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Journal articles on the topic 'Kinematic identification- Vision based techniques'

Author: Grafiati

Published: 6 September 2023

Last updated: 31 July 2025

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1

Seah, Shao Xuan, Yan Han Lau, and Sutthiphong Srigrarom. "Multiple Aerial Targets Re-Identification by 2D- and 3D- Kinematics-Based Matching." Journal of Imaging 8, no. 2 (2022): 26. http://dx.doi.org/10.3390/jimaging8020026.

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This paper presents two techniques in the matching and re-identification of multiple aerial target detections from multiple electro-optical devices: 2-dimensional and 3-dimensional kinematics-based matching. The main advantage of these methods over traditional image-based methods is that no prior image-based training is required; instead, relatively simpler graph matching algorithms are used. The first 2-dimensional method relies solely on the kinematic and geometric projections of the detected targets onto the images captured by the various cameras. Matching and re-identification across frame

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Chen, Biao, Chaoyang Chen, Jie Hu, et al. "Computer Vision and Machine Learning-Based Gait Pattern Recognition for Flat Fall Prediction." Sensors 22, no. 20 (2022): 7960. http://dx.doi.org/10.3390/s22207960.

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Background: Gait recognition has been applied in the prediction of the probability of elderly flat ground fall, functional evaluation during rehabilitation, and the training of patients with lower extremity motor dysfunction. Gait distinguishing between seemingly similar kinematic patterns associated with different pathological entities is a challenge for the clinician. How to realize automatic identification and judgment of abnormal gait is a significant challenge in clinical practice. The long-term goal of our study is to develop a gait recognition computer vision system using artificial int

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Huang, Jianbing, and Chia-Hsiang Menq. "Identification and Characterization of Regular Surfaces from Unorganized Points by Normal Sensitivity Analysis." Journal of Computing and Information Science in Engineering 2, no. 2 (2002): 115–24. http://dx.doi.org/10.1115/1.1509075.

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In this paper, the concept of free motion subspace is introduced and utilized to characterize the special kinematic properties of regular surfaces, including planes, natural quadrics, and regular swept surfaces. Based on the concept, a general approach is developed to automatically identify the surface type and calculate the associated geometric parameters of an unknown surface from unorganized measurement points. In the approach, a normal sensitivity matrix, that characterizes the normal perturbation of surface points under differential motions, is derived. With the normal sensitivity matrix,

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Amami, Mustafa M. "Fast and Reliable Vision-Based Navigation for Real Time Kinematic Applications." International Journal for Research in Applied Science and Engineering Technology 10, no. 2 (2022): 922–32. http://dx.doi.org/10.22214/ijraset.2022.40395.

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Abstract: Automatic Image Matching (AIM) is the term used to identify the automatic detection of corresponding points located on the overlapping areas of multiple images. AIM is extensively used with Mobile Mapping System (MMS) for different engineering applications, such as highway infrastructure mapping, monitoring of road surface quality and markings, telecommunication, emergency response, and collecting data for Geographical Information Systems (GIS). Robotics community and Simultaneous Localization And Mapping (SLAM) based applications are other important areas that require fact and welld

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Wei, Hong, Fulong Liu, Guoxing Li, Xingchen Yun, Muhammad Yousaf Iqbal, and Fengshou Gu. "Fault identification of the vehicle suspension system based on binocular vision and kinematic decoupling." Mechanical Sciences 15, no. 2 (2024): 445–60. http://dx.doi.org/10.5194/ms-15-445-2024.

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Abstract. Suspension faults have a detrimental impact on the safety and handling stability of a vehicle. Therefore, monitoring the condition of suspension systems is significant to ensuring the safe operation of modern vehicles. This paper proposes an online monitoring scheme that utilizes binocular vision and kinematic decoupling, to fulfill real-time monitoring requirements for suspensions. To implement the proposed method, a system consisting of a binocular camera and an inertial measurement unit (IMU) is established for acquiring vibration signals from the vehicle body. Additionally, the v

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Sanchez Guinea, Alejandro, Simon Heinrich, and Max Mühlhäuser. "Activity-Free User Identification Using Wearables Based on Vision Techniques." Sensors 22, no. 19 (2022): 7368. http://dx.doi.org/10.3390/s22197368.

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In order to achieve the promise of smart spaces where the environment acts to fulfill the needs of users in an unobtrusive and personalized manner, it is necessary to provide means for a seamless and continuous identification of users to know who indeed is interacting with the system and to whom the smart services are to be provided. In this paper, we propose a new approach capable of performing activity-free identification of users based on hand and arm motion patterns obtained from an wrist-worn inertial measurement unit (IMU). Our approach is not constrained to particular types of movements

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Heydarzadeh, Mohsen, Nima Karbasizadeh, Mehdi Tale Masouleh, and Ahmad Kalhor. "Experimental kinematic identification and position control of a 3-DOF decoupled parallel robot." Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 233, no. 5 (2018): 1841–55. http://dx.doi.org/10.1177/0954406218775906.

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This paper aims at using a kinematic identification procedure in order to enhance the control of a 3-DOF fully decoupled parallel robot, the so-called “Tripteron.” From a practical standpoint, manufacture errors lead to some kinematic uncertainties in the robot which cause real kinematic equations of the robot to be different from the theoretical ones. In this paper, using a white box identification procedure, the independence of degrees-of-freedom in the robot is studied. Considering the fact that the kinematic identification of a robotic manipulator requires the position of its end-effector

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Silva, José Luís, Rui Bordalo, José Pissarra, and Paloma de Palacios. "Computer Vision-Based Wood Identification: A Review." Forests 13, no. 12 (2022): 2041. http://dx.doi.org/10.3390/f13122041.

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Wood identification is an important tool in many areas, from biology to cultural heritage. In the fight against illegal logging, it has a more necessary and impactful application. Identifying a wood sample to genus or species level is difficult, expensive and time-consuming, even when using the most recent methods, resulting in a growing need for a readily accessible and field-applicable method for scientific wood identification. Providing fast results and ease of use, computer vision-based technology is an economically accessible option currently applied to meet the demand for automated wood

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Bryła, Jakub, Adam Martowicz, Maciej Petko, Konrad Gac, Konrad Kobus, and Artur Kowalski. "Wear Analysis of 3D-Printed Spur and Herringbone Gears Used in Automated Retail Kiosks Based on Computer Vision and Statistical Methods." Materials 16, no. 16 (2023): 5554. http://dx.doi.org/10.3390/ma16165554.

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This paper focuses on a wear evaluation conducted for prototype spur and herringbone gears made from PET-G filament using additive manufacturing. The main objective of this study is to verify if 3D-printed gears can be considered a reliable choice for long-term exploitation in selected mechanical systems, specifically automated retail kiosks. For this reason, two methods were applied, utilizing: (1) vision-based inspection of the gears’ cross-sectional geometry and (2) the statistical characterization of the selected kinematic parameters and torques generated by drives. The former method invol

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T.A, Sathish Shankar. "Computer Vision based Greenhouse Fruits and Vegetables Identification – A Review." Journal of Innovative Image Processing 6, no. 3 (2024): 290–304. http://dx.doi.org/10.36548/jiip.2024.3.006.

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In recent years, computer vision has emerged as a powerful tool for automating various processes in agriculture, particularly in greenhouse environments. The identification and classification of vegetables and fruits within greenhouses play a crucial role in monitoring crop growth, assessing health status, and optimizing resource utilization. This review study provides a comprehensive overview of recent advancements in computer vision techniques for the identification of vegetables and fruits in greenhouse settings. This study discusses about various methodologies and challenges in this domain

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Huang, Xueqi, and Jinzhu Huang. "Research and Design of Network RTK High-precision Positioning and Computer Vision Service Platform Based on Beidou." Journal of Industry and Engineering Management 2, no. 1 (2024): 1–6. http://dx.doi.org/10.62517/jiem.202403101.

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Beidou RTK (real-time kinematic) high-precision positioning is based on the Beidou Satellite Navigation System, utilizing differential processing techniques with ground-based augmentation systems, reference stations, and other means to achieve high-precision positioning. It has a wide range of application prospects. Integrating Beidou RTK high-precision positioning with computer vision, a service platform based on Beidou RTK high-precision positioning and computer vision is constructed. This will further expand the application areas of the Beidou Satellite Navigation System. This article, base

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RADHIKA, K. R., S. V. SHEELA, M. K. VENKATESHA, and G. N. SEKHAR. "SIGNATURE AND IRIS AUTHENTICATION BASED ON DERIVED KINEMATIC VALUES." International Journal of Pattern Recognition and Artificial Intelligence 24, no. 08 (2010): 1237–60. http://dx.doi.org/10.1142/s021800141000841x.

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Authentication systems which are covenant with a measurable behavioral and physiological traits are essential for an online system. In this paper, two types of biometric sample authentication from different databases on a common algorithm using Continuous Dynamic Programming [CDP] are discussed. Using a common algorithm, a method for user-dependent threshold decisions can be achieved for both biometrics in a uniform fashion. The integration of static iris information and dynamic signature information are done at decision level. Inferences are drawn using voting techniques. The derived kinemati

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Cosenza, C., R. Brancati, V. Niola, and S. Savino. "Experimental Investigation on the Kinematics of an Underactuated Mechanical Finger through Vision-Based Technology." WSEAS TRANSACTIONS ON ENVIRONMENT AND DEVELOPMENT 18 (February 9, 2022): 322–32. http://dx.doi.org/10.37394/232015.2022.18.32.

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Marker-less vision techniques represent a promising route in the development of experimental methods to study the kinematic and the dynamic parameters of mechanical systems. The knowledge of a great number of these parameters is a fundamental issue in the system behaviour analysis and represents an even more crucial aspect for underactuated mechanical systems. In this paper, a technique is proposed to identify the kinematics of the phalanges of an underactuated mechanical finger, starting from the acquisition of the finger point cloud data by means of contactless vision system devices. The ana

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Dang, Minh. "Efficient Vision-Based Face Image Manipulation Identification Framework Based on Deep Learning." Electronics 11, no. 22 (2022): 3773. http://dx.doi.org/10.3390/electronics11223773.

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Image manipulation of the human face is a trending topic of image forgery, which is done by transforming or altering face regions using a set of techniques to accomplish desired outputs. Manipulated face images are spreading on the internet due to the rise of social media, causing various societal threats. It is challenging to detect the manipulated face images effectively because (i) there has been a limited number of manipulated face datasets because most datasets contained images generated by GAN models; (ii) previous studies have mainly extracted handcrafted features and fed them into mach

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Knyaz, V. A., A. A. Maksimov, and M. M. Novikov. "VISION BASED AUTOMATED ANTHROPOLOGICAL MEASUREMENTS AND ANALYSIS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W12 (May 9, 2019): 117–22. http://dx.doi.org/10.5194/isprs-archives-xlii-2-w12-117-2019.

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<p><strong>Abstract.</strong> Modern techniques of optical 3D measurements such as photogrammetry, computer tomography, laser 3D scanning provide new possibilities for acquiring accurate 2D and 3D data of high resolution, thus creating new conditions for anthropological data analysis. Traditional anthropological manual point measurements can be substituted by analysis of accurate textured 3D models, which allow to retrieve more information about studied object and easily to share data for independent analysis. The paper presents the vision-based techniques for anthropological

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Tahir, Muhammad, and Saeed Anwar. "Transformers in Pedestrian Image Retrieval and Person Re-Identification in a Multi-Camera Surveillance System." Applied Sciences 11, no. 19 (2021): 9197. http://dx.doi.org/10.3390/app11199197.

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Person Re-Identification is an essential task in computer vision, particularly in surveillance applications. The aim is to identify a person based on an input image from surveillance photographs in various scenarios. Most Person re-ID techniques utilize Convolutional Neural Networks (CNNs); however, Vision Transformers are replacing pure CNNs for various computer vision tasks such as object recognition, classification, etc. The vision transformers contain information about local regions of the image. The current techniques take this advantage to improve the accuracy of the tasks underhand. We

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Jung, SeHee, Bingyi Su, Hanwen Wang, et al. "A computer vision-based lifting task recognition method." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 66, no. 1 (2022): 1210–14. http://dx.doi.org/10.1177/1071181322661507.

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Low-back musculoskeletal disorders (MSDs) are major cause of work-related injury among workers in manual material handling (MMH). Epidemiology studies show that excessive repetition is one of major risk factors of low-back MSDs. Thus, it is essential to monitor the frequency of lifting tasks for an ergonomics intervention. In the current field practice, safety practitioners need to manually observe workers to identify their lifting frequency, which is time consuming and labor intensive. In this study, we propose a method that can recognize lifting actions from videos using computer vision and

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Jurczyk, Karolina, Paweł Piskur, and Piotr Szymak. "Parameters Identification of the Flexible Fin Kinematics Model Using Vision and Genetic Algorithms." Polish Maritime Research 27, no. 2 (2020): 39–47. http://dx.doi.org/10.2478/pomr-2020-0025.

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AbstractRecently a new type of autonomous underwater vehicle uses artificial fins to imitate the movements of marine animals, e.g. fish. These vehicles are biomimetic and their driving system is an undulating propulsion. There are two main methods of reproducing undulating motion. The first method uses a flexible tail fin, which is connected to a rigid hull by a movable axis. The second method is based on the synchronised operation of several mechanical joints to imitate the tail movement that can be observed among real marine animals such as fish. This paper will examine the first method of r

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Kwon, Jaerock, Yunju Lee, and Jehyung Lee. "Comparative Study of Markerless Vision-Based Gait Analyses for Person Re-Identification." Sensors 21, no. 24 (2021): 8208. http://dx.doi.org/10.3390/s21248208.

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The model-based gait analysis of kinematic characteristics of the human body has been used to identify individuals. To extract gait features, spatiotemporal changes of anatomical landmarks of the human body in 3D were preferable. Without special lab settings, 2D images were easily acquired by monocular video cameras in real-world settings. The 2D and 3D locations of key joint positions were estimated by the 2D and 3D pose estimators. Then, the 3D joint positions can be estimated from the 2D image sequences in human gait. Yet, it has been challenging to have the exact gait features of a person

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Chen, Yen-Lin, Chin-Hsuan Liu, Chao-Wei Yu, Posen Lee, and Yao-Wen Kuo. "An Upper Extremity Rehabilitation System Using Efficient Vision-Based Action Identification Techniques." Applied Sciences 8, no. 7 (2018): 1161. http://dx.doi.org/10.3390/app8071161.

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This study proposes an action identification system for home upper extremity rehabilitation. In the proposed system, we apply an RGB-depth (color-depth) sensor to capture the image sequences of the patient’s upper extremity actions to identify its movements. We apply a skin color detection technique to assist with extremity identification and to build up the upper extremity skeleton points. We use the dynamic time warping algorithm to determine the rehabilitation actions. The system presented herein builds up upper extremity skeleton points rapidly. Through the upper extremity of the human ske

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Gokaraju, Jnana Sai Abhishek Varma, Weon Keun Song, Min-Ho Ka, and Somyot Kaitwanidvilai. "Human and bird detection and classification based on Doppler radar spectrograms and vision images using convolutional neural networks." International Journal of Advanced Robotic Systems 18, no. 3 (2021): 172988142110105. http://dx.doi.org/10.1177/17298814211010569.

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The study investigated object detection and classification based on both Doppler radar spectrograms and vision images using two deep convolutional neural networks. The kinematic models for a walking human and a bird flapping its wings were incorporated into MATLAB simulations to create data sets. The dynamic simulator identified the final position of each ellipsoidal body segment taking its rotational motion into consideration in addition to its bulk motion at each sampling point to describe its specific motion naturally. The total motion induced a micro-Doppler effect and created a micro-Dopp

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Hoang, Xuan-Bach, Phu-Cuong Pham, and Yong-Lin Kuo. "Collision Detection of a HEXA Parallel Robot Based on Dynamic Model and a Multi-Dual Depth Camera System." Sensors 22, no. 15 (2022): 5923. http://dx.doi.org/10.3390/s22155923.

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This paper introduces a Hexa parallel robot and obstacle collision detection method based on dynamic modeling and a computer vision system. The processes to deal with the collision issues refer to collision detection, collision isolation, and collision identification applied to the Hexa robot, respectively, in this paper. Initially, the configuration, kinematic and dynamic characteristics during movement trajectories of the Hexa parallel robot are analyzed to perform the knowledge extraction for the method. Next, a virtual force sensor is presented to estimate the collision detection signal cr

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Rohling, Robert N., and John M. Hollerbach. "Calibrating the Human Hand for Haptic Interfaces." Presence: Teleoperators and Virtual Environments 2, no. 4 (1993): 281–96. http://dx.doi.org/10.1162/pres.1993.2.4.281.

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Determination of human hand poses from hand master measurements of joint angles requires an accurate human hand model for each operator. A new method for human hand calibration is proposed, based on open-loop kinematic calibration. The parameters of a kinematic model of the human index finger are determined as an example. Singular value decomposition is used as a tool for analyzing the kinematic model and the identification process. It was found that accurate and reliable results are obtained only when the numerical condition is minimized through parameter scaling, model reduction and pose set

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Sultan, Ahmed, Walied Makram, Mohammed Kayed, and Abdelmaged Amin Ali. "Sign language identification and recognition: A comparative study." Open Computer Science 12, no. 1 (2022): 191–210. http://dx.doi.org/10.1515/comp-2022-0240.

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Abstract Sign Language (SL) is the main language for handicapped and disabled people. Each country has its own SL that is different from other countries. Each sign in a language is represented with variant hand gestures, body movements, and facial expressions. Researchers in this field aim to remove any obstacles that prevent the communication with deaf people by replacing all device-based techniques with vision-based techniques using Artificial Intelligence (AI) and Deep Learning. This article highlights two main SL processing tasks: Sign Language Recognition (SLR) and Sign Language Identific

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Andreff, Nicolas, and Philippe Martinet. "Unifying Kinematic Modeling, Identification, and Control of a Gough–Stewart Parallel Robot Into a Vision-Based Framework." IEEE Transactions on Robotics 22, no. 6 (2006): 1077–86. http://dx.doi.org/10.1109/tro.2006.882931.

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Parkhi, Abhinav, and Atish Khobragade. "Review on deep learning based techniques for person re-identification." 3C TIC: Cuadernos de desarrollo aplicados a las TIC 11, no. 2 (2022): 208–23. http://dx.doi.org/10.17993/3ctic.2022.112.208-223.

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In-depth study has recently been concentrated on human re-identification, which is a crucial component of automated video surveillance. Re-identification is the act of identifying someone in photos or videos acquired from other cameras after they have already been recognized in an image or video from one camera. Re-identification, which involves generating consistent labelling between several cameras, or even just one camera, is required to reconnect missing or interrupted tracks. In addition to surveillance, it may be used in forensics, multimedia, and robotics.Re-identification of the person

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Zhang, Dashan, Andong Zhu, Wenhui Hou, Lu Liu, and Yuwei Wang. "Vision-Based Structural Modal Identification Using Hybrid Motion Magnification." Sensors 22, no. 23 (2022): 9287. http://dx.doi.org/10.3390/s22239287.

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As a promising alternative to conventional contact sensors, vision-based technologies for a structural dynamic response measurement and health monitoring have attracted much attention from the research community. Among these technologies, Eulerian video magnification has a unique capability of analyzing modal responses and visualizing modal shapes. To reduce the noise interference and improve the quality and stability of the modal shape visualization, this study proposes a hybrid motion magnification framework that combines linear and phase-based motion processing. Based on the assumption that

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Madan Mohan Mishra. and Pramod Singh. "Pattern Based Leaves Disease Classification Using AI." International Journal of Latest Technology in Engineering Management & Applied Science 14, no. 6 (2025): 908–14. https://doi.org/10.51583/ijltemas.2025.1406000100.

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Abstract- Artificial Intelligence (AI) is an overarching domain that integrates a variety of techniques, tools, and systems designed to enable machines to learn from data and perform predictive or decision-making tasks. Within this domain, computer vision stands out as a pivotal subfield, offering substantial contributions across multiple sectors, including agriculture. The integration of AI and computer vision has given rise to smart farming an advanced form of agriculture where traditional cultivation practices are optimized through intelligent technologies to enhance productivity, precision

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Rajagounder, Rajamani. "A machine vision system for inspecting mechanical parts." Machine Graphics and Vision 34, no. 1 (2025): 75–86. https://doi.org/10.22630/mgv.2025.34.1.4.

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Computer vision-based inspection has become widely used in manufacturing industries for part identification, dimensional inspection, and guiding material handling systems. Defect-free production cannot be achieved with sampling inspection methods; therefore, a 100 percentage inspection approach is mandatory to meet the zero-defect goals of manufacturing industries. Achieving this is possible with advanced technologies, such as vision-based inspection systems. In this study, a vision-based inspection system is proposed for part identification, defect detection, and dimensional measurement. The

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Barranco Gutiérrez, Alejandro Israel, and José de Jesús Medel Juárez. "Artificial vision and identification for intelligent orientation using a compass." Revista Facultad de Ingeniería Universidad de Antioquia, no. 58 (February 27, 2013): 191–98. http://dx.doi.org/10.17533/udea.redin.14614.

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A method to determine the orientation of an object relative to Magnetic North using computer vision and identification techniques, by hand compass is presented. This is a necessary condition for intelligent systems with movements rather than the responses of GPS, which only locate objects within a region. Commonly, intelligent systems have vision tools and identification techniques that show their position on the hand compass without relying on a satellite network or external objects that indicate their location. The methodof intelligent guidance is based on image recognition for the red needl

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Liu, Chien-Pin, Ting-Yang Lu, Hsuan-Chih Wang, Chih-Ya Chang, Chia-Yeh Hsieh, and Chia-Tai Chan. "Inertial Measurement Unit-Based Frozen Shoulder Identification from Daily Shoulder Tasks Using Machine Learning Approaches." Sensors 24, no. 20 (2024): 6656. http://dx.doi.org/10.3390/s24206656.

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Frozen shoulder (FS) is a common shoulder condition accompanied by shoulder pain and a loss of shoulder range of motion (ROM). The typical clinical assessment tools such as questionnaires and ROM measurement are susceptible to subjectivity and individual bias. To provide an objective evaluation for clinical assessment, this study proposes an inertial measurement unit (IMU)-based identification system to automatically identify shoulder tasks whether performed by healthy subjects or FS patients. Two groups of features (time-domain statistical features and kinematic features), seven machine learn

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Liang, Yunan, Bin Zhang, Wenwen Wang, et al. "Deep Learning-Based Fluid Identification with Residual Vision Transformer Network (ResViTNet)." Processes 13, no. 6 (2025): 1707. https://doi.org/10.3390/pr13061707.

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The tight sandstone gas reservoirs in the LX area of the Ordos Basin are characterized by low porosity, poor permeability, and strong heterogeneity, which significantly complicate fluid type identification. Conventional methods based on petrophysical logging and core analysis have shown limited effectiveness in this region, often resulting in low accuracy of fluid identification. To improve the precision of fluid property identification in such complex tight gas reservoirs, this study proposes a hybrid deep learning model named ResViTNet, which integrates ResNet (residual neural network) with

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Raza, Ali, Mohammad Rustom Al Nasar, Essam Said Hanandeh, Raed Abu Zitar, Ahmad Yacoub Nasereddin, and Laith Abualigah. "A Novel Methodology for Human Kinematics Motion Detection Based on Smartphones Sensor Data Using Artificial Intelligence." Technologies 11, no. 2 (2023): 55. http://dx.doi.org/10.3390/technologies11020055.

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Kinematic motion detection aims to determine a person’s actions based on activity data. Human kinematic motion detection has many valuable applications in health care, such as health monitoring, preventing obesity, virtual reality, daily life monitoring, assisting workers during industry manufacturing, caring for the elderly. Computer vision-based activity recognition is challenging due to problems such as partial occlusion, background clutter, appearance, lighting, viewpoint, and changes in scale. Our research aims to detect human kinematic motions such as walking or running using smartphones

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Feng, Wenfeng, Guoying Sun, and Xin Zhang. "Plant Disease Identification Based on Encoder–Decoder Model." Agronomy 14, no. 10 (2024): 2208. http://dx.doi.org/10.3390/agronomy14102208.

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Plant disease identification is a crucial issue in agriculture, and with the advancement of deep learning techniques, early and accurate identification of plant diseases has become increasingly critical. In recent years, the rise of vision transformers has attracted significant attention from researchers in various vision-based application areas. We designed a model with an encoder–decoder architecture to efficiently classify plant diseases using a transfer learning approach, which effectively recognizes a large number of plant diseases in multiple crops. The model was tested on the “PlantVill

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WANG, MIN, XIADONG LV, and XINHAN HUANG. "VISION BASED MOTION CONTROL AND TRAJECTORY TRACKING FOR MICROASSEMBLY ROBOTS." International Journal of Information Acquisition 04, no. 03 (2007): 237–49. http://dx.doi.org/10.1142/s0219878907001319.

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This paper presents a vision based motion control and trajectory tracking strategies for microassembly robots including a self-optimizing visual servoing depth motion control method and a novel trajectory snake tracking strategy. To measure micromanipulator depth motion, a normalized gray-variance focus measure operator is developed using depth from focus techniques. The extracted defocus features are theoretically distributed with one peak point which can be applied to locate the microscopic focal depth via self-optimizing control. Tracking differentiators are developed to suppress noises and

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Kong, Xuan, Tengyi Wang, Jie Zhang, et al. "Tire Contact Force Equations for Vision-Based Vehicle Weight Identification." Applied Sciences 12, no. 9 (2022): 4487. http://dx.doi.org/10.3390/app12094487.

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Overloaded vehicles have a variety of adverse effects; they not only damage pavements, bridges, and other infrastructure but also threaten the safety of human life. Therefore, it is necessary to address the problem of overloading, and this requires the accurate identification of the vehicle weight. Many methods have been used to identify vehicle weights. Most of them use contact methods that require sensors attached to or embedded in the road or bridge, which have disadvantages such as high cost, low accuracy, and poor durability. The authors have developed a vehicle weight identification meth

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Meng, Hua, Lina Zhang, Fan Yang, et al. "Livestock Biometrics Identification Using Computer Vision Approaches: A Review." Agriculture 15, no. 1 (2025): 102. https://doi.org/10.3390/agriculture15010102.

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In the domain of animal management, the technology for individual livestock identification is in a state of continuous evolution, encompassing objectives such as precise tracking of animal activities, optimization of vaccination procedures, effective disease control, accurate recording of individual growth, and prevention of theft and fraud. These advancements are pivotal to the efficient and sustainable development of the livestock industry. Recently, visual livestock biometrics have emerged as a highly promising research focus due to their non-invasive nature. This paper aims to comprehensiv

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Alonso Mijangos, Roberto, ENARA SANTIAGO CORTES, AITZIBER MANCISIDOR BARINAGARREMENTERIA, IBAN LEIZEA ALONSO, and ITZIAR CABANES AXPE. "INTELLIGENT ROBOTIC SYSTEM FOR AUTOMATIC IDENTIFICATION OF MARKS ON PARTS." DYNA 100, no. 3 (2025): 199–203. https://doi.org/10.52152/d11305.

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The increasing market demands are driving companies to develop intelligent robotic cells capable of making autonomous decisions based on their environment. This work addresses the industrial challenge of distinguishing and tracking marked parts throughout the production process, where precision in identifying these parts and their marks poses a significant obstacle. As a solution, the integration of advanced learning techniques into a vision system for pick and place applications with a parallel delta robot is proposed. The developed system uses cubes with serigraphed letters on their top surf

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Slutski, Leonid. "Online Telecontrol Techniques Based on Object Parameter Adjusting." Presence: Teleoperators and Virtual Environments 6, no. 3 (1997): 255–67. http://dx.doi.org/10.1162/pres.1997.6.3.255.

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An approach to telerobotic system organization with manipulator variable parameters is developed. It is intended for solution of manipulation problems when fast transportation operations are combined with high-precision positioning operations. Based on previous research, manipulator gain was chosen as a means for system quality control. It was proposed that the human operator should personally adjust the robot parameters in compliance with situation requirements. Besides such direct parameter adjustment, another implementation of this concept based on indirect adjustment (such as by analog cir

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Qamar, Tehreem, Narmeen Zakaria Bawany, Jawwad Ahmed Shamsi, and Kanwal Zahoor. "VISION-BASED ACCIDENT IDENTIFICATION IN TRAFFIC VIDEOS USING DEEP LEARNING." Suranaree Journal of Science and Technology 30, no. 6 (2024): 010266(1–11). http://dx.doi.org/10.55766/sujst-2023-06-e0939.

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Traffic collisions have emerged as a prominent factor contributing to a significant rise in injury-related fatalities and injuries, making them a pressing concern for public safety. Statistics show that an estimated 1.4 million people die in traffic accidents every year and around fifty million are injured. With the increasing population, ensuring road safety has become one of the critical challenges for city administration. In response to this challenge, the field of computer vision has gained prominence, focusing on the development of digital systems capable of processing, analyzing, and int

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Kaur, Surleen, and Prabhpreet Kaur. "Plant Species Identification based on Plant Leaf Using Computer Vision and Machine Learning Techniques." Journal of Multimedia Information System 6, no. 2 (2019): 49–60. http://dx.doi.org/10.33851/jmis.2019.6.2.49.

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Cruz Ulloa, Christyan, Lourdes Sánchez, Jaime Del Cerro, and Antonio Barrientos. "Deep Learning Vision System for Quadruped Robot Gait Pattern Regulation." Biomimetics 8, no. 3 (2023): 289. http://dx.doi.org/10.3390/biomimetics8030289.

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Robots with bio-inspired locomotion systems, such as quadruped robots, have recently attracted significant scientific interest, especially those designed to tackle missions in unstructured terrains, such as search-and-rescue robotics. On the other hand, artificial intelligence systems have allowed for the improvement and adaptation of the locomotion capabilities of these robots based on specific terrains, imitating the natural behavior of quadruped animals. The main contribution of this work is a method to adjust adaptive gait patterns to overcome unstructured terrains using the ARTU-R (A1 Res

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Gupta, Ashish Kumar, Ayan Seal, Mukesh Prasad, and Pritee Khanna. "Salient Object Detection Techniques in Computer Vision—A Survey." Entropy 22, no. 10 (2020): 1174. http://dx.doi.org/10.3390/e22101174.

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Detection and localization of regions of images that attract immediate human visual attention is currently an intensive area of research in computer vision. The capability of automatic identification and segmentation of such salient image regions has immediate consequences for applications in the field of computer vision, computer graphics, and multimedia. A large number of salient object detection (SOD) methods have been devised to effectively mimic the capability of the human visual system to detect the salient regions in images. These methods can be broadly categorized into two categories b

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Chaudhari, Tejas. "Fruit Scan - Disease Identification in Fruits Using Image Processing." International Journal for Research in Applied Science and Engineering Technology 12, no. 12 (2024): 3061–65. http://dx.doi.org/10.22214/ijraset.2024.59572.

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Abstract: The agricultural industry plays a crucial role in sustaining global food security, and the health of fruit crops is paramount in ensuring a steady food supply. Fruit diseases pose a significant threat to crop yield and quality, making their early detection and management essential. In recent years, the integration of technology and artificial intelligence has transformed fruit disease detection, offering more accurate and efficient solutions. This abstract provides an overview of the techniques and challenges associated with fruit disease detection. This review highlights various met

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Buoli, E., E. Bassoli, G. Eslami Varzaneh, F. Ponsi, and L. Vincenzi. "Vision-based dynamic monitoring of a steel footbridge." Journal of Physics: Conference Series 2647, no. 12 (2024): 122010. http://dx.doi.org/10.1088/1742-6596/2647/12/122010.

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Abstract The increasing slenderness and lightness of modern footbridges make the vibration serviceability assessment a key aspect for their design and maintenance. A promising approach to the characterization of footbridge dynamic behaviour is represented by computer vision-based techniques. In contrast to traditional monitoring systems relied on dense sensor networks, computer vision-based monitoring requires the installation of one or more cameras together with, if necessary, some targets on the monitored structure. The article presents some preliminary results of the short-term dynamic moni

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Singh, Law Kumar, Pooja, Hitendra Garg, and Munish Khanna. "An Artificial Intelligence-Based Smart System for Early Glaucoma Recognition Using OCT Images." International Journal of E-Health and Medical Communications 12, no. 4 (2021): 32–59. http://dx.doi.org/10.4018/ijehmc.20210701.oa3.

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Glaucoma is a progressive and constant eye disease that leads to a deficiency of peripheral vision and, at last, leads to irrevocable loss of vision. Detection and identification of glaucoma are essential for earlier treatment and to reduce vision loss. This motivates us to present a study on intelligent diagnosis system based on machine learning algorithm(s) for glaucoma identification using three-dimensional optical coherence tomography (OCT) data. This experimental work is attempted on 70 glaucomatous and 70 healthy eyes from combination of public (Mendeley) dataset and private dataset. For

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Pimentel de Figueiredo, Rui, Stefan Nordborg Eriksen, Ignacio Rodriguez, and Simon Bøgh. "A Complete System for Automated Semantic–Geometric Mapping of Corrosion in Industrial Environments." Automation 6, no. 2 (2025): 23. https://doi.org/10.3390/automation6020023.

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Corrosion, a naturally occurring process leading to the deterioration of metallic materials, demands diligent detection for quality control and the preservation of metal-based objects, especially within industrial contexts. Traditional techniques for corrosion identification, including ultrasonic testing, radiographic testing, and magnetic flux leakage, necessitate the deployment of expensive and bulky equipment on-site for effective data acquisition. An unexplored alternative involves employing lightweight, conventional camera systems and state-of-the-art computer vision methods for its ident

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Lin, Huei-Yung, Jyun-Min Dai, Lu-Ting Wu, and Li-Qi Chen. "A Vision-Based Driver Assistance System with Forward Collision and Overtaking Detection." Sensors 20, no. 18 (2020): 5139. http://dx.doi.org/10.3390/s20185139.

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One major concern in the development of intelligent vehicles is to improve the driving safety. It is also an essential issue for future autonomous driving and intelligent transportation. In this paper, we present a vision-based system for driving assistance. A front and a rear on-board camera are adopted for visual sensing and environment perception. The purpose is to avoid potential traffic accidents due to forward collision and vehicle overtaking, and assist the drivers or self-driving cars to perform safe lane change operations. The proposed techniques consist of lane change detection, forw

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Wang, Zhengpu, Baoshi Cao, Zongwu Xie, Boyu Ma, Kui Sun, and Yang Liu. "Kinematic Calibration of a Space Manipulator Based on Visual Measurement System with Extended Kalman Filter." Machines 11, no. 3 (2023): 409. http://dx.doi.org/10.3390/machines11030409.

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The calibration of kinematic parameters has been widely used to improve the pose (position and orientation) accuracy of the robot arm. Intelligent measuring equipment with high accuracy is usually provided for the industrial manipulator. Unfortunately, large noise exists in the vision measurement system, which is provided for space manipulators. To overcome the adverse effect of measuring noise and improve the optimality of calibrating time, a calibration method based on extended Kalman filter (EKF) for space manipulators is proposed in this paper. Firstly, the identification model based on th

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Taha, Zahari, Jouh Yeong Chew, and Hwa Jen Yap. "Omnidirectional Vision for Mobile Robot Navigation." Journal of Advanced Computational Intelligence and Intelligent Informatics 14, no. 1 (2010): 55–62. http://dx.doi.org/10.20965/jaciii.2010.p0055.

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Machine vision has been widely studied, leading to the discovery of many image-processing and identification techniques. Together with this, rapid advances in computer processing speed have triggered a growing need for vision sensor data and faster robot response. In considering omnidirectional camera use in machine vision, we have studied omnidirectional image features in depth to determine correlation between parameters and ways to flatten 3-dimensional images into 2 dimensions. We also discuss ways to process omnidirectional images based on their individual features.

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