Visual Images Research Articles

No-reference image quality assessment based on visual explanation images and deep transfer learning

Quantifying image quality in the absence of a reference image continues to be a challenge despite the introduction of numerous no-reference image quality assessments (NR-IQA) in recent years. Unlike most existing NRIQA methods, this paper proposes an efficient NR-IQA method based on deep visual interpretations. Specifically, the main components of the proposed method are: i) generating a pseudo-reference image (PRI) for the input distorted images, ii) employing a pretrained convolutional network to extract feature maps from the distorted image and the corresponding PRI, iii) producing visual explanation images (VEIs) by using the feature maps of the distorted image and the corresponding PRI, iv) measuring the similarity between the two VEIs using an image similarity metric, and v) employing a non-linear mapping function for quality score alignment. In our experiments, we evaluated the efficacy of the proposed method across various forms of distortion using four benchmark datasets (LIVE, SIQAD, CSIQ, and TID2013). The proposed approach demonstrates parity with the latest methods, as evidenced by comparisons with both hand-crafted NR-IQA and deep learning-based approaches.

Thermal imaging-based identification of facial features in noisy environment

<p class="Abstract">Face identification is amongst the most efficacious and extensive applications in biometrics involving extraction and locating facial features. With identification being monotonous task attributable to reliance on parameters like varied cameras, fluctuating backgrounds, and exposure to the environment in which an individual is present. Thermal imaging is endeavoring to resolve the accuracy issue of apparent imaging, such as lighting and brightness intensity, among all biometric variables. This paper presents a study of thermal imaging and effective methods involved in the feature extraction process for facial features with thermal imaging under the influence of varied noise. A novel face dataset is created TID comprising 27 thermal images and its corresponding visual band image using Fluke 480 Ti Pro camera. The study analyses detection efficiency of six feature extraction techniques in visible and thermal bands in facial features identification. Also, the influence of noise in the thermal band within the region of interest using feature points FIN, FOUT has been estimated. Throughout TID dataset, ORB extraction technique has been able to identify strongest inlier features FIN to a maximum extent with detection around the nose, eyes, and mouth. Further, results indicate feature detection in thermal images being invariant to effect of noise for detecting facial features.</p>

Optimization of building thermal environment in industrial heritage landscape regeneration design simulation based on image visual visualization

With the increasing attention paid to the regeneration design of industrial heritage landscape, how to effectively improve the thermal energy environment in these areas has become a research hotspot. The thermal efficiency of buildings not only affects the energy consumption, but also relates to the comfort of users and the sustainable development of the region. This study aims to explore the optimization method of building thermal energy environment based on image visual visualization, so as to achieve efficient regeneration of industrial heritage landscape, and improve the thermal energy performance and environmental adaptability of buildings through scientific design and simulation means. Through the use of computer aided design (CAD) and building information modeling (BIM) techniques, combined with heat flow simulation and visual image analysis, a detailed thermal environment assessment is carried out for selected industrial heritage buildings. Through data collection and analysis, we take into account factors such as sunlight, ventilation and heat loss in the building to develop an optimization plan. The simulation results show that the energy consumption of buildings with optimized thermal environment design is significantly reduced and the indoor thermal comfort is greatly improved. Visual tools effectively help the design team identify problem areas and provide intuitive data support for all parties during the design process. The optimization method of building thermal energy environment based on image visual visualization provides a new idea for the regeneration design of industrial heritage landscape, which helps to realize the dual goals of protecting the historical value and meeting the needs of modern environmental protection.

The Effect of Seeing Paintings or Photojournalistic Images Depicting Refugees on Their Infrahumanisation

Abstract Images are powerful cultural agents that can inform, shape and change the attitudes and behaviours of viewers. The ways in which marginalised groups are depicted in photographs shape people's attitudes towards them. For example, viewing photojournalistic images portraying refugees in large rather than small groups results in the infrahumanisation of refugees. However, the impact of artworks vs photojournalistic images depicting refugees on their infrahumanisation remains a key and unexplored topic. In this pre-registered study, we investigated whether viewing different types of visual images (e.g., paintings vs photographs) of refugees using distinct visual framings (e.g., large or small groups) influences viewers' infrahumanisation of refugees and the intensity and valence of the viewers' felt emotions. Contrary to our predictions, no evidence was found for an effect of visual framing (large vs small groups) and different image types (paintings vs photos) portraying refugees on their infrahumanisation. Moreover, and in line with our predictions, photojournalistic images were evaluated as more emotionally intense than paintings, whereas paintings rather than photojournalistic images engaged more positive emotions, demonstrating that different types of images involve distinct affective responses. Furthermore, exploratory findings revealed that increased positive appraisals of photojournalistic images depicting small groups rather than large groups led to greater humanisation of refugees. This suggests that positive emotions can decrease the infrahumanisation of marginalized groups, particularly when these are evoked by photojournalistic images. Overall, these findings shed new light on the effects of audience exposure to different forms of visual representation and their social and emotional consequences.

Urban expansion and agricultural land loss: a GIS-Based analysis and policy implications in Hawassa city, Ethiopia

This study investigated the historical and future trends of urban expansion and its subsequent impact on agricultural land-use in Hawassa city, Ethiopia. A time-series of remote-sensing imageries from Landsat Thematic Mapper for the years 1984, 1990, 2000, and 2010 and Operational Land Imager for 2021 were used to extract the LULC information from the study area. Seven major land-cover classes’ waterbody, built-up, agricultural land, wetland, grassland, woody vegetation, and agroforestry were identified with visual image interpretation along with supervised image classification techniques using the maximum-likelihood algorithm for the study years. The urban and agricultural lands were then extracted from the original LULC data to quantify the extent, rates, and number of area conversion between the two. The Land Change Modeler module of TerrSet software was used to predict the spatial extents of built-up and agricultural lands in 2030 and 2050. The results showed that there have been significant changes between the LULC types in Hawassa city within the past 37 years, from which built-up and agricultural land have shown the most prevalent changes. It showed that built-up land has increased from 584.73 ha in 1,984–3,939.03 ha in 2021, representing a 573.65% increase at an annual growth rate of 15.50%. However, agricultural land decreased from 8,324.64 ha to 3,595.68 ha in the respective years, with a 56.81% decrease at a rate of −1.54% each year. A total of 3,148.74 ha (37.82%) of agricultural land was converted into built-up land within the past 37 years (85.10 ha per year, a rate of 1.02%. The built-up land is projected to increase to 5,009.85 ha and 6,794.73 ha from 2021 to 2030 and 2050, with annual growth rates of 3.02% and 2.50%, respectively. In the same years, agricultural land will decrease to 2,849.58 ha and 2033.46 ha by 2.31% and 1.50% annually, respectively, from which 64.76 ha (1.80%) and 48.41 ha (1.35%) will be converted into built-up land, respectively. Future planning and development in the city should consider the rapid increase in built-up land toward agricultural land areas and develop appropriate adaptation mechanisms for the local community, which is highly dependent on agriculture.

„Jestem specjalistą od samego siebie” – cytowanie modeli autokreacji

The article is focused on the ways of quoting the artistic legends. Sentence in the title by Tadeusz Kantor is specific for his own building the image, the legend. Connection the work of art, commentary and biography (in private, as well in art) was performed in correct form of communication. Kantor was building himself as a text, he created his own legend, but also ways of reading his creation and his image. It was performed during interviews, meetings etc. Kantor didn’t hide his self-creation (it was one of the forms of this creation). One of the ways of it was appointment for predecessors. It wasn’t only forms of intertextuality – quotations, motives, hints etc.), but he appointed the legends, the former artists as a texts. The most important was two of them. The first was Stanisław Wyspiański – Kantor underlined community of motives (f.e. motive of returning of Odysseus), but also he created the continuation of legends (community of Kanonicza street in Cracow, elegance etc.). The another one was Witold Gombrowicz. Kantor many times has appointed to him, but in art there wasn’t so much relations. The most interesting is quotation of Gombrowicz as a text. Very similar in both cases are ways of self-creation. Very important is problem of visuality (in art and also besides it). Comparison the Kantor and Gombrowicz is interesting because the author of Ferdydurke didn’t create the visual image (opposite to Kantor), but strategies of self-creation are similar.

Research on the Visual Image Shaping of Tourism Brand in Panyu District, Guangzhou

Research on the Visual Image Shaping of Tourism Brand in Panyu District, Guangzhou

Knowledge Guided Transformer Network for Compositional Zero-Shot Learning

Compositional Zero-shot Learning (CZSL) attempts to recognise images of new compositions of states and objects when images of only a subset of state-object compositions are available as training data. An example of CZSL is to recognise images of peeled apple by a model when it is trained using images of peeled orange , ripe apple and ripe orange . There are two major challenges in solving CZSL. First, the visual features of a state vary depending on the context of a state-object composition. For example state like ripe produces distinct visual properties in the compositions ripe orange and ripe banana . Hence, understanding the context dependency of state features is a necessary requirement to solve CZSL. Second, the extent of association between the features of a state and an object varies significantly in different images of same composition. For example, in different images of peeled oranges , the oranges may be peeled to different extents. As a consequence, the visual features of images of the class peeled orange may vary. Hence, there exists a significant amount of intra-class variability among the visual features of different images of a composition. Existing approaches merely look for the existence or absence of features of particular state or object in a composition. Our approach not only looks for the existence of a particular state features or object features but also the extent of association of state features and object features to better tackle the intra-class variability in visual features of compositional images. The proposed architecture is constructed using a novel Knowledge Guided Transformer . The transformer-based framework is utilised for processing larger context dependency between the state and object. Extensive experiments on C-GQA, MIT-States and UT-Zappos50k datasets demonstrate the superiority of the proposed approach in comparison with the state-of-the-art in both open-world and closed-world CZSL settings.

Analyzing Multimodal Construction of China’s Image Based on the Illustrated Articles in The Economist

The shaping of national image is a two-way process, including “self-shaping” and “shaping by others”. Magazines are one of the important channels for disseminating information. Taking The Economist’s China-related reports in 2023 as a case, this article focuses on 195 reports on politics and diplomacy as well as economy, conducting quantitative and qualitative analyses of the visual images from the perspective of visual grammar, and further exploring the multimodal construction of China's image in The Economist. The research shows that the images in The Economist fully realize the representational meaning, interactive meaning and compositional meaning. In terms of the representational meaning, narrative representation is the major process. Images that reflect the reactional process, action process and symbolic process are used more frequently. In terms of the interactive meaning, “offer” images, long shot images and images with horizontal perspective appear more frequently. In addition, information value, salience, and framing are interrelated to achieve the compositional meaning. The political and diplomatic image of China constructed by The Economist is that China is a country with increasing power, which is authoritarian internally and threatening externally. In terms of economy, China is portrayed as a country with a fast growing economy which can withstand the global economic crisis, but its economic prospects are not optimistic, and the “China threat theory” still exists.

Opto-Electrical Decoupled Phototransistor for Starlight Detection.

Highly sensitive shortwave infrared (SWIR) detectors are essential for detecting weak radiation (typically below 10-8 W·Sr-1·cm-2·µm-1) with high-end passive image sensors. However, mainstream SWIR detection based on epitaxial photodiodes cannot effectively detect ultraweak infrared radiation due to the lack of inherent gain. Here, we develop a heterojunction-gated field-effect transistor (HGFET) consisting of a colloidal quantum dot (CQD)-based p-i-n heterojunction and a carbon nanotube (CNT) field-effect transistor, which achieves a high inherent gain based on an opto-electric decoupling mechanism for suppressing noise. The stacked heterojunction absorbs infrared radiation and separates electron-hole pairs. Then, the generated photovoltage tunes the drain current of the CNT FET through an Y2O3 gate insulator. As a result, the HGFET significantly detects and amplifies SWIR signals with a high inherent gain while minimally amplifying noise, leading to a recorded specific detectivity above 1014 Jones at 1300nm and a recorded maximum gain-bandwidth product of 69.2 THz. Direct comparative testing indicates that the HGFET can detect weak infrared radiation at 0.46 nWcm-2 levels; thus, compared to commercial and reported SWIR detectors, this detector is much more sensitive and enables starlight detection or vision. As the fabrication process is very compatible with CMOS readout integrated circuits, the HGFET is a promising SWIR detector for realizing passive night vision imaging sensors with high resolutions that are high-end, highly sensitive, and inexpensive.

Ultra‐Sensitive, Self‐powered, CMOS‐Compatible Near‐Infrared Photodetectors for Wide‐Ranging Applications

AbstractSelf‐powered near‐infrared (NIR) photodetectors are essential for surveillance systems, sensing in IoT electronics, facial recognition, health monitoring, optical communication networks, night vision, and biomedical imaging. However, silicon commercial detectors need external power to operate and cooling to suppress large dark currents. This work demonstrates a new class of CMOS‐compatible self‐powered NIR photodetector based on ferroelectric 5‐nm thick ZrO2 films which do not require cooling and therefore have two key advantages over Si, and at the same time have comparable performance metrics. At room‐temperature, under 940 nm wavelength illumination (1.4 mW cm−2 power density, 10 Hz repetition rate), and without any power applied, fast rise and fall times of ≈2 and 4 µs, respectively, are achieved in Al/Si/SiOx/ZrO2/ITO devices, along with responsivity, detectivity and sensitivity values of up to ≈3.4 A W−1, 1.2 × 1010 Jones and 4.2 × 103, respectively, far exceeding all other emerging self‐powered systems. Furthermore, dual‐band NIR detection is shown for different NIR wavelengths, proof‐of‐concept feasibility being demonstrated for the smart identification of NIR targets. Therefore, it is demonstrated, for the first time, that coupling together the pyroelectric effect, the photovoltaic effect, and the ferroelectric effect is a novel method to significantly enhance the performance of CMOS‐compatible ZrO2‐based self‐powered photodetectors in the NIR region.

Creativity, credit, and copyright in the age of artificial art

ABSTRACT Generative artificial intelligence is transforming the way we make, and think about, art. With prompting from human users, these generative systems now produce aesthetically compelling and seemingly creative works in a variety of artistic domains. In doing so, they challenge the ways we think about artistic credit, about creativity, and about the mechanism of legal copyright, which is meant to protect and promote creativity in a capitalist art market. All of this is currently at play in the courtroom, as artists contest the ways in which their artworks can be rightfully fed into these artificial systems (“the problem of the inputs”), and other artists are challenged over whether they might be credited for the visual images these systems generate (“the problem of the outputs”). Here, we explore these problems as they arise in visual art. We argue that the contested legal landscape surrounding these artificial systems reflects the ways they challenge our received notions of artistic credit and creativity. And we suggest that clarifying or changing the application of copyright in light of their generative capacities will ultimately involve revising our conception of art.

Teacher-student learning of generative adversarial network-guided diffractive neural networks for visual tracking and imaging

Teacher-student learning of generative adversarial network-guided diffractive neural networks for visual tracking and imaging

Accuracy of the Image Interpretation Capability of ChatGPT-4 Vision in Analysis of Hess Screen and Visual Field Abnormalities.

OpenAI, the owner of ChatGPT, publicly released the GPT-4 Vision in September 2023. This multimedia chatbot has the capability to receive and analyze various images presented to it by the user. We assessed the accuracy of its interpretation of 2 of the images commonly used in neuro-ophthalmology, namely Hess screen and automated visual field images. We separately uploaded typical images of 5 abnormal Hess screen charts related to third, fourth, and sixth cranial nerve palsy, Brown syndrome, and inferior orbital wall fracture with entrapment of the inferior rectus muscle. Likewise, 5 classic images of automated visual field grayscale maps related to lesions of the optic nerve, the chiasma, the optic tract, the optic radiations, and the occipital lobe were presented. The chatbot was instructed to select the best option among the 5 choices presented in each question. The GPT-4 Vision was able to select the right choice in 2/5 questions on Hess screens and 3/5 of the visual field questions. Despite selection of the correct option, qualitative evaluation of GPT-4 responses revealed flawed analysis of certain aspects of some image findings, such as the side of involvement or the misinterpretation of the physiologic blind spot as a central scotoma. The performance of GPT-4 Vision in the interpretation of abnormalities of Hess screen and visual field involvement was highly variable, even with simple typical cases of classic disorders. As the chatbot's image recognition is currently evolving, its capacity to accurately interpret ophthalmologic images is still limited at this time.

Advances in Emerging Non-Destructive Technologies for Detecting Raw Egg Freshness: A Comprehensive Review

Eggs are a rich food source of proteins, fats, vitamins, minerals, and other nutrients. However, the egg industry faces some challenges such as microbial invasion due to environmental factors, leading to damage and reduced usability. Therefore, detecting the freshness of raw eggs using various technologies, including traditional and non-destructive methods, can overcome these challenges. As the traditional methods of assessing egg freshness are often subjective and time-consuming, modern non-destructive technologies, including near-infrared (NIR) spectroscopy, Raman spectroscopy, fluorescence spectroscopy, computer vision (color imaging), hyperspectral imaging, electronic noses, and nuclear magnetic resonance, have offered objective and rapid results to address these limitations. The current review summarizes and discusses the recent advances and developments in applying non-destructive technologies for detecting raw egg freshness. Some of these technologies such as NIR spectroscopy, computer vision, and hyperspectral imaging have achieved an accuracy of more than 96% in detecting egg freshness. Therefore, this review provides an overview of the current trends in the state-of-the-art non-destructive technologies recently utilized in detecting the freshness of raw eggs. This review can contribute significantly to the field of emerging technologies in this research track and pique the interests of both food scientists and industry professionals.

Vahagn: VisuAl Haptic Attention Gate Net for slip detection

IntroductionSlip detection is crucial for achieving stable grasping and subsequent operational tasks. A grasp action is a continuous process that requires information from multiple sources. The success of a specific grasping maneuver is contingent upon the confluence of two factors: the spatial accuracy of the contact and the stability of the continuous process.MethodsIn this paper, for the task of perceiving grasping results using visual-haptic information, we propose a new method for slip detection, which synergizes visual and haptic information from spatial-temporal dual dimensions. Specifically, the method takes as input a sequence of visual images from a first-person perspective and a sequence of haptic images from a gripper. Then, it extracts time-dependent features of the whole process and spatial features matching the importance of different parts with different attention mechanisms. Inspired by neurological studies, during the information fusion process, we adjusted temporal and spatial information from vision and haptic through a combination of two-step fusion and gate units.Results and discussionTo validate the effectiveness of method, we compared it with traditional CNN net and models with attention. It is anticipated that our method achieves a classification accuracy of 93.59%, which is higher than that of previous works. Attention visualization is further presented to support the validity.

Spatiotemporal trends and drivers of forest cover change in Metekel Zone forest areas, Northwest Ethiopia.

The spatiotemporal dynamics of forest cover are essential for understanding the patterns and processes of forest change over time and space. This research focused on the spatiotemporal trends and drivers of forest cover change in the Metekel Zone of Northwest Ethiopia. Landsat 5, Landsat 7, and Landsat 8 imagery, covering the period from 1986 to 2019, were used for land use/cover classification. Land use/cover classification was performed using random forest (RF) and support vector machine (SVM) algorithms in the Google Earth Engine (GEE) platform, with training samples obtained through visual image interpretation. Spectral indices, such as the normalized difference vegetation index, soil-adjusted vegetation index, leaf area index, and normalized difference water index, were analyzed to examine forest cover dynamics over time. In addition, key informant interviews (KIIs) and focus group discussions (FGDs) were conducted. Findings revealed that forest cover decreased significantly from 51.37% in 1986 to 17.20% in 2019, driven largely by human activities such as agricultural expansion, increased demand for firewood, and urban expansion. Findings from spectral indices further corroborated the finding that forest cover in the study region (mainly in the southwestern part) substantially decreased from 1986 to 2019. Concerning forest depletion, the lack of local community awareness has become a key challenge. This problem is attributed to communities prioritizing immediate needs such as fuel and land for agriculture over long-term forest conservation. To combat ongoing deforestation, the Metekel Zone Administration, in collaboration with the land administration office and other stakeholders, revisited and strengthened existing forest policies and control systems. It is also suggested that community awareness, chiefly among youth, should be enhanced through the strategic expansion of formal and nonformal educational initiatives, which empower the youth as agents of change and promote the dissemination of knowledge throughout the community.

Research on motion control strategy of athlete muscle training based on blockchain and visual image analysis

Muscle training is an important part of athletes’ physical training. Its goal is to keep athletes in high intensity and improve sports performance in physical training. With the continuous improvement of the level of competitive sports, there are still many problems in the training of competitive athletes. The research on the control strategies of some technical movements in the process of athletes’ muscle development would help to improve the efficiency and effect of athletes’ training. By analyzing and quant description of muscle movement control process based on blockchain and visual image processing technology, the muscle movement control rule is discussed and then a complete movement database is constructed. At the same time, combined with the sports experiment method and cognitive behavioral theory, this paper analyzed the exercise load, exercise time allocation, muscle training time allocation methods and the corresponding muscle training strategy selection methods under different muscle states, providing theoretical basis for athletes to conduct muscle strength training scientifically and efficiently. This paper first analyzed the importance of athletes’ muscle training action control. After that, the research was mainly carried out from the following two aspects. The first is to analyze the motion control strategy of athlete muscle training based on blockchain and visual image, and summarize its characteristics. The second is to use electronic imaging technology to analyze the data related to posture control in athletes’ muscle training, and explore the characteristics and changes of muscle posture. At last, this paper put forward relevant algorithms about visual images and conducted experimental research on athletes’ physical indicators and performance under different muscle training according to the research in this paper. It was concluded that the score ratio of athletes’ performance after muscle training action control was 7.39% higher than that of general muscle training. Therefore, it is very important to strengthen the research of movement technology in muscle training and analyze the influence of some training control strategies on the development of athletes’ muscles in training.

Effect of Stimulus Regularities on Eye Movement Characteristics

Humans have the unique ability to discern spatial and temporal regularities in their surroundings. However, the effect of learning these regularities on eye movement characteristics has not been studied enough. In the present study, we investigated the effect of the frequency of occurrence and the presence of common chunks in visual images on eye movement characteristics like the fixation duration, saccade amplitude and number, and gaze number across sequential experimental epochs. The participants had to discriminate the patterns presented in pairs as the same or different. The order of pairs was repeated six times. Our results show an increase in fixation duration and a decrease in saccade amplitude in the sequential epochs, suggesting a transition from ambient to focal information processing as participants acquire knowledge. This transition indicates deeper cognitive engagement and extended analysis of the stimulus information. Interestingly, contrary to our expectations, the saccade number increased, and the gaze number decreased. These unexpected results might imply a reduction in the memory load and a narrowing of attentional focus when the relevant stimulus characteristics are already determined.

Phosphor-in-Ceramic-Converted Laser-Driven Near-Infrared Light Sources for Multiple Intelligent Spectroscopy Applications.

Ultrabright broadband near-infrared (NIR) phosphor-converted laser diode (pc-LD) as a light source is increasingly essential for improving the sensitivity and spatial resolution of intelligent NIR spectroscopy technologies. However, the performance of NIR pc-LD is greatly hindered by the low external quantum efficiency (EQE) and poor thermal resistance of phosphor materials. Herein, a highly stable phosphor-in-ceramic (PiC) film deposited on high thermal conductivity substrate, in which the NIR-emitting Ca3MgHfGe3O12:Cr3+ phosphor is incorporated into a glass-crystallized Ca3Ga2Ge3O12 ceramic matrix, along with the formation of a new type PiC composite material with high efficiency, absorbance, and thermal conductivity, is designed and prepared. The obtained PiC exhibits an impressive EQE of 57.7%, high thermal conductivity of 17.1 W m-1 K-1, and the PiC wheel demonstrates a broadband NIR emission exceeding 5 W when excited by a 450 nm laser. Finally, a groundbreaking electrically driven pc-LD device based on PiC achieves 1.6 W of NIR output, enabling multiple intelligent spectroscopy applications in archaeology and night vision imaging. This work paves the way for advancing broadband NIR light sources in a diverse range of photonic applications.