scholarly journals A new fast efficient non-maximum suppression algorithm based on image segmentation

2020 ◽  
Vol 19 (2) ◽  
pp. 1062
Author(s):  
Oday Jasim Al-Furaiji ◽  
Nguyen Anh Tuan ◽  
Viktar Yurevich Tsviatkou
Keyword(s):  
Image Segmentation ◽  
Computational Complexity ◽  
Image Reconstruction ◽  
High Speed ◽  
Local Extremum ◽  
Local Maxima ◽  
Iterative Processing ◽  
Local Extrema ◽  
Single Pixel ◽  
High Computational Complexity

<span>In this paper, the problem of finding local extrema in grayscale images is considered. The known non-maximum suppression algorithms provide high speed, but only single-pixel extrema are extracted, skipping regions formed by multi-pixel extrema. Morphological algorithms allow to</span><span>extract all extrema but its maxima and minima are processed separately with high computational complexity by iterative processing based on image reconstruction using image morphological dilation and erosion. In this paper a new fast efficient non-maximum suppression algorithm based on image segmentation and border analysis is proposed. The proposed algorithm considers homogeneous areas, which are formed by multi-pixel extrema and are the local maxima or minima in relation to adjacent areas, eliminating iterative processing of non-extreme pixels and assigning label numbers to local extrema during their search. The proposed algorithm allowed to increase the accuracy of local extremum extraction in comparison with known non-maximum suppression algorithms and reduce the computational complexity and the use of RAM in comparison with the morphological algorithms.</span>

scholarly journals Block-segment search of local extrema of images based on analysis of brightnesses of related pixels and areas

2019 ◽  
pp. 4-9
Author(s):  
A. T. Nguyen ◽  
V. Yu. Tsviatkou
Keyword(s):  
Computational Complexity ◽  
Search Algorithm ◽  
Local Maximum ◽  
High Accuracy ◽  
Search Algorithms ◽  
Low Computational Complexity ◽  
Local Extrema ◽  
Single Pixel ◽  
Maximum Minimum ◽  
High Computational Complexity

The aim of the work is to develop an algorithm for extracting local extremes of images with low computational complexity and high accuracy. The known algorithms for block search for local extrema have low computational complexity, but only strict maxima and minima are distinguished without errors. The morphological search gives accurate results, highlighting the extreme areas formed by non-severe extremes, however, it has high computational complexity. The paper proposes a block-segment search algorithm for local extremums of images based on an analysis of the brightness of adjacent pixels and regions. The essence of the algorithm is to search for single-pixel local extremes and regions of uniform brightness, comparing the values of their boundary pixels with the values of the corresponding pixels of adjacent regions: the region is a local maximum (minimum) if the values of all its boundary pixels are larger (smaller) or equal to the values of all adjacent pixels. The developed algorithm, as well as the morphological search algorithm, allows detecting all single-pixel local extremes, as well as extreme areas, which exceeds the block search algorithms. At the same time, the developed algorithm in comparison with the morphological search algorithm requires much less time and RAM.

scholarly journals Accumulator-free Hough Transform for Sequence Collinear Points

2021 ◽  
Vol 10 (2) ◽  
pp. 74-83
Author(s):  
Rudi Kurniawan ◽  
Zahrul Fuadi ◽  
Ramzi Adriman
Keyword(s):  
Computational Complexity ◽  
Hough Transform ◽  
High Speed ◽  
Laser Scanner ◽  
Measurement Data ◽  
Low Complexity ◽  
Autonomous Mobile Robots ◽  
Straight Line ◽  
Good Agreement ◽  
High Computational Complexity

The perception, localization, and navigation of its environment are essential for autonomous mobile robots and vehicles. For that reason, a 2D Laser rangefinder sensor is used popularly in mobile robot applications to measure the origin of the robot to its surrounding objects. The measurement data generated by the sensor is transmitted to the controller, where the data is processed by one or multiple suitable algorithms in several steps to extract the desired information. Universal Hough Transform (UHT) is one of the appropriate and popular algorithms to extract the primitive geometry such as straight line, which later will be used in the further step of data processing. However, the UHT has high computational complexity and requires the so-called accumulator array, which is less suitable for real-time applications where a high speed and low complexity computation is highly demanded. In this study, an Accumulator-free Hough Transform (AfHT) is proposed to reduce the computational complexity and eliminate the need for the accumulator array. The proposed algorithm is validated using the measurement data from a 2D laser scanner and compared to the standard Hough Transform. As a result, the extracted value of AfHT shows a good agreement with that of UHT but with a significant reduction in the complexity of the computation and the need for computer memory.

scholarly journals Division of images into areas of local extrema with a monotonic change in pixel brightness

Doklady BGUIR ◽  
2021 ◽  
Vol 19 (4) ◽  
pp. 61-69
Author(s):  
A. T. Nguyen ◽  
V. Yu. Tsviatkou
Keyword(s):  
Local Maximum ◽  
Local Extremum ◽  
Maximum Increase ◽  
Low Contrast ◽  
Monotonic Change ◽  
Local Maxima ◽  
Average Value ◽  
Local Extrema ◽  
Segmentation Algorithms ◽  
Pixel Brightness

In this paper, the problem of segmentation of halftone images is considered, in which areas of local maxima and minima (extrema) are distinguished with a monotonic change in the brightness of pixels from local extrema to the boundaries of areas. To solve this problem, a mathematical model is proposed and a segmentation algorithm is developed on the basis of counter-wave growing of local extremum regions. The developed algorithm differs from the known segmentation algorithms by using a set of brightness thresholds (by the number of regions), varying by one in each cycle, starting from the values of local extrema, taking into account the increase or decrease in brightness to select adjacent pixels that are attached to the regions formed from these local extrema. The algorithm provides a greater deviation of pixel brightness from the average value within the region compared to known segmentation algorithms. This does not allow evaluating its efficiency using known indicators based on the variance of the brightness within the region. In this regard, estimates of the monotonicity of changes in the brightness of regions are proposed based on a) the shortest distances from each pixel of the region to the corresponding local extremum along the routes determined by the maximum increase (for the region of the local maximum) or decrease (for the region of the local minimum) the brightness of pixels and b) taking into account the number pixels that break the monotony of the segment brightness change. Using these estimates, it is shown that the proposed algorithm provides segmentation of artificial and natural grayscale images with a monotonic change in the brightness of pixels in the areas of local extrema. These properties allow us to consider the developed algorithm as a basis for the selection of texels, spots, low-contrast objects in images.

Multiresolution Bio-Medical Image Segmentation using Fuzzy C-Means Clustering

2019 ◽  
Vol 8 (4) ◽  
pp. 9548-9551
Keyword(s):  
Image Segmentation ◽  
Medical Image Segmentation ◽  
Experimental Result ◽  
Segmentation Method ◽  
Fuzzy C Means ◽  
Fuzzy C Means Clustering ◽  
Robust Image ◽  
Multiple Clusters ◽  
Single Pixel ◽  
Popular Image

Fuzzy c-means clustering is a popular image segmentation technique, in which a single pixel belongs to multiple clusters, with varying degree of membership. The main drawback of this method is it sensitive to noise. This method can be improved by incorporating multiresolution stationary wavelet analysis. In this paper we develop a robust image segmentation method using Fuzzy c-means clustering and wavelet transform. The experimental result shows that the proposed method is more accurate than the Fuzzy c-means clustering.

scholarly journals LiDAR Point Cloud Recognition and Visualization with Deep Learning for Overhead Contact Inspection

Sensors ◽  
2020 ◽  
Vol 20 (21) ◽  
pp. 6387 ◽  
Author(s):  
Xiaohan Tu ◽  
Cheng Xu ◽  
Siping Liu ◽  
Shuai Lin ◽  
Lipei Chen ◽  
...  
Keyword(s):  
Deep Learning ◽  
Computational Complexity ◽  
Point Cloud ◽  
High Speed ◽  
Point Cloud Data ◽  
Embedded Devices ◽  
Cloud Data ◽  
Manual Inspection ◽  
Order Of Magnitude ◽  
Supply Systems

As overhead contact (OC) is an essential part of power supply systems in high-speed railways, it is necessary to regularly inspect and repair abnormal OC components. Relative to manual inspection, applying LiDAR (light detection and ranging) to OC inspection can improve efficiency, accuracy, and safety, but it faces challenges to efficiently and effectively segment LiDAR point cloud data and identify catenary components. Recent deep learning-based recognition methods are rarely employed to recognize OC components, because they have high computational complexity, while their accuracy needs to be improved. To track these problems, we first propose a lightweight model, RobotNet, with depthwise and pointwise convolutions and an attention module to recognize the point cloud. Second, we optimize RobotNet to accelerate its recognition speed on embedded devices using an existing compilation tool. Third, we design software to facilitate the visualization of point cloud data. Our software can not only display a large amount of point cloud data, but also visualize the details of OC components. Extensive experiments demonstrate that RobotNet recognizes OC components more accurately and efficiently than others. The inference speed of the optimized RobotNet increases by an order of magnitude. RobotNet has lower computational complexity than other studies. The visualization results also show that our recognition method is effective.

scholarly journals A Novel Hybrid Precoding-Companding Technique for Peak-to-Average Power Ratio Reduction in 5G and beyond

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1410
Author(s):  
Mohamed Mounir ◽  
Mohamed B. El_Mashade ◽  
Salah Berra ◽  
Gurjot Singh Gaba ◽  
Mehedi Masud
Keyword(s):  
Computational Complexity ◽  
Orthogonal Frequency Division Multiplexing ◽  
High Speed ◽  
Average Power ◽  
Papr Reduction ◽  
Power Ratio ◽  
Error Vector Magnitude ◽  
Hybrid Precoding ◽  
High Power Amplifier ◽  
Reduction Techniques

Several high-speed wireless systems use Orthogonal Frequency Division Multiplexing (OFDM) due to its advantages. 5G has adopted OFDM and is expected to be considered beyond 5G (B5G). Meanwhile, OFDM has a high Peak-to-Average Power Ratio (PAPR) problem. Hybridization between two PAPR reduction techniques gains the two techniques’ advantages. Hybrid precoding-companding techniques are attractive as they require small computational complexity to achieve high PAPR reduction gain. Many precoding-companding techniques were introduced to increasing the PAPR reduction gain. However, reducing Bit Error Rate (BER) and out-of-band (OOB) radiation are more significant than increasing PAPR reduction gain. This paper proposes a new precoding-companding technique to better reduce the BER and OOB radiation than previous precoding-companding techniques. Results showed that the proposed technique outperforms all previous precoding-companding techniques in BER enhancement and OOB radiation reduction. The proposed technique reduces the Error Vector Magnitude (EVM) by 15 dB compared with 10 dB for the best previous technique. Additionally, the proposed technique increases high power amplifier efficiency (HPA) by 11.4%, while the best previous technique increased HPA efficiency by 9.8%. Moreover, our proposal achieves PAPR reduction gain better than the most known powerful PAPR reduction technique with a 99% reduction in required computational complexity.

Moth-Flame Optimization Algorithm Based Multilevel Thresholding for Image Segmentation

2017 ◽  
Vol 8 (4) ◽  
pp. 58-83 ◽  
Author(s):  
Abdul Kayom Md Khairuzzaman ◽  
Saurabh Chaudhury
Keyword(s):  
Image Segmentation ◽  
Computational Complexity ◽  
Signal To Noise Ratio ◽  
Fitness Function ◽  
Multilevel Thresholding ◽  
Bacterial Foraging Optimization ◽  
Kapur’S Entropy ◽  
Moth Flame Optimization Algorithm ◽  
Popular Image ◽  
Better Than

Multilevel thresholding is a popular image segmentation technique. However, computational complexity of multilevel thresholding increases very rapidly with increasing number of thresholds. Metaheuristic algorithms are applied to reduce computational complexity of multilevel thresholding. A new method of multilevel thresholding based on Moth-Flame Optimization (MFO) algorithm is proposed in this paper. The goodness of the thresholds is evaluated using Kapur's entropy or Otsu's between class variance function. The proposed method is tested on a set of benchmark test images and the performance is compared with PSO (Particle Swarm Optimization) and BFO (Bacterial Foraging Optimization) based methods. The results are analyzed objectively using the fitness function and the Peak Signal to Noise Ratio (PSNR) values. It is found that MFO based multilevel thresholding method performs better than the PSO and BFO based methods.

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