scholarly journals EXPLOITING 3D ULTRASOUND FOR FETAL DIAGNOSTIC PURPOSE THROUGH FACIAL LANDMARKING

2014 ◽  
Vol 33 (3) ◽  
pp. 167 ◽  
Author(s):  
Enrico Vezzetti ◽  
Domenico Speranza ◽  
Federica Marcolin ◽  
Giulia Fracastoro ◽  
Giorgia Buscicchio
Keyword(s):  
State Of The Art ◽  
Three Dimensional ◽  
Aesthetic Surgery ◽  
Point Clouds ◽  
3D Ultrasound ◽  
Diagnostic Purpose ◽  
Expression Recognition ◽  
Principal Curvatures ◽  
Extraction Algorithm ◽  
The Mean

In the last decade, three-dimensional landmarking has gained attention for different applications, such as face recognition for both identification of suspects and authentication, facial expression recognition, corrective and aesthetic surgery, syndrome study and diagnosis. This work focuses on the last one by proposing a geometrically-based landmark extraction algorithm aimed at diagnosing syndromes on babies before their birth. Pivotal role in this activity is the support provided by physicians and 3D ultrasound tools for working on real faces. In particular, the landmarking algorithm here proposed only relies on descriptors coming from Differential Geometry (Gaussian, mean, and principal curvatures, derivatives, coefficients of first and second fundamental forms, Shape and Curvedness indexes) and is tested on nine facial point clouds referred to nine babies taken by a three-dimensional ultrasound tool at different weeks' gestation. The results obtained, validated with the support of four practitioners, show that the localization is quite accurate. All errors lie in the range between 0 and 3.5 mm and the mean distance for each shell is in the range between 0.6 and 1.6 mm. The landmarks showing the highest errors are the ones belonging to the mouth region. Instead, the most precise landmark is the pronasal, on the nose tip, with a mean distance of 0.55 mm. Relying on current literature, this study is something missing in the state-of-the-art of the field, as present facial studies on 3D ultrasound do not work on automatic landmarking yet.

scholarly journals 3D SURFACE RECONSTRUCTION FROM MULTI-DATE SATELLITE IMAGES

2021 ◽  
Vol XLIII-B2-2021 ◽  
pp. 313-320
Author(s):  
S. Bullinger ◽  
C. Bodensteiner ◽  
M. Arens
Keyword(s):  
Large Scale ◽  
Satellite Images ◽  
Stereo Matching ◽  
State Of The Art ◽  
Satellite Image ◽  
Three Dimensional ◽  
Point Clouds ◽  
Reconstruction Algorithms ◽  
Depth Maps ◽  
Median Error

Abstract. The reconstruction of accurate three-dimensional environment models is one of the most fundamental goals in the field of photogrammetry. Since satellite images provide suitable properties for obtaining large-scale environment reconstructions, there exist a variety of Stereo Matching based methods to reconstruct point clouds for satellite image pairs. Recently, a Structure from Motion (SfM) based approach has been proposed, which allows to reconstruct point clouds from multiple satellite images. In this work, we propose an extension of this SfM based pipeline that allows us to reconstruct not only point clouds but watertight meshes including texture information. We provide a detailed description of several steps that are mandatory to exploit state-of-the-art mesh reconstruction algorithms in the context of satellite imagery. This includes a decomposition of finite projective camera calibration matrices, a skew correction of corresponding depth maps and input images as well as the recovery of real-world depth maps from reparameterized depth values. The paper presents an extensive quantitative evaluation on multi-date satellite images demonstrating that the proposed pipeline combined with current meshing algorithms outperforms state-of-the-art point cloud reconstruction algorithms in terms of completeness and median error. We make the source code of our pipeline publicly available.

Three Dimensional Point Clouds Watermarking Algorithm Based on Sphere Degenerated Octree

2011 ◽  
Vol 314-316 ◽  
pp. 2064-2070 ◽  
Author(s):  
Jian Xin Liu ◽  
Han Guo Cui ◽  
Xing Dai
Keyword(s):  
Three Dimensional ◽  
Point Clouds ◽  
Component Analysis ◽  
Primary Component ◽  
Experimental Results ◽  
Synchronization Error ◽  
Blind Detection ◽  
Extraction Algorithm ◽  
Uniform Scaling ◽  
Original Point

To ensure the security and integrity of three dimensional point clouds model during transmission in the network, according to their own characteristics, after the pretreatment of PCA (Primary Component Analysis), three dimensional point clouds watermarking embedding algorithm based on sphere degenerated octree was proposed, and the octree that contained the original point clouds model was subdivided multilevel from top to bottom, and the nodes of the octree were ordered. And three dimensional point clouds watermarking extraction algorithm was proposed, and the original point clouds model and the watermarked model were registered accurately, and the occurrence of synchronization error was avoided. Experimental results showed that the presented algorithm was robust to many attacks such as rotation, translation, uniform scaling, vertices reordering, simplifying, noise and cropping under blind detection.

scholarly journals Mobile Laser Scanned Point-Clouds for Road Object Detection and Extraction: A Review.

2021 ◽  
Author(s):  
Lingfei Ma ◽  
Ying Li ◽  
Jonathan Li ◽  
Cheng Wang ◽  
Ruisheng Wang ◽  
...  
Keyword(s):  
Object Detection ◽  
Laser Scanning ◽  
State Of The Art ◽  
Three Dimensional ◽  
Point Clouds ◽  
Extraction Methods ◽  
The State ◽  
Integrated Analysis ◽  
High Definition ◽  
Accurate Localization

The mobile laser scanning (MLS) technique has attracted considerable attention for providing high-density, high-accuracy, unstructured, three-dimensional (3D) geo-referenced point-cloud coverage of the road environment. Recently, there has been an increasing number of applications of MLS in the detection and extraction of urban objects. This paper presents a systematic review of existing MLS related literature. This paper consists of three parts. Part 1 presents a brief overview of the state-of-the-art commercial MLS systems. Part 2 provides a detailed analysis of on-road and off-road information inventory methods, including the detection and extraction of on-road objects (e.g., road surface, road markings, driving lines, and road crack) and off-road objects (e.g., pole-like objects and power lines). Part 3 presents a refined integrated analysis of challenges and future trends. Our review shows that MLS technology is well proven in urban object detection and extraction, since the improvement of hardware and software accelerate the efficiency and accuracy of data collection and processing. When compared to other review papers focusing on MLS applications, we review the state-of-the-art road object detection and extraction methods using MLS data and discuss their performance and applicability. The main contribution of this review demonstrates that the MLS systems are suitable for supporting road asset inventory, ITS-related applications, high-definition maps, and other highly accurate localization services.

scholarly journals Dynamic polygon clouds: representation and compression for VR/AR

2018 ◽  
Vol 7 ◽  
Author(s):  
Eduardo Pavez ◽  
Philip A. Chou ◽  
Ricardo L. de Queiroz ◽  
Antonio Ortega
Keyword(s):  
Point Cloud ◽  
State Of The Art ◽  
Three Dimensional ◽  
Point Clouds ◽  
Polygonal Meshes ◽  
Triangle Meshes ◽  
Previous State ◽  
Temporal Redundancy ◽  
Inter Frame ◽  
Robust To Noise

We introduce the polygon cloud, a compressible representation of three-dimensional geometry (including attributes, such as color), intermediate between polygonal meshes and point clouds. Dynamic polygon clouds, like dynamic polygonal meshes and dynamic point clouds, can take advantage of temporal redundancy for compression. In this paper, we propose methods for compressing both static and dynamic polygon clouds, specifically triangle clouds. We compare triangle clouds to both triangle meshes and point clouds in terms of compression, for live captured dynamic colored geometry. We find that triangle clouds can be compressed nearly as well as triangle meshes, while being more robust to noise and other structures typically found in live captures, which violate the assumption of a smooth surface manifold, such as lines, points, and ragged boundaries. We also find that triangle clouds can be used to compress point clouds with significantly better performance than previously demonstrated point cloud compression methods. For intra-frame coding of geometry, our method improves upon octree-based intra-frame coding by a factor of 5–10 in bit rate. Inter-frame coding improves this by another factor of 2–5. Overall, our proposed method improves over the previous state-of-the-art in dynamic point cloud compression by 33% or more.

scholarly journals Three-Stream Convolutional Neural Network with Squeeze-and-Excitation Block for Near-Infrared Facial Expression Recognition

Electronics ◽  
2019 ◽  
Vol 8 (4) ◽  
pp. 385 ◽  
Author(s):  
Ying Chen ◽  
Zhihao Zhang ◽  
Lei Zhong ◽  
Tong Chen ◽  
Juxiang Chen ◽  
...  
Keyword(s):  
Neural Network ◽  
Facial Expression ◽  
Facial Expression Recognition ◽  
Near Infrared ◽  
Recognition Accuracy ◽  
State Of The Art ◽  
Recognition Rate ◽  
Three Dimensional ◽  
Expression Recognition ◽  
Facial Expression Database

Near-infrared (NIR) facial expression recognition is resistant to illumination change. In this paper, we propose a three-stream three-dimensional convolution neural network with a squeeze-and-excitation (SE) block for NIR facial expression recognition. We fed each stream with different local regions, namely the eyes, nose, and mouth. By using an SE block, the network automatically allocated weights to different local features to further improve recognition accuracy. The experimental results on the Oulu-CASIA NIR facial expression database showed that the proposed method has a higher recognition rate than some state-of-the-art algorithms.

scholarly journals Reconstruction of Complex Roof Semantic Structures from 3D Point Clouds Using Local Convexity and Consistency

2021 ◽  
Vol 13 (10) ◽  
pp. 1946
Author(s):  
Pingbo Hu ◽  
Yiming Miao ◽  
Miaole Hou
Keyword(s):  
State Of The Art ◽  
Three Dimensional ◽  
Reconstruction Algorithm ◽  
Point Clouds ◽  
Urban Environments ◽  
Automated Modeling ◽  
3D Point Clouds ◽  
Building Models ◽  
Complex Building ◽  
Topology Information

Three-dimensional (3D) building models are closely related to human activities in urban environments. Due to the variations in building styles and complexity in roof structures, automatically reconstructing 3D buildings with semantics and topology information still faces big challenges. In this paper, we present an automated modeling approach that can semantically decompose and reconstruct the complex building light detection and ranging (LiDAR) point clouds into simple parametric structures, and each generated structure is an unambiguous roof semantic unit without overlapping planar primitive. The proposed method starts by extracting roof planes using a multi-label energy minimization solution, followed by constructing a roof connection graph associated with proximity, similarity, and consistency attributes. Furthermore, a progressive decomposition and reconstruction algorithm is introduced to generate explicit semantic subparts and hierarchical representation of an isolated building. The proposed approach is performed on two various datasets and compared with the state-of-the-art reconstruction techniques. The experimental modeling results, including the assessment using the International Society for Photogrammetry and Remote Sensing (ISPRS) benchmark LiDAR datasets, demonstrate that the proposed modeling method can efficiently decompose complex building models into interpretable semantic structures.

scholarly journals Mobile Laser Scanned Point-Clouds for Road Object Detection and Extraction: A Review

2018 ◽  
Vol 10 (10) ◽  
pp. 1531 ◽  
Author(s):  
Lingfei Ma ◽  
Ying Li ◽  
Jonathan Li ◽  
Cheng Wang ◽  
Ruisheng Wang ◽  
...  
Keyword(s):  
Object Detection ◽  
Laser Scanning ◽  
State Of The Art ◽  
Three Dimensional ◽  
Point Clouds ◽  
Extraction Methods ◽  
The State ◽  
Integrated Analysis ◽  
High Definition ◽  
Accurate Localization

The mobile laser scanning (MLS) technique has attracted considerable attention for providing high-density, high-accuracy, unstructured, three-dimensional (3D) geo-referenced point-cloud coverage of the road environment. Recently, there has been an increasing number of applications of MLS in the detection and extraction of urban objects. This paper presents a systematic review of existing MLS related literature. This paper consists of three parts. Part 1 presents a brief overview of the state-of-the-art commercial MLS systems. Part 2 provides a detailed analysis of on-road and off-road information inventory methods, including the detection and extraction of on-road objects (e.g., road surface, road markings, driving lines, and road crack) and off-road objects (e.g., pole-like objects and power lines). Part 3 presents a refined integrated analysis of challenges and future trends. Our review shows that MLS technology is well proven in urban object detection and extraction, since the improvement of hardware and software accelerate the efficiency and accuracy of data collection and processing. When compared to other review papers focusing on MLS applications, we review the state-of-the-art road object detection and extraction methods using MLS data and discuss their performance and applicability. The main contribution of this review demonstrates that the MLS systems are suitable for supporting road asset inventory, ITS-related applications, high-definition maps, and other highly accurate localization services.

scholarly journals Mobile Laser Scanned Point-Clouds for Road Object Detection and Extraction: A Review.

2021 ◽  
Author(s):  
Lingfei Ma ◽  
Ying Li ◽  
Jonathan Li ◽  
Cheng Wang ◽  
Ruisheng Wang ◽  
...  
Keyword(s):  
Object Detection ◽  
Laser Scanning ◽  
State Of The Art ◽  
Three Dimensional ◽  
Point Clouds ◽  
Extraction Methods ◽  
The State ◽  
Integrated Analysis ◽  
High Definition ◽  
Accurate Localization

The mobile laser scanning (MLS) technique has attracted considerable attention for providing high-density, high-accuracy, unstructured, three-dimensional (3D) geo-referenced point-cloud coverage of the road environment. Recently, there has been an increasing number of applications of MLS in the detection and extraction of urban objects. This paper presents a systematic review of existing MLS related literature. This paper consists of three parts. Part 1 presents a brief overview of the state-of-the-art commercial MLS systems. Part 2 provides a detailed analysis of on-road and off-road information inventory methods, including the detection and extraction of on-road objects (e.g., road surface, road markings, driving lines, and road crack) and off-road objects (e.g., pole-like objects and power lines). Part 3 presents a refined integrated analysis of challenges and future trends. Our review shows that MLS technology is well proven in urban object detection and extraction, since the improvement of hardware and software accelerate the efficiency and accuracy of data collection and processing. When compared to other review papers focusing on MLS applications, we review the state-of-the-art road object detection and extraction methods using MLS data and discuss their performance and applicability. The main contribution of this review demonstrates that the MLS systems are suitable for supporting road asset inventory, ITS-related applications, high-definition maps, and other highly accurate localization services.

scholarly journals Three Dimensional Reconstruction of Botanical Trees with Simulatable Geometry

2021 ◽  
Vol 4 (3) ◽  
pp. 1-16
Author(s):  
Ed Quigley ◽  
Winnie Lin ◽  
Yilin Zhu ◽  
Ronald Fedkiw
Keyword(s):  
Physical Simulation ◽  
State Of The Art ◽  
Three Dimensional ◽  
Image Data ◽  
Point Clouds ◽  
Future Research ◽  
Geometric Accuracy ◽  
Rigid Body Model ◽  
Dimensional Reconstruction ◽  
Pros And Cons

We tackle the challenging problem of creating full and accurate three dimensional reconstructions of botanical trees with the topological and geometric accuracy required for subsequent physical simulation, e.g. in response to wind forces. Although certain aspects of our approach would benefit from various improvements, our results exceed the state of the art especially in geometric and topological complexity and accuracy. Starting with two dimensional RGB image data acquired from cameras attached to drones, we create point clouds, textured triangle meshes, and a simulatable and skinned cylindrical articulated rigid body model. We discuss the pros and cons of each step of our pipeline, and in order to stimulate future research we make the raw and processed data from every step of the pipeline as well as the final geometric reconstructions publicly available.

scholarly journals Role of the State-of-the-Art Three-dimensional Ultrasound in the Differentiation of Benign and Malignant Ovarian Masses

2015 ◽  
Vol 9 (4) ◽  
pp. 446-461
Author(s):  
Francisco Raga ◽  
Oscar Caballero ◽  
Francisco Bonilla ◽  
Fernando Bonilla-Musoles ◽  
Juan Carlos Castillo ◽  
...  
Keyword(s):  
System Architecture ◽  
Obstet Gynecol ◽  
State Of The Art ◽  
Three Dimensional ◽  
3D Ultrasound ◽  
3D Reconstructions ◽  
Essential Information ◽  
Role Of The State ◽  
Ovarian Masses

ABSTRACT Modern three-dimensional (3D) ultrasound tools, HDlive and radiance system architecture or silhouette offer unique ways for assessing women with ovarian masses; providing realistic 3D reconstructions of the ovarian tumors, together with essential information to facilitate the differentiation between benignity and malignancy of ovarian masses. How to cite this article Bonilla F Jr, Raga F, Caballero O, Castillo JC, Machado LE, Bonilla-Musoles F. Role of the Stateof- the-Art Three-dimensional Ultrasound in the Differentiation of Benign and Malignant Ovarian Masses. Donald School J Ultrasound Obstet Gynecol 2015;9(4):446-461.

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