scholarly journals Fast VMZ : code enhancements for video mosaicing and summarization

2021 ◽  
Author(s):  
◽  
Nafis Ahmed
Keyword(s):  
Feature Matching ◽  
Affine Invariant ◽  
Illumination Variation ◽  
Difference Problem ◽  
Illumination Problem ◽  
Speeded Up Robust Features ◽  
Video Mosaicing ◽  
Homography Estimation ◽  
Speed Up ◽  
Run Time

Video Mosaicing and Summarization (VMZ) is a novel image processing pipeline that summarizes the content of a long sequence of geospatial or biomedical videos using a few coverage maps or mini mosaics. The existing VMZ algorithm uses Normalized Cross-Correlation (NCC), Structure Tensor (ST), Affine-Invariant SIFT (ASIFT), Speeded up robust features for its feature matching and homography estimation pipeline, which are the most computationally expensive modules in the VMZ pipeline. Due to these long-running compute-intensive modules, the VMZ pipeline is not suitable for real-time mosaic formation in drones or UAVs. For instance, VMZ takes around 4 hours to generate mini-mosaics from an image sequence containing 9291 image frames. The blending algorithms used for mini-mosaic generation suffer from illumination variation due to the illumination difference in image frames. Such illumination inconsistency causes severe problems for biomedical scene understanding where curvilinear or tiny biological structures are present. VMZ pipeline is also dependent on 3rd party libraries not aligned with the flow of VMZ, which introduces redundant computation. One of the main reasons for the slow processing of the VMZ pipeline is not leveraging any parallel processing techniques and available graphics processing hardware. Therefore, the objective of this thesis is mainly three-fold: (i) speeding up the computeintensive and long-running modules in the VMZ pipeline, (ii) modifying the existing libraries and interfaces for better alignment with VMZ workflow, and (iii) resolving the illumination difference problem of the blending algorithms. Selected longrunning modules with the most impact on the overall run-time have been improved using CPU-based Multi-Threading, GPU-based Parallelization, and better integration with the existing VMZ pipeline. An illumination-matched blending algorithm has been proposed to improve the illumination problem. Besides, to evaluate the performance of different blending algorithms, a novel metric named Maximum Overall Illumination Difference (MOID) has been proposed. The improvement of VMZ modules has resulted in more than 100x speed-up in certain modules, with a 4.4x speed-up for the total VMZ run-time. The novel illumination matched blending resulted in a better MOID value for image sequences not having illumination variance in a single frame.

scholarly journals A graphical heuristic for reduction and partitioning of large datasets for scalable supervised training

2019 ◽  
Vol 6 (1) ◽  
Author(s):  
Sumedh Yadav ◽  
Mathis Bode
Keyword(s):  
Prediction Accuracy ◽  
Clustering Algorithm ◽  
Nearest Neighbor ◽  
Large Datasets ◽  
Test Results ◽  
Accuracy Test ◽  
Reasonable Proportion ◽  
Speed Up ◽  
Run Time ◽  
Information Graph

Abstract A scalable graphical method is presented for selecting and partitioning datasets for the training phase of a classification task. For the heuristic, a clustering algorithm is required to get its computation cost in a reasonable proportion to the task itself. This step is succeeded by construction of an information graph of the underlying classification patterns using approximate nearest neighbor methods. The presented method consists of two approaches, one for reducing a given training set, and another for partitioning the selected/reduced set. The heuristic targets large datasets, since the primary goal is a significant reduction in training computation run-time without compromising prediction accuracy. Test results show that both approaches significantly speed-up the training task when compared against that of state-of-the-art shrinking heuristics available in LIBSVM. Furthermore, the approaches closely follow or even outperform in prediction accuracy. A network design is also presented for a partitioning based distributed training formulation. Added speed-up in training run-time is observed when compared to that of serial implementation of the approaches.

scholarly journals AC_ICAP: A Flexible High Speed ICAP Controller

2015 ◽  
Vol 2015 ◽  
pp. 1-15 ◽  
Author(s):  
Luis Andres Cardona ◽  
Carles Ferrer
Keyword(s):  
High Speed ◽  
Access Port ◽  
Field Programmable ◽  
Programmable Gate Arrays ◽  
Speed Up ◽  
Run Time ◽  
Ip Cores ◽  
Similar Solutions ◽  
Internal Configuration ◽  
Run Time Reconfiguration

The Internal Configuration Access Port (ICAP) is the core component of any dynamic partial reconfigurable system implemented in Xilinx SRAM-based Field Programmable Gate Arrays (FPGAs). We developed a new high speed ICAP controller, named AC_ICAP, completely implemented in hardware. In addition to similar solutions to accelerate the management of partial bitstreams and frames, AC_ICAP also supports run-time reconfiguration of LUTs without requiring precomputed partial bitstreams. This last characteristic was possible by performing reverse engineering on the bitstream. Besides, we adapted this hardware-based solution to provide IP cores accessible from the MicroBlaze processor. To this end, the controller was extended and three versions were implemented to evaluate its performance when connected to Peripheral Local Bus (PLB), Fast Simplex Link (FSL), and AXI interfaces of the processor. In consequence, the controller can exploit the flexibility that the processor offers but taking advantage of the hardware speed-up. It was implemented in both Virtex-5 and Kintex7 FPGAs. Results of reconfiguration time showed that run-time reconfiguration of single LUTs in Virtex-5 devices was performed in less than 5 μs which implies a speed-up of more than 380x compared to the Xilinx XPS_HWICAP controller.

scholarly journals Fast Image Registration for Spacecraft Autonomous Navigation Using Natural Landmarks

2018 ◽  
Vol 2018 ◽  
pp. 1-12
Author(s):  
Yun-Hua Wu ◽  
Lin-Lin Ge ◽  
Feng Wang ◽  
Bing Hua ◽  
Zhi-Ming Chen ◽  
...  
Keyword(s):  
Image Registration ◽  
Autonomous Navigation ◽  
Computational Cost ◽  
Segmentation Algorithm ◽  
Speeded Up Robust Features ◽  
Speed Up ◽  
Random Sample Consensus ◽  
Invariant Properties ◽  
Low Computational Cost ◽  
Ransac Algorithm

In order to satisfy the real-time requirement of spacecraft autonomous navigation using natural landmarks, a novel algorithm called CSA-SURF (chessboard segmentation algorithm and speeded up robust features) is proposed to improve the speed without loss of repeatability performance of image registration progress. It is a combination of chessboard segmentation algorithm and SURF. Here, SURF is used to extract the features from satellite images because of its scale- and rotation-invariant properties and low computational cost. CSA is based on image segmentation technology, aiming to find representative blocks, which will be allocated to different tasks to speed up the image registration progress. To illustrate the advantages of the proposed algorithm, PCA-SURF, which is the combination of principle component analysis and SURF, is also analyzed in this paper for comparison. Furthermore, random sample consensus (RANSAC) algorithm is applied to eliminate the false matches for further accuracy improvement. The simulation results show that the proposed strategy obtains good results, especially in scaling and rotation variation. Besides, CSA-SURF decreased 50% of the time in extraction and 90% of the time in matching without losing the repeatability performance by comparing with SURF algorithm. The proposed method has been demonstrated as an alternative way for image registration of spacecraft autonomous navigation using natural landmarks.

scholarly journals Natural Feature Detection on Mobile Phones with 3D FAST

2010 ◽  
Vol 9 (4) ◽  
pp. 29-34 ◽  
Author(s):  
Achim Weimert ◽  
Xueting Tan ◽  
Xubo Yang
Keyword(s):  
Feature Detection ◽  
Feature Matching ◽  
Hessian Matrix ◽  
Detection Algorithm ◽  
Scale Invariant ◽  
Natural Feature ◽  
Detection Approach ◽  
Scale Levels ◽  
Speed Up ◽  
Low Performance

In this paper, we present a novel feature detection approach designed for mobile devices, showing optimized solutions for both detection and description. It is based on FAST (Features from Accelerated Segment Test) and named 3D FAST. Being robust, scale-invariant and easy to compute, it is a candidate for augmented reality (AR) applications running on low performance platforms. Using simple calculations and machine learning, FAST is a feature detection algorithm known to be efficient but not very robust in addition to its lack of scale information. Our approach relies on gradient images calculated for different scale levels on which a modified9 FAST algorithm operates to obtain the values of the corner response function. We combine the detection with an adapted version of SURF (Speed Up Robust Features) descriptors, providing a system with all means to implement feature matching and object detection. Experimental evaluation on a Symbian OS device using a standard image set and comparison with SURF using Hessian matrix-based detector is included in this paper, showing improvements in speed (compared to SURF) and robustness (compared to FAST)

scholarly journals Intrinsic Run-time Row Hammer PUFs: Leveraging the Row Hammer Effect for Run-Time Cryptography and Improved Security

2018 ◽  
Author(s):  
Nikolaos Athanasios Anagnostopoulos ◽  
Tolga Arul ◽  
Yufan Fan ◽  
Christian Hatzfeld ◽  
André Schaller ◽  
...  
Keyword(s):  
Random Access ◽  
Physical Unclonable Functions ◽  
Generation Times ◽  
Speed Up ◽  
Run Time ◽  
Commercial Off The Shelf ◽  
Cost Efficient ◽  
Statistical Relationships ◽  
The Times ◽  
Time Required

Physical Unclonable Functions (PUFs) based on the retention times of the cells of a Dynamic Random Access Memory (DRAM) can be utilised for the implementation of cost-efficient and lightweight cryptographic protocols. However, as recent work has demonstrated, the times needed in order to generate their responses may prohibit their widespread usage. In order to address this issue, the Row Hammer PUF has been proposed by Schaller et al. [1], which leverages the row hammer effect in DRAM modules to reduce the retention times of their cells and, therefore, significantly speed up the generation times for the responses of PUFs based on these retention times. In this work, we extend the work of Schaller et al. by presenting a run-time accessible implementation of this PUF and further reducing the time required for the generation of its responses. Additionally, we also provide a more thorough investigation of the effects of temperature variations on the the Row Hammer PUF and briefly discuss potential statistical relationships between the cells used to implement it. As our results prove, the Row Hammer PUF could potentially provide an adequate level of security for Commercial Off-The-Shelf (COTS) devices, if its dependency on temperature is mitigated, and, may therefore, be commercially adopted in the near future.

Improvement of Image Mosaic Algorithm Based on SURF

2013 ◽  
Vol 427-429 ◽  
pp. 1625-1630
Author(s):  
Xu Lin Long ◽  
Qiang Chen ◽  
Jun Wei Bao
Keyword(s):  
Coordinate System ◽  
Random Sample ◽  
Clustering Algorithm ◽  
Feature Matching ◽  
Consensus Method ◽  
Image Mosaic ◽  
Matching Accuracy ◽  
Speeded Up Robust Features ◽  
Random Sample Consensus ◽  
Improved Algorithm

The present study concerns about feature matching in image mosaic. In order to solve the problems of low accuracy and poor applicability in the traditional speeded up robust features algorithm, this paper presents an improved algorithm. Clustering algorithm based on density instead of random sample consensus method is used to eliminate mismatching pairs. The initial matching pairs are mapped onto a plane coordinate system, which can be regarded as points, by calculating the density of each point to extract the final matching pairs. The results show that this algorithm overcomes the limitations of the traditional speeded up robust features mosaic method, improving the matching accuracy and speed, and the mosaic effect. It has certain theoretical and practical value.

scholarly journals SOC program for dust continuum radiative transfer

2019 ◽  
Vol 622 ◽  
pp. A79 ◽  
Author(s):  
Mika Juvela
Keyword(s):  
Monte Carlo ◽  
Radiative Transfer ◽  
Convergence Rates ◽  
Dust Emission ◽  
Interstellar Clouds ◽  
Physical Conditions ◽  
Cloud Models ◽  
Order Of Magnitude ◽  
Speed Up ◽  
Run Time

Context. Thermal dust emission carries information on physical conditions and dust properties in many astronomical sources. Because observations represent a sum of emission along the line of sight, their interpretation often requires radiative transfer (RT) modelling. Aims. We describe a new RT program, SOC, for computations of dust emission, and examine its performance in simulations of interstellar clouds with external and internal heating. Methods. SOC implements the Monte Carlo RT method as a parallel program for shared-memory computers. It can be used to study dust extinction, scattering, and emission. We tested SOC with realistic cloud models and examined the convergence and noise of the dust-temperature estimates and of the resulting surface-brightness maps. Results. SOC has been demonstrated to produce accurate estimates for dust scattering and for thermal dust emission. It performs well with both CPUs and GPUs, the latter providing a speed-up of processing time by up to an order of magnitude. In the test cases, accelerated lambda iterations (ALIs) improved the convergence rates but was also sensitive to Monte Carlo noise. Run-time refinement of the hierarchical-grid models did not help in reducing the run times required for a given accuracy of solution. The use of a reference field, without ALI, works more robustly, and also allows the run time to be optimised if the number of photon packages is increased only as the iterations progress. Conclusions. The use of GPUs in RT computations should be investigated further.

scholarly journals A JOINT EFFORT OF SPEEDED-UP ROBUST FEATURES ALGORITHM AND A DISPARITY-BASED MODEL FOR 3D INDOOR MAPPING USING RGB-D DATA

2018 ◽  
Vol 24 (3) ◽  
pp. 351-366
Author(s):  
Marcos Aurélio Basso ◽  
Daniel Rodrigues dos Santos
Keyword(s):  
Indoor Environments ◽  
3D Mapping ◽  
Joint Effort ◽  
Plane Model ◽  
Loop Closure ◽  
Speeded Up Robust Features ◽  
Speed Up ◽  
Coarse To Fine ◽  
Fine Registration ◽  
Different Levels

Abstract In this paper, we present a method for 3D mapping of indoor environments using RGB-D data. The contribution of our proposed method is two-fold. First, our method exploits a joint effort of the speed-up robust features (SURF) algorithm and a disparity-to-plane model for a coarse-to-fine registration procedure. Once the coarse-to-fine registration task accumulates errors, the same features can appear in two different locations of the map. This is known as the loop closure problem. Then, the variance-covariance matrix that describes the uncertainty of transformation parameters (3D rotation and 3D translation) for view-based loop closure detection followed by a graph-based optimization are proposed to achieve a 3D consistent indoor map. To demonstrate and evaluate the effectiveness of the proposed method, experimental datasets obtained in three indoor environments with different levels of details are used. The experimental results shown that the proposed framework can create 3D indoor maps with an error of 11,97 cm into object space that corresponds to a positional imprecision around 1,5% at the distance of 9 m travelled by sensor.

scholarly journals A Vision based Indian Traffic Sign Classification

2020 ◽  
Vol 8 (6) ◽  
pp. 3132-3141
Keyword(s):  
Support Vector ◽  
Svm Classifier ◽  
Traffic Sign ◽  
Illumination Variation ◽  
Svm Algorithm ◽  
Speed Up ◽  
Surf Features ◽  
Overall Performance ◽  
Whisker Plot ◽  
Traffic Sign Classification

In this paper, an algorithm is proposed to classify the Indian traffic sign as mandatory cautionary and informatory class. In order to complete the task, system extracted the speed up robust features (SURF) from the Indian traffic sign data, and exploited these features to train support vector machine (SVM) algorithm. Combination of SURF features and SVM classifier makes system robust for scale variation, rotation, translation and illumination variation as well as generalization is achieved. Dimension of features have been reduced by choosing a sub set of features. Whisker and box plot visualization utilized to understand the features data. Whisker plot visualization concluded about the range, skewness, median and outliers of feature data therefore, it makes the system capable to keep good features and back out from irrelevant features. Feature refinement reduces the computational complexity. The results evaluated narrate that the overall performance of proposed algorithm is efficient.

scholarly journals UAV AND LIDAR IMAGE REGISTRATION: A SURF-BASED APPROACH FOR GROUND CONTROL POINTS SELECTION

2019 ◽  
Vol XLII-2/W13 ◽  
pp. 413-418 ◽  
Author(s):  
B. Kalantar ◽  
N. Ueda ◽  
H. A. H. Al-Najjar ◽  
H. Moayedi ◽  
A. A. Halin ◽  
...  
Keyword(s):  
Image Registration ◽  
Water Resource Management ◽  
Feature Matching ◽  
Image Data ◽  
Ground Control ◽  
Control Points ◽  
Speeded Up Robust Features ◽  
Ground Control Points ◽  
Edge Detectors ◽  
Edge Features

<p><strong>Abstract.</strong> Multisource remote sensing image data provides synthesized information to support many applications including land cover mapping, urban planning, water resource management, and GIS modelling. Effectively utilizing such images however requires proper image registration, which in turn highly relies on accurate ground control points (GCP) selection. This study evaluates the performance of the interest point descriptor SURF (Speeded-Up Robust Features) for GCPs selection from UAV and LiDAR images. The main motivation for using SURF is due to it being invariant to scaling, blur and illumination, and partially invariant to rotation and view point changes. We also consider features generated by the Sobel and Canny edge detectors as complements to potentially increase the accuracy of feature matching between the UAV and LiDAR images. From our experiments, the red channel (Band-3) produces the most accurate and practical results in terms of registration, while adding the edge features seems to produce lacklustre results.</p>

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