scholarly journals Rough or Noisy? Metrics for Noise Estimation in SfM Reconstructions

Sensors ◽  
2020 ◽  
Vol 20 (19) ◽  
pp. 5725
Author(s):  
Ivan Nikolov ◽  
Claus Madsen
Keyword(s):  
Image Data ◽  
Ground Truth ◽  
Noise Removal ◽  
Difficult Problem ◽  
Noise Estimation ◽  
Real Surface ◽  
Reconstructed Surfaces ◽  
Noise Assessment ◽  
Global Smoothing ◽  
Statistical Metrics

Structure from Motion (SfM) can produce highly detailed 3D reconstructions, but distinguishing real surface roughness from reconstruction noise and geometric inaccuracies has always been a difficult problem to solve. Existing SfM commercial solutions achieve noise removal by a combination of aggressive global smoothing and the reconstructed texture for smaller details, which is a subpar solution when the results are used for surface inspection. Other noise estimation and removal algorithms do not take advantage of all the additional data connected with SfM. We propose a number of geometrical and statistical metrics for noise assessment, based on both the reconstructed object and the capturing camera setup. We test the correlation of each of the metrics to the presence of noise on reconstructed surfaces and demonstrate that classical supervised learning methods, trained with these metrics can be used to distinguish between noise and roughness with an accuracy above 85%, with an additional 5–6% performance coming from the capturing setup metrics. Our proposed solution can easily be integrated into existing SfM workflows as it does not require more image data or additional sensors. Finally, as part of the testing we create an image dataset for SfM from a number of objects with varying shapes and sizes, which are available online together with ground truth annotations.

scholarly journals Fig Plant Segmentation from Aerial Images Using a Deep Convolutional Encoder-Decoder Network

2019 ◽  
Vol 11 (10) ◽  
pp. 1157 ◽  
Author(s):  
Jorge Fuentes-Pacheco ◽  
Juan Torres-Olivares ◽  
Edgar Roman-Rangel ◽  
Salvador Cervantes ◽  
Porfirio Juarez-Lopez ◽  
...  
Keyword(s):  
Precision Agriculture ◽  
Image Data ◽  
Ground Truth ◽  
Aerial Images ◽  
Aerial Image ◽  
Data Set ◽  
Visual Appearance ◽  
Aerial Robots ◽  
Lighting Conditions ◽  
Convolutional Encoder

Crop segmentation is an important task in Precision Agriculture, where the use of aerial robots with an on-board camera has contributed to the development of new solution alternatives. We address the problem of fig plant segmentation in top-view RGB (Red-Green-Blue) images of a crop grown under open-field difficult circumstances of complex lighting conditions and non-ideal crop maintenance practices defined by local farmers. We present a Convolutional Neural Network (CNN) with an encoder-decoder architecture that classifies each pixel as crop or non-crop using only raw colour images as input. Our approach achieves a mean accuracy of 93.85% despite the complexity of the background and a highly variable visual appearance of the leaves. We make available our CNN code to the research community, as well as the aerial image data set and a hand-made ground truth segmentation with pixel precision to facilitate the comparison among different algorithms.

Color image analysis for vehicle speed measurement

1993 ◽  
Vol 20 (2) ◽  
pp. 228-235 ◽  
Author(s):  
Yean-Jye Lu ◽  
Xidong Yuan
Keyword(s):  
Image Analysis ◽  
Traffic Control ◽  
Road Traffic ◽  
Color Image ◽  
Image Data ◽  
Difficult Problem ◽  
Vehicle Speed ◽  
Color Image Analysis ◽  
Speed Measurement ◽  
Monochrome Image

Image analysis for traffic data collection has been studied throughout the world for more than a decade. A survey of existing systems shows that research was focused mainly on the monochrome image analysis and that the field of color image analysis was rarely studied. With the application of color image analysis in mind, this paper proposes a new algorithm for vehicle speed measurement in daytime. The new algorithm consists of four steps: (i) image input, (ii) pixel analysis, (iii) single image analysis, and (iv) image sequence analysis. It has three significant advantages. First, the algorithm can distinguish the shadows caused by moving vehicles outside the detection area from the actual vehicles passing through the area, which is a difficult problem for the monochrome image analysis technique to handle. Second, the algorithm significantly reduces the image data to be processed; thus only a personal computer is required without the addition of any special hardware. The third advantage is the flexible placement of detection spots at any position in the camera's field of view. The accuracy of the algorithm is also discussed. Key words: speed measurement, vehicle detection, image analysis, image processing, traffic control, traffic measurement and road traffic.

Background Noise Removal Technique using Deep Learning Segmentation Network without Segmentation Map

2021 ◽  
Vol 263 (2) ◽  
pp. 4441-4445
Author(s):  
Hyunsuk Huh ◽  
Seungchul Lee
Keyword(s):  
Deep Learning ◽  
Background Noise ◽  
Ground Truth ◽  
Noise Removal ◽  
Industrial Manufacturing ◽  
Proposed Model ◽  
Audio Data ◽  
Passive Noise ◽  
Removal Technique ◽  
Noise Canceling

Audio data acquired at industrial manufacturing sites often include unexpected background noise. Since the performance of data-driven models can be worse by background noise. Therefore, it is important to get rid of unwanted background noise. There are two main techniques for noise canceling in a traditional manner. One is Active Noise Canceling (ANC), which generates an inverted phase of the sound that we want to remove. The other is Passive Noise Canceling (PNC), which physically blocks the noise. However, these methods require large device size and expensive cost. Thus, we propose a deep learning-based noise canceling method. This technique was developed using audio imaging technique and deep learning segmentation network. However, the proposed model only needs the information on whether the audio contains noise or not. In other words, unlike the general segmentation technique, a pixel-wise ground truth segmentation map is not required for this method. We demonstrate to evaluate the separation using pump sound of MIMII dataset, which is open-source dataset.

scholarly journals Automated single particle detection and tracking for large microscopy datasets

2016 ◽  
Vol 3 (5) ◽  
pp. 160225 ◽  
Author(s):  
Rhodri S. Wilson ◽  
Lei Yang ◽  
Alison Dun ◽  
Annya M. Smyth ◽  
Rory R. Duncan ◽  
...  
Keyword(s):  
Single Molecule ◽  
Single Particle ◽  
Image Data ◽  
Ground Truth ◽  
Detection Algorithm ◽  
Large Datasets ◽  
Single Particle Tracking ◽  
Synthetic Image ◽  
Particle Detection ◽  
Very Large Datasets

Recent advances in optical microscopy have enabled the acquisition of very large datasets from living cells with unprecedented spatial and temporal resolutions. Our ability to process these datasets now plays an essential role in order to understand many biological processes. In this paper, we present an automated particle detection algorithm capable of operating in low signal-to-noise fluorescence microscopy environments and handling large datasets. When combined with our particle linking framework, it can provide hitherto intractable quantitative measurements describing the dynamics of large cohorts of cellular components from organelles to single molecules. We begin with validating the performance of our method on synthetic image data, and then extend the validation to include experiment images with ground truth. Finally, we apply the algorithm to two single-particle-tracking photo-activated localization microscopy biological datasets, acquired from living primary cells with very high temporal rates. Our analysis of the dynamics of very large cohorts of 10 000 s of membrane-associated protein molecules show that they behave as if caged in nanodomains. We show that the robustness and efficiency of our method provides a tool for the examination of single-molecule behaviour with unprecedented spatial detail and high acquisition rates.

scholarly journals Hand-Held Stereovision System for Image Updating in Open Spine Surgery

2020 ◽  
Vol 19 (4) ◽  
pp. 461-470
Author(s):  
Xiaoyao Fan ◽  
Maxwell S Durtschi ◽  
Chen Li ◽  
Linton T Evans ◽  
Songbai Ji ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Spine Surgery ◽  
Surgical Navigation ◽  
Image Data ◽  
Ground Truth ◽  
Acquisition Time ◽  
Fiducial Markers ◽  
Image Pair ◽  
3 Dimensional ◽  
Surgical Field

Abstract BACKGROUND Image guidance in open spinal surgery is compromised by changes in spinal alignment between preoperative images and surgical positioning. We evaluated registration of stereo-views of the surgical field to compensate for vertebral alignment changes. OBJECTIVE To assess accuracy and efficiency of an optically tracked hand-held stereovision (HHS) system to acquire images of the exposed spine during surgery. METHODS Standard midline posterior approach exposed L1 to L6 in 6 cadaver porcine spines. Fiducial markers were placed on each vertebra as “ground truth” locations. Spines were positioned supine with accentuated lordosis, and preoperative computed tomography (pCT) was acquired. Spines were re-positioned in a neutral prone posture, and locations of fiducials were acquired with a tracked stylus. Intraoperative stereovision (iSV) images were acquired and 3-dimensional (3D) surfaces of the exposed spine were reconstructed. HHS accuracy was assessed in terms of distances between reconstructed fiducial marker locations and their tracked counterparts. Level-wise registrations aligned pCT with iSV to account for changes in spine posture. Accuracy of updated computed tomography (uCT) was assessed using fiducial markers and other landmarks. RESULTS Acquisition time for each image pair was <1 s. Mean reconstruction time was <1 s for each image pair using batch processing, and mean accuracy was 1.2 ± 0.6 mm across 6 cases. Mean errors of uCT were 3.1 ± 0.7 and 2.0 ± 0.5 mm on the dorsal and ventral sides, respectively. CONCLUSION Results suggest that a portable HHS system offers potential to acquire accurate image data from the surgical field to facilitate surgical navigation during open spine surgery.

Brain tumor classification and segmentation using sparse coding and dictionary learning

2016 ◽  
Vol 61 (4) ◽  
pp. 413-429 ◽  
Author(s):  
Saif Dawood Salman Al-Shaikhli ◽  
Michael Ying Yang ◽  
Bodo Rosenhahn
Keyword(s):  
Brain Tumor ◽  
Dictionary Learning ◽  
Sparse Coding ◽  
Image Data ◽  
Ground Truth ◽  
Tumor Classification ◽  
Training Data ◽  
Tumor Segmentation ◽  
Feature Dictionary ◽  
The Brain

AbstractThis paper presents a novel fully automatic framework for multi-class brain tumor classification and segmentation using a sparse coding and dictionary learning method. The proposed framework consists of two steps: classification and segmentation. The classification of the brain tumors is based on brain topology and texture. The segmentation is based on voxel values of the image data. Using K-SVD, two types of dictionaries are learned from the training data and their associated ground truth segmentation: feature dictionary and voxel-wise coupled dictionaries. The feature dictionary consists of global image features (topological and texture features). The coupled dictionaries consist of coupled information: gray scale voxel values of the training image data and their associated label voxel values of the ground truth segmentation of the training data. For quantitative evaluation, the proposed framework is evaluated using different metrics. The segmentation results of the brain tumor segmentation (MICCAI-BraTS-2013) database are evaluated using five different metric scores, which are computed using the online evaluation tool provided by the BraTS-2013 challenge organizers. Experimental results demonstrate that the proposed approach achieves an accurate brain tumor classification and segmentation and outperforms the state-of-the-art methods.

Expanding the Capabilities of Radar-Based Vehicle Detection Systems: Noise Characterization and Removal Procedures

2019 ◽  
Vol 2673 (11) ◽  
pp. 150-160
Author(s):  
Kelvin R. Santiago-Chaparro ◽  
David A. Noyce
Keyword(s):  
Performance Measures ◽  
Vehicle Detection ◽  
Ground Truth ◽  
Noise Removal ◽  
Performance Measure ◽  
Signalized Intersections ◽  
Volume Data ◽  
Trajectory Data ◽  
Potential Accuracy ◽  
Detection Systems

The capabilities of radar-based vehicle detection (RVD) systems used at signalized intersections for stop bar and advanced detection are arguably underutilized. Underutilization happens because RVD systems can monitor the position and speed (i.e., trajectory) of multiple vehicles at the same time but these trajectories are only used to emulate the behavior of legacy detection systems such as inductive loop detectors. When full vehicle trajectories tracked by an RVD system are collected, detailed traffic operations and safety performance measures can be calculated for signalized intersections. Unfortunately, trajectory datasets obtained from RVD systems often contain significant noise which makes the computation of performance measures difficult. In this paper, a description of the type of trajectory datasets that can be obtained from RVD systems is presented along with a characterization of the noise expected in these datasets. Guidance on the noise removal procedures that can be applied to these datasets is also presented. This guidance can be applied to the use of data from commercially-available RVD systems to obtain advanced performance measures. To demonstrate the potential accuracy of the noise removal procedures, the procedures were applied to trajectory data obtained from an existing intersection, and data on a basic performance measure (vehicle volume) were extracted from the dataset. Volume data derived from the de-noised trajectory dataset was compared with ground truth volume and an absolute average difference of approximately one vehicle every 5 min was found, thus highlighting the potential accuracy of the noise removal procedures introduced.

scholarly journals LAND COVER CLASSIFICATION FROM FULL-WAVEFORM LIDAR DATA BASED ON SUPPORT VECTOR MACHINES

2016 ◽  
Vol XLI-B3 ◽  
pp. 447-452
Author(s):  
M. Zhou ◽  
C. R. Li ◽  
L. Ma ◽  
H. C. Guan
Keyword(s):  
Support Vector Machines ◽  
Land Cover ◽  
Prediction Model ◽  
Image Data ◽  
Ground Truth ◽  
Land Cover Classification ◽  
Training Data ◽  
Support Vector ◽  
Vector Machines ◽  
Waveform Lidar

In this study, a land cover classification method based on multi-class Support Vector Machines (SVM) is presented to predict the types of land cover in Miyun area. The obtained backscattered full-waveforms were processed following a workflow of waveform pre-processing, waveform decomposition and feature extraction. The extracted features, which consist of distance, intensity, Full Width at Half Maximum (FWHM) and back scattering cross-section, were corrected and used as attributes for training data to generate the SVM prediction model. The SVM prediction model was applied to predict the types of land cover in Miyun area as ground, trees, buildings and farmland. The classification results of these four types of land covers were obtained based on the ground truth information according to the CCD image data of Miyun area. It showed that the proposed classification algorithm achieved an overall classification accuracy of 90.63%. In order to better explain the SVM classification results, the classification results of SVM method were compared with that of Artificial Neural Networks (ANNs) method and it showed that SVM method could achieve better classification results.

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