from the Hubble Space Telescope (HST). Useability assessment of materials is an important phase of production before integration into structured programs, and we used a multi-phased approach in our prior research to create and test appropriate textures for 3D astronomical prints. This paper describes the last step of reviewing our 3D prints through informal useability sessions with diverse individuals. The assessment indicated our 3D prints provide reliable, informative representations of astronomical data appropriate for public use especially for public information, outreach programs, and science education for individuals with B/VI.

A Complete Scheme for Multi-Character Classification Using EEG Signals From Speech Imagery.

Some classification studies of brain-computer interface (BCI) based on speech imagery show potential for improving communication skills in patients with amyotrophic lateral sclerosis (ALS). However, current research on speech imagery is limited in scope and primarily focuses on vowels or a few selected words. In this paper, we propose a complete research scheme for multi-character classification based on EEG signals derived from speech imagery. Firstly, we record 31 speech imagery contents, including 26 alphabets and five commonly used punctuation marks, from seven subjects using a 32-channel electroencephalogram (EEG) device. Secondly, we introduce the wavelet scattering transform (WST), which shares a structural resemblance to Convolutional Neural Networks (CNNs), for feature extraction. The WST is a knowledge-driven technique that preserves high-frequency information and maintains the deformation stability of EEG signals. To reduce the dimensionality of wavelet scattering coefficient features, we employ Kernel Principal Component Analysis (KPCA). Finally, the reduced features are fed into an Extreme Gradient Boosting (XGBoost) classifier within a multi-classification framework. The XGBoost classifier is optimized through hyperparameter tuning using grid search and 10-fold cross-validation, resulting in an average accuracy of 78.73% for the multi-character classification task. We utilize t-Distributed Stochastic Neighbor Embedding (t-SNE) technology to visualize the low-dimensional representation of multi-character speech imagery. This visualization effectively enables us to observe the clustering of similar characters. The experimental results demonstrate the effectiveness of our proposed multi-character classification scheme. Furthermore, our classification categories and accuracy exceed those reported in existing research.

Dual-stage color calibration of UAV imagery using multivariate regression and deep learning

Accurate color representation in UAV-based imagery is critical in high-throughput plant phenotyping and precision agriculture for monitoring crop health and identifying nutrient deficiencies or water stress. However, achieving color accuracy is challenging due to varying lighting conditions and flight altitudes in UAV operations. To overcome these challenges, we propose an automated dual-phase approach for consistent color calibration in UAV imagery, integrating traditional color calibration methods with cutting-edge deep learning algorithms. In the first phase, we derived a color calibration matrix (M) for four color boards placed within the imaging area. This involved mapping the observed color values in the UAV images to their true color values, as measured by a colorimeter, using multivariate linear regression (MLR). The individual matrices from each board were then merged into a single, weighted average matrix, which was then applied to the UAV images for color correction, resulting in standardized color outputs. In the second phase, we developed a deep learning model, trained on the MLR-corrected images as a ground truth. This model assigns varying weights for different pixels across the image scene, translating the image from a distorted to a color-corrected image using image-to-image regression without manual intervention. We validated this approach using 184 UAV images of various sizes, ranging from 3969 × 2589 to 15257 × 15098 pixels, captured under a wide array of lighting conditions and altitudes. The results show that our deep learning model significantly outperforms existing methods, with a marginal color error of 1.52 %. This two-phase color correction method greatly improves operational efficiency by removing the necessity for physical color boards in each image and allowing for automated processing after training.

VINCENT: Cyber-threat detection through vision transformers and knowledge distillation

Vision Transformers (ViTs) denote a family of attention-based deep learning techniques that have recently achieved amazing results in various problems related to the field of computer vision. In this paper, we explore the use of ViTs in problems of cyber-threat detection related to malware and network intrusion detection. In particular, we propose VINCENT, that is a novel deep neural method, which resorts to a color imagery representation of cyber-data by encoding related cyber-data features into neighboring color pixels. ViTs are trained from cyber-data images as teacher models, to extract explainable imagery signatures of cyber-data classes. This knowledge is extracted by leveraging the self-attention mechanism to give paired attention values between pairs of imagery patches. The signature knowledge, extracted through the ViT teacher, is, finally, used to train a smaller neural student model according to the knowledge distillation theory. Experiments with various benchmark cybersecurity datasets assess the accuracy of the student model VINCENT also compared to that of several state-of-the-art methods. In addition, it shows that VINCENT can obtain insights from explanations recovered through the self-attention mechanism of the ViT teacher.

Inequivalent and uncorrelated response priming in motor imagery and execution.

Theoretical considerations on motor imagery and motor execution have long been dominated by the functional equivalence view. Previous empirical works comparing these two modes of actions, however, have largely relied on subjective judgments on the imagery process, which may be exposed to various biases. The current study aims to re-examine the commonality and distinguishable aspects of motor imagery and execution via a response repetition paradigm. This framework aims to offer an alternative approach devoid of self-reporting, opening the opportunity for less subjective evaluation of the disparities and correlations between motor imagery and motor execution. Participants performed manual speeded-choice on prime-probe pairs in each trial under three conditions distinguished by the modes of response on the prime: mere observation (Perceptual), imagining response (Imagery), and actual responses (Execution). Responses to the following probe were all actual execution of button press. While Experiment 1 compared the basic repetition effects in the three prime conditions, Experiment 2 extended the prime duration to enhance the quality of MI and monitored electromyography (EMG) for excluding prime imagery with muscle activities to enhance specificity of the underlying mechanism. In Experiment 1, there was no significant repetition effect after mere observation. However, significant repetition effects were observed in both imagery and execution conditions, respectively, which were also significantly correlated. In Experiment 2, trials with excessive EMG activities were excluded before further statistical analysis. A consistent repetition effect pattern in both Imagery and Execution but not the Perception condition. Now the correlation between Imagery and Execution conditions were not significant. Findings from the current study provide a novel application of a classical paradigm, aiming to minimize the subjectivity inherent in imagery assessments while examining the relationship between motor imagery and motor execution. By highlighting differences and the absence of correlation in repetition effects, the study challenges the functional equivalence hypothesis of imagery and execution. Motor representations of imagery and execution, when measured without subjective judgments, appear to be more distinguishable than traditionally thought. Future studies may examine the neural underpinnings of the response repetition paradigm to further elucidating the common and separable aspects of these two modes of action.

Icônica representação feminina e mítica de Medeia no Mundo Antigo

We are interested in rescuing the actions and textual and imagery representations of the mythical figure of Medea that make it clear that narrative can define in three cycles, namely: the Cycles of Yolkos, the Cycle of the Argonauts, and the Roman Cycle. The cycles demarcate the different temporalities, and changes in the highlighted theme, but maintain an identity structure that we call the identity core. The character maintains as her identity core the fact that she is a mature woman, married to Jason, has children and has expertise in the manipulation of herbs and magic drugs. The nucleus does not change and goes beyond time, as the recurrent narratives in Medea's texts and images reveal. Key-words: Medeia image representation narrative re-signification

Investigating the different mechanisms in related neural activities: a focus on auditory perception and imagery.

Neuroimaging studies have shown that the neural representation of imagery is closely related to the perception modality; however, the undeniable different experiences between perception and imagery indicate that there are obvious neural mechanism differences between them, which cannot be explained by the simple theory that imagery is a form of weak perception. Considering the importance of functional integration of brain regions in neural activities, we conducted correlation analysis of neural activity in brain regions jointly activated by auditory imagery and perception, and then brain functional connectivity (FC) networks were obtained with a consistent structure. However, the connection values between the areas in the superior temporal gyrus and the right precentral cortex were significantly higher in auditory perception than in the imagery modality. In addition, the modality decoding based on FC patterns showed that the FC network of auditory imagery and perception can be significantly distinguishable. Subsequently, voxel-level FC analysis further verified the distribution regions of voxels with significant connectivity differences between the 2 modalities. This study complemented the correlation and difference between auditory imagery and perception in terms of brain information interaction, and it provided a new perspective for investigating the neural mechanisms of different modal information representations.

Inferring cascading effects of natural and built environments failures from social media videos in disasters

The objective of this study is to infer the cascading effects between disruptive events in disasters using content coding and imagery representation techniques on social media videos. It is well recognized that disaster events are cascading due to the dynamic interactions between natural and built environments. Understanding why and how the failures of natural and built environments such as road and vehicle damages cascade is critical for retrospective investigation and disaster adaptation. Social media provides high-volume and real-time data, which has been broadly used in disaster management. What's more, the immense availability of social media videos, which record the facts of disaster events with high spatial and temporal resolutions, provides a unique opportunity to review and uncover the cascading process in disasters. Here, we propose an analytical method from two theoretical perspectives: content understanding and the philosophy of cascading effects. First, we establish a content classification system to code social media videos with respect to specific disaster events. Then, an imagery representation approach is developed to capture the cascading effects based on the observed relations from the social media videos. To examine the application of these methods and to derive new insights for the cascading effects, we leverage empirical social media video data collected from Weibo, a popular Chinese microblogging platform, during Typhoon Lekima. The coding results show that social media videos contain a wealth of information regarding disaster impacts, enabling us to unveil the occurrences and processes of the natural and built environment failures. By establishing cascading relations among disaster failures, we can present a whole picture of the complex disaster events induced by typhoons. The proposed method and findings of this study can contribute to not only computational modeling of cascading failures within natural and built environment, but also informing the design and planning of cities in adapting to future disasters.

Representation Of Color Imagery in Usman Nasir's Poetry

The article analyzes the poetry of Usman Nasir, one of the talented representatives of 20th century Uzbek literature. It also depicted the poet's skill in the color image used in the poet's poetry.

Development of a Mathematical Apparatus with an Imagery Representation of Information for Neuromorphic Systems

Development of a Mathematical Apparatus with an Imagery Representation of Information for Neuromorphic Systems

Drawing as a tool for investigating the nature of imagery representations of blind people: The case of the canonical size phenomenon.

Several studies have shown that blind people, including those with congenital blindness, can use raised-line drawings, both for "reading" tactile graphics and for drawing unassisted. However, research on drawings produced by blind people has mainly been qualitative. The current experimental study was designed to investigate the under-researched issue of the size of drawings created by people with blindness. Participants (N = 59) varied in their visual status. Adventitiously blind people had previous visual experience and might use visual representations (e.g., when visualising objects in imagery/working memory). Congenitally blind people did not have any visual experience. The participant's task was to draw from memory common objects that vary in size in the real world. The findings revealed that both groups of participants produced larger drawings of objects that have larger actual sizes. This means that the size of familiar objects is a property of blind people's mental representations, regardless of their visual status. Our research also sheds light on the nature of the phenomenon of canonical size. Since we have found the canonical size effect in a group of people who are blind from birth, the assumption of the visual nature of this phenomenon - caused by the ocular-centric biases present in studies on drawing performance - should be revised.

REPRESENTATION OF THE CITY IN TRADITIONAL CHINESE GOHUA PAINTING: MOTIFS, TYPOLOGY, PICTORIAL AND STYLISTIC FEATURES

The representation of the city in traditional Chinese painting has certain features and traditions that distinguish it from the representation of urban imagery in Western art. First of all, this is an artistic generalization of the urban landscape as part of the artistic context of nature. It is also important to focus attention on the depicted identities of the urban space (houses, streets, bridges, as well as the nature that surrounds the city, etc.). In addition to this, gohua in its historical development has an obvious tendency to panoramicity and a generalized way of perception, which actualizes a special system of artistic motifs. Within the historical context of Chinese painting, the city often has a symbolic meaning, and its visuality is represented through the symbolism of color, space or emblems. After all, the use of traditional artistic materials and techniques is of great importance, which determines the palette of artistic techniques and directly affects the artistic motifs of the representation of the city. The representation of the city within the gohua reflects the peculiarities of Chinese culture and perception of the world. Cities are considered as part of nature and interconnected with it. This approach gives works of art depth and symbolic meaning. It is substantiated that the cityscape in the context of the general phenomenology of guohua has its own conceptual and terminological niche, which can be translated as “urban ink painting” (都市水墨). In turn, within this niche, the most popular artistic technique is “meticulous painting” (gongbi /工笔画; literally, “working with a brush”). The following typical pictorial and stylistic features of the urban landscape genre within Chinese ink painting, which are characteristic of the modern stage of development of Chinese fine art, have been established: 1) Expressiveness, which is expressed in the artist’s effort to convey not only the appearance of the city landscape, but also the emotional reaction to the landscape; 2) Minimalism: the desire to build an artistic foundation with the minimum number of colors and elements and focus on the expressiveness of details; 3) Line graphics: as a sign reflects the European approach to understanding the technique and imagery of Chinese painting; in the urban landscape genre, it affects imagery by conveying the shapes and contours of objects; 4) Contrast, with the help of which artists create volume, depth and texture of the urban landscape; 5) A separate artistic parameter is symbolism, which is a field of interpretation of the general pictorial and stylistic repertoire

Different Mechanisms for Supporting Mental Imagery and Perceptual Representations: Modulation Versus Excitation.

Recent research suggests imagery is functionally equivalent to a weak form of visual perception. Here we report evidence across five independent experiments on adults that perception and imagery are supported by fundamentally different mechanisms: Whereas perceptual representations are largely formed via increases in excitatory activity, imagery representations are largely supported by modulating nonimagined content. We developed two behavioral techniques that allowed us to first put the visual system into a state of adaptation and then probe the additivity of perception and imagery. If imagery drives similar excitatory visual activity to perception, pairing imagery with perceptual adapters should increase the state of adaptation. Whereas pairing weak perception with adapters increased measures of adaptation, pairing imagery reversed their effects. Further experiments demonstrated that these nonadditive effects were due to imagery weakening representations of nonimagined content. Together these data provide empirical evidence that the brain uses categorically different mechanisms to represent imagery and perception.