Vision Technology Research Articles

Study on Measurement Method of Three-dimensional Position of Unlabeled Microspheres under Bright Background

Abstract: Using computer vision technology to obtain the position and trajectory data of particle probe microspheres from microscope images has significance and value in the molecular field. However, most of the existing microsphere measurement methods are based on transmission, which can only be measured under transparent samples and substrates and are not suitable for the application scenario of living cell measurement. In this paper, a method based on reflectivity imaging is proposed to measure the three-dimensional position of the dark microspheres in the bright field. Based on the outermost ring radius method, the relationship between the inner ring radius of the microsphere spot and the out-of-focus distance was explored to measure the coordinates in the Z direction. Cardiomyocytes were combined with 10um size silica microspheres. Experiments show that in a bright field with a high perturbation environment, it can achieve high precision measurement of dark microspheres and achieve three-dimensional position measurement with an accuracy of 50nm in XY direction and 100nm in Z direction.

Guidance device for visually impaired people based on ultrasonic signals and open hardware

<span>Visual impairment is a complex challenge that affects people of all ages, and it is estimated that around 2.2 billion people worldwide lack adequate access to medical treatment and support. In Latin America, there is a lack of attention to people with visual disabilities, evidenced by poor urban infrastructure and lack of compliance with inclusion laws. Some projects stand out for the use of prototypes with artificial vision technology, global positioning system (GPS) and smart canes. Therefore, the objective of the project is to use ultrasonic sensors and a low-cost electronic device coupled to canes, for obstacle detection and mobility using an open hardware embedded system. The results confirmed the efficiency in the detection and operation of the ultrasonic sensor by activating the light emitting diode (LED), the buzzer and the vibrating motor according to the programmed distances. Challenges were identified, such as adapting the sensor to the tilt of the cane and the importance of accurate calibration of the ultrasonic sensor. The system met its objectives by detecting objects in a range of 2 to 50 cm and providing sound alerts to improve the perception of blind people.</span>

The effect of visual displays on the mastery of the forms and functions of English intonation by Chinese L2 students

This study examines the effectiveness of visual displays in improving the mastery of the forms and functions of English intonation among Chinese L2 students. Over a four-week period, two groups of learners (visual-training and auditory-training) took part in five sessions of intonation training, while a control group received no training. Each session began with a 25-minute lesson on the forms and functions of intonation, followed by a 40-minute practice session. All sentences were presented in communicative contexts. With the aid of speech visualization technology, the visual-training group was taught linguistic categories and English intonation structures, and was encouraged to imitate and practice intonation patterns using visual displays of the intonation of each sentence. The auditory-training group learned and practiced intonation by carefully listening to audio clips of each sentence. Intonation production was assessed using speech data from pre- and post-tests. Results showed that the visual-training group outperformed the other two groups, making the greatest progress in mastering the forms of English intonation (pitch accents and boundary tones) and its functions (expressing prominence and marking modality).

A Study on the Effectiveness of Climate Adaptation Materials for Urban Heat Islands using Digital Twin and Computational Fluid Dynamics

This study analyzed the impact of climate adaptation materials on the mitigation of urban heat islands (UHI) using digital twin visualization and computational fluid dynamics (CFD) technology. The effects of UHI due to increased impervious surfaces have become more pronounced, and climate-adaptation materials are gaining attention as potential solutions. In this study, the impact of climate adaptation materials on UHI mitigation was analyzed using the CFD program STAR-CCM+ based on the finite volume method (FVM). The selected research site was Byeoryang-dong, Gwacheon City, and four simulation scenarios were created based on the emissivity values of the asphalt and concrete materials. The results showed that application of climate adaptation materials decreased the surface and aboveground (1.5 m) temperatures by 6.3 and 0.8 °C, respectively. This study suggests that climate adaptation materials can significantly mitigate UHI.

Use of Artificial Intelligence in Fashion Sales Techniques

This article discusses the prominent elements of artificial intelligence innovation in fashion sales techniques and details artificial intelligence-supported sales strategies such as virtual reality, augmented reality, virtual try-on rooms, smart mirrors, artificial intelligence-supported style consultants, visual search technologies and chatbot. This study was written with the compilation method. The compilation method is written with the aim of collecting the literature written so far on the research subjects from a scientific perspective and making a collective contribution to the literature. While virtual reality and augmented reality offer customers interactive shopping experiences and the opportunity to try products in their own environment without going to the store, virtual trial rooms and smart mirrors; Studies have shown that it has the potential to make online and face-to-face shopping easier, faster and more interactive. It has been stated that visual search technologies enable customers to easily find the products in the style they want, and artificial intelligence-supported style consultants and chatbots play an important role in optimizing the shopping experience by making personalized suggestions to customers. New and innovative solutions can be produced by conducting studies that encourage interdisciplinary collaboration between fashion designers, software developers and academicians. In this period when we live intertwined with technology, artificial intelligence-focused modules can be added to academic curricula for students studying in the field of design, and these modules will provide benefits for students to gain knowledge and skills on how to integrate artificial intelligence technologies into fashion design. The effective use of artificial intelligence in fashion sales techniques will provide a competitive advantage against competitors in the industry.

Advanced Plant Phenotyping: Unmanned Aerial Vehicle Remote Sensing and CimageA Software Technology for Precision Crop Growth Monitoring

In production activities and breeding programs, large-scale investigations of crop high-throughput phenotype information are needed to help improve management and decision-making. The development of UAV (unmanned aerial vehicle) remote sensing technology provides a new means for the large-scale, efficient, and accurate acquisition of crop phenotypes, but its practical application and popularization are hindered due to the complicated data processing required. To date, there is no automated system that can utilize the canopy images acquired through UAV to conduct a phenotypic character analysis. To address this bottleneck, we developed a new scalable software called CimageA. CimageA uses crop canopy images obtained by UAV as materials. It can combine machine vision technology and machine learning technology to conduct the high-throughput processing and phenotyping of crop remote sensing data. First, zoning tools are applied to draw an area-of-interest (AOI). Then, CimageA can rapidly extract vital remote sensing information such as the color, texture, and spectrum of the crop canopy in the plots. In addition, we developed data analysis modules that estimate and quantify related phenotypes (such as leaf area index, canopy coverage, and plant height) by analyzing the association between measured crop phenotypes and CimageA-derived remote sensing eigenvalues. Through a series of experiments, we confirmed that CimageA performs well in extracting high-throughput remote sensing information regarding crops, and verified the reliability of retrieving LAI (R2 = 0.796) and estimating plant height (R2 = 0.989) and planting area using CimageA. In short, CimageA is an efficient and non-destructive tool for crop phenotype analysis, which is of great value for monitoring crop growth and guiding breeding decisions.

Naked-eye 3D visualization of cultural relics based on integrated imaging

AbstractDigital technology has become crucial for the protection of cultural relics, enabling their acquisition, preservation, exhibition, and dissemination in a non-contact manner.At present, the three-dimensional(3D) visualization technology of cultural relics focuses on the research of non-true 3D visualization, which cannot provide continuous viewing angle and takes a long time to render. This paper focuses on the acquisition and reproduction of digital information related to cultural relics, and proposes to achieve the true 3D display of cultural relics from a continuous perspective based on integrated imaging(InIm). Building up on traditional InIm, an Element image array (EIA) generation algorithm based on local depth template matching is designed by using spatial geometry relation. Experimental results show that the algorithm not only enables naked-eye 3D visualization of cultural relics, but also the generation speed is more than twice that of the compared 3D display data source generation algorithm.

Advanced modeling of seepage dynamics and control strategies in thick coal seams under high-confined aquifer conditions: A case study

The hydraulic behavior of the connection between the floor failure area and the aquifer water-conductive zone is considered to be the root cause of mine water inrush disasters. Therefore, unraveling the floor failure mechanism is particularly important for safe coal mining above the high-confined aquifer. This paper estimates the depth of the baseplate failure to be 18.4–27.3 m by combining network parallel electrical methods with drilling visualization technology. The FLAC3D-based numerical model considering the strain hardening of caved rock was established with rigorous calibration and verification. The results showed that the depth of damage to the floor is 23.1 m, and the dominating floor failure mechanism is shear failure caused by the vertical stress exceeding the rock bearing capacity. Moreover, the stress recovery process of the baseplate does not alter the failure morphology of the baseplate. Based on the above research findings, the in-situ floor control technique of the working face No. 4305 is proposed and practiced in the field. Field measurements show that floor control performance is satisfactory with water inflow in the goaf being roughly stable at 50 m3/h. Our results can provide useful reference for safe mining above confined aquifer and prevention and mitigation of water-related hazards.

Personalized Super Resolution with Face Prior

Face Super-Resolution (FSR) is a critical technology in computer vision that aims to reconstruct high-resolution facial images from low-resolution inputs. Despite recent advancements, current FSR methods struggle to accurately reconstruct personalized and detailed features. This paper proposes a novel FSR approach that addresses these challenges through a personalized feature extraction and fusion framework. Our method integrates a U-Net based downsampling mechanism to extract individual- specific features from high-resolution reference images, which are then fused with a pre-trained Generative Adversarial Network (GAN) for enhanced reconstruction. We introduce a comprehensive loss function that combines reconstruction, adversarial, facial component, and identity preservation losses to guide the learning process. Extensive experiments on the augmented FFHQ dataset demonstrate that our approach significantly improves the reconstruction of rich facial features, particularly for older individuals, outperforming existing state-of-the-art methods in both quantitative metrics and qualitative visual assessments.

Robot intelligent perception and control

Abstract. In recent years, with the rapid development of robotics, intelligent perception and control has become the core research direction in the field of robotics. This technology plays a key role in many fields such as industrial automation, medical assistance, and autonomous driving, significantly improving production efficiency and service quality, and also promoting the intelligent processes of various industries. On the basis of an in-depth analysis of robot intelligent perception and control technology, this paper systematically reviews the development of visual and non-visual perception technology and discusses the application of multi-modal perception in complex environments. The latest development of motion control and force control technology is analyzed, and its importance in fine operation is expounded. The future development direction of robot intelligent perception and control technology is proposed, and the necessity of enhancing real-time robustness and system integration is emphasized, which provides theoretical support and technical reference for promoting the continuous innovation of robot intelligent perception and control technology.

ViCo: Human visual perception-based contour extraction of Chinese Tang dynasty textile patterns

The study of ancient textile patterns not only enriches historical material but also inspires modern costume art design. Recent developments in computer vision technology provide novel analytical methods for the study of ancient textile patterns. As contours are one of the most significant features in computer vision tasks, contour extraction serves as a prerequisite to accomplishing intelligent understanding and automatic design of textile patterns. In this paper, a perception-consistent contour extraction method (ViCo) that simulates the human visual system from the retina, the lateral geniculate nucleus (LGN), to the V1 area in the cortex is proposed, along with an ancient textile pattern dataset represented by TuanKe in the Chinese Tang dynasty. Experiments were conducted both qualitatively and quantitatively to verify the effectiveness of the proposed method on the TuanKe dataset. Analysis of the results implies the effectiveness of ViCo because the retinal and LGN filter enhances edge information and restrains tiny pixel changes, and the cortex simulation is direction selective. This study is among the initial efforts to employ computer vision technology for ancient textile pattern studies. The study presents a TuanKe dataset and a novel bio-inspired contour extraction method, which has significant historical research importance, fabric art design inspiration, and computer application value.

A Review of Abnormal Crowd Behavior Recognition Technology Based on Computer Vision

Abnormal crowd behavior recognition is one of the research hotspots in computer vision. Its goal is to use computer vision technology and abnormal behavior detection models to accurately perceive, predict, and intervene in potential abnormal behaviors of the crowd and monitor the status of the crowd system in public places in real time, to effectively prevent and deal with public security risks and ensure public life safety and social order. To this end, focusing on the abnormal crowd behavior recognition technology in the computer vision system, a systematic review study of its theory and cutting-edge technology is conducted. First, the crowd level and abnormal behaviors in public places are defined, and the challenges faced by abnormal crowd behavior recognition are expounded. Then, from the dimensions based on traditional methods and based on deep learning, the mainstream technologies of abnormal behavior recognition are discussed, and the design ideas, advantages, and limitations of various methods are analyzed. Next, the mainstream software tools are introduced to provide a comprehensive reference for the technical framework. Secondly, typical abnormal behavior datasets at home and abroad are sorted out, and the characteristics of these datasets are compared in detail from multiple perspectives such as scale, characteristics, and uses, and the performance indicators of different algorithms on the datasets are compared and analyzed. Finally, the full text is summarized and the future development direction of abnormal crowd behavior recognition technology is prospected.

ADDON FOR BLENDER3D. AUTOMATION OF THE OBJECT TEXTURING PROCESS

Abstract. Today, the use of 3D visualization technologies is one of the key directions in the field of information technologies. The 3D direction has become extremely popular in many fields, such as architecture, interior design, game industry, medicine, automotive and other fields. This method opens up new opportunities for perception and interpretation of information, adding it to a three-dimensional virtual space. The topic of the research work is the creation of a software module (addon) for the Blender3D program, which will provide the user with a library of ready-made materials, divided by categories. This addon has similar functionality to Substance Painter and Quixel Mixer, the main functions of which are to create and adjust materials and textures for imported ready-made 3D models. The addon has its own name "QuickShader" and will provide the user with 8 shaders and 35 PBR materials. Throughout the development of the project, the Waterfall life cycle model was used, stability and a clear structure were put first, which helped to efficiently work through each step of creating an addon for the Blender program. In the conclusion to the scientific work, it can also be noted that the development of a software addon for Blender3D, which includes a library of shader operators and PBR materials, will greatly facilitate the work of users when assigning textures to objects on the scene. A library of shader operators makes it easy to implement and customize shaders for light, glass, and some metals, giving you more control over the visual aspect of the scene. PBR materials provide realism due to the accurate representation of physical properties using Physically Based Rendering technologies. This addon gives users the ability to interact more effectively with procedural materials, providing a quick means to create realistic renders. This module automates the creation of materials by providing a list of prepared shaders and textures that can be applied to scene objects. The user will be provided with a set of ready-made shaders (glass, light material, metals) and PBR materials (wood, fabric, metals, and others), which can be applied with one button on the module panel.

The Srgb Color Space Based Density Analysis for Brain Tumor Segmentation

One of the important areas of study in medical image processing is early disease detection and diagnosis. A subfield of medical image processing includes the brain tumour segmentation procedure. Medical professionals have an efficient means of diagnosing illnesses because to computer vision and machine learning technology. The Srgb based density analysis is used in this study to isolate the area of brain tumours in MRI images. To differentiate the tumour area from the surrounding area, the intensity values of the input are normalised using the Gaussian filter and the Srgb colour space. In brain MRI samples, the adaptive threshold approach aids in locating potential tumour space. By calculating region parameters like area and density function, the actual space occupied by brain tumours is recovered. Ultimately, by applying morphological functions and removing potential false positives, the accurate tumour space is identified. Recall, accuracy, and F-measure are a few of the performance indicators that are used to evaluate how effective the suggested strategy.

Identification of Technical Requirements for the Initial Design of an Autonomous Fruit-Harvesting Robot

Automation in agriculture is revolutionizing the management of field tasks. This paper focuses on identifying the essential needs and preliminary technical requirements for designing an autonomous robot specifically for fruit harvesting. The study examines the integration of advanced machine vision technologies and neural networks for the accurate detection of ripe fruits and obstacles, optimizing harvesting efficiency. Key features such as proximity and touch sensors are also evaluated to ensure the safe handling of fruits, minimizing damage during harvesting. The technical specifications, including traction, speed, and the robot’s ability to operate on uneven terrain, are analyzed to ensure adaptability across various crops and ground conditions. Perception systems, incorporating cameras and sensors, are crucial for crop inspection and route planning. The main findings of this review establish a set of technical requirements that serve as a foundation for developing efficient and economically viable fruit-harvesting robots. These systems offer significant potential to transform agriculture by addressing labor shortages and improving harvesting precision. However, challenges remain, particularly in adapting to diverse crops and environments, emphasizing the need for further research and development.

Application and Integration of Computer Vision Technologies for Automated Recognition and Recording of Chemical Experiments

Abstract Advancements in computer vision have significantly improved motion and object recognition accuracy. These advancements should aid the automatic recognition of chemical experiments, potentially contributing to the recording of experiments. Creating an electronic laboratory notebook from experiment filming enhances convenience and allows more detailed information storage compared to traditional manual recording methods. Our previous research focused on employing object detection and action recognition to automate the recognition of chemical experiments. This paper presents a novel system that combines object detection, action recognition, multiple object tracking, and barcode recognition to automatically generate experimental flowcharts. We implemented our system as a graphical user interface-based application for laboratory use that successfully constructs flowcharts from videos of chemical experiments, including simple chemical manipulations.

Research on the Storage Process Management System Based on RFID

With the continuous development and complexity of the global supply chain, warehouse automation and intelligent technology have become the key means to improve the efficiency and accuracy. The application of radio frequency identification (RFID) in storage process management is discussed, Use RFID technology to manage the storage process, pretreatment the goods, and improve the overall logistics speed. At the same time, the visualization technology is used to monitor the status of the goods, The full state of cargo preprocessing to the end of the task is visible.

Doing Futures-Anthropology Research in Visions of Aging Technology

Doing Futures-Anthropology Research in Visions of Aging Technology

Short-Wave Infrared Upconverting Nanoparticles.

Optical technologies enable real-time, noninvasive analysis of complex systems but are limited to discrete regions of the optical spectrum. While wavelengths in the short-wave infrared (SWIR) window (typically, 1700-3000 nm) should enable deep subsurface penetration and reduced photodamage, there are few luminescent probes that can be excited in this region. Here, we report the discovery of lanthanide-based upconverting nanoparticles (UCNPs) that efficiently convert 1740 or 1950 nm excitation to wavelengths compatible with conventional silicon detectors. Screening of Ln3+ ion combinations by differential rate equation modeling identifies Ho3+/Tm3+ or Tm3+ dopants with strong visible or NIR-I emission following SWIR excitation. Experimental upconverted photoluminescence excitation (U-PLE) spectra find that 10% Tm3+-doped NaYF4 core/shell UCNPs have the strongest 800 nm emission from SWIR wavelengths, while UCNPs with an added 2% or 10% Ho3+ show the strongest red emission when excited at 1740 or 1950 nm. Mechanistic modeling shows that addition of a low percentage of Ho3+ to Tm3+-doped UCNPs shifts their emission from 800 to 652 nm by acting as a hub of efficient SWIR energy acceptance and redistribution up to visible emission manifolds. Parallel experimental and computational analysis shows rate equation models are able to predict compositions for specific wavelengths of both excitation and emission. These SWIR-responsive probes open a new IR bioimaging window, and are responsive at wavelengths important for vision technologies.

An integrated three-stream network model for discriminating fish feeding intensity using multi-feature analysis and deep learning.

Feed costs constitute a significant part of the expenses in the aquaculture industry. However, feeding practices in fish farming often rely on the breeder's experience, leading to feed wastage and environmental pollution. To achieve precision in feeding, it is crucial to adjust the feed according to the fish's feeding state. Existing computer vision-based methods for assessing feeding intensity are limited by their dependence on a single spatial feature and manual threshold setting for determining feeding status constraints. These models lack practicality due to their specificity to certain scenarios and objectives. To address these limitations, we propose an integrated approach that combines computer vision technology with a Convolutional Neural Net-work (CNN) to assess the feeding intensity of farmed fish. Our method incorporates temporal, spatial, and data statistical features to provide a comprehensive evaluation of feeding intensity. Using computer vision techniques, we preprocessed feeding images of pearl gentian grouper, extracting temporal features through optical flow, spatial features via binarization, and statistical features using the gray-level co-occurrence matrix. These features are input into their respective specific feature discrimination networks, and the classification results of the three networks are fused to construct a three-stream network for feeding intensity discrimination. The results of our proposed three-stream network achieved an impressive accuracy of 99.3% in distinguishing feeding intensity. The model accurately categorizes feeding states into none, weak, and strong, providing a scientific basis for intelligent fish feeding in aquaculture. This advancement holds promise for promoting sustainable industry development by minimizing feed wastage and optimizing environmental impact.