LiDAR Odometry by Deep Learning-based Feature Points with Two-step Pose Estimation

KITTI dataset is collected from three types of environments, i.e., country, urban and highway The types of feature point cover a variety of scenes. The KITTI dataset provides 22 sequences of LiDAR data. 11 sequences of them from sequence 00 to sequence 10 are "training" data. The training data are provided with ground truth translation and rotation. In addition, field experiment data is collected by low-resolution LiDAR, VLP-16 in Wuhan Research and Innovation Center.

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On the objectivity, reliability, and validity of deep learning enabled bioimage analyses

eLife ◽

10.7554/elife.59780 ◽

2020 ◽

Vol 9 ◽

Cited By ~ 1

Author(s):

Dennis Segebarth ◽

Matthias Griebel ◽

Nikolai Stein ◽

Cora R von Collenberg ◽

Corinna Martin ◽

...

Keyword(s):

Deep Learning ◽

Signal To Noise Ratio ◽

Biological Effects ◽

Reliability And Validity ◽

Ground Truth ◽

Training Data ◽

Model Organisms ◽

Data Annotation ◽

Bioimage Analysis ◽

Model Training

Bioimage analysis of fluorescent labels is widely used in the life sciences. Recent advances in deep learning (DL) allow automating time-consuming manual image analysis processes based on annotated training data. However, manual annotation of fluorescent features with a low signal-to-noise ratio is somewhat subjective. Training DL models on subjective annotations may be instable or yield biased models. In turn, these models may be unable to reliably detect biological effects. An analysis pipeline integrating data annotation, ground truth estimation, and model training can mitigate this risk. To evaluate this integrated process, we compared different DL-based analysis approaches. With data from two model organisms (mice, zebrafish) and five laboratories, we show that ground truth estimation from multiple human annotators helps to establish objectivity in fluorescent feature annotations. Furthermore, ensembles of multiple models trained on the estimated ground truth establish reliability and validity. Our research provides guidelines for reproducible DL-based bioimage analyses.

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Synthesizing Depth Hand Images with GANs and Style Transfer for Hand Pose Estimation

Sensors ◽

10.3390/s19132919 ◽

2019 ◽

Vol 19 (13) ◽

pp. 2919 ◽

Cited By ~ 2

Author(s):

Wangyong He ◽

Zhongzhao Xie ◽

Yongbo Li ◽

Xinmei Wang ◽

Wendi Cai

Keyword(s):

Pose Estimation ◽

Ground Truth ◽

Training Image ◽

Training Data ◽

Generative Adversarial Network ◽

Style Transfer ◽

Visual Appearance ◽

Hand Pose Estimation ◽

Adversarial Network ◽

Hand Pose

Hand pose estimation is a critical technology of computer vision and human-computer interaction. Deep-learning methods require a considerable amount of tagged data. Accordingly, numerous labeled training data are required. This paper aims to generate depth hand images. Given a ground-truth 3D hand pose, the developed method can generate depth hand images. To be specific, a ground truth can be 3D hand poses with the hand structure contained, while the synthesized image has an identical size to that of the training image and a similar visual appearance to the training set. The developed method, inspired by the progress in the generative adversarial network (GAN) and image-style transfer, helps model the latent statistical relationship between the ground-truth hand pose and the corresponding depth hand image. The images synthesized using the developed method are demonstrated to be feasible for enhancing performance. On public hand pose datasets (NYU, MSRA, ICVL), comprehensive experiments prove that the developed method outperforms the existing works.

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Deep learning-driven velocity model building workflow

The Leading Edge ◽

10.1190/tle38110872a1.1 ◽

2019 ◽

Vol 38 (11) ◽

pp. 872a1-872a9 ◽

Cited By ~ 4

Author(s):

Mauricio Araya-Polo ◽

Stuart Farris ◽

Manuel Florez

Keyword(s):

Deep Learning ◽

Seismic Data ◽

Model Building ◽

Ground Truth ◽

Velocity Model ◽

Training Data ◽

Quality Data ◽

Generative Adversarial Network ◽

Data Set ◽

Velocity Models

Exploration seismic data are heavily manipulated before human interpreters are able to extract meaningful information regarding subsurface structures. This manipulation adds modeling and human biases and is limited by methodological shortcomings. Alternatively, using seismic data directly is becoming possible thanks to deep learning (DL) techniques. A DL-based workflow is introduced that uses analog velocity models and realistic raw seismic waveforms as input and produces subsurface velocity models as output. When insufficient data are used for training, DL algorithms tend to overfit or fail. Gathering large amounts of labeled and standardized seismic data sets is not straightforward. This shortage of quality data is addressed by building a generative adversarial network (GAN) to augment the original training data set, which is then used by DL-driven seismic tomography as input. The DL tomographic operator predicts velocity models with high statistical and structural accuracy after being trained with GAN-generated velocity models. Beyond the field of exploration geophysics, the use of machine learning in earth science is challenged by the lack of labeled data or properly interpreted ground truth, since we seldom know what truly exists beneath the earth's surface. The unsupervised approach (using GANs to generate labeled data)illustrates a way to mitigate this problem and opens geology, geophysics, and planetary sciences to more DL applications.

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3D Human Pose Estimation Using Spatio-Temporal Networks with Explicit Occlusion Training

Proceedings of the AAAI Conference on Artificial Intelligence ◽

10.1609/aaai.v34i07.6689 ◽

2020 ◽

Vol 34 (07) ◽

pp. 10631-10638

Author(s):

Yu Cheng ◽

Bo Yang ◽

Bo Wang ◽

Robby T. Tan

Keyword(s):

Pose Estimation ◽

Ground Truth ◽

Video Data ◽

Training Data ◽

Human Pose Estimation ◽

Ground Truth Data ◽

Public Data ◽

Spatio Temporal ◽

Human Pose ◽

3D Human Pose Estimation

Estimating 3D poses from a monocular video is still a challenging task, despite the significant progress that has been made in the recent years. Generally, the performance of existing methods drops when the target person is too small/large, or the motion is too fast/slow relative to the scale and speed of the training data. Moreover, to our knowledge, many of these methods are not designed or trained under severe occlusion explicitly, making their performance on handling occlusion compromised. Addressing these problems, we introduce a spatio-temporal network for robust 3D human pose estimation. As humans in videos may appear in different scales and have various motion speeds, we apply multi-scale spatial features for 2D joints or keypoints prediction in each individual frame, and multi-stride temporal convolutional networks (TCNs) to estimate 3D joints or keypoints. Furthermore, we design a spatio-temporal discriminator based on body structures as well as limb motions to assess whether the predicted pose forms a valid pose and a valid movement. During training, we explicitly mask out some keypoints to simulate various occlusion cases, from minor to severe occlusion, so that our network can learn better and becomes robust to various degrees of occlusion. As there are limited 3D ground truth data, we further utilize 2D video data to inject a semi-supervised learning capability to our network. Experiments on public data sets validate the effectiveness of our method, and our ablation studies show the strengths of our network's individual submodules.

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EVICAN—a balanced dataset for algorithm development in cell and nucleus segmentation

Bioinformatics ◽

10.1093/bioinformatics/btaa225 ◽

2020 ◽

Vol 36 (12) ◽

pp. 3863-3870

Author(s):

Mischa Schwendy ◽

Ronald E Unger ◽

Sapun H Parekh

Keyword(s):

Deep Learning ◽

Cell Biology ◽

Ground Truth ◽

Training Data ◽

Training Dataset ◽

Visual Cell ◽

Application Development ◽

Ground Truth Data ◽

Quantitative Image ◽

Nucleus Segmentation

Abstract Motivation Deep learning use for quantitative image analysis is exponentially increasing. However, training accurate, widely deployable deep learning algorithms requires a plethora of annotated (ground truth) data. Image collections must contain not only thousands of images to provide sufficient example objects (i.e. cells), but also contain an adequate degree of image heterogeneity. Results We present a new dataset, EVICAN—Expert visual cell annotation, comprising partially annotated grayscale images of 30 different cell lines from multiple microscopes, contrast mechanisms and magnifications that is readily usable as training data for computer vision applications. With 4600 images and ∼26 000 segmented cells, our collection offers an unparalleled heterogeneous training dataset for cell biology deep learning application development. Availability and implementation The dataset is freely available (https://edmond.mpdl.mpg.de/imeji/collection/l45s16atmi6Aa4sI?q=). Using a Mask R-CNN implementation, we demonstrate automated segmentation of cells and nuclei from brightfield images with a mean average precision of 61.6 % at a Jaccard Index above 0.5.

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Simulation and deep learning on point clouds for robot grasping

Assembly Automation ◽

10.1108/aa-07-2020-0096 ◽

2021 ◽

Vol ahead-of-print (ahead-of-print) ◽

Author(s):

Zhengtuo Wang ◽

Yuetong Xu ◽

Guanhua Xu ◽

Jianzhong Fu ◽

Jiongyan Yu ◽

...

Keyword(s):

Deep Learning ◽

Pose Estimation ◽

Point Clouds ◽

Estimation Algorithm ◽

Training Data ◽

Learning Method ◽

Data Set ◽

Content Type ◽

Experimental Platform ◽

Robot Grasping

Purpose In this work, the authors aim to provide a set of convenient methods for generating training data, and then develop a deep learning method based on point clouds to estimate the pose of target for robot grasping. Design/methodology/approach This work presents a deep learning method PointSimGrasp on point clouds for robot grasping. In PointSimGrasp, a point cloud emulator is introduced to generate training data and a pose estimation algorithm, which, based on deep learning, is designed. After trained with the emulation data set, the pose estimation algorithm could estimate the pose of target. Findings In experiment part, an experimental platform is built, which contains a six-axis industrial robot, a binocular structured-light sensor and a base platform with adjustable inclination. A data set that contains three subsets is set up on the experimental platform. After trained with the emulation data set, the PointSimGrasp is tested on the experimental data set, and an average translation error of about 2–3 mm and an average rotation error of about 2–5 degrees are obtained. Originality/value The contributions are as follows: first, a deep learning method on point clouds is proposed to estimate 6D pose of target; second, a convenient training method for pose estimation algorithm is presented and a point cloud emulator is introduced to generate training data; finally, an experimental platform is built, and the PointSimGrasp is tested on the platform.

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Neural Supermodeling

10.5194/egusphere-egu2020-12601 ◽

2020 ◽

Author(s):

Wim Wiegerinck

Keyword(s):

Deep Learning ◽

Domain Knowledge ◽

Model Performance ◽

Ground Truth ◽

Training Data ◽

Combination Method ◽

Neural Network Approach ◽

Modeling Approach ◽

Promising Tool ◽

Spatially Extended

Deep learning is a modeling approach that has shown impressive results in image processing and is arguably a promising tool for dealing with spatially extended complex systems such earth atmosphere with its visually interpretable patterns. A disadvantage of the neural network approach is that it typically requires an enormous amount of training data.&#160;Another recently proposed modeling approach is supermodeling. In supermodeling it is assumed that a dynamical system &#8211; the truth &#8211; is modelled by a set of good but imperfect models. The idea is to improve model performance by dynamically combining imperfect models during the simulation. The resulting combination of models is called the supermodel. The combination strength has to be learned from data. However, since supermodels do not start from scratch, but make use of existing domain knowledge, they may learn from less data.&#160;One of the ways to combine models is to define the tendencies of the supermodel as linear (weighted) combinations of the imperfect model tendencies. Several methods including linear regression have been proposed to optimize the weights. &#160;However, the combination method might also be nonlinear. In this work we propose and explore a novel combination of deep learning and supermodeling, in which convolutional neural networks are used as tool to combine the predictions of the imperfect models. &#160;The different supermodeling strategies are applied in simulations in a controlled environment with a three-level, quasi-geostrophic spectral model that serves as ground truth and perturbed models that serve as the imperfect models.

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Automated Ground Truth Generation for Learning-Based Crack Detection on Concrete Surfaces

Applied Sciences ◽

10.3390/app112210966 ◽

2021 ◽

Vol 11 (22) ◽

pp. 10966

Author(s):

Hsiang-Chieh Chen ◽

Zheng-Ting Li

Keyword(s):

Deep Learning ◽

Crack Detection ◽

Ground Truth ◽

Experimental Results ◽

Training Data ◽

Generation Process ◽

Detection Model ◽

Ground Truth Generation ◽

Labeling Approach ◽

The Cost

This article introduces an automated data-labeling approach for generating crack ground truths (GTs) within concrete images. The main algorithm includes generating first-round GTs, pre-training a deep learning-based model, and generating second-round GTs. On the basis of the generated second-round GTs of the training data, a learning-based crack detection model can be trained in a self-supervised manner. The pre-trained deep learning-based model is effective for crack detection after it is re-trained using the second-round GTs. The main contribution of this study is the proposal of an automated GT generation process for training a crack detection model at the pixel level. Experimental results show that the second-round GTs are similar to manually marked labels. Accordingly, the cost of implementing learning-based methods is reduced significantly because data labeling by humans is not necessitated.

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Visual Scan-Path based Data-Augmentation for CNN-based 360-degree Image Quality Assessment

London Imaging Meeting ◽

10.2352/issn.2694-118x.2021.lim-21 ◽

2021 ◽

Vol 2021 (1) ◽

pp. 21-26

Author(s):

Abderrezzaq Sendjasni ◽

Mohamed-Chaker Larabi ◽

Faouzi Alaya Cheikh

Keyword(s):

Deep Learning ◽

Image Quality ◽

Quality Assessment ◽

Data Augmentation ◽

Image Quality Assessment ◽

Ground Truth ◽

Training Data ◽

Training Scheme ◽

Scan Path ◽

Scan Paths

360-degree Image quality assessment (IQA) is facing the major challenge of lack of ground-truth databases. This problem is accentuated for deep learning based approaches where the performances are as good as the available data. In this context, only two databases are used to train and validate deep learning-based IQA models. To compensate this lack, a dataaugmentation technique is investigated in this paper. We use visual scan-path to increase the learning examples from existing training data. Multiple scan-paths are predicted to account for the diversity of human observers. These scan-paths are then used to select viewports from the spherical representation. The results of the data-augmentation training scheme showed an improvement over not using it. We also try to answer the question of using the MOS obtained for the 360-degree image as the quality anchor for the whole set of extracted viewports in comparison to 2D blind quality metrics. The comparison showed the superiority of using the MOS when adopting a patch-based learning.

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