Traditional NR-IQA metrics usually train complex models such as support vector machine , neural network , and probability graph model , which result in long computational time and poor robustness . To overcome these weaknesses , the paper proposes a fast no-reference image quality assessment via hash coding method , named NRHC . First , the image is divided into overlapped patches to extract the spatial statistical features of natural scene images . However , since image quality is the integrated set of perceptions of the overall degree of excellence of an image , or in other words , image quality is the perceptually weighted combination of significant attributes ( contrast , graininess … ) of an image when considered in its marketplace or application , there is no standard definition on overall image or video quality especially for the no-reference case ( without a standard chart as reference ) . In this study , Subjective Quality Factor ( SQF ) and acutance are used for no-reference image quality evaluation . Basic image quality parameters , like sharpness , color accuracy , size of obstruction and transmission of obstruction , are used as subparameter to define the rating scale for image quality evaluation or comparison . The no-reference image quality evaluation is an important method in image processing technologies when the original image can not be obtained . In this paper , the basic problems in no-reference image quality evaluation methods are discussed and several methods based on block effects , image blurs and interactive neural evolution used in no-reference image quality evaluation are explained and analyzed . An overall no-reference image quality evaluation principle is presented in accordance with the shortnesses above mentioned , and it suggests that the evaluation principle should be a synthetical index applying fuzzy measures and fuzzy integrals , incorporating Human Vision System(HVS ) characteristics and adhering to the philosophy of quantifying quality by blind distortion measurements . Furthermore , unlike previous methods , our unsupervised feature learning strategy enables our method to adapt to different domains . CORNIA ( Codebook Representation for No-Reference Image Assessment ) is tested on LIVE database and shown to perform statistically better than the full-reference quality measure , structural similarity index ( SSIM ) and is shown to be comparable to state-of-the-art general purpose NR-IQA algorithms . Table 1 reports Pearsons linear correlation coefficient ( PLCC ) and Spearmans rank-order correlation coefficient ( SRCC ) between 3D image quality mean opinion scores ( 3DIQMOS ) and averaging some state-of-the-art 2D-BIQA estimations of both views on LIVE 3D Image Database Phase II [ 3 ] , WaterlooIVC 3D Image Database Phase I [ 5 ] and Phase II [ 12 ] . The ten tested state-of-the-art 2D-BIQA algorithms include Blind Image Quality Index ( BIQI ) [ 13 ] , BLind Image Integrity Notator using DCT-Statistics II ( BLIINDS-II ) [ 14 ] , Blind/Referenceless Image Spatial QUality Evaluator ( BRISQUE ) [ 15 ] , Codebook Representation for No-Reference Image Assessment ( CORNIA ) [ 16 ] , Distortion Identification-based Image Verity and INtegrity Evalutation ( DIIVINE ) [ 17 ] , Local Pattern Statistics Index ( LPSI ) [ 18 ] , M3 [ 19 ] , Naturalness Image Quality Evaluator ( NIQE ) [ 20 ] , Quality-Aware Clustering ( QAC ) [ 21 ] and Distortion Type Classification and Label Transfer ( TCLT ) [ 22 ] . Among them , BIQI , BLIINDS-II , BRISQUE , CORNIA , DIIVINE , M3 , TCLT are opinion-aware BIQA methods that require subject-rated images for training , and are trained using all images from LIVE Image Quality Assessment Database Release 2 [ 26 ] . In this paper , a new measure for no-reference assessment of image quality based on the use of the structural properties of an image is proposed . As a measure , it is proposed to use the estimate of the Weibull distribution shape parameter , obtained by the set of image gradient magnitudes . The aim of research on the no-reference image quality assessment problem is to design models that can predict the quality of distorted images consistently with human visual perception . Due to the little prior knowledge of the images , it is still a difficult problem . We show that this approach is able to develop a better mapping between the image and its output . We analyze additional properties of our approach and show its effectiveness on one synthetic and two real-world vision tasks - no-reference image quality estimation and image tampering detection - by its performance improvement over existing strong baselines . For example , it is used in edge detection for threshold setting ( Davies , 2000 ) , image filtering ( Touzi , 2002 ; Lee et al. , 2009 ) including denoising techniques based on orthogonal transforms ( Mallat , 1998 ; Sen‐ dur&Selesnick , 2002 ; Egiazarian et al. , 1999 ) , image reconstruction ( Katsaggelos , 1991 ) , lossy compression of noisy images ( Bekhtin , 2011 ) , non-reference assessment of image visual quality ( Choi et al. , 2009 ) , etc . Third , although there can be initial assumptions on noise type and a range of variations of its statistical parameters , these parameters can be quite different even for a given imaging system depending upon conditions of its operation . Referenceless image quality assessment is a challenging and critical problem in today 's multimedia applica\-tions . Texture patterns in images are normally at high frequencies compared to lower ones . In surveillance and security , for example , matching of 2D face images with 3D face models permits to exploit both appearance and structural information to perform target identification , so superseding the limitations of traditional 2D face matching . While 3D face databases are becoming more and more available , 3D face matching is becoming an important topic of investigation for advanced security applications . New recognition and tracking applications will fully exploit 3D body behaviors . It includes efficient steps to filter out extracted point candidates that have low contrast or are poorly localized along edges or ridges . In addition , it achieves , for the first time , full 3D orientation invariance of the descriptors , which is essential for 3D feature matching . An application of this technique is demonstrated to the feature-based automated registration and segmentation of clinical datasets in the context of radiation therapy . While the offline camera registration problem can be considered as essentially solved , robust online tracking remains an open issue because many real-time algorithms described in the literature still lack robustness and are prone to drift and jitter . To solve these problems , we have developed a robust approach to 3D feature matching that can handle wide-baseline matching : our method merges the information from preceding frames in traditional recursive tracking fashion with that provided by a very limited number of keyframes created during an offline stage . This combination results in a system that does not suffer from the above difficulties and can deal with drastic aspect changes . Computation is based on massively parallel architecture of NVIDIA CUDA . The presented concept of 3D data matching is based on parallel computation used for fast nearest neighbor search . Nearest neighbor search procedure is using 3D space decomposition into cubic buckets , therefore the time of matching is deterministic . The first approach is based on 3D matching and the second on 2D matching . The comparison shows that 3D image matching crucially depends upon on the quality of the camera and its correct image matching accuracy ranges from 62 - 92 % depending on the dataset . In contrast , the matching accuracy of 2D image matching is consistent across all cameras and ranges from 80 - 95 % . This is a challenging task especially for large-scale problems . As the map size grows bigger , many 3D points in the wider geographical area can be visually very similar – or even identical – causing severe ambiguities in 2D-3D feature matching . The key is to quickly and unambiguously find the correct matches between a query image and the large 3D map . Efficient detection of three dimensional ( 3D ) objects in point clouds is a challenging problem . Performing 3D descriptor matching or 3D scanning-window search with detector are both time-consuming due to the 3-dimensional complexity . One solution is to project 3D point cloud into 2D images and thus transform the 3D detection problem into 2D space , but projection at multiple viewpoints and rotations produce a large amount of 2D detection tasks , which limit the performance and complexity of the 2D detection algorithm choice . In this paper , we propose a method to register and stitch ultrasound volumes , which are scanned by dedicated ultrasound probe , based on an improved 3D Scale Invariant Feature Transform ( SIFT ) algorithm . We propose methods to exclude artifacts from ultrasound images in order to improve the overall performance in 3D feature point extraction and matching . Our method has been validated on both phantom and clinical data sets of human liver . The Kinect looking up toward the ceiling can track the robot 's trajectory through visual odometry method , which can provide more accurate motion estimation compared to wheel motion measurement and can not be disturbed under wheel slippage . This is because the Kinect can provide a color image as well as depth information such that robust 3D feature points matching using invariant 2D feature descriptors such as SURF and FAST is possible . Furthermore , the straight line features on the ceiling can provide additional constraints on the inter-frame motion of the camera and the loop closure leading to a more accurate pose estimate . Double camera measure dispose of the two laser stripe images which gained by the two camera at the same time , via digital image process such as acquiring the feature,3D matching to get two images pixels . Based on the relation between the object coordinate and the image coordinate that established by the camera calibration , using lease square-method to acquire 3D points coordinate.because lease square-method do not consider geometric meaning that the equation is represent for , so the measure precision is lower . Two objectives of 3D computer vision are high processing speed and precise recovery of object boundaries . This paper addresses these issues by presenting an algorithm that combines feature-based 3D matching with Compressed Image Correlation . The algorithm uses an image compression scheme that retains pixel values in high intensity gradient areas while eliminating pixels with little correlation information in smooth surface regions . Besides , Weighted Parameter Distance Cost ( WPDC ) is proposed as the cost function . To the best of our knowledge , this is the first attempt to solve the 3D face alignment with CNN . To enable the training of the 3DDFA , we construct a face database containing pairs of 2D face images and 3D face models . The predicted 2D landmarks are used to adjust the model parameters to better fit the current user . Jeni et al . proposed a 3D cascade regression method to implement 3D face alignment , which can maintain the pose invariance of facial landmarks within the range of around degrees . There are several occlusion - free face alignment methods . intelligently ’ ’ , rather than operating blindly over the entire domain . We show that field probing is significantly more efficient than 3DCNNs , while providing state - of - the - art performance , on classification tasks for 3D object recognition benchmark datasets . section : Introduction However , such approaches can not work on high resolution 3D data , as the computational complexity is a cubic function of the voxel grid resolution . Since CNNs for images have been extensively studied , 3D shapes can be rendered into 2D images , and be represented by the CNN features of the images , which , surprisingly , outperforms any 3D CNN approaches , in a 3D shape classification task . Recently , Qi et al . presented an extensive study of these volumetric and multi - view CNNs and refreshed the performance records . section : Related Work 3D object recognition is a well - studied problem in the computer vision literature . Early efforts often combined simple image classification methods with hand - crafted shape descriptors , requiring intensive effort on the side of the human data annotator . To supervise and facilitate the 3D face model learning , we introduce four selfsupervision losses , including the self - critic which is employed to weakly supervise the training samples that without 3D annotations . Our 2DASL make the abundant " inthe - wild " face images could be used to aid 3D face analysis without any 2D - to - 3D supervision . Experiments on two challenging face datasets illustrate the effectiveness of 2DASL on both 3D face reconstruction and dense face alignment by comparing with other state - of - the - arts . instance - level model alignments for 6D object pose estimation as well as to find surface correspondences in 3D meshes . To facilitate further research in the area of 3D keypoint matching and geometric registration , we provide a correspondence matching benchmark as well as a surface registration benchmark similar to [ reference ] , but with real - world scan data . section : Related Work Learning local geometric descriptors for matching 3D data lies at the intersection of computer vision and graphics . We briefly review the related work in both domains . Wu et al . [ reference ] introduced the volumetric representation of a depth image and surpassed the existing hand - crafted descriptor - based methods in 3D shape classification and retrieval . They represented each voxel as a binary random variable and used a convolutional deep belief network to learn the probability distribution for each voxel . He was with University of Texas at San Antonio ( UTSA ) and Johns Hopkins University ( JHU ) as a research scholar . His research interests include image retrieval and classification , 3D shape recognition , person re - identification , semantic segmentation and deep learning . More information can be found in his homepage : . In this paper , we propose to learn a deep fitting degree scoring network for monocular 3D object detection , which aims to score fitting degree between proposals and object conclusively . Different from most existing monocular frameworks which use tight constraint to get 3D location , our approach achieves high-precision localization through measuring the visual fitting degree between the projected 3D proposals and the object . Besides our methodological innovations , we critically review the AP metric used in KITTI3D , which emerged as the most important dataset for comparing 3D detection results . We identify and resolve a flaw in the 11-point interpolated AP metric , affecting all previously published detection results and particularly biases the results of monocular 3D detection . We provide extensive experimental evaluations and ablation studies and set a new state-of-the-art on the KITTI3D Car class . Then , an example of this method for classifying lines with a vanishing point is described and tested on images from indoor scenes . This method has also been successfully applied to other vision segmentation problems ( Brillault , A probabilistic approach to 3D interpretation of monocular images , Doctoral dissertation , City University , March ( 1992 ) ) . In this work , the goal is to predict 3D bounding boxes from single images without using temporal data nor any explicit depth estimation . We propose an approach for 3D monocular object detection based on Deep3DBox [ 20 ] . We aim to replace the geometric constraints taken into account to predict the 3D location of objects by a deep learning module . The goal of this method is to obtain the position ( x ; y ) and orientation θ of the humanoid robot inside the field of play . The methods proposed include some digital image processing algorithms and geometric interpretation to perform a 3D monocular reconstruction , that allows to measure the relative position between the robot and some known objects ( goals and beacons ) , and to use them to obtain the robot 's absolute position , by means of a triangulation method . Finally , the results obtained in Webots and in the real platform are presented . We present a novel approach to 3D object detection in scenes scanned by LiDAR sensors , based on a probabilistic representation of free , occupied , and hidden space that extends the concept of occupancy grids from robot mapping algorithms . This scene representation naturally handles LiDAR sampling issues , can be used to fuse multiple LiDAR data sets , and captures the inherent uncertainty of the data due to occlusions and clutter . This paper is the first description of combining 3D shape structure with 2D texture . A simple and effective technique using iterative closest point ( ICP ) was applied for 3D periocular region matching . It proved its strength for relatively unconstrained eye region capture , and does not require any training . Previous research mainly focus on detecting objects using only one modality , which ignores exploiting the cross-modality cues . In this work , we propose a cross-modality deep learning framework based on deep Boltzmann Machines for 3D Scenes object detection . In particular , we demonstrate that by learning cross-modality feature from RGBD data , it is possible to capture their joint information to reinforce detector trainings in individual modalities . 3D visual object detection is a fundamental requirement for autonomous vehicles . However , accurately detecting 3D objects was until recently a quality unique to expensive LiDAR ranging devices . Training our method only uses the 3D properties of the object without the need for external networks or supervision data . Our method is the first real-time system for monocular image 3D detection while achieves state-of-the-art performance on the KITTI benchmark . Code will be released at this https URL . section : Introduction We focus on 3D object detection , which is a fundamental computer vision problem impacting most autonomous robotics systems including self - driving cars and drones . The goal of 3D object detection is to recover the 6 DoF pose and the 3D bounding box dimensions for all objects of interest in the scene . While recent advances in convolutional neural networks have enabled accurate 2D detection in complex environments , the 3D object detection problem still remains an open challenge . Methods for 3D box regression from a single image , even including recent deep learning methods such as , still have relatively low accuracy especially in depth estimates at longer ranges . We demonstrate that the dense prediction architecture outperforms the architecture that regresses 3D corner locations directly by a large margin . We evaluate our model on two distinctive 3D object detection datasets . The KITTI dataset focuses on the outdoor urban driving scenario in which pedestrians , cyclists , and cars are annotated in data acquired with a camera - lidar system . They represented each voxel as a binary random variable and used a convolutional deep belief network to learn the probability distribution for each voxel . Several recent works [ reference ][ reference ] also represented 3D input data as a volumetric form for 3D object classification and detection . Our work follows the strategy from [ reference ] , wherein several types of volumetric representation ( i.e. , occupancy grid models ) were proposed to fully utilize the rich source of 3D information and efficiently deal with large amounts of point cloud data . 3D reconstruction based on slice-image data is a main topic of visualization of 3D data fields . 3D model of reconstruction is also the premise of analysis and emulation . The freeware Java tool Point Analysis in Java ( PAJ ) , created to perform 3D point analysis , was tested in an independent laboratory setting . The input data consisted of images of the hippocampal perforant pathway from serial immunocytochemical localizations of the rat brain in multiple views at different resolutions . Autonomous 3D Printing in Extreme Environments . Implementation of Robotics and Automation has revolutionised the Manufacturing Industry , generating unprecedented levels of efficiency , boosted productivity and lower levels of risks . This paper describes the development of an industrial robot manipulator control system for 3D scanning of surfaces prior to robotic plasma processing and cutting of products with complex shape . Preliminary 3D scanning of the surface by a robot manipulator is needed to create a 3D model of a product in order to automatically generate a program for moving the robot to ensure robotic precise plasma processing or cutting of the product . The new algorithm of synthesis of the robot manipulator motioncontrolling system by dynamics and perturbations compensation method has been developed . Accurate and high-speed three-dimensional ( 3D ) shape measurements are becoming increasingly important in many industries . Recent advances in the development of high-resolution detectors , light sources , MEMS , and computational power are enabling the availability of noncontact and noninvasive optical methods , which can be applied to new and challenging applications requiring 3D and high-speed measurements . The folding process is simulated by describing the path of the vertex . This method explores a unified approach to 3D folding model simulation in Rhinoceros/Grasshopper . ABSTRACT This paper is focused on meeting the demands for better detection and evaluation of the spinal deformities that go beyond a basic static 3D analysis . It is expected that by observing the spine while it moves an improved 3D deformity analysis and measurements can be achieved . In this paper , we present a novel approach for the automatic back symmetry curve sensing . Technologies of underground mobile positioning were proposed based on LiDAR data and coded sequence pattern landmarks for mine shafts and tunnels environment to meet the needs of fast and accurate positioning and navigation of equipments in the mine underground without satellite navigation signals . A coded sequence pattern was employed for automatic matching of 3D scans . The methods of SIFT feature , Otsu segmentation and fast hough transformation were described for the identification , positioning and interpretation of the coded sequence patterns , respectively . Currently , a lot of different scanning techniques are used for 3D imaging of human body . Most of existing systems are based on static registration of internal structures using MRI or CT techniques as well as 3D scanning of outer surface of human body by laser triangulation or structured light methods . As has been shown in previous benchmarks some of the presently used methods suffer from low convergence rates and/or low accuracy of the results and exhibit very long computation times1 , 2 which makes application to extended 2D layout patterns impractical . We address 3D simulation tasks by using a finite-element solver which has been shown to be superior to competing methods by several orders of magnitude in accuracy and computational time for typical microlithography simulations.2 We report on the current status of the solver , incorporating higher order edge elements , adaptive refinement methods , and fast solution algorithms . Further , we investigate the performance of the solver in the 3D simulation project of light diffraction off an alternating phase-shift contact-hole mask . In field probing layers , we typically use , , and ( distance and normal fields ) , while in 3D CNN , and . Compared with convolutional layers , field probing layers save a majority of computation ( ) , as the probing filters in field probing layers are capable of learning where to ‘ The number of kernels in each convolutional layer , from shallow to deeper , is as follows : 25 , 25 , 25 , 50 , 50 , 50 , 75 , 75 and 75 . To achieve this depth in a 3D CNN , we employ small kernels of size . Moreover , a unit stride is used for all convolutions to preserve spatial resolution . Furthermore , we make the implementation of our algorithm publicly available , which we believe can be of great benefit to the community , given the lack of robust open - source implementations for fitting 3DMMs . Due to lack of ground - truth data , the majority of the 3D face reconstruction papers report only qualitative results . In this paper , in order to provide quantitative evaluations , we collected a new dataset of 3D facial surfaces , using Kinect Fusion , which has many “ in - the - wild ” characteristics , even though it is captured indoors . further boosts the performance , except for a minor drop on orientation estimation of pedestrian . Evaluation on 2D Segmentation and 3D Localization . 3DVPs enable us to transfer the meta data to the detect objects , so our method is able to segment the boundary of object . In the following paragraph , we provide examples from 5 predominant approaches : In the 3D Morphable Model ( 3DMM ) , the most popular approach for estimating the full 3D facial structure from a single image ( among others ) , training includes an iterative flow procedure for dense image correspondence which is prone to failure . Additionally , testing requires a careful initialisation for solving a difficult highly non - convex optimization problem , which is slow . We compare the performance of our method with that of a similar method in Section [ reference ] . To alleviate the aforementioned learning problem , we propose to reformulate the problem of 3D face reconstruction as one of 2D to 3D image segmentation : in particular , we convert each 3D facial scan into a 3D binary volume by discretizing the 3D space into voxels , assigning a value of 1 to all points enclosed by the 3D facial scan , and 0 otherwise . That is to say is the ground truth for voxel and is equal to 1 , We observe impressive performance in shape generation and shape arithmetic in a large dataset . For both general 3D model building and single image reconstruction , we show that our model is fully unsupervised , requiring no extra human - generated information about segmentation , keypoints , or pose information . We show that our model outperforms current state - of - the - art approaches in unsupervised ( object ) model classification while requiring significantly fewer learned feature extractors ( a vector with less than 100 dimensions compared to the 3D - GAN ’s 2.5 million dimensional vector ) . section : Conclusions For many practical purposes , face alignment requires only global , parametric 2D or 3D transformations . This is often the case in state - of - the - art face recognition pipelines and a wide variety of other face understanding tasks . Recent direct approaches make use of deep learning . Regression - based approaches train an end - to - end network to predict 3D joint locations directly from the image . Li incorporate model joint dependencies in the CNN via a max - margin formalism , others impose kinematic constraints by embedding a differentiable kinematic model into the deep learning architecture . Earlier methods used to focus on extracting features , invariant to factors such as background scenes , lighting , and skin color from images and mapping them into 3D human pose . With the success of deep networks , recent methods tend to focus on training a deep convolutional neural network ( CNN ) end - to - end to estimate 3D poses from images directly . Some approaches divided the 3D pose estimation task into first predicting the joint locations in 2D using 2D pose estimators and then back - projecting them to estimate the 3D joint locations .
Traditional NR-IQA metrics usually train complex models such as support vector machine , neural network , and probability graph model , which result in long computational time and poor robustness . To overcome these weaknesses , the paper proposes a fast no-reference image quality assessment via hash coding method , named NRHC . First , the image is divided into overlapped patches to extract the spatial statistical features of natural scene images .
However , since image quality is the integrated set of perceptions of the overall degree of excellence of an image , or in other words , image quality is the perceptually weighted combination of significant attributes ( contrast , graininess … ) of an image when considered in its marketplace or application , there is no standard definition on overall image or video quality especially for the no-reference case ( without a standard chart as reference ) . In this study , Subjective Quality Factor ( SQF ) and acutance are used for no-reference image quality evaluation . Basic image quality parameters , like sharpness , color accuracy , size of obstruction and transmission of obstruction , are used as subparameter to define the rating scale for image quality evaluation or comparison .
The no-reference image quality evaluation is an important method in image processing technologies when the original image can not be obtained . In this paper , the basic problems in no-reference image quality evaluation methods are discussed and several methods based on block effects , image blurs and interactive neural evolution used in no-reference image quality evaluation are explained and analyzed . An overall no-reference image quality evaluation principle is presented in accordance with the shortnesses above mentioned , and it suggests that the evaluation principle should be a synthetical index applying fuzzy measures and fuzzy integrals , incorporating Human Vision System(HVS ) characteristics and adhering to the philosophy of quantifying quality by blind distortion measurements .
Furthermore , unlike previous methods , our unsupervised feature learning strategy enables our method to adapt to different domains . CORNIA ( Codebook Representation for No-Reference Image Assessment ) is tested on LIVE database and shown to perform statistically better than the full-reference quality measure , structural similarity index ( SSIM ) and is shown to be comparable to state-of-the-art general purpose NR-IQA algorithms .
Table 1 reports Pearsons linear correlation coefficient ( PLCC ) and Spearmans rank-order correlation coefficient ( SRCC ) between 3D image quality mean opinion scores ( 3DIQMOS ) and averaging some state-of-the-art 2D-BIQA estimations of both views on LIVE 3D Image Database Phase II [ 3 ] , WaterlooIVC 3D Image Database Phase I [ 5 ] and Phase II [ 12 ] . The ten tested state-of-the-art 2D-BIQA algorithms include Blind Image Quality Index ( BIQI ) [ 13 ] , BLind Image Integrity Notator using DCT-Statistics II ( BLIINDS-II ) [ 14 ] , Blind/Referenceless Image Spatial QUality Evaluator ( BRISQUE ) [ 15 ] , Codebook Representation for No-Reference Image Assessment ( CORNIA ) [ 16 ] , Distortion Identification-based Image Verity and INtegrity Evalutation ( DIIVINE ) [ 17 ] , Local Pattern Statistics Index ( LPSI ) [ 18 ] , M3 [ 19 ] , Naturalness Image Quality Evaluator ( NIQE ) [ 20 ] , Quality-Aware Clustering ( QAC ) [ 21 ] and Distortion Type Classification and Label Transfer ( TCLT ) [ 22 ] . Among them , BIQI , BLIINDS-II , BRISQUE , CORNIA , DIIVINE , M3 , TCLT are opinion-aware BIQA methods that require subject-rated images for training , and are trained using all images from LIVE Image Quality Assessment Database Release 2 [ 26 ] .
In this paper , a new measure for no-reference assessment of image quality based on the use of the structural properties of an image is proposed . As a measure , it is proposed to use the estimate of the Weibull distribution shape parameter , obtained by the set of image gradient magnitudes .
The aim of research on the no-reference image quality assessment problem is to design models that can predict the quality of distorted images consistently with human visual perception . Due to the little prior knowledge of the images , it is still a difficult problem .
We show that this approach is able to develop a better mapping between the image and its output . We analyze additional properties of our approach and show its effectiveness on one synthetic and two real-world vision tasks - no-reference image quality estimation and image tampering detection - by its performance improvement over existing strong baselines .
For example , it is used in edge detection for threshold setting ( Davies , 2000 ) , image filtering ( Touzi , 2002 ; Lee et al. , 2009 ) including denoising techniques based on orthogonal transforms ( Mallat , 1998 ; Sen‐ dur&Selesnick , 2002 ; Egiazarian et al. , 1999 ) , image reconstruction ( Katsaggelos , 1991 ) , lossy compression of noisy images ( Bekhtin , 2011 ) , non-reference assessment of image visual quality ( Choi et al. , 2009 ) , etc . Third , although there can be initial assumptions on noise type and a range of variations of its statistical parameters , these parameters can be quite different even for a given imaging system depending upon conditions of its operation .
Referenceless image quality assessment is a challenging and critical problem in today 's multimedia applica\-tions . Texture patterns in images are normally at high frequencies compared to lower ones .
In surveillance and security , for example , matching of 2D face images with 3D face models permits to exploit both appearance and structural information to perform target identification , so superseding the limitations of traditional 2D face matching . While 3D face databases are becoming more and more available , 3D face matching is becoming an important topic of investigation for advanced security applications . New recognition and tracking applications will fully exploit 3D body behaviors .
It includes efficient steps to filter out extracted point candidates that have low contrast or are poorly localized along edges or ridges . In addition , it achieves , for the first time , full 3D orientation invariance of the descriptors , which is essential for 3D feature matching . An application of this technique is demonstrated to the feature-based automated registration and segmentation of clinical datasets in the context of radiation therapy .
While the offline camera registration problem can be considered as essentially solved , robust online tracking remains an open issue because many real-time algorithms described in the literature still lack robustness and are prone to drift and jitter . To solve these problems , we have developed a robust approach to 3D feature matching that can handle wide-baseline matching : our method merges the information from preceding frames in traditional recursive tracking fashion with that provided by a very limited number of keyframes created during an offline stage . This combination results in a system that does not suffer from the above difficulties and can deal with drastic aspect changes .
Computation is based on massively parallel architecture of NVIDIA CUDA . The presented concept of 3D data matching is based on parallel computation used for fast nearest neighbor search . Nearest neighbor search procedure is using 3D space decomposition into cubic buckets , therefore the time of matching is deterministic .
The first approach is based on 3D matching and the second on 2D matching . The comparison shows that 3D image matching crucially depends upon on the quality of the camera and its correct image matching accuracy ranges from 62 - 92 % depending on the dataset . In contrast , the matching accuracy of 2D image matching is consistent across all cameras and ranges from 80 - 95 % .
This is a challenging task especially for large-scale problems . As the map size grows bigger , many 3D points in the wider geographical area can be visually very similar – or even identical – causing severe ambiguities in 2D-3D feature matching . The key is to quickly and unambiguously find the correct matches between a query image and the large 3D map .
Efficient detection of three dimensional ( 3D ) objects in point clouds is a challenging problem . Performing 3D descriptor matching or 3D scanning-window search with detector are both time-consuming due to the 3-dimensional complexity . One solution is to project 3D point cloud into 2D images and thus transform the 3D detection problem into 2D space , but projection at multiple viewpoints and rotations produce a large amount of 2D detection tasks , which limit the performance and complexity of the 2D detection algorithm choice .
In this paper , we propose a method to register and stitch ultrasound volumes , which are scanned by dedicated ultrasound probe , based on an improved 3D Scale Invariant Feature Transform ( SIFT ) algorithm . We propose methods to exclude artifacts from ultrasound images in order to improve the overall performance in 3D feature point extraction and matching . Our method has been validated on both phantom and clinical data sets of human liver .
The Kinect looking up toward the ceiling can track the robot 's trajectory through visual odometry method , which can provide more accurate motion estimation compared to wheel motion measurement and can not be disturbed under wheel slippage . This is because the Kinect can provide a color image as well as depth information such that robust 3D feature points matching using invariant 2D feature descriptors such as SURF and FAST is possible . Furthermore , the straight line features on the ceiling can provide additional constraints on the inter-frame motion of the camera and the loop closure leading to a more accurate pose estimate .
Double camera measure dispose of the two laser stripe images which gained by the two camera at the same time , via digital image process such as acquiring the feature,3D matching to get two images pixels . Based on the relation between the object coordinate and the image coordinate that established by the camera calibration , using lease square-method to acquire 3D points coordinate.because lease square-method do not consider geometric meaning that the equation is represent for , so the measure precision is lower .
Two objectives of 3D computer vision are high processing speed and precise recovery of object boundaries . This paper addresses these issues by presenting an algorithm that combines feature-based 3D matching with Compressed Image Correlation . The algorithm uses an image compression scheme that retains pixel values in high intensity gradient areas while eliminating pixels with little correlation information in smooth surface regions .
Besides , Weighted Parameter Distance Cost ( WPDC ) is proposed as the cost function . To the best of our knowledge , this is the first attempt to solve the 3D face alignment with CNN . To enable the training of the 3DDFA , we construct a face database containing pairs of 2D face images and 3D face models .
The predicted 2D landmarks are used to adjust the model parameters to better fit the current user . Jeni et al . proposed a 3D cascade regression method to implement 3D face alignment , which can maintain the pose invariance of facial landmarks within the range of around degrees . There are several occlusion - free face alignment methods .
intelligently ’ ’ , rather than operating blindly over the entire domain . We show that field probing is significantly more efficient than 3DCNNs , while providing state - of - the - art performance , on classification tasks for 3D object recognition benchmark datasets . section : Introduction
However , such approaches can not work on high resolution 3D data , as the computational complexity is a cubic function of the voxel grid resolution . Since CNNs for images have been extensively studied , 3D shapes can be rendered into 2D images , and be represented by the CNN features of the images , which , surprisingly , outperforms any 3D CNN approaches , in a 3D shape classification task . Recently , Qi et al . presented an extensive study of these volumetric and multi - view CNNs and refreshed the performance records .
section : Related Work 3D object recognition is a well - studied problem in the computer vision literature . Early efforts often combined simple image classification methods with hand - crafted shape descriptors , requiring intensive effort on the side of the human data annotator .
To supervise and facilitate the 3D face model learning , we introduce four selfsupervision losses , including the self - critic which is employed to weakly supervise the training samples that without 3D annotations . Our 2DASL make the abundant " inthe - wild " face images could be used to aid 3D face analysis without any 2D - to - 3D supervision . Experiments on two challenging face datasets illustrate the effectiveness of 2DASL on both 3D face reconstruction and dense face alignment by comparing with other state - of - the - arts .
instance - level model alignments for 6D object pose estimation as well as to find surface correspondences in 3D meshes . To facilitate further research in the area of 3D keypoint matching and geometric registration , we provide a correspondence matching benchmark as well as a surface registration benchmark similar to [ reference ] , but with real - world scan data . section : Related Work Learning local geometric descriptors for matching 3D data lies at the intersection of computer vision and graphics . We briefly review the related work in both domains .
Wu et al . [ reference ] introduced the volumetric representation of a depth image and surpassed the existing hand - crafted descriptor - based methods in 3D shape classification and retrieval . They represented each voxel as a binary random variable and used a convolutional deep belief network to learn the probability distribution for each voxel .
He was with University of Texas at San Antonio ( UTSA ) and Johns Hopkins University ( JHU ) as a research scholar . His research interests include image retrieval and classification , 3D shape recognition , person re - identification , semantic segmentation and deep learning . More information can be found in his homepage : .
In this paper , we propose to learn a deep fitting degree scoring network for monocular 3D object detection , which aims to score fitting degree between proposals and object conclusively . Different from most existing monocular frameworks which use tight constraint to get 3D location , our approach achieves high-precision localization through measuring the visual fitting degree between the projected 3D proposals and the object .
Besides our methodological innovations , we critically review the AP metric used in KITTI3D , which emerged as the most important dataset for comparing 3D detection results . We identify and resolve a flaw in the 11-point interpolated AP metric , affecting all previously published detection results and particularly biases the results of monocular 3D detection . We provide extensive experimental evaluations and ablation studies and set a new state-of-the-art on the KITTI3D Car class .
Then , an example of this method for classifying lines with a vanishing point is described and tested on images from indoor scenes . This method has also been successfully applied to other vision segmentation problems ( Brillault , A probabilistic approach to 3D interpretation of monocular images , Doctoral dissertation , City University , March ( 1992 ) ) .
In this work , the goal is to predict 3D bounding boxes from single images without using temporal data nor any explicit depth estimation . We propose an approach for 3D monocular object detection based on Deep3DBox [ 20 ] . We aim to replace the geometric constraints taken into account to predict the 3D location of objects by a deep learning module .
The goal of this method is to obtain the position ( x ; y ) and orientation θ of the humanoid robot inside the field of play . The methods proposed include some digital image processing algorithms and geometric interpretation to perform a 3D monocular reconstruction , that allows to measure the relative position between the robot and some known objects ( goals and beacons ) , and to use them to obtain the robot 's absolute position , by means of a triangulation method . Finally , the results obtained in Webots and in the real platform are presented .
We present a novel approach to 3D object detection in scenes scanned by LiDAR sensors , based on a probabilistic representation of free , occupied , and hidden space that extends the concept of occupancy grids from robot mapping algorithms . This scene representation naturally handles LiDAR sampling issues , can be used to fuse multiple LiDAR data sets , and captures the inherent uncertainty of the data due to occlusions and clutter .
This paper is the first description of combining 3D shape structure with 2D texture . A simple and effective technique using iterative closest point ( ICP ) was applied for 3D periocular region matching . It proved its strength for relatively unconstrained eye region capture , and does not require any training .
Previous research mainly focus on detecting objects using only one modality , which ignores exploiting the cross-modality cues . In this work , we propose a cross-modality deep learning framework based on deep Boltzmann Machines for 3D Scenes object detection . In particular , we demonstrate that by learning cross-modality feature from RGBD data , it is possible to capture their joint information to reinforce detector trainings in individual modalities .
3D visual object detection is a fundamental requirement for autonomous vehicles . However , accurately detecting 3D objects was until recently a quality unique to expensive LiDAR ranging devices .
Training our method only uses the 3D properties of the object without the need for external networks or supervision data . Our method is the first real-time system for monocular image 3D detection while achieves state-of-the-art performance on the KITTI benchmark . Code will be released at this https URL .
section : Introduction We focus on 3D object detection , which is a fundamental computer vision problem impacting most autonomous robotics systems including self - driving cars and drones . The goal of 3D object detection is to recover the 6 DoF pose and the 3D bounding box dimensions for all objects of interest in the scene . While recent advances in convolutional neural networks have enabled accurate 2D detection in complex environments , the 3D object detection problem still remains an open challenge . Methods for 3D box regression from a single image , even including recent deep learning methods such as , still have relatively low accuracy especially in depth estimates at longer ranges .
We demonstrate that the dense prediction architecture outperforms the architecture that regresses 3D corner locations directly by a large margin . We evaluate our model on two distinctive 3D object detection datasets . The KITTI dataset focuses on the outdoor urban driving scenario in which pedestrians , cyclists , and cars are annotated in data acquired with a camera - lidar system .
They represented each voxel as a binary random variable and used a convolutional deep belief network to learn the probability distribution for each voxel . Several recent works [ reference ][ reference ] also represented 3D input data as a volumetric form for 3D object classification and detection . Our work follows the strategy from [ reference ] , wherein several types of volumetric representation ( i.e. , occupancy grid models ) were proposed to fully utilize the rich source of 3D information and efficiently deal with large amounts of point cloud data .
3D reconstruction based on slice-image data is a main topic of visualization of 3D data fields . 3D model of reconstruction is also the premise of analysis and emulation .
The freeware Java tool Point Analysis in Java ( PAJ ) , created to perform 3D point analysis , was tested in an independent laboratory setting . The input data consisted of images of the hippocampal perforant pathway from serial immunocytochemical localizations of the rat brain in multiple views at different resolutions .
Autonomous 3D Printing in Extreme Environments . Implementation of Robotics and Automation has revolutionised the Manufacturing Industry , generating unprecedented levels of efficiency , boosted productivity and lower levels of risks .
This paper describes the development of an industrial robot manipulator control system for 3D scanning of surfaces prior to robotic plasma processing and cutting of products with complex shape . Preliminary 3D scanning of the surface by a robot manipulator is needed to create a 3D model of a product in order to automatically generate a program for moving the robot to ensure robotic precise plasma processing or cutting of the product . The new algorithm of synthesis of the robot manipulator motioncontrolling system by dynamics and perturbations compensation method has been developed .
Accurate and high-speed three-dimensional ( 3D ) shape measurements are becoming increasingly important in many industries . Recent advances in the development of high-resolution detectors , light sources , MEMS , and computational power are enabling the availability of noncontact and noninvasive optical methods , which can be applied to new and challenging applications requiring 3D and high-speed measurements .
The folding process is simulated by describing the path of the vertex . This method explores a unified approach to 3D folding model simulation in Rhinoceros/Grasshopper .
ABSTRACT This paper is focused on meeting the demands for better detection and evaluation of the spinal deformities that go beyond a basic static 3D analysis . It is expected that by observing the spine while it moves an improved 3D deformity analysis and measurements can be achieved . In this paper , we present a novel approach for the automatic back symmetry curve sensing .
Technologies of underground mobile positioning were proposed based on LiDAR data and coded sequence pattern landmarks for mine shafts and tunnels environment to meet the needs of fast and accurate positioning and navigation of equipments in the mine underground without satellite navigation signals . A coded sequence pattern was employed for automatic matching of 3D scans . The methods of SIFT feature , Otsu segmentation and fast hough transformation were described for the identification , positioning and interpretation of the coded sequence patterns , respectively .
Currently , a lot of different scanning techniques are used for 3D imaging of human body . Most of existing systems are based on static registration of internal structures using MRI or CT techniques as well as 3D scanning of outer surface of human body by laser triangulation or structured light methods .
As has been shown in previous benchmarks some of the presently used methods suffer from low convergence rates and/or low accuracy of the results and exhibit very long computation times1 , 2 which makes application to extended 2D layout patterns impractical . We address 3D simulation tasks by using a finite-element solver which has been shown to be superior to competing methods by several orders of magnitude in accuracy and computational time for typical microlithography simulations.2 We report on the current status of the solver , incorporating higher order edge elements , adaptive refinement methods , and fast solution algorithms . Further , we investigate the performance of the solver in the 3D simulation project of light diffraction off an alternating phase-shift contact-hole mask .
In field probing layers , we typically use , , and ( distance and normal fields ) , while in 3D CNN , and . Compared with convolutional layers , field probing layers save a majority of computation ( ) , as the probing filters in field probing layers are capable of learning where to ‘
The number of kernels in each convolutional layer , from shallow to deeper , is as follows : 25 , 25 , 25 , 50 , 50 , 50 , 75 , 75 and 75 . To achieve this depth in a 3D CNN , we employ small kernels of size . Moreover , a unit stride is used for all convolutions to preserve spatial resolution .
Furthermore , we make the implementation of our algorithm publicly available , which we believe can be of great benefit to the community , given the lack of robust open - source implementations for fitting 3DMMs . Due to lack of ground - truth data , the majority of the 3D face reconstruction papers report only qualitative results . In this paper , in order to provide quantitative evaluations , we collected a new dataset of 3D facial surfaces , using Kinect Fusion , which has many “ in - the - wild ” characteristics , even though it is captured indoors .
further boosts the performance , except for a minor drop on orientation estimation of pedestrian . Evaluation on 2D Segmentation and 3D Localization . 3DVPs enable us to transfer the meta data to the detect objects , so our method is able to segment the boundary of object .
In the following paragraph , we provide examples from 5 predominant approaches : In the 3D Morphable Model ( 3DMM ) , the most popular approach for estimating the full 3D facial structure from a single image ( among others ) , training includes an iterative flow procedure for dense image correspondence which is prone to failure . Additionally , testing requires a careful initialisation for solving a difficult highly non - convex optimization problem , which is slow .
We compare the performance of our method with that of a similar method in Section [ reference ] . To alleviate the aforementioned learning problem , we propose to reformulate the problem of 3D face reconstruction as one of 2D to 3D image segmentation : in particular , we convert each 3D facial scan into a 3D binary volume by discretizing the 3D space into voxels , assigning a value of 1 to all points enclosed by the 3D facial scan , and 0 otherwise . That is to say is the ground truth for voxel and is equal to 1 ,
We observe impressive performance in shape generation and shape arithmetic in a large dataset . For both general 3D model building and single image reconstruction , we show that our model is fully unsupervised , requiring no extra human - generated information about segmentation , keypoints , or pose information . We show that our model outperforms current state - of - the - art approaches in unsupervised ( object ) model classification while requiring significantly fewer learned feature extractors ( a vector with less than 100 dimensions compared to the 3D - GAN ’s 2.5 million dimensional vector ) .
section : Conclusions For many practical purposes , face alignment requires only global , parametric 2D or 3D transformations . This is often the case in state - of - the - art face recognition pipelines and a wide variety of other face understanding tasks .
Recent direct approaches make use of deep learning . Regression - based approaches train an end - to - end network to predict 3D joint locations directly from the image . Li incorporate model joint dependencies in the CNN via a max - margin formalism , others impose kinematic constraints by embedding a differentiable kinematic model into the deep learning architecture .
Earlier methods used to focus on extracting features , invariant to factors such as background scenes , lighting , and skin color from images and mapping them into 3D human pose . With the success of deep networks , recent methods tend to focus on training a deep convolutional neural network ( CNN ) end - to - end to estimate 3D poses from images directly . Some approaches divided the 3D pose estimation task into first predicting the joint locations in 2D using 2D pose estimators and then back - projecting them to estimate the 3D joint locations .
Fast image quality assessment via supervised iterative quantization method The study of surgical image quality evaluation system by subjective quality factor method Design principle of no-reference image quality evaluation Unsupervised feature learning framework for no-reference image quality assessment Blind Quality Prediction of Stereoscopic 3D Images Novel Technique for Ro-Reference Image Quality Assessment No-reference image quality assessment based on hybrid model Capturing Localized Image Artifacts through a CNN-based Hyper-image Representation Methods for Blind Estimation of Speckle Variance in SAR Images: Simulation Results and Verification for Real-Life Data Generalized referenceless image quality assessment framework using texture energy measures and pattern strength features Proceedings of the 2011 joint ACM workshop on Human gesture and behavior understanding, J-HGBU@MM 2011, Scottsdale, Arizona, USA, December 1, 2011 Full orientation invariance and improved feature selectivity of 3D SIFT with application to medical image analysis Fusing online and offline information for stable 3D tracking in real-time On-line data registration in OUTDOOR environment 3D versus 2D based indoor image matching analysis on images from low cost mobile devices Efficient Global 2D-3D Matching for Camera Localization in a Large-Scale 3D Map 3D point cloud object detection with multi-view convolutional neural network Volumetric Ultrasound Panorama Based on 3D SIFT An efficient SLAM system only using RGBD sensors Research on how to get object's 3D coordinate on two CCD camera measure system A fast and robust feature-based 3D algorithm using compressed image correlation Face Alignment Across Large Poses: A 3D Solution Deep Multi-Center Learning for Face Alignment FPNN: Field Probing Neural Networks for 3D Data Learning a Hierarchical Latent-Variable Model of 3D Shapes Joint 3D Face Reconstruction and Dense Face Alignment from A Single Image with 2D-Assisted Self-Supervised Learning 3DMatch: Learning Local Geometric Descriptors from RGB-D Reconstructions V2V-PoseNet: Voxel-to-Voxel Prediction Network for Accurate 3D Hand and Human Pose Estimation from a Single Depth Map PCL: Proposal Cluster Learning for Weakly Supervised Object Detection Deep Fitting Degree Scoring Network for Monocular 3D Object Detection Disentangling Monocular 3D Object Detection A maximum likelihood approach to feature segmentation 3D Bounding box detection from monocular images Monocular visual self-localization for humanoid soccer robots A probabilistic representation of LiDAR range data for efficient 3D object detection Combining 3D and 2D for less constrained periocular recognition Towards 3D object detection with bimodal deep Boltzmann machines over RGBD imagery 3D Visual Object Detection from Monocular Images RTM3D: Real-time Monocular 3D Detection from Object Keypoints for Autonomous Driving PointFusion: Deep Sensor Fusion for 3D Bounding Box Estimation V2V-PoseNet: Voxel-to-Voxel Prediction Network for Accurate 3D Hand and Human Pose Estimation from a Single Depth Map A Simulated Annealing Genetic Algorithm in Contours Tiling Point Analysis in Java Applied to Histological Images of the Perforant Pathway: A User’s Account A Reconfigurable Modular Swarm Robotic System for ISRU (In-Situ Resource Utilisation) Autonomous 3D Printing in Extreme Environments Development of Control System for Robotic Surface Tracking High-Speed Shape Measurements by Fringe Projection Method: SOPRA 3D Computational Methods for Curved Surface Modeling Based on Muria-Ori 3D sensing of back symmetry curve suited for dynamic analysis of spinal deformities Key technology of mine underground mobile positioning based on LiDAR and coded sequence pattern Multidirectional four-dimensional shape measurement system 3D simulations of electromagnetic fields in nanostructures using the time-harmonic finite-element method FPNN: Field Probing Neural Networks for 3D Data Deep CNN ensembles and suggestive annotations for infant brain MRI segmentation 3D Face Morphable Models "In-the-Wild" Subcategory-Aware Convolutional Neural Networks for Object Proposals and Detection Large Pose 3D Face Reconstruction from a Single Image via Direct Volumetric CNN Regression Learning a Hierarchical Latent-Variable Model of 3D Shapes FacePoseNet: Making a Case for Landmark-Free Face Alignment Lifting from the Deep: Convolutional 3D Pose Estimation from a Single Image Exploiting Temporal Information for 3D Human Pose Estimation
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Medical Imaging: Image Perception, Observer Performance, and Technology Assessment CVPR HVEI CVPR Workshops CVPR (2) MMAR IVCNZ ICCV ICPR MICCAI (2) ROBIO 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Neurocomputing NIPS 2018 International Conference on 3D Vision (3DV) ArXiv 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition IEEE Transactions on Pattern Analysis and Machine Intelligence CONIELECOMP CVPR Workshops BTAS CVPR ISVC (1) 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Three-Dimensional Image Processing (3DIP) and Applications NIPS Comput. Medical Imaging Graph. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2017 IEEE Winter Conference on Applications of Computer Vision (WACV) 2017 IEEE International Conference on Computer Vision (ICCV) 2018 International Conference on 3D Vision (3DV) 2017 IEEE International Conference on Computer Vision Workshops (ICCVW) 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) ECCV
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