Image processing by interpolation using polyharmonic function and increase in processing speed

2010 ◽  
Vol 6 (S1) ◽  
pp. S1-S6
Author(s):  
Tadashi Kobayashi ◽  
Narihei Kawashima ◽  
Yoshihiro Ochiai
Keyword(s):  
Image Processing ◽  
Processing Speed ◽  
Polyharmonic Function

FPGA Implementation of Real-Time Adaptive Bidirectional Equalization for Histogram

2012 ◽  
Vol 461 ◽  
pp. 215-219
Author(s):  
Yu Qian Zhao ◽  
Zhi Gang Li
Keyword(s):  
Image Processing ◽  
Real Time ◽  
Processing Speed ◽  
Local Maximum ◽  
Histogram Equalization ◽  
Infrared Images ◽  
Single Frame ◽  
Processing Effects ◽  
Simulation Results ◽  
Enhancement Algorithm

According to the characteristics of infrared images, a contrast enhancement algorithm was presented. The principium of FPGA-based adaptive bidirectional plateau histogram equalization was given in this paper. The plateau value was obtained by finding local maximum and whole maximum in statistical histogram based on dimensional histogram statistic. Statistical histogram was modified by the plateau value and balanced in gray scale and gray spacing. Test data generated by single frame image, which was simulated by FPGA-based real-time adaptive bidirectional plateau histogram equalization. The simulation results indicates that the precept meet the requests well in both the image processing effects and processing speed

Fast Face Recognition on GPUs

2014 ◽  
Vol 905 ◽  
pp. 543-547
Author(s):  
Yi Lei ◽  
Xiao Ya Fan ◽  
Meng Zhang
Keyword(s):  
Image Processing ◽  
Principal Component Analysis ◽  
Pattern Recognition ◽  
Face Recognition ◽  
Processing Speed ◽  
Principal Component ◽  
Great Accuracy ◽  
Analysis Algorithm ◽  
Training Images ◽  
Principal Component Analysis Algorithm

Face recognition is popular in the field of pattern recognition and image processing. However, traditional recognition technologies spend too long there are a lot of images to be recognized or trained for great accuracy in the recognition. Parallel computing is an effective way to improve the processing speed. With the improvement of GPU performance, its widely applied in computing-concentrated data operations. This paper presents a study of performance speedup achieved by applying GPU for face recognition based on PCA (Principal Component Analysis) algorithm. We successfully accelerated the testing phase by 6868-folds compared to a sequential C implementation when it has 100 test images and 2400 training images.

The FPGA Design and Implementation of Pipeline Image Processing in the GPU System

2013 ◽  
Vol 380-384 ◽  
pp. 3807-3810
Author(s):  
Ke Nian Wang ◽  
Hui Min Du
Keyword(s):  
Image Processing ◽  
Processing Speed ◽  
Data Transmission ◽  
Pipeline Architecture ◽  
Fpga Design ◽  
Foam Extrusion ◽  
Design And Implementation ◽  
Transmission Channels ◽  
Processing Procedure

In the GPU system, pipeline image processing is facing the problem that a large amount of data to be processed, complicated processing procedure, more data transmission channels, and etc. All of these lead to low processing speed and large circuit area. This paper proposed a FPGA design of the pipeline image processing in GPU. The design has been implemented by foam extrusion pipeline architecture and validated on Xilinx Virtex XC6VLX550T FPGA. The results show that the consumption of resources is 390726.09 and the speed is 200MHz.

Design of Nut Machine Vision Inspection and Screening Systems Based on PC

2013 ◽  
Vol 712-715 ◽  
pp. 2733-2737
Author(s):  
Zhong An Yu ◽  
Chun Li Wang ◽  
Pei Yu Guo ◽  
Kong Kan
Keyword(s):  
Image Processing ◽  
Machine Vision ◽  
Processing Speed ◽  
High Reliability ◽  
Single Chip ◽  
The Core ◽  
Vision Inspection ◽  
System Use ◽  
Fast Processing ◽  
Machine Vision Inspection

This system use PC as the core of image analysis and processing, with the single chip processor as the control core execution, combining with machine vision image processing technology, using advanced image processing algorithms, to achieve separation of the nut, and through experiments to test the correctness of the algorithm. The system has the advantage of a fast processing speed and high reliability. It not only save the manpower cost, but also improve the efficiency of the nut sorting.

scholarly journals Using high-performance deep learning platform to accelerate object detection

2019 ◽  
pp. 354-360 ◽  
Author(s):  
S O Stepanenko ◽  
P Y Yakimov
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Image Processing ◽  
Processing Speed ◽  
High Performance ◽  
Object Classification ◽  
High Accuracy ◽  
Learning Platform ◽  
The Neural Network

Object classification with use of neural networks is extremely current today. YOLO is one of the most often used frameworks for object classification. It produces high accuracy but the processing speed is not high enough especially in conditions of limited performance of a computer. This article researches use of a framework called NVIDIA TensorRT to optimize YOLO with the aim of increasing the image processing speed. Saving efficiency and quality of the neural network work TensorRT allows us to increase the processing speed using an optimization of the architecture and an optimization of calculations on a GPU.

scholarly journals Improved Image Processing Techniques for User Immersion Problem Alleviation in Virtual Reality Environments

2020 ◽  
Vol 2 (2) ◽  
pp. 77-84
Author(s):  
Dr. Dhaya R.
Keyword(s):  
Image Processing ◽  
Virtual Reality ◽  
Processing Speed ◽  
Processing Technique ◽  
Image Processing Technique ◽  
Image Processing Techniques ◽  
Processing Techniques

The latest advertisements on the advancements of the virtual reality has paved way for diverse studies, in manifold fields that can benefit by utilizing the technologies of the virtual reality, not excluding the design, gaming and the simulated understanding. Yet whenever a virtual reality device conveys information in form of images with the assistance of the display that is positioned closer to the user’s eyes it faces problems like minimizing the speed of the process and degradation in the quality of images ending up in huge variations across the virtual realism and the realism causing user immersion problems. So to mitigate the immersion problems of the user because of the low quality of image and the minimization of processing speed in the virtual reality environments the paper puts forth an improved image processing technique to improvise the sharpness of the images in order to enhance quality of the images and heighten the processing speed.

Study on an Improved Median Filtering Algorithm

2014 ◽  
Vol 989-994 ◽  
pp. 2273-2277
Author(s):  
Heng Zhang ◽  
Min Gao ◽  
Hai Long Ren
Keyword(s):  
Image Processing ◽  
Processing Speed ◽  
Median Filter ◽  
Median Filtering ◽  
Matlab Simulation ◽  
Filtering Algorithm ◽  
Noise Point ◽  
Real Time Image ◽  
Linear Smoothing ◽  
Time Image

Median filter is a very effective method of non-linear smoothing filtering. However, the data ordering for traditional median filtering (TMF) is very time-consuming and hardly satisfy real-time image processing. This article proposes a kind of fast median filtering algorithm based on grey histogram, the filter seeks the median through the grey histogram of mask window, not the numeric sort, which decreases the comparison times. Moreover, the update of histogram by using the overlap of mask window increases the arithmetic processing speed. Meanwhile, in the proposed algorithm, the two-level self-adapting threshold comparison, with a higher precision of detection, is used to implement the inspection of noise point and improve the image quality and increase the signal-noise ratio by processing the noise point and non-noise point respectively. The experiments by matlab simulation can prove the availability of this algorithm.

scholarly journals An Improved Adaptive Weighted Mean Filtering Approach for Metallographic Image Processing

2021 ◽  
Vol 30 (1) ◽  
pp. 470-478
Author(s):  
Chonglei Shao ◽  
Preet Kaur ◽  
Rajeev Kumar
Keyword(s):  
Image Processing ◽  
Processing Speed ◽  
Noise Detection ◽  
Weighted Mean ◽  
Filtering Method ◽  
Large Window ◽  
Weight Calculation ◽  
Metallographic Images ◽  
Filtering Effect ◽  
Mean Filtering

Abstract Background As noise brings great error in the analysis of metallographic images, an adaptive weighted mean filtering method proposed to overcome the shortcomings of the standard mean filtering method. Methods The method used to detect the pulse noise points in the image, and then the modified mean method used to filter out the detected noise points. Patents on metallographic image processing have discussed for the development of the proposed methodology. Results It is shown that filter window can be filtered in comparison with the conventional 3×3, 5×5 and 7×7 filt window to reduce noise detection and reduce the complexity of the weight calculation. Conclusion It can be concluded that this method can better protect the details of the image, has better filtering effect than the standard mean filtering, and its processing speed is faster than the median filtering of the large window, which has profound significance for the edge detection and processing of the metallographic image.

Image Mosaicing Using Binary Edge Detection Algorithm in a Cloud-Computing Environment

2016 ◽  
Vol 11 (3) ◽  
pp. 1-14 ◽  
Author(s):  
Abdullah Alamareen ◽  
Omar Al-Jarrah ◽  
Inad A. Aljarrah
Keyword(s):  
Image Processing ◽  
Cloud Computing ◽  
Edge Detection ◽  
Processing Speed ◽  
Message Passing ◽  
Detection Algorithm ◽  
Computing Environment ◽  
Cloud Computing Environment ◽  
Edge Detection Algorithm ◽  
Binary Edge

Image Mosaicing is an image processing technique that arises from the need of having a more realistic view of the real world wider than the view captured by the lenses of the available cameras. In this paper, a sequence of images will be mosaiced using binary edge detection algorithm in a cloud-computing environment to improve processing speed and accuracy. The authors have used Platform as a Service (PaaS) to provide a number of nodes in the cloud to run the computational intensive image processing and stitching algorithms. This increased the processing speed as most of image processing algorithms deal with every single pixel in the image. Message Passing Interface (MPI) is used for message passing among the compute-nodes in the cloud and a MapReduce technique is used for image distribution and collection, where the root node is used as reducer and the others as mappers. After applying the algorithm on different sequence of images and different machines on JUST cloud, the authors have achieved high mosaicing accuracy, and the execution time has been improved when comparing it with sequential execution on the images.

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