The Impact of the Image Feature Detector and Descriptor Choice on Visual SLAM Accuracy

2015 ◽  
pp. 203-210 ◽  
Author(s):  
Adam Schmidt ◽  
Marek Kraft
Keyword(s):  
Image Feature ◽  
Visual Slam ◽  
Feature Detector ◽  
The Impact

scholarly journals IMPACT ASSESSMENT OF IMAGE FEATURE EXTRACTORS ON THE PERFORMANCE OF SLAM SYSTEMS

2015 ◽  
Vol 2 (2) ◽  
pp. 45-50 ◽  
Author(s):  
Taihú Pire ◽  
Thomas Fischer ◽  
Jan Faigl
Keyword(s):  
Ground Truth ◽  
Mapping Method ◽  
Image Feature ◽  
Feature Descriptor ◽  
Visual Slam ◽  
Feature Detector ◽  
Trade Off ◽  
Feature Detectors ◽  
Localization Precision ◽  
Ground Truth Information

This work evaluates an impact of image feature extractors on the performance of a visual SLAM method in terms of pose accuracy and computational requirements. In particular, the S-PTAM (Stereo Parallel Tracking and Mapping) method is considered as the visual SLAM framework for which both the feature detector and feature descriptor are parametrized. The evaluation was performed with a standard dataset with ground-truth information and six feature detectors and four descriptors. The presented results indicate that the combination of the GFTT detector and the BRIEF descriptor provides the best trade-off between the localization precision and computational requirements among the evaluated combinations of the detectors and descriptors.

Feature Selection Based on GA and PNN

2011 ◽  
Vol 217-218 ◽  
pp. 1753-1757 ◽  
Author(s):  
Min Hao ◽  
Shuo Shi Ma ◽  
Xiao Dong Hao ◽  
Li Li Ma ◽  
Li Juan Wang
Keyword(s):  
Genetic Algorithm ◽  
Feature Selection ◽  
Fitness Function ◽  
Feature Selection Method ◽  
Selection Method ◽  
Zernike Moments ◽  
Image Feature ◽  
Detection Accuracy ◽  
Shape Feature ◽  
The Impact

A new image feature selection method with the combination of Genetic Algorithm(GA) and Probabilistic Neural Network(PNN) is proposed and applied to potato shape feature selection and classification. The classifier selecting principle is investigated by combining with the genetic algorithm. A new feature selection method based on GA and PNN is put forward firstly. Comprehensively considering the factor of classification accuracy,selected feature number and the impact of the two factors, a new fitness function is proposed. The initial Zernike moments parameters of potatoes are optimized using improved genetic algorithm, and nineteen Zernike moments are extracted to form the shape feature. The shape detection accuracy can reach 93% and 100% respectively for the perfect and malformation potatoes. The tests indicate that the fitness function and feature selection method can be used for searching the best feature combination.

scholarly journals Medical Cloud Computing Data Processing to Optimize the Effect of Drugs

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Fengxia Li ◽  
Zhi Qu ◽  
Ruiling Li
Keyword(s):  
Cloud Computing ◽  
Large Scale ◽  
Vascular Endothelial Cells ◽  
Senile Dementia ◽  
Data Retrieval ◽  
Strong Stability ◽  
Image Feature ◽  
Computing Platform ◽  
Image Set ◽  
The Impact

In recent years, cloud computing technology is maturing in the process of growing. Hadoop originated from Apache Nutch and is an open-source cloud computing platform. Moreover, the platform is characterized by large scale, virtualization, strong stability, strong versatility, and support for scalability. It is necessary and far-reaching, based on the characteristics of unstructured medical images, to combine content-based medical image retrieval with the Hadoop cloud platform to conduct research. This study combines the impact mechanism of senile dementia vascular endothelial cells with cloud computing to construct a corresponding data retrieval platform of the cloud computing image set. Moreover, this study uses Hadoop’s core framework distributed file system HDFS to upload images, store the images in the HDFS and image feature vectors in HBase, and use MapReduce programming mode to perform parallel retrieval, and each of the nodes cooperates with each other. The results show that the proposed method has certain effects and can be applied to medical research.

Sparse and limited-angle CT image reconstruction using dictionary learning constrained by L1 norm

2019 ◽  
Vol 61 (10) ◽  
pp. 584-590
Author(s):  
Jun-Nian Gou ◽  
Pan-Pan Zhai ◽  
Hai-Ying Dong
Keyword(s):  
Dictionary Learning ◽  
Absolute Error ◽  
Image Feature ◽  
Projection Data ◽  
Reconstruction Technique ◽  
L1 Norm ◽  
Noisy Projection ◽  
The Impact ◽  
Better Than ◽  
Limited Angle

Reconstructed images from computed tomography (CT) using the algebraic reconstruction technique (ART) and simultaneous ART (SART) algorithms often suffer from obvious artefacts when only sparse and limited-angle projection data are available. Using the ability of dictionary learning (DL) in image feature extraction and sparse signal representation, a new iterative reconstruction algorithm, ART-DL-L1, is proposed to overcome the aforementioned limitations. This new algorithm is based on DL and an L1 norm constraint, combined with ART. An alternate iterative solving strategy based on an approach of 'ART first, then adaptive dictionary learning' is suggested and is explicitly described in a flowchart depicting the ART-DL-L1 algorithm. For both a noisy projection of 360° sparse data and limitedangle data of 120°, simulation reconstruction results from the classic Shepp-Logan image obtained using ART-DL-L1 appear to be better than those obtained using SART and total variation (TV) algorithms and also better than the cutting-edge ART-DL-L2 algorithm. Five evaluation metrics corresponding to the root-mean-square error (RMSE), the mean absolute error (MAE), the peak signal-to-noise ratio (PSNR), the residuals and the structural similarity (SSIM) index are adopted to estimate the reconstruction effect. The results suggest that the five metrics obtained using ART-DL-L1 outperform those obtained using the other three algorithms. The impact of using patches of various sizes played by the DL part in ART-DL-L1 is considered in the simulations and the patch size achieving the best reconstructed image quality is identified in this case as 25 (5 × 5). Overall, the proposed ART-DL-L1 algorithm may reduce artefacts and suppress noise from incomplete noisy projection CT imaging to some degree.

scholarly journals Improving Wavelet Threshold De-noising Applied on Parts Detection

2018 ◽  
Vol 232 ◽  
pp. 04084
Author(s):  
Lizhou Cheng ◽  
Jian Mao ◽  
Hongyuan Wei
Keyword(s):  
Wavelet Transform ◽  
Noise Reduction ◽  
Detection System ◽  
Signal To Noise Ratio ◽  
Median Filter ◽  
Image Features ◽  
Image Feature ◽  
Threshold Function ◽  
Image Noise Reduction ◽  
The Impact

The traditional filtering methods such as median filter and mean filter always blurrs image features, resulting in poor noise reduction effect. Wavelet transform has unique adaptability due to its variable resolution, which can better implement wavelet denoising on the basis of image feature. Aiming at the shortcoming of traditional wavelet transform threshold denoising, based on the hard threshold and soft threshold function, this paper proposes improved adaptive thresholding function. By comparing and validating, this method obtains the smaller mean square error (MSE) and higher peak signal to noise ratio. Meanwhile, this method improves the quality of detection images, and reduces the impact on images brought by noise from external enviroment and internal system. So, this can be applied to image noise reduction of the detection system.

scholarly journals Vehicle Detection in Aerial Images Using a Fast Oriented Region Search and the Vector of Locally Aggregated Descriptors

Sensors ◽  
2019 ◽  
Vol 19 (15) ◽  
pp. 3294 ◽  
Author(s):  
Liu ◽  
Ding ◽  
Zhu ◽  
Xiu ◽  
Li ◽  
...  
Keyword(s):  
Vehicle Detection ◽  
Aerial Images ◽  
Image Feature ◽  
Superior Performance ◽  
Detection Scheme ◽  
Military Applications ◽  
Detection Stage ◽  
Overhead View ◽  
Region Search ◽  
The Impact

Vehicle detection in aerial images plays a significant role in civil and military applications and it faces many challenges including the overhead-view perspective, the highly complex background, and the variants of vehicles. This paper presents a robust vehicle detection scheme to overcome these issues. In the detection stage, we propose a novel algorithm to generate oriented proposals that could enclose the vehicle objects properly as rotated rectangles with orientations. To discriminate the object and background in the proposals, we propose a modified vector of locally aggregated descriptors (VLAD) image representation model with a recently proposed image feature, i.e., local steering kernel (LSK) feature. By applying non-maximum suppression (NMS) after classification, we show that each vehicle object is detected with a single-oriented bounding box. Experiments are conducted on aerial images to compare the proposed method with state-of-art methods and evaluate the impact of the components in the model. The results have proven the robustness of the proposed method under various circumstances and the superior performance over other existing vehicle detection approaches.

Derivative-Based Scale Invariant Image Feature Detector With Error Resilience

2014 ◽  
Vol 23 (5) ◽  
pp. 2380-2391 ◽  
Author(s):  
Pradip Mainali ◽  
Gauthier Lafruit ◽  
Klaas Tack ◽  
Luc Van Gool ◽  
Rudy Lauwereins
Keyword(s):  
Error Resilience ◽  
Image Feature ◽  
Feature Detector ◽  
Scale Invariant
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