Visual Images Research Articles

Development and validation of a nonverbal consensus-based semantic memory paradigm in patients with epilepsy.

Brain areas implicated in semantic memory can be damaged in patients with epilepsy (PWE). However, it is challenging to delineate semantic processing deficits from acoustic, linguistic, and other verbal aspects in current neuropsychological assessments. We developed a new Visual-based Semantic Association Task (ViSAT) to evaluate nonverbal semantic processing in PWE. The ViSAT was adapted from similar predecessors (Pyramids & Palm Trees test, PPT; Camels & Cactus Test, CCT) comprised of 100 unique trials using real-life color pictures that avoid demographic, cultural, and other potential confounds. We obtained performance data from 23 PWE participants and 24 control participants (Control), along with crowdsourced normative data from 54 Amazon Mechanical Turk (Mturk) workers. ViSAT reached a consensus >90% in 91.3% of trials compared to 83.6% in PPT and 82.9% in CCT. A deep learning model demonstrated that visual features of the stimulus images (color, shape; i.e., non-semantic) did not influence top answer choices (p = 0.577). The PWE group had lower accuracy than the Control group (p = 0.019). PWE had longer response times than the Control group in general and this was augmented for the semantic processing (trial answer) stage (both p < 0.001). This study demonstrated performance impairments in PWE that may reflect dysfunction of nonverbal semantic memory circuits, such as seizure onset zones overlapping with key semantic regions (e.g., anterior temporal lobe). The ViSAT paradigm avoids confounds, is repeatable/longitudinal, captures behavioral data, and is open-source, thus we propose it as a strong alternative for clinical and research assessment of nonverbal semantic memory.

Vestibulo-Ocular Reflex Suppression: Clinical Relevance and Assessment in the Digital Age.

Visual acuity and image stability are crucial for daily activities, particularly during head motion. The vestibulo-ocular reflex (VOR) and its suppression (VORS) support stable fixation of objects of interest. The VOR drives a reflexive eye movement to counter retinal slip of a stable target during head motion. In contrast, VORS inhibits this countermovement when the target stimulus is in motion. The VORS allows for object fixation when it aligns with the direction of the head's movement, or when an object within or outside the peripheral vision needs to be focused upon. Deficits of the VORS have been linked to age-related diseases such as balance deficits associated with an increased fall risk. Therefore, the accurate assessment of the VORS is of particular clinical relevance. However, current clinical assessment methods for VORS are mainly qualitative and not sufficiently standardised. Recent advances in digital health technology, such as smartphone-based videooculography, offer a promising alternative for assessing VORS in a more accessible, efficient, and quantitative manner. Moreover, integrating mobile eye-tracking technology with virtual reality environments allows for the implementation of controlled VORS assessments with different visual inputs. These assessment approaches allow the extraction of novel parameters with potential pathomechanistic and clinical relevance. We argue that researchers and clinicians can obtain a more nuanced understanding of this ocular stabilisation reflex and its associated pathologies by harnessing digital health technology for VORS assessment. Further research is warranted to explore the technologies' full potential and utility in clinical practice.

A SEMIOTIC ANALYSIS OF VISUAL ELEMENTS ON SLIDE PRESENTATIONS USED IN COLLEGE ENGLISH CLASS

The purpose of this study is to establish that visual elements found in slide presentation used in College English Class convey mean-ing by its interpretation through semiotic analysis. Using the semiotic analysis, fifty-one (51) visual elements were identified and underwent in-depth analysis and interpretation. The study’s findings revealed that the visual elements carry meaning and can be interpreted in various ways. The study obtained significant information that helped derive the semiotic features of the visual ele-ments based on their signifier, signified, denotative meaning, connotative meaning, and myth. Moreover, the signifier interpretation of the visual images provided information on how each visual element were classified into- icon, symbol, and index. It was also dis-cussed in the study the detailed description of the signified meanings of each visual element. Additionally, the relevance to the social context of the college English class were examined intensively by analyzing its denotations, connotations, and myths. Indeed, visual elements carry meaning; thus, there is a need for close monitoring and evaluation of the visual elements used in slide presentations as it could affect the learning of the students. Ultimately, the study suggests that the incorporation of visual elements in instructional materials should be carefully considered because this may affect how students learn and perceive the subject matter and discussion in class. KEYWORDS: Semiotic Analysis, visual elements, slide presentations, signifier, signified, denotation, connotation, myth, Davao de Oro

Enhancing Spatial Ability among Undergraduate First-Year Engineering and Architecture Students

Spatial ability (SA) refers to the ability to generate, retain and manipulate abstract visual images in one’s head. Research of this ability conducted during the last few decades had shown that SA is an essential ability to the development of mathematical skills in children. Later studies addressed adult SA development and accordingly found that SA was a predictor of success in Science, Technology, Engineering and Mathematics (STEM) fields of academic studies. Recently, focus has also been directed to another related field which is architecture. This group has not been extensively researched in relation to its spatial thinking abilities. The current research examines the efficiency of a new training program aimed at improving engineering and architecture student’s SA abilities. This program was based on previous intervention programs but was innovative in its combination of both traditional and computer-based teaching methods and combining frontal learning, hands on model building and augmented reality (AR) tools, all in a short program of 20 h. We asked whether the training program will be effective in improving student’s SA ability and whether this improvement will remain across different factors such as gender, faculty and math level. The results showed a significant improvement in SA abilities among the students who took the training program, compared with the control group. These results were true for both engineering and architecture students. Moreover, the results showed the training program to be efficient regardless of gender or prior math level.

Diagnostic capabilities of PET/CT with &lt;sup&gt;18&lt;/sup&gt;F-DOPA in biochemical recurrence of medullary thyroid carcinoma: a retrospective study

INTRODUCTION: Medullary thyroid carcinoma (MTС) is a rare neuroendocrine malignant neoplasm of the thyroid gland. In most cases, sporadic MTС is diagnosed at late stages due to the absence of specific symptoms. The main method of treatment of MTС is surgical — thyroidectomy and in most cases cervical lymphodissection. Current international guidelines suggest the use of basal calcitonin levels and cancer embryonic antigen (CEA) as markers of biochemical recurrence. In biochemical recurrence of the disease, it is advisable to search for tumor foci regardless of the level of cancer markers. Positron emission tomography combined with computed tomography (PET/CT) has the highest sensitivity and specificity for searching for local recurrence and distant metastases. Anatomical imaging methods (computed tomography (CT), magnetic resonance imaging (MRI)) have suboptimal sensitivity and specificity in detection of a recurrent tumor.OBJECTIVE: To explore the diagnostic capabilities of PET/CT with 18F-DOPA in patients with biochemical recurrence of medullary thyroid carcinoma, depending on the concentration of calcitonin in blood plasma.MATERIALS AND METHODS: To evaluate the diagnostic capabilities of PET/CT with 18F-DOPA in patients with biochemical recurrence of medullary thyroid carcinoma, 81 PET/CT studies were analyzed in patients after thyroidectomy. In most cases (76/81), at the time of the study, patients had elevated basal calcitonin levels (&gt;10 pg/ml, including in 52/81 cases &gt;150 pg/ml), which corresponded to a biochemical recurrence of the disease. In 6 cases, the calcitonin level was &lt;10 pg/ml. PET/CT results were processed by visual image analysis, measurement of a semi-quantitative indicator of the maximal standardized level of radiopharmaceutical accumulation normalized to lean body mass (SUVlbm max) and the total volume of PET-positive tumor tissue (metabolic tumor volume).RESULTS: In 41 studies, foci of pathological accumulation of 18F-DOPA were identified, corresponding to recurrent tumor foci. DOPA-negative results in patients with elevated calcitonin levels were regarded as false negative. The highest sensitivity of the method was achieved at calcitonin levels &gt;150 pg/ml. A noticeable positive correlation was found between the concentration of basal serum calcitonin, the number of pathological foci of 18F-DOPA hyperaccumulation and the total metabolic volume of tumor tissue. In most cases of biochemical recurrence, the PET method was superior to the CT method in detecting recurrent tumor foci.CONCLUSION: PET/CT with 18F-DOPA is the most informative method for molecular and structural imaging in patients with biochemical recurrence of medullary thyroid carcinoma. The results of the study directly correlate with the level of basal calcitonin in the blood.

UGLS: an uncertainty guided deep learning strategy for accurate image segmentation

Accurate image segmentation plays a crucial role in computer vision and medical image analysis. In this study, we developed a novel uncertainty guided deep learning strategy (UGLS) to enhance the performance of an existing neural network (i.e., U-Net) in segmenting multiple objects of interest from images with varying modalities. In the developed UGLS, a boundary uncertainty map was introduced for each object based on its coarse segmentation (obtained by the U-Net) and then combined with input images for the fine segmentation of the objects. We validated the developed method by segmenting optic cup (OC) regions from color fundus images and left and right lung regions from Xray images. Experiments on public fundus and Xray image datasets showed that the developed method achieved a average Dice Score (DS) of 0.8791 and a sensitivity (SEN) of 0.8858 for the OC segmentation, and 0.9605, 0.9607, 0.9621, and 0.9668 for the left and right lung segmentation, respectively. Our method significantly improved the segmentation performance of the U-Net, making it comparable or superior to five sophisticated networks (i.e., AU-Net, BiO-Net, AS-Net, Swin-Unet, and TransUNet).

Fashioning Landscapes with Bruno Latour’s Immutable Mobiles and Epistemological Matrix or: How We Pass on Knowledge

Landscape poetry and art is informed by place and place is understood at least in part by quantitative data about the place. This essay delineates Bruno Latour’s method of understanding how data is gathered in the field and translated into visual and textual images of place (which he calls immutable mobiles). Textual representations reduce the material specificity of place, but in return representations facilitate understanding of a place by creating greater compatibility, standardization, calculation, and circulation of knowledge. Latour explains how instruments create representations and how the instruments and texts are part of a broader implicate epistemological matrix. This matrix determines in advance how we think about, measure, see, and represent the world around us. The essay then develops how landscape poetry and art participate in the broader cultural epistemological matrix.

Idols and Icons: Franciscan Theology and Artistic Inculturation in Mexico, 1524–1699

Indigenous people in Mexico encountered and interacted with the visual images of Mary, Jesus, and the Saints via Catholic missionaries in the sixteenth century. I examine the complexity of Franciscan changing attitudes and practices toward transforming what they perceived as idols into icons in the sixteenth and seventeenth centuries. Indigenous Christians also adapted and synthesized artistic forms and meanings. Furthermore, artists from various cultures drew sources from Indigenous Mexican, European, and Asian traditions and created a visual Christian culture.

Infrared and Visual Image Fusion Based on a Local-Extrema-Driven Image Filter.

The objective of infrared and visual image fusion is to amalgamate the salient and complementary features of the infrared and visual images into a singular informative image. To accomplish this, we introduce a novel local-extrema-driven image filter designed to effectively smooth images by reconstructing pixel intensities based on their local extrema. This filter is iteratively applied to the input infrared and visual images, extracting multiple scales of bright and dark feature maps from the differences between continuously filtered images. Subsequently, the bright and dark feature maps of the infrared and visual images at each scale are fused using elementwise-maximum and elementwise-minimum strategies, respectively. The two base images, representing the final-scale smoothed images of the infrared and visual images, are fused using a novel structural similarity- and intensity-based strategy. Finally, our fusion image can be straightforwardly produced by combining the fused bright feature map, dark feature map, and base image together. Rigorous experimentation conducted on the widely used TNO dataset underscores the superiority of our method in fusing infrared and visual images. Our approach consistently performs on par or surpasses eleven state-of-the-art image-fusion methods, showcasing compelling results in both qualitative and quantitative assessments.

A RESEARCH OF THE LATEST APPROACHES TO VISUAL IMAGE RECOGNITION AND CLASSIFICATION

Context. The paper provides an overview of current methods for recognizing and classifying visual images in static images or video stream. The paper will discuss various approaches, including machine learning, current problems of these methods and possible improvements. The biggest challenges of the visual image retrieval and classification task are discussed. The main emphasis is placed on the review of such promising algorithms as SSD, YOLO, R-CNN, an overview of the principles of these methods, network architectures.&#x0D; Objective. The aim of the work is to analyze existing studies and find the best algorithm for recognizing and classifying visual images for further activities.&#x0D; Method. Primary method is to compare different factors of algorithms in order to select the most perspective one. There are different marks to compare, like image processing speed, accuracy. There are a number of studies and publications that propose methods and algorithms for solving the problem of finding and classifying images in an image [3–6]. It should be noted that most promising approaches are based on machine learning methods. It is worth noting that the proposed methods have drawbacks due to the imperfect implementation of the Faster R-CNN, YOLO, SSD algorithms for working with streaming video. The impact of these drawbacks can be significantly reduced by applying the following solutions: development of combined identification methods, processing of edge cases – tracking the position of identified objects, using the difference between video frames, additional preliminary preparation of input data. Another major area for improvement is the optimization of methods to work with real-time video data, as most current methods focus on images.&#x0D; Results. As an outcome of the current research we have found an optimal algorithm for further researches and optimizations.&#x0D; Conclusions. Analysis of existent papers and researches has demonstrated the most promising algorithm for further optimizations and experiments. Also current approaches still have some space for further. The next step is to take the chosen algorithm and investigate possibilities to enhance it.

Visual impact control of urban waterfront development on the background mountain view: Examining its justifiability through two types of immersive virtual reality experiments

Several cities built along the waterfront are also accompanied by mountainous backgrounds, and thus have characteristic panoramas contributing to their visual image and identity. However, urban waterfronts are also often subject to high-rise development, which may damage that identity by shielding the mountainous background. Building regulations are adopted as visual impact management policies to retain mountain visibility, but it is difficult to find either scientific evidence to support the effectiveness of such private property control or further empirical guidance for its elaboration. Through a case study of Seoul's Han River, this study thus analyzed the actual impact of relevant building characteristics on the visual preference of urban waterfront views with mountainous backgrounds. Immersive virtual reality experiments using both actual and simulated cityscape images were conducted. Hierarchical linear regression and quasi-rank-based conjoint analysis results confirm that controls on buildings to retain background mountain visibility would actually serve as reasonable visual impact management measures. Insights into what kinds of regulation could be desirable in which circumstances were further obtained—excessively high buildings are especially unrecommended when the height of the background mountain is low, and in all cases a combination of view corridors with convex skylines are advisable for mitigating the negative impacts of mid-to-high building heights.

"Mercury and Argus" by P. I. Sokolov: aesthetics of the painting, search for an artistic image, perception by contemporaries

Purpose of this article is analysis of painting by P. I. Sokolov (1753-1791) «Mercury and Argus» (1776) in context preferences of era. Objectives of study: analyze preparatory drawings and picture; study archival sources, artist reports; reveal process of painting; represent aesthetic ideals of epoch and perception of personality. Our focus will be on artistic process of creation historical picture in second half of XVIII century on example of work by P. I. Sokolov «Mercury and Argus». Research subject is role of sketches from nature, sketch composition in work of P. I. Sokolov on picture «Mercury and Argus». Author considers of visual technique, graphic-plastic composition, preparatory graphic, identifying process of working on picture based on academic education system. Citing quotes from letters, reports and writings of second half of XVIII century author analyses what was typical for art of period under study, what tasks artist set for himself, what conditions were necessary for expressiveness of visual image and idea of picture as well as her positive evaluation by her contemporaries. During study were applied: formal, formal-stylistic, art criticism analysis. Author comes to conclusion that work on historical painting in second half of XVIII century included a large systematic creative process dictated by academic education system at Imperial Academy of Arts. Analysis showed that execution of sketches and drawings from nature became basis for subsequent search for better image. Historical painting of second half of XVIII century reflected aesthetic views and ideals of era, being an artistic embodiment of ideas of theoretical treatises aimed at improving visual image. Novelty of research lies in considering painting not only as part of artistic heritage, but also as a reflection of academic education system at Imperial Academy of Arts in second half of XVIII century and aesthetics of educational classicism.

Knowledge-Embedded Mutual Guidance for Visual Reasoning.

Visual reasoning between visual images and natural language is a long-standing challenge in computer vision. Most of the methods aim to look for answers to questions only on the basis of the analysis of the offered questions and images. Other approaches treat knowledge graphs as flattened tables to search for the answer. However, there are two major problems with these works: 1) the model disregards the fact that the world we surrounding us interlinks our hearing and speaking of natural language and 2) the model largely ignores the structure of the acrlong KG. To overcome these challenging deficiencies, a model should jointly consider two modalities of vision and language, as well as the rich structural and logical information embedded in knowledge graphs. To this end, we propose a general joint representation learning framework for visual reasoning, namely, knowledge-embedded mutual guidance. It realizes mutual guidance not only between visual data and natural language descriptions but also between knowledge graphs and reasoning models. In addition, it exploits the knowledge derived from the reasoning model to boost knowledge graphs when applying the visual relation detection task. The experimental results demonstrate that the proposed approach performs dramatically better than state-of-the-art methods on two benchmarks for visual reasoning.

DesTrans: A medical image fusion method based on Transformer and improved DenseNet

Medical image fusion can provide doctors with more detailed data and thus improve the accuracy of disease diagnosis. In recent years, deep learning has been widely used in the field of medical image fusion. The traditional method of medical image fusion is to operate by superimposing and other methods of pixels. The introduction of deep learning methods has improved the effectiveness of medical image fusion. However, these methods still have problems such as edge blurring and information redundancy. In this paper, we propose a deep learning network model based on Transformer and an improved DenseNet network module integration that can be applied to medical images and solve the above problems. At the same time, the method can be moved to natural images. The use of Transformer and dense concatenation enhances the feature extraction capability of the method by limiting the feature loss which reduces the risk of edge blurring. We compared several representative traditional methods and more advanced deep learning methods with this method. The experimental results show that the Transformer and the improved DenseNet network module have a strong capability of feature extraction. The method yields good results both in terms of visual quality and objective image evaluation metrics.

Domain generalization for retinal vessel segmentation via Hessian-based vector field

Blessed by vast amounts of data, learning-based methods have achieved remarkable performance in countless tasks in computer vision and medical image analysis. Although these deep models can simulate highly nonlinear mapping functions, they are not robust with regard to the domain shift of input data. This is a significant concern that impedes the large-scale deployment of deep models in medical images since they have inherent variation in data distribution due to the lack of imaging standardization. Therefore, researchers have explored many domain generalization (DG) methods to alleviate this problem. In this work, we introduce a Hessian-based vector field that can effectively model the tubular shape of vessels, which is an invariant feature for data across various distributions. The vector field serves as a good embedding feature to take advantage of the self-attention mechanism in a vision transformer. We design paralleled transformer blocks that stress the local features with different scales. Furthermore, we present a novel data augmentation method that introduces perturbations in image style while the vessel structure remains unchanged. In experiments conducted on public datasets of different modalities, we show that our model achieves superior generalizability compared with the existing algorithms. Our code and trained model are publicly available at https://github.com/MedICL-VU/Vector-Field-Transformer.

Detection of Oscillations in Process Control Loops from Visual Image Space Using Deep Convolutional Networks

Detection of Oscillations in Process Control Loops from Visual Image Space Using Deep Convolutional Networks

Monitoring Welding Torch Position and Posture Using Reversed Electrode Images – Part II, Experimental and Analysis for the REI-TPA Model

This paper is a sequel to the previous paper. In the previous research (Ref. 1), a REI-TPA model was established to quantitively relate reversed electrode images (REIs) to welding torch position and attitude (TPA). In this research, the REI-TPA model was validated with bead-on-plate welding experiments on S304 stainless steel plates. The contours of the REI and electrode as well as the weld pool geometry were extracted from the image with a developed, robust algorithm, and the arc length was calculated with welding voltage. The offset distance and deflection angle of the welding torch relative to the correct position and attitude were calculated by inputting the extracted parameters into the REI-TPA model. The computational result was compared to the experimental data. The result showed that the model is correct and the monitoring of the welding torch with the REI-TPA model is available. The REI- TPA model can be applied to real-time control of TPA, which is a supplement to the application of the weld passive visual image and an extension of multi- information acquisition and processing methods in the welding process.

Intersubject representational similarity analysis uncovers the impact of state anxiety on brain activation patterns in the human extrastriate cortex.

The current study used functional magnetic resonance imaging (fMRI) and showed that state anxiety modulated extrastriate cortex activity in response to emotionally-charged visual images. State anxiety and neuroimaging data from 53 individuals were subjected to an intersubject representational similarity analysis (ISRSA), wherein the geometries between neural and behavioral data were compared. This analysis identified the extrastriate cortex (fusiform gyrus and area MT) to be the sole regions whose activity patterns covaried with state anxiety. Importantly, we show that this brain-behavior association is revealed when treating state anxiety data as a multidimensional response pattern, rather than a single composite score. This suggests that ISRSA using multivariate distances may be more sensitive in identifying the shared geometries between self-report questionnaires and brain imaging data. Overall, our findings demonstrate that a transient state of anxiety may influence how visual information - especially those relevant to the valence dimension - is processed in the extrastriate cortex.

Defining metrics of visual acuity from theoretical models of observers.

Many experimental studies show that metrics of visual image quality can predict changes in visual acuity due to optical aberrations. Here we use statistical decision theory and Fourier optics formalism to demonstrate that two metrics known in the field of vision sciences are approximations of two different theoretical models of linear observers. The theory defines metrics of visual acuity to potentially predict changes in visual acuity due to optical aberrations, without needing a posteriori scale or offset. We illustrate our approach with experiments, using combinations of defocus and spherical aberration, and pure coma.

Animal behavior: Mosquitos ride the wave

Some insects have a frustrating knack for avoiding a swatter. A new study shows that mosquitos not only evade the visual image of the looming threat, they also surf the wave of air the swatter creates.