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Journal articles on the topic 'Landmarks detection'

Author: Grafiati

Published: 4 June 2021

Last updated: 20 June 2025

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1

Zhao, Can, Tao Wu, Jianlin Zhang, Zhiyong Xu, Meihui Li, and Dongxu Liu. "Siamese comparative transformer-based network for unsupervised landmark detection." PLOS ONE 19, no. 12 (2024): e0313518. https://doi.org/10.1371/journal.pone.0313518.

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Landmark detection is a common task that benefits downstream computer vision tasks. Current landmark detection algorithms often train a sophisticated image pose encoder by reconstructing the source image to identify landmarks. Although a well-trained encoder can effectively capture landmark information through image reconstruction, it overlooks the semantic relationships between landmarks. This contradicts the goal of achieving semantic representations in landmark detection tasks. To address these challenges, we introduce a novel Siamese comparative transformer-based network that strengthens t

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Lee, Dong-Hyuk, Hyungpil Moon, and Hyouk Ryeol Choi. "Landmark detection methods for in-pipe robot traveling in urban gas pipelines." Robotica 34, no. 3 (2014): 601–18. http://dx.doi.org/10.1017/s0263574714001726.

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SUMMARYElbows and branches in pipelines produce unique image patterns, which can be used as landmarks for autonomous navigation inside the pipelines. This paper presents two landmark detection methods, known as shadow-based method and laser projection method. The first method uses the landmark's unique patterns of shadow produced by the robot's illuminator. The other method exploits special line features generated by its own line-laser beam projector. The basic algorithms for extracting the landmarks are given and special sensor mechanisms are addressed respectively. Finally, the detection per

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Su, Kai, and Xin Geng. "Soft Facial Landmark Detection by Label Distribution Learning." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 5008–15. http://dx.doi.org/10.1609/aaai.v33i01.33015008.

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Most existing facial landmark detection algorithms regard the manually annotated landmarks as precise hard labels, therefore, the accurate annotated landmarks are essential to the training of these algorithms. However, in many cases, there exist deviations in manual annotations, and the landmarks marked for facial parts with occlusion and large poses are not always accurate, which means that the “ground truth” landmarks are usually not annotated precisely. In such case, it is more reasonable to use soft labels rather than explicit hard labels. Therefore, this paper proposes to associate a biva

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Kim, Taehyung, Jiwon Mok, and Euichul Lee. "Detecting Facial Region and Landmarks at Once via Deep Network." Sensors 21, no. 16 (2021): 5360. http://dx.doi.org/10.3390/s21165360.

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For accurate and fast detection of facial landmarks, we propose a new facial landmark detection method. Previous facial landmark detection models generally perform a face detection step before landmark detection. This greatly affects landmark detection performance depending on which face detection model is used. Therefore, we propose a model that can simultaneously detect a face region and a landmark without performing the face detection step before landmark detection. The proposed single-shot detection model is based on the framework of YOLOv3, a one-stage object detection method, and the los

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Ed-dhahraouy, Mohammed, Hicham Riri, Manal Ezzahmouly, Abdelmajid El Moutaouakkil, Hakima Aghoutan, and Farid Bourzgui. "Proposition of local automatic algorithm for landmark detection in 3D cephalometry." Bulletin of Electrical Engineering and Informatics 10, no. 2 (2021): 707–15. http://dx.doi.org/10.11591/eei.v10i2.1827.

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This study proposes a new contribution to solve the problem of automatic landmarks detection in three-dimensional cephalometry. 3D images obtained from CBCT (cone beam computed tomography) equipment were used for automatic identification of twelve landmarks. The proposed method is based on a local geometry and intensity criteria of skull structures. After the step of preprocessing and binarization, the algorithm segments the skull into three structures using the geometry information of nasal cavity and intensity information of the teeth. Each targeted landmark was detected using local geometri

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Mohammed, Ed-dhahraouy, Riri Hicham, Ezzahmouly Manal, El Moutaouakkil Abdelmajid, Aghoutan Hakima, and Bourzgui Farid. "Proposition of local automatic algorithm for landmark detection in 3D cephalometry." Bulletin of Electrical Engineering and Informatics 10, no. 2 (2021): 707~715. https://doi.org/10.11591/eei.v10i2.1827.

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This study proposes a new contribution to solve the problem of automatic landmarks detection in three-dimensional cephalometry. 3D images obtained from CBCT (cone beam computed tomography) equipment were used for automatic identification of twelve landmarks. The proposed method is based on a local geometry and intensity criteria of skull structures. After the step of preprocessing and binarization, the algorithm segments the skull into three structures using the geometry information of nasal cavity and intensity information of the teeth. Each targeted landmark was detected using local geometri

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Gupta, S., G. H. Smith, E. A. Wakelin, T. Van Der Veen, C. Plaskos, and J. W. Pierrepont. "AUTOMATED LANDMARK DETECTION IN FUNCTIONAL LATERAL RADIOGRAPHS USING DEEP LEARNING." Orthopaedic Proceedings 105-B, SUPP_3 (2023): 70. http://dx.doi.org/10.1302/1358-992x.2023.3.070.

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Evaluation of patient specific spinopelvic mobility requires the detection of bony landmarks in lateral functional radiographs. Current manual landmarking methods are inefficient, and subjective. This study proposes a deep learning model to automate landmark detection and derivation of spinopelvic measurements (SPM).A deep learning model was developed using an international multicenter imaging database of 26,109 landmarked preoperative, and postoperative, lateral functional radiographs (HREC: Bellberry: 2020-08-764-A-2). Three functional positions were analysed: 1) standing, 2) contralateral s

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Deshmane, Prof Priyanka. "Continent Based Geo-Landmark Detection and Recognition Using CNN." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 05 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem32987.

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In the modern world, we need the development of an automated system that can accurately detect landmarks and provide essential services is important. The 'Continent-based Geo-Landmark Detection and Recognition using CNN" project develop the Continent-based Landmark Detection system using deep learning, specifically Convolutional neural network (CNNs). The system will be capable of identifying landmarks from uploaded images. It will provide a short summary about the identified landmarks, and extract geographic data such as landmark address, latitude and longitude and display them on the map. Ad

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Wang, Yuan, Min Cao, Zhenfeng Fan, and Silong Peng. "Learning to Detect 3D Facial Landmarks via Heatmap Regression with Graph Convolutional Network." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 3 (2022): 2595–603. http://dx.doi.org/10.1609/aaai.v36i3.20161.

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3D facial landmark detection is extensively used in many research fields such as face registration, facial shape analysis, and face recognition. Most existing methods involve traditional features and 3D face models for the detection of landmarks, and their performances are limited by the hand-crafted intermediate process. In this paper, we propose a novel 3D facial landmark detection method, which directly locates the coordinates of landmarks from 3D point cloud with a well-customized graph convolutional network. The graph convolutional network learns geometric features adaptively for 3D facia

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Hwang, Hye-Won, Ji-Hoon Park, Jun-Ho Moon, et al. "Automated Identification of Cephalometric Landmarks: Part 2- Might It Be Better Than human?" Angle Orthodontist 90, no. 1 (2019): 69–76. http://dx.doi.org/10.2319/022019-129.1.

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ABSTRACT Objectives To compare detection patterns of 80 cephalometric landmarks identified by an automated identification system (AI) based on a recently proposed deep-learning method, the You-Only-Look-Once version 3 (YOLOv3), with those identified by human examiners. Materials and Methods The YOLOv3 algorithm was implemented with custom modifications and trained on 1028 cephalograms. A total of 80 landmarks comprising two vertical reference points and 46 hard tissue and 32 soft tissue landmarks were identified. On the 283 test images, the same 80 landmarks were identified by AI and human exa

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Kim, Hyeon-Woo, Hyung-Joon Kim, Seungmin Rho, and Eenjun Hwang. "Augmented EMTCNN: A Fast and Accurate Facial Landmark Detection Network." Applied Sciences 10, no. 7 (2020): 2253. http://dx.doi.org/10.3390/app10072253.

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Facial landmarks represent prominent feature points on the face that can be used as anchor points in many face-related tasks. So far, a lot of research has been done with the aim of achieving efficient extraction of landmarks from facial images. Employing a large number of feature points for landmark detection and tracking usually requires excessive processing time. On the contrary, relying on too few feature points cannot accurately represent diverse landmark properties, such as shape. To extract the 68 most popular facial landmark points efficiently, in our previous study, we proposed a mode

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Schwendicke, Falk, Akhilanand Chaurasia, Lubaina Arsiwala, et al. "Deep learning for cephalometric landmark detection: systematic review and meta-analysis." Clinical Oral Investigations 25, no. 7 (2021): 4299–309. http://dx.doi.org/10.1007/s00784-021-03990-w.

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Abstract Objectives Deep learning (DL) has been increasingly employed for automated landmark detection, e.g., for cephalometric purposes. We performed a systematic review and meta-analysis to assess the accuracy and underlying evidence for DL for cephalometric landmark detection on 2-D and 3-D radiographs. Methods Diagnostic accuracy studies published in 2015-2020 in Medline/Embase/IEEE/arXiv and employing DL for cephalometric landmark detection were identified and extracted by two independent reviewers. Random-effects meta-analysis, subgroup, and meta-regression were performed, and study qual

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Morfin, Edgar, Jeung-Yoon Choi, and Stefanie Shattuck-Hufnagel. "Automatic detection and labeling of glides for the English and Spanish language." Journal of the Acoustical Society of America 155, no. 3_Supplement (2024): A338. http://dx.doi.org/10.1121/10.0027738.

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This study outlines the development of a module for automatic detection of glide landmarks. We define glide landmarks as acoustic events that are observed during a narrowing in the oral cavity that does not result in cessation of airflow or in the conditions for turbulence noise. Glide landmarks are commonly associated with standard productions of underlying glides, a set that includes semivowels, liquids, and sounds produced by narrowing at the glottis, such as glottal stops, or the aspirant /h/. We lay out a framework that can be used to determine the acoustic measurements that are useful fo

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Gdoura, Ahmed, Markus Degünther, Birgit Lorenz, and Alexander Effland. "Combining CNNs and Markov-like Models for Facial Landmark Detection with Spatial Consistency Estimates." Journal of Imaging 9, no. 5 (2023): 104. http://dx.doi.org/10.3390/jimaging9050104.

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The accurate localization of facial landmarks is essential for several tasks, including face recognition, head pose estimation, facial region extraction, and emotion detection. Although the number of required landmarks is task-specific, models are typically trained on all available landmarks in the datasets, limiting efficiency. Furthermore, model performance is strongly influenced by scale-dependent local appearance information around landmarks and the global shape information generated by them. To account for this, we propose a lightweight hybrid model for facial landmark detection designed

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Le, Van Nhat Thang, Junhyeok Kang, Il-Seok Oh, Jae-Gon Kim, Yeon-Mi Yang, and Dae-Woo Lee. "Effectiveness of Human–Artificial Intelligence Collaboration in Cephalometric Landmark Detection." Journal of Personalized Medicine 12, no. 3 (2022): 387. http://dx.doi.org/10.3390/jpm12030387.

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Detection of cephalometric landmarks has contributed to the analysis of malocclusion during orthodontic diagnosis. Many recent studies involving deep learning have focused on head-to-head comparisons of accuracy in landmark identification between artificial intelligence (AI) and humans. However, a human–AI collaboration for the identification of cephalometric landmarks has not been evaluated. We selected 1193 cephalograms and used them to train the deep anatomical context feature learning (DACFL) model. The number of target landmarks was 41. To evaluate the effect of human–AI collaboration on

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Yun, Hye Sun, Chang Min Hyun, Seong Hyeon Baek, Sang-Hwy Lee, and Jin Keun Seo. "A semi-supervised learning approach for automated 3D cephalometric landmark identification using computed tomography." PLOS ONE 17, no. 9 (2022): e0275114. http://dx.doi.org/10.1371/journal.pone.0275114.

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Identification of 3D cephalometric landmarks that serve as proxy to the shape of human skull is the fundamental step in cephalometric analysis. Since manual landmarking from 3D computed tomography (CT) images is a cumbersome task even for the trained experts, automatic 3D landmark detection system is in a great need. Recently, automatic landmarking of 2D cephalograms using deep learning (DL) has achieved great success, but 3D landmarking for more than 80 landmarks has not yet reached a satisfactory level, because of the factors hindering machine learning such as the high dimensionality of the

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Handels, H., W. Plötz, S. J. Pöppl, and J. Ehrhardt. "Atlas-based Recognition of Anatomical Structures and Landmarks and the Automatic Computation of Orthopedic Parameters." Methods of Information in Medicine 43, no. 04 (2004): 391–97. http://dx.doi.org/10.1055/s-0038-1633882.

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Summary Objective: This paper describes methods for the automatic atlas-based segmentation of bone structures of the hip, the automatic detection of anatomical point landmarks and the computation of orthopedic parameters to avoid the interactive, time-consuming preprocessing steps for the virtual planning of hip operations. Methods: Based on the CT data of the Visible Human Data Sets, two three-dimensional atlases of the human pelvis have been built. The atlases consist of labeled CT data sets, 3D surface models of the separated structures and associated anatomical point landmarks. The atlas i

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Han, Miaomiao, Zhengqun Huo, Jiangyan Ren, et al. "Automated Landmark Detection and Lip Thickness Classification Using a Convolutional Neural Network in Lateral Cephalometric Radiographs." Diagnostics 15, no. 12 (2025): 1468. https://doi.org/10.3390/diagnostics15121468.

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Objective: The objective of this study is to develop a convolutional neural network (CNN) for the automatic detection of soft and hard tissue landmarks and the classification of lip thickness on lateral cephalometric radiographs. Methods: A dataset of 1019 pre-orthodontic lateral cephalograms from patients with diverse malocclusions was utilized. A CNN-based model was trained to automatically detect 22 cephalometric landmarks. Upper and lower lip thicknesses were measured using some of these landmarks, and a pre-trained decision tree model was employed to classify lip thickness into the thin,

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Ye, Chengwei, Kaiwei Che, Yibing Yao, et al. "A deep learning-based system for accurate detection of anatomical landmarks in colon environment." Intelligence & Robotics 4, no. 2 (2024): 164–78. http://dx.doi.org/10.20517/ir.2024.10.

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Colonoscopy is a standard imaging tool for examining the lower gastrointestinal tract of patients to capture lesion areas. However, if a lesion area is found during the colonoscopy process, it is difficult to record its location relative to the colon for subsequent therapy or recheck without any reference landmark. Thus, automatic detection of biological anatomical landmarks is highly demanded to improve clinical efficiency. In this article, we propose a novel deep learning-based approach to detect biological anatomical landmarks in colonoscopy videos. First, raw colonoscopy video sequences ar

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Rühaak, J., R. Werner, H. Handels, J. Modersitzki, and T. Polzin. "Lung Registration Using Automatically Detected Landmarks." Methods of Information in Medicine 53, no. 04 (2014): 250–56. http://dx.doi.org/10.3414/me13-01-0125.

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SummaryObjectives: Accurate registration of lung CT images is inevitable for numerous clinical applications. Usually, nonlinear intensity-based methods are used. Their accuracy is typically evaluated using corresponding anatomical points (landmarks; e.g. bifurcations of bronchial and vessel trees) annotated by medical experts in the images to register. As image registration can be interpreted as correspond ence finding problem, these corresponding landmarks can also be used in feature-based registration techniques. Recently, approaches for automated identification of such landmark corresponden

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Zeng, Qingxi, Yuchao Kan, Xiaodong Tao, and Yixuan Hu. "LiDAR Positioning Algorithm Based on ICP and Artificial Landmarks Assistance." Sensors 21, no. 21 (2021): 7141. http://dx.doi.org/10.3390/s21217141.

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As one of the automated guided vehicle (AGV) positioning methods, the LiDAR positioning method, based on artificial landmarks, has been widely used in warehousing logistics industries in recent years. However, the traditional LiDAR positioning method based on artificial landmarks mainly depends on the three-point positioning method, the performance of which is limited due to landmarks’ layout and detection requirements. This paper proposes a LiDAR positioning algorithm based on iterative closest point (ICP) and artificial landmarks assistance. It provides improvements based on the traditional

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Linnenbrügger, N. I., R. L. Webber, L. P. Kobbelt, and T. M. Lehmann. "Automated Hybrid TACT® Volume Reconstructions." Methods of Information in Medicine 43, no. 04 (2004): 315–19. http://dx.doi.org/10.1055/s-0038-1633874.

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Summary Objectives: To design, implement in Java™, and evaluate a method and means for the automated localization of artificial landmarks in optical images for tuned-aperture computed tomography® (TACT®) that allows the replacement of radiographic with optical landmarks. Methods: Circular, colored, optical landmarks were designed to provide flexibility with regard to landmark constellation, imaging equipment, and lighting conditions. The landmark detection was based on Hough transforms (HT) for ellipses and lines. The HT for ellipses was extended to enable selective detection of bright ellipse

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Manira*, Nishika, Swelia Monteiro, Tashya Alberto, Tracy Niasso, and Supriya Patil. "Geo-Landmark Recognition and Detection." Regular issue 10, no. 7 (2021): 95–98. http://dx.doi.org/10.35940/ijitee.g8983.0510721.

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The widespread use of smartphones and mobile data in the present-day society has exponentially led to the interaction with the physical world. The increase in the amount of image data in web and mobile applications makes image search slow and inaccurate. Landmark recognition, an image retrieval task, faces its challenges due to the uncommon structure it possesses, such as, buildings, cathedrals, castles or museums. These are shot from various angles which are often different from each other, for instance, the exterior and interior of a landmark. This paper makes use of a Convolutional Neural N

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Nishika, Manira, Monteiro Swelia, Alberto Tashya, Niasso Tracy, and Patil Supriya. "Geo-Landmark Recognition and Detection." International Journal of Innovative Technology and Exploring Engineering (IJITEE) 10, no. 7 (2021): 95–98. https://doi.org/10.35940/ijitee.G8983.0510721.

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The widespread use of smartphones and mobile data in the present-day society has exponentially led to the interaction with the physical world. The increase in the amount of image data in web and mobile applications makes image search slow and inaccurate. Landmark recognition, an image retrieval task, faces its challenges due to the uncommon structure it possesses, such as, buildings, cathedrals, castles or museums. These are shot from various angles which are often different from each other, for instance, the exterior and interior of a landmark. This paper makes use of a Convolutional Neural N

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Nguyen-Huu, Khanh, and Seon-Woo Lee. "A Multi-Floor Indoor Pedestrian Localization Method Using Landmarks Detection for Different Holding Styles." Mobile Information Systems 2021 (March 1, 2021): 1–15. http://dx.doi.org/10.1155/2021/6617417.

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The pedestrian dead reckoning (PDR) technique is widely used due to its ease of implementation on portable devices such as smartphones. However, the position error that accumulates over time is the main drawback of this technology. In this paper, we propose a fusion method combining a PDR technique and the landmark recognition methods for multi-floor indoor environments using a smartphone in different holding styles. The proposed method attempts to calibrate the position of a pedestrian by detecting whether the pedestrian passes by specific locations called landmarks. Three kinds of landmarks

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Park, Janette, Jeung-Yoon Choi, and Stefanie Shattuck-Hufnagel. "Automatic detection of nasal closure and nasal release landmark acoustic cues." Journal of the Acoustical Society of America 155, no. 3_Supplement (2024): A337. http://dx.doi.org/10.1121/10.0027732.

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This study describes the detection of nasal closure and nasal release landmarks, as part of a larger system for speech recognition based on acoustic cues. Landmarks are produced as a result of closures and releases in the oral region and are indicated by abrupt changes in the speech signal. Nasal closure and release landmarks have proven particularly challenging to detect and are the focus of this report. The process for implementing the nasal detection module includes extracting and processing a set of speech-related measurements, such as formant frequencies, spectral band energies, and their

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Nguyen, Xuan-Ha, Van-Huy Nguyen, and Thanh-Tung Ngo. "A New Landmark Detection Approach for Slam Algorithm Applied in Mobile Robot." Journal of Science and Technology - Technical Universities 30.7, no. 146 (2020): 31–36. http://dx.doi.org/10.51316/30.7.6.

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Simultaneous Localization and Mapping is a key technique for mobile robot applications and has received much research effort over the last three decades. A precondition for a robust and life-long landmark-based SLAM algorithm is the stable and reliable landmark detector. However, traditional methods are based on laserbased data which are believed very unstable, especially in dynamic-changing environments. In this work, we introduce a new landmark detection approach using vision-based data. Based on this approach, we exploit a deep neural network for processing images from a stereo camera syste

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Wu, Hai, Hongtao Xie, Chuanbin Liu, Zheng-Jun Zha, Jun Sun, and Yongdong Zhang. "CircleNet for Hip Landmark Detection." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 07 (2020): 12370–77. http://dx.doi.org/10.1609/aaai.v34i07.6922.

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Landmark detection plays a critical role in diagnosis of Developmental Dysplasia of the Hip (DDH). Heatmap and anchor-based object detection techniques could obtain reasonable results. However, they have limitations in both robustness and precision given the complexities and inhomogeneity of hip X-ray images. In this paper, we propose a much simpler and more efficient framework called CircleNet to improve the accuracy of landmark detection by predicting landmark and corresponding radius. Using the CircleNet, we not only constrain the relationship between landmarks but also integrate landmark d

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Azad, Mohammad, Said Elaiwat, and Mohammad Khursheed Alam. "A Computational Tool for Detection of Soft Tissue Landmarks and Cephalometric Analysis." Electronics 11, no. 15 (2022): 2408. http://dx.doi.org/10.3390/electronics11152408.

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In facial aesthetics, soft tissue landmark recognition and linear and angular measurement play a critical role in treatment planning. Visual identification and judgment by hand are time-consuming and prone to errors. As a result, user-friendly software solutions are required to assist healthcare practitioners in improving treatment planning. Our first goal in this paper is to create a computational tool that may be used to identify and save critical landmarks from patient X-ray pictures. The second goal is to create automated software that can assess the soft tissue facial profiles of patients

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Amarasinghe, Dilan, George K. I. Mann, and Raymond G. Gosine. "Landmark detection and localization for mobile robot applications: a multisensor approach." Robotica 28, no. 5 (2009): 663–73. http://dx.doi.org/10.1017/s0263574709990312.

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SUMMARYThis paper describes a landmark detection and localization using an integrated laser-camera sensor. Laser range finder can be used to detect landmarks that are direction invariant in the laser data such as protruding edges in walls, edges of tables, and chairs. When such features are unavailable, the dependant processes will fail to function. However, in many instances, larger number of landmarks can be detected using computer vision. In the proposed method, camera is used to detect landmarks while the location of the landmark is measured by the laser range finder using laser-camera cal

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Song, Min Sun, Seong-Oh Kim, Ik-Hwan Kim, Chung-min Kang, and Je Seon Song. "Accuracy of Automatic Cephalometric Analysis Programs on Lateral Cephalograms of Preadolescent Children." JOURNAL OF THE KOREAN ACADEMY OF PEDTATRIC DENTISTRY 48, no. 3 (2021): 245–54. http://dx.doi.org/10.5933/jkapd.2021.48.3.245.

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The aim of this study was to evaluate the accuracy of 3 different automatic landmark identification programs on lateral cephalgrams and the clinical acceptability in pediatric dentistry. Sixty digital cephalometric radiographs of 7 to 12 years old healthy children were randomly selected. Fourteen landmarks were chosen for assessment and the mean of 3 measurements of each landmark by a single examiner was defined as the baseline landmarks. The mean difference between an automatically identified landmark and the baseline landmark was measured for each landmark on each image. The total mean diffe

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Song, Yu, Xu Qiao, Yutaro Iwamoto, and Yen-wei Chen. "Automatic Cephalometric Landmark Detection on X-ray Images Using a Deep-Learning Method." Applied Sciences 10, no. 7 (2020): 2547. http://dx.doi.org/10.3390/app10072547.

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Accurate automatic quantitative cephalometry are essential for orthodontics. However, manual labeling of cephalometric landmarks is tedious and subjective, which also must be performed by professional doctors. In recent years, deep learning has gained attention for its success in computer vision field. It has achieved large progress in resolving problems like image classification or image segmentation. In this paper, we propose a two-step method which can automatically detect cephalometric landmarks on skeletal X-ray images. First, we roughly extract a region of interest (ROI) patch for each l

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Adnan, Muhammad, Muhammad Sardaraz, Muhammad Tahir, Muhammad Najam Dar, Mona Alduailij, and Mai Alduailij. "A Robust Framework for Real-Time Iris Landmarks Detection Using Deep Learning." Applied Sciences 12, no. 11 (2022): 5700. http://dx.doi.org/10.3390/app12115700.

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Iris detection and tracking plays a vital role in human–computer interaction and has become an emerging field for researchers in the last two decades. Typical applications such as virtual reality, augmented reality, gaze detection for customer behavior, controlling computers, and handheld embedded devices need accurate and precise detection of iris landmarks. A significant improvement has been made so far in iris detection and tracking. However, iris landmarks detection in real-time with high accuracy is still a challenge and a computationally expensive task. This is also accompanied with the

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Wan, Xintong, Yifan Wu, and Xiaoqiang Li. "Learning Robust Shape-Indexed Features for Facial Landmark Detection." Applied Sciences 12, no. 12 (2022): 5828. http://dx.doi.org/10.3390/app12125828.

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In facial landmark detection, extracting shape-indexed features is widely applied in existing methods to impose shape constraint over landmarks. Commonly, these methods crop shape-indexed patches surrounding landmarks of a given initial shape. All landmarks are then detected jointly based on these patches, with shape constraint naturally embedded in the regressor. However, there are still two remaining challenges that cause the degradation of these methods. First, the initial shape may seriously deviate from the ground truth when presented with a large pose, resulting in considerable noise in

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Ngo, Ba-Viet, Thanh-Hai Nguyen, and Chi Cuong Vu. "Automatic Labeling of Natural Landmarks for Wheelchair Motion Planning." Electronics 12, no. 14 (2023): 3093. http://dx.doi.org/10.3390/electronics12143093.

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Labeling landmarks for the mobile plan of the automatic electric wheelchair is essential, because it can assist disabled people. In particular, labeled landmark images will help the wheelchairs to locate landmarks and move more accurately and safely. Here, we propose an automatic detection of natural landmarks in RGBD images for navigation of mobile platforms in an indoor environment. This method can reduce the time for manually collecting and creating a dataset of landmarks. The wheelchair, equipped with a camera system, is allowed to move along corridors to detect and label natural landmarks

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Liu, Shuang, Hongli Xu, Yang Lin, and Lei Gao. "Visual Navigation for Recovering an AUV by Another AUV in Shallow Water." Sensors 19, no. 8 (2019): 1889. http://dx.doi.org/10.3390/s19081889.

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Autonomous underwater vehicles (AUVs) play very important roles in underwater missions. However, the reliability of the automated recovery of AUVs has still not been well addressed. We propose a vision-based framework for automatically recovering an AUV by another AUV in shallow water. The proposed framework contains a detection phase for the robust detection of underwater landmarks mounted on the docking station in shallow water and a pose-estimation phase for estimating the pose between AUVs and underwater landmarks. We propose a Laplacian-of-Gaussian-based coarse-to-fine blockwise (LCB) met

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Song, Chanho, Yoosoo Jeong, Hyungkyu Huh, et al. "Multi-Scale 3D Cephalometric Landmark Detection Based on Direct Regression with 3D CNN Architectures." Diagnostics 14, no. 22 (2024): 2605. http://dx.doi.org/10.3390/diagnostics14222605.

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Background: Cephalometric analysis is important in diagnosing and planning treatments for patients, traditionally relying on 2D cephalometric radiographs. With advancements in 3D imaging, automated landmark detection using deep learning has gained prominence. However, 3D imaging introduces challenges due to increased network complexity and computational demands. This study proposes a multi-scale 3D CNN-based approach utilizing direct regression to improve the accuracy of maxillofacial landmark detection. Methods: The method employs a coarse-to-fine framework, first identifying landmarks in a g

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Suputra, Putu Hendra, Anggraini Dwi Sensusiati, Myrtati Dyah Artaria, Gijsbertus Jacob Verkerke, Eko Mulyanto Yuniarno, and I. Ketut Eddy Purnama. "Automatic 3D Cranial Landmark Positioning based on Surface Curvature Feature using Machine Learning." Knowledge Engineering and Data Science 5, no. 1 (2022): 27. http://dx.doi.org/10.17977/um018v5i12022p27-40.

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Cranial anthropometric reference points (landmarks) play an important role in craniofacial reconstruction and identification. Knowledge to detect the position of landmarks is critical. This work aims to locate landmarks automatically. Landmarks positioning using Surface Curvature Feature (SCF) is inspired by conventional methods of finding landmarks based on morphometrical features. Each cranial landmark has a unique shape. With the appropriate 3D descriptors, the computer can draw associations between shapes and landmarks using machine learning. The challenge in classification and detection i

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Dewi, Christine, Rung-Ching Chen, Xiaoyi Jiang, and Hui Yu. "Adjusting eye aspect ratio for strong eye blink detection based on facial landmarks." PeerJ Computer Science 8 (April 18, 2022): e943. http://dx.doi.org/10.7717/peerj-cs.943.

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Blink detection is an important technique in a variety of settings, including facial movement analysis and signal processing. However, automatic blink detection is very challenging because of the blink rate. This research work proposed a real-time method for detecting eye blinks in a video series. Automatic facial landmarks detectors are trained on a real-world dataset and demonstrate exceptional resilience to a wide range of environmental factors, including lighting conditions, face emotions, and head position. For each video frame, the proposed method calculates the facial landmark locations

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Shi, Belinda, Jeung-Yoon Choi, and Stefanie Shattuck-Hufnagel. "Processing methods for the detection of landmark acoustic cues." Journal of the Acoustical Society of America 150, no. 4 (2021): A355. http://dx.doi.org/10.1121/10.0008569.

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Landmarks (Stevens, 2002) are acoustic cues that are correlated with certain changes in speech articulation, and can be used to infer some of the distinctive features useful for speech recognition, largely the manner features. This project identifies and organizes the processing steps involved in extracting eight types of Landmark acoustic cues for a feature-based hierarchical automatic speech recognition system, in which each module detects one landmark cue from samples of continuous speech, based on knowledge about human speech production and acoustic measurements such as formant frequency a

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Dronne, Clea, Helena Durrant, Angela Davey, Edward Henderson, Eliana Vasquez Osorio, and Marianne Aznar. "AUTOMATIC DETECTION OF FACIAL LOCATIONS TO MEASURE FACIAL ASYMMETRY AFTER PAEDIATRIC RADIOTHERAPY." Neuro-Oncology 25, Supplement_3 (2023): iii14—iii15. http://dx.doi.org/10.1093/neuonc/noad147.059.

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Abstract AIMS Children receiving radiotherapy for head and neck tumours, such as Head and Neck Rhabdomyosarcoma (HN- RMS), often experience facial asymmetry later in life. Here, we present a convolutional neural network (CNN) that will enable the automatic detection of facial anatomical landmarks on MR images taken during follow-up examinations. This model will facilitate quantitative tracking of facial asymmetry, thus revealing the centres of growth that are most affected by radiation, with the ultimate goal of defining precise dose tolerance levels. METHOD A dataset of 61 paediatric MRI imag

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Qian, Yonglin, Guoqiang Zheng, Yifan Xie, Xiangshuai Lv, and Weizhen Zhang. "Fatigue Driving Detection Based on Driver Facial Temporal Sequences." Academic Journal of Science and Technology 10, no. 3 (2024): 37–41. http://dx.doi.org/10.54097/t3pmh008.

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Addressing the issues of low real-time performance and high false positive rates in driver fatigue detection methods based on deep learning, this paper proposes a temporal sequence Transformer-based fatigue detection method grounded in the localization of facial landmarks in drivers. Initially, the facial positions are obtained using the single-stage face detection algorithm RetinaFace. Subsequently, a lightweight GM module is designed as the principal feature extraction module for constructing a multi-scale fusion facial landmark detection network, and facial fatigue feature parameters based

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Yang, Hyeonsik, Jieun Park, Eunyoung Regina Kim, et al. "A Novel Landmark-based Semi-supervised Deep Learning Method for Cerebral Aneurysm Detection Using TOF-MRA." Journal of the Korean Neurological Association 42, no. 4 (2024): 322–30. http://dx.doi.org/10.17340/jkna.2024.0050.

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Background: Time-of-flight (TOF) magnetic resonance angiography (MRA) is widely used to identify aneurysm in human brain. Various deep learning models have been developed to help TOF-MRA reading in the field. The performance of those TOF-MRA analysis tools, however, faces several limitations in cerebral aneurysm detection. These challenges primarily come from the fact that cerebral aneurysms occupy less than 0.1% of the total TOF-MRA voxel size. This study aims to improve the efficiency of cerebral aneurysm detection by developing a landmark-based semi-supervised deep learning method, a techno

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Feng, Shuaichao, Xinpeng Miao, Shukui Ma, Fei Ma, and Guangping Zhuo. "Cephalometric Landmark Localization Model Based on Polarized Self-Attention Mechanism." International Journal of Computer Science and Information Technology 5, no. 1 (2025): 127–38. https://doi.org/10.62051/ijcsit.v5n1.12.

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Precise localization of cephalometric landmarks is crucial in the fields of orthodontics and craniofacial surgery. Traditional manual cephalometric analysis and computer-aided cephalometric analysis have significant drawbacks, including large errors, low accuracy, and being time-consuming. To achieve efficient and accurate localization of cephalometric landmarks, this study proposes a detection algorithm, CenterNet-PSA, which integrates the Polarized Self-Attention Mechanism. The algorithm first uses a pre-trained DLA-34 as the feature extraction network to extract features, and then incorpora

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Zhao, Qijie, Junhao Zhu, Junjun Zhu, Anwen Zhou, and Hui Shao. "Bone anatomical landmark localization with cascaded spatial configuration network." Measurement Science and Technology 33, no. 6 (2022): 065401. http://dx.doi.org/10.1088/1361-6501/ac5436.

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Abstract Accurate localization of bone anatomical landmarks in medical images is of great significance for decision-making in surgical plans and orthopedic surgery visual navigation systems development. However, due to the high dimensionality and large size of medical images, current automatic anatomical landmarks detection methods still have issues in terms of accuracy, robustness, and efficiency. To improve the accuracy of bone anatomical landmark detection in medical images, a new network model with a two-step strategy (coarse localizing and fine localizing) was proposed in this study. By c

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Sakai, Atsushi, Teppei Saitoh, and Yoji Kuroda. "Robust Landmark Estimation and Unscented Particle Sampling for SLAM in Dynamic Outdoor Environment." Journal of Robotics and Mechatronics 22, no. 2 (2010): 140–49. http://dx.doi.org/10.20965/jrm.2010.p0140.

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In this paper, we propose a set of techniques for accurate and practical Simultaneous Localization And Mapping (SLAM) in dynamic outdoor environments. The techniques are categorized into Landmark estimation and Unscented particle sampling. Landmark estimation features stable feature detection and data management for estimating landmarks accurately, robustly, and at a low-calculation cost. The stable feature detection removes dynamic objects and sensor noise with scan subtraction, detects feature points sparsely and evenly, and sets data association parameters with landmark density. The data ma

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Farkhod, Akhmedov, Akmalbek Bobomirzaevich Abdusalomov, Mukhriddin Mukhiddinov, and Young-Im Cho. "Development of Real-Time Landmark-Based Emotion Recognition CNN for Masked Faces." Sensors 22, no. 22 (2022): 8704. http://dx.doi.org/10.3390/s22228704.

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Owing to the availability of a wide range of emotion recognition applications in our lives, such as for mental status calculation, the demand for high-performance emotion recognition approaches remains uncertain. Nevertheless, the wearing of facial masks has been indispensable during the COVID-19 pandemic. In this study, we propose a graph-based emotion recognition method that adopts landmarks on the upper part of the face. Based on the proposed approach, several pre-processing steps were applied. After pre-processing, facial expression features need to be extracted from facial key points. The

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Albrecht, C., S. Kraus, and U. Stilla. "CONCEPT ON LANDMARK DETECTION IN ROAD SCENE IMAGES TAKEN FROM A TOP-VIEW CAMERA SYSTEM." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B1-2020 (August 6, 2020): 205–9. http://dx.doi.org/10.5194/isprs-archives-xliii-b1-2020-205-2020.

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Abstract. In this paper, we demonstrate the inclusion of a top-view camera system mounted on a city bus in an existing sensor setup. A novel sensor setup with five down-facing cameras is mounted on the roof of a MAN Lion’s City 12 city bus to extract landmarks in road scene images. Its positioning is validated by an exemplary detection of lane markings. The concept for further landmark detection with the help of the presented camera system is explained in this paper and sensor data fusion methods are proposed. Based on our previous findings (Albrecht et al., 2019), strengths of the novel senso

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Geldenhuys, Dylan S., Shane Josias, Willie Brink, et al. "Deep learning approaches to landmark detection in tsetse wing images." PLOS Computational Biology 19, no. 6 (2023): e1011194. http://dx.doi.org/10.1371/journal.pcbi.1011194.

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Morphometric analysis of wings has been suggested for identifying and controlling isolated populations of tsetse (Glossina spp), vectors of human and animal trypanosomiasis in Africa. Single-wing images were captured from an extensive data set of field-collected tsetse wings of species Glossina pallidipes and G. m. morsitans. Morphometric analysis required locating 11 anatomical landmarks on each wing. The manual location of landmarks is time-consuming, prone to error, and infeasible for large data sets. We developed a two-tier method using deep learning architectures to classify images and ma

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Wirtz, Andreas, Julian Lam, and Stefan Wesarg. "Automated Cephalometric Landmark Localization using a Coupled Shape Model." Current Directions in Biomedical Engineering 6, no. 3 (2020): 56–59. http://dx.doi.org/10.1515/cdbme-2020-3015.

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AbstractCephalometric analysis is an important method in orthodontics for the diagnosis and treatment of patients. It is performed manually in clinical practice, therefore automation of this time consuming task would be of great assistance. In order to provide dentists with such tools, a robust and accurate identification of the necessary landmarks is required. However, poor image quality of lateral cephalograms like low contrast or noise make this task difficult. In this paper, an approach for automatic landmark localization is presented and used to find 19 landmarks in lateral cephalometric

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