Field Of Visual Perception Research Articles

The Interaction of Information Technology Habits and Learning in Young Adulthood

Abstract This study examines the relationship between the habits of young adults in the use of information technologies and the cognitive processes involved in learning. It was found that information technologies have become an irreplaceable part of modern education, offering vast opportunities to access information and resources, thus promoting the learning of young adults. A large research phase was held to qualitatively analyze the impact of expert interviews on information technology usage habits on young adults' cognitive processes and to investigate the changes it can create in the learning process. Data was collected using semi-structured interviews withopen-ended questions. At the end of the research stage, it was found that using information technology in the learning process has positive and negative consequences, as it has changed the way knowledge is acquired and has made information accessible; however, the use of technology creates an illusion that a person is able to multitask. Learning with the help of information technology reduces the visual perception field; moreover, under its influence, a different attitude towards time and the pace of life arises, creating a feeling of lacking time. Various cognitive, emotional, and social disorders intensify; directly affecting young adults' well-being. This study highlights the importance of the interaction of information technology usage habits and cognitive processes in the learning of young adults.

Vision‐audio fusion SLAM in dynamic environments

AbstractMoving humans, agents, and subjects bring many challenges to robot self‐localisation and environment perception. To adapt to dynamic environments, SLAM researchers typically apply several deep learning image segmentation models to eliminate these moving obstacles. However, these moving obstacle segmentation methods cost too much computation resource for the onboard processing of mobile robots. In the current industrial environment, mobile robot collaboration scenario, the noise of mobile robots could be easily found by on‐board audio‐sensing processors and the direction of sound sources can be effectively acquired by sound source estimation algorithms, but the distance estimation of sound sources is difficult. However, in the field of visual perception, the 3D structure information of the scene is relatively easy to obtain, but the recognition and segmentation of moving objects is more difficult. To address these problems, a novel vision‐audio fusion method that combines sound source localisation methods with a visual SLAM scheme is proposed, thereby eliminating the effect of dynamic obstacles on multi‐agent systems. Several heterogeneous robots experiments in different dynamic scenes indicate very stable self‐localisation and environment reconstruction performance of our method.

Edge preserving image denoising with deep networks and ensemble convolutional neural network architectures

In the field of visual perception, the edges of images tend to be rich in effective visual stimuli, which contribute to the neural network's understanding of various scenes. Image smoothing is an image processing method used to highlight the wide area, low-frequency components, and main part of the image or to suppress image noise and high-frequency interference components, which can make the image's brightness smooth and gradual, reduce the abrupt gradient, and improve the image quality. Reducing noise is treated as one of the important problems in image processing. At the same time, preserving the edges of objects is of critical importance to protect the visual appearance of the objects. Deep networks have marked a trend in computer vision applications and this paper presents a customized Gaussian noise minimizing network with edge preserving filers. Here, ensemble architecture of convolutional neural network is used in minimizing the Gaussian noise and image denoising. The ensemble architecture is combined with VGG-19 and Xception design of CNN. The ensemble Convolutional Neural Networks (CNNs) are classified for Gaussian noisy images, real noisy images, blind denoising, and hybrid noisy images, representing the combination of noisy, blurred, and lowresolution images. Following the classification, motivations and principles of various deep learning methods are analyzed. Subsequently, a comparison of state-of-the-art methods on public denoising datasets is conducted, considering both quantitative and qualitative analyses. The experimental analysis is carried out in terms of PSNR, accuracy, precision, recall and F-measure.

Percepcja wzrokowa uczniów z lekką niepełnosprawnością intelektualną

Introduction: Visual perception is an important cognitive ability, which influences other skills, including the mastery of school techniques by students with mild intellectual disabilities. An important goal of supporting of childhood education is to develop visual perception in all its aspects. Developing aspects such as hand-eye coordination, perception of figures and background, perception stability, spatial position and perception of spatial relationships has a positive effect on the math skills and writing and reading of early schoolchildren. Research Aim : The aim of the research was to learn about the functioning of students with mild intellectual disability in terms of 5 aspects of visual perception: visual and motor coordination, perception of the figure and background, perception of the constancy of shape, figure position and spatial relations, and to develop practical implications for developing this function. Method: The research used the method of diagnostic survey. The observation technique and the tool of M. Frostig and D. Horne “Visual Perception Test” were used to collect the data. Results: The level of functioning of the examined students with mild intellectual disability in terms of visual perception is below the norm. In all the subjects, a decrease in the level of visual perception was found in 3 of the examined areas; perception of the figure and background, shape and position of the figure. In terms of eye-hand coordination and the perception of spatial relations, the dominant students are those with the tested feature below their age norm. Conclusions : On the basis of the obtained results, guidelines for working with students were formulated in order to improve their functioning in the field of visual perception in all examined aspects.

On the Correspondence between the Spectral Sensitivity Characteristics of Human Eye Retina Photo-Receivers and the Frequencies of the Universal Period-Tripling System

The article is devoted to the discussion of the evolutionary adjustment of the photodetectors of the human retina to the frequencies of the UPTS (the so-called universal period-tripling system). This is a new scientific direction in the study of complex systems. The article invites lighting engineers who study the issues of visual perception to get acquainted with the phenomenon of UPTS. To date, an experimental research material has been accumulated and sufficient theoretical grounds have been obtained for the formulation of the assumption that the UPTS and its main parameter Тk, m are manifested in almost all time scales. The values of Тk, m are described with great accuracy by means of the empirical Puetz’s formula. In human vision, UPTS manifests itself in the form of nine periods of Тk, m, corresponding to the cases of m = –11, –8, –5, –3, 0, 3, 5, 8, and 11, where the five central ones dominate –5, –3, 0, 3, and 5, corresponding to the rods, as well as the B- and R-cones. Some questions remain about the G-cones and the ipRGC cells that respond to the extreme, “weakened” values of the index m = –11, –8, 8, and 11. The determination of the proposed calculated values of the wavelengths of light corresponding to the maximum spectral sensitivity of retinal photodetectors is important for specialists in the field of visual perception, medicine, optics, as well as for applications in a number of technical applications.

Local Light Alignment for Multi‐Scale Shape Depiction

AbstractMotivated by recent findings in the field of visual perception, we present a novel approach for enhancing shape depiction and perception of surface details. We propose a shading‐based technique that relies on locally adjusting the direction of light to account for the different components of materials. Our approach ensures congruence between shape and shading flows, leading to an effective enhancement of the perception of shape and details while impairing neither the lighting nor the appearance of materials. It is formulated in a general way allowing its use for multiple scales enhancement in real‐time on the GPU, as well as in global illumination contexts. We also provide artists with fine control over the enhancement at each scale.

A Case Study of Baidu's Wholly-owned Acquisition of xPerception

With the development of economic globalization, more and more enterprises in the world begin to adopt cross-border m&a as their main means of international operation. On April 13, 2017, Baidu wholly-owned xPerception, an American artificial intelligence company, to further strengthen its hardware and software capabilities in the field of visual perception, enabling AR, autonomous driving and other core businesses, which is an important step to speed up the productization of artificial intelligence technology. The acquisition is mainly for Baidu to accelerate the industrialization of ARTIFICIAL intelligence business matrix including AR, autonomous driving and robot, and gain strategic resources and other advantages, but at the same time, it also faces many risks in capital and culture. Based on baidu company wholly owned buying American xPerception as an example, from the company of the current situation and existing problems of transnational merger and acquisition, respectively introduces the acquirers company and acquired American company xPerception an overview of the artificial intelligence, and then analyzes the reason of the acquisition and risk, and finally summarizes the result of the acquisition, has carried on the corresponding summary to acquisition, evaluation and reflection, aims to provide reference for other enterprises transnational mergers and acquisitions in the future.

Road surface detection and differentiation considering surface damages

A challenge still to be overcome in the field of visual perception for vehicle and robotic navigation on heavily damaged and unpaved roads is the task of reliable path and obstacle detection. The vast majority of the researches have as scenario roads in good condition, from developed countries. These works cope with few situations of variation on the road surface and even fewer situations presenting surface damages. In this paper we present an approach for road detection considering variation in surface types, identifying paved and unpaved surfaces and also detecting damage and other information on other road surface that may be relevant to driving safety. We also present a new Ground Truth with image segmentation, used in our approach and that allowed us to evaluate our results. Our results show that it is possible to use passive vision for these purposes, even using images captured with low cost cameras.

Knowledge Graph-Based Image Recognition Transfer Learning Method for On-Orbit Service Manipulation

Visual perception provides state information of current manipulation scene for control system, which plays an important role in on-orbit service manipulation. With the development of deep learning, deep convolutional neural networks (CNNs) have achieved many successful applications in the field of visual perception. Deep CNNs are only effective for the application condition containing a large number of training data with the same distribution as the test data; however, real space images are difficult to obtain during large-scale training. Therefore, deep CNNs can not be directly adopted for image recognition in the task of on-orbit service manipulation. In order to solve the problem of few-shot learning mentioned above, this paper proposes a knowledge graph-based image recognition transfer learning method (KGTL), which learns from training dataset containing dense source domain data and sparse target domain data, and can be transferred to the test dataset containing large number of data collected from target domain. The average recognition precision of the proposed method is 80.5%, and the average recall is 83.5%, which is higher than that of ResNet50-FC; the average precision is 60.2%, and the average recall is 67.5%. The proposed method significantly improves the training efficiency of the network and the generalization performance of the model.

A novel image edge smoothing method based on convolutional neural network

In the field of visual perception, the edges of images tend to be rich in effective visual stimuli, which contribute to the neural network’s understanding of various scenes. Image smoothing is an image processing method used to highlight the wide area, low-frequency components, main part of the image or to suppress image noise and high-frequency interference components, which could make the image’s brightness smooth and gradual, reduce the abrupt gradient, and improve the image quality. At present, there are still problems such as easy blurring of the edges of the image, poor overall smoothing effect, obvious step effect, and lack of robustness to noise on image smoothing. Based on the convolutional neural network, this article proposes a method for edge detection and deep learning for image smoothing. The results show that the research method proposed in this article solves the problem of edge detection and information capture better, significantly improves the edge effect, and protects the effectiveness of edge information. At the same time, it reduces the signal-to-noise ratio of the smoothed image and greatly improves the effect of image smoothing.

Contextualización en matemáticas: uso del teorema del ángulo inscrito en la geometrización de la percepción visual

La contextualización en matemáticas adquiere relevancia debido al interés actual de que los estudiantes puedan usar lo que aprenden en la escuela para explicar fenómenos de la realidad. Por ello, el propósito de esta investigación es caracterizar las dificultades que surgen cuando estudiantes de secundaria abordan un problema del mundo real en el ámbito de la percepción visual. El método empleado corresponde a una ingeniería didáctica mediante la cual se confrontan una indagación histórico-epistemológica de la Óptica de Euclides y las respuestas de los estudiantes al abordar un problema diseñado sobre la base de dicha indagación. Los resultados revelan dificultades tanto en la tendencia que manifiestan los estudiantes a justificar sus respuestas desde el ámbito del fenómeno estudiado, como en las restricciones que genera el tratamiento escolar del teorema del ángulo inscrito.

The very large electrode array for retinal stimulation (VLARS)—A concept study

Objective. The restoration of vision in blind patients suffering from degenerative retinal diseases like retinitis pigmentosa may be obtained by local electrical stimulation with retinal implants. In this study, a very large electrode array for retinal stimulation (VLARS) was introduced and tested regarding its safety in implantation and biocompatibility. Further, the array’s stimulation capabilities were tested in an acute setting. Approach. The polyimide-based implants have a diameter of 12 mm, cover approximately 110 mm2 of the retinal surface and carrying 250 iridium oxide coated gold electrodes. The implantation surgery was established in cadaveric porcine eyes. To analyze biocompatibility, ten rabbits were implanted with the VLARS device, and observed for 12 weeks using slit lamp examination, fundus photography, optical coherence tomography (OCT) as well as ultrasound imaging. After enucleation, histological examinations were performed. In acute stimulation experiments, electrodes recorded cortical field potentials upon retinal stimulation in the visual cortex in rabbits. Main results. Implantation studies in rabbits showed that the implantation surgery is safe but difficult. Retinal detachment induced by retinal tears was observed in five animals in varying severity. In five cases, corneal edema reduced the quality of the follow-up examinations. Findings in OCT-imaging and funduscopy suggested that peripheral fixation was insufficient in various animals. Results of the acute stimulation demonstrated the array’s ability to elicit cortical responses. Significance. Overall, it was possible to implant very large epiretinal arrays. On retinal stimulation with the VLARS responses in the visual cortex were recorded. The VLARS device offers the opportunity to restore a much larger field of visual perception when compared to current available retinal implants.

Monocular Vision-Based Obstacle Detection and Avoidance for a Multicopter

This article presents a monocular vision-based algorithm for detecting obstacles and identifying obstacle-aware regions, developed to be used for collision avoidance by a multicopter. The first step of our algorithm is to predict a disparity image from a single-view image via implementing a deep encoder-decoder network. All pixels in this disparity prediction are then categorized as one of three classes, obstacle, road, or obstacle-free, by combining V-disparity analysis and a fuzzy inference system. For pixels belonging to obstacle objects, obstacle-aware regions are generated within the field of visual perception. To accommodate the safety margins of a multicopter, intermediate waypoints are then added to obtain a new flyable path that passes through an unknown environment safely. Experimental results verified the effectiveness of the detection of obstacles and the identification of obstacle-aware regions. The accuracy of disparity prediction and monocular depth estimation were quantitatively compared to support the feasibility of monocular vision in obstacle avoidance. Furthermore, the entire algorithm was successfully tested on a robotic platform, autonomously flying a hexacopter in an outdoor space with obstacles. In conclusion, the proposed monocular algorithm performs well for obstacle detection and depth estimation and is potentially an alternative to a binocular solution.

ОСНОВНІ ПРИНЦИПИ СТВОРЕННЯ ЕФЕКТИВНОГО ВІЗУАЛЬНОГО ПОВІДОМЛЕННЯ

The article is devoted to the definition of the basic principles of creating a visual message, the adherence to which contributes to the effectiveness of its influence on the target audience. In the context of Russia's hybrid war against Ukraine, counter-terrorism issues are more urgent than ever. In light of world research in psychology, the effectiveness of perceiving information depends, in particular, on the dominant channel of perception (audio, visual, kinesthetic). However, the results of a number of psychological experiments in the field of visual perception have shown that it is better to understand, memorize and reproduce the information we receive through our visual channels. Modern research of social problems leads to understanding - the world has become visual. Man of our time is under constant influence of visual content. So reporting to the consciousness of the necessary information effectively through this channel. Thus, the very visual message is an effective tool for counterpropaganda and for informing the population in the interests of the Armed Forces of Ukraine. The result of the research is based on the study of the peculiarities of the visual perception of information about the world and, related to this, the possibilities of using two-dimensional space in the interests of creating products of psychological influence. In addition, an analysis was conducted of the psychological effect of color on the human subconscious, the emotiogenic structure of color combinations, the psychological effects of symbolic coding of information, general rules of composition, examples of successful graphic marketing. In forming practical recommendations for the implementation of the proposed principles, general publishing approaches to the creation of printed visual products, the technological features of different types of media, the effectiveness of distribution channels for products of psychological influence and the results of the analysis of the target audience were taken into account.

Objective Evaluation of Impression of Faces with Various Female Hairstyles Using Field of Visual Perception

Objective Evaluation of Impression of Faces with Various Female Hairstyles Using Field of Visual Perception

Knowledge-based Taxonomic Scheme for Full-Reference Objective Image Quality Measurement Models

In this article, we propose a knowledge-based taxonomic scheme of the objective image quality assessment metrics including the key concepts involved for each approach. Our classification is constructed according to six criteria based on the information available at each stage of the design process. The novelty of the present classification scheme is that the six layers are linked via a single concept where each layer represents a single type of knowledge about: 1) the reference image, 2) the degradation type, 3) the visual perception field, 4) the human visual physiology and psychophysical mechanisms, 5) the processes of the visual information analysis, and finally 6) knowledge about perceptual image representation and coding. The first layer helps delineate boundaries between full-reference (FR) image quality assessment metrics, that are further classified through layers 2–6, and other families (reduced-reference [RR] and no-reference [NR]). In addition, gradual degrees are considered for knowledge about specific areas related to visual quality evaluation processes. The proposed taxonomic framework is intended to be stepwise, to help sorting out the fundamental ideas behind the development of objective image quality metrics often working on the luminance channel or marginally on the RGB channels. The aim is to congregate the already published classification schemes and to methodologically expand new aspects according to which an efficient and straightforward classification of the image quality assessment algorithms becomes possible. This is significant because of the increasing number of developed metrics. Furthermore, a systematic summarization is necessary in order to facilitate the research and application of image quality techniques. © 2016 Society for Imaging Science and Technology.

The evolvement of discrete representations from continuous stimulus properties: A possible overarching principle of cognition.

Leibovich et al. propose that non-symbolic numerosity abilities develop from the processing of more basic, continuous magnitudes such as size, area, and density. Here I review similar arguments arising in the visual perception field and further propose that the evolvement of discrete representations from continuous stimulus properties may be a fundamental characteristic of cognitive development.

デジタル画像における視知覚の場の算出の高速化および疑似濃淡画像への適用の検討

デジタル画像における視知覚の場の算出の高速化および疑似濃淡画像への適用の検討

Knowledge-based Taxonomic Scheme for Full-Reference Objective Image Quality Measurement Models

In this paper, we propose a knowledge-based taxonomic scheme of the objective image quality assessment metrics including the key concepts involved for each approach. Our classification is constructed according to six criteria based on the information available at each stage of the design process. The novelty of the present classification scheme is that the six layers are linked via a single concept where each layer represents a single type of knowledge about: 1) the reference image, 2) the degradation type, 3) the visual perception field, 4) the human visual physiology and psychophysical mechanisms, 5) the processes of the visual information analysis, and finally 6) knowledge about perceptual image representation and coding. The first layer helps delineate boundaries between full-reference image quality assessment metrics, that are further classified trough layers 2 to 6, and other families (reduced-reference and no-reference). In addition, gradual degrees are considered for knowledge about specific areas related to visual quality evaluation processes. The proposed taxonomic framework is intended to be stepwise, to help sorting out the fundamental ideas behind the development of objective image quality metrics often working on the luminance channel or marginally on the RGB channels. The aim is to congregate the already published classification schemes and to methodologically expand new aspects according to which an efficient and straightforward classification of the image quality assessment algorithms becomes possible. This is significant because of the increasing number of developed metrics. Furthermore, a systematic summarisation is necessary in order to facilitate the search and application of image quality techniques.

Harmonic context influences pitch class equivalence judgments through gestalt and congruency effects

The context in which a stimulus is presented shapes the way it is processed. This effect has been studied extensively in the field of visual perception. Our understanding of how context affects the processing of auditory stimuli is, however, rather limited. Western music is primarily built on melodies (succession of pitches) typically accompanied by chords (harmonic context), which provides a natural template for the study of context effects in auditory processing. Here, we investigated whether pitch class equivalence judgments of tones are affected by the harmonic context within which the target tones are embedded. Nineteen musicians and 19 non-musicians completed a change detection task in which they were asked to determine whether two successively presented target tones, heard either in isolation or with a chordal accompaniment (same or different chords), belonged to the same pitch class. Both musicians and non-musicians were most accurate when the chords remained the same, less so in the absence of chordal accompaniment, and least when the chords differed between both target tones. Further analysis investigating possible mechanisms underpinning these effects of harmonic context on task performance revealed that both a change in gestalt (change in either chord or pitch class), as well as incongruency between change in target tone pitch class and change in chords, led to reduced accuracy and longer reaction times. Our results demonstrate that, similarly to visual processing, auditory processing is influenced by gestalt and congruency effects.