Bubble Image Processing Algorithm and Application in Vacuum Casting Equipment

Bubbles in the manufacturing process are common. The bubbles often lead to the decrease of the product’s surface quality and internal performance. This paper summarized the published researches and applications of the detection and processing for bubble images, of which the advantages and disadvantages were also presented. Based on the above mentioned results, this paper then proposed a new bubble image processing algorithm for vacuum casting process, in which the characteristics of the bubbles in vacuum casting process and the problems possibly caused in detail were analyzed. According to the characteristics of bubbles in vacuum casting process, an image processing algorithms was designed using Matlab. The simulation result showed the efficiency of the proposed algorithm.

Download Full-text

Realization of Image Processing Algorithm based on FPGA

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.c5338.098319 ◽

2019 ◽

Vol 8 (3) ◽

pp. 2882-2885

Keyword(s):

Image Processing ◽

Processing Algorithm ◽

Model Design ◽

Image Processing Algorithm ◽

Matlab Simulink ◽

System Generator ◽

Image Processing Algorithms ◽

Processing Algorithms ◽

Xilinx System Generator

This paper proposes the use of Xilinx System Generator for image processing. Several categories of algorithms are assisted by necessary libraries in Xilinx System Generator. This work integrates Matlab Simulink environment. The image processing algorithms are implemented by design approaches based on model design. The results are verified by hardware co-simulation. The system generator blocks are used for various algorithms of image processing for image negatives, RGB to grayscale, dilation, etc

Download Full-text

Development of Digital Image-Processing Algorithm to Compute Unified Crack Index for Salt Lake City

Transportation Research Record Journal of the Transportation Research Board ◽

10.1177/0361198196152600118 ◽

1996 ◽

Vol 1526 (1) ◽

pp. 142-148 ◽

Cited By ~ 3

Author(s):

Siriphan Jitprasithsiri ◽

Hosin Lee ◽

Robert G. Sorcic ◽

Richard Johnston

Keyword(s):

Image Processing ◽

Digital Images ◽

Salt Lake ◽

Salt Lake City ◽

Processing Algorithm ◽

Image Processing Algorithm ◽

Crack Index ◽

Image Processing Algorithms ◽

Processing Algorithms ◽

Lake City

This paper presents the recent efforts in developing an image processing algorithm for computing a unified pavement crack index for Salt Lake City. The pavement surface images were collected using a digital camera mounted on a van. Each image covers a pavement area of 2.13 m (7 ft) × 1.52 m (5 ft), taken at every 30-m (100-ft) station. The digital images were then transferred onto a 1-gigabyte hard disk from a set of memory cards each of which can store 21 digital images. Approximately 1,500 images are then transferred from the hard disk to a compact disc. The image-processing algorithm, based on a variable thresholding technique, was developed on a personal computer to automatically process pavement images. The image is divided into 140 smaller tiles, each tile consisting of 40 × 40 pixels. To measure the amount of cracking, a variable threshold value is computed based on the average gray value of each tile. The program then automatically counts the number of cracked tiles and computes a unified crack index for each pavement image. The crack indexes computed from the image-processing algorithms are compared against the manual rating procedure in this paper. The image-processing algorithms were applied to process more than 450 surveyed miles of Salt Lake City street network.

Download Full-text

Stress Distribution Analysis on Hyperspectral Corn Leaf Images for Improved Phenotyping Quality

Sensors ◽

10.3390/s20133659 ◽

2020 ◽

Vol 20 (13) ◽

pp. 3659

Author(s):

Dongdong Ma ◽

Liangju Wang ◽

Libo Zhang ◽

Zhihang Song ◽

Tanzeel U. Rehman ◽

...

Keyword(s):

Image Processing ◽

Distribution Patterns ◽

Plant Phenotyping ◽

Distribution Analysis ◽

Image Processing Algorithm ◽

Plant Image ◽

Image Processing Algorithms ◽

Processing Algorithms ◽

Nitrogen Treatments

High-throughput imaging technologies have been developing rapidly for agricultural plant phenotyping purposes. With most of the current crop plant image processing algorithms, the plant canopy pixels are segmented from the images, and the averaged spectrum across the whole canopy is calculated in order to predict the plant’s physiological features. However, the nutrients and stress levels vary significantly across the canopy. For example, it is common to have several times of difference among Soil Plant Analysis Development (SPAD) chlorophyll meter readings of chlorophyll content at different positions on the same leaf. The current plant image processing algorithms cannot provide satisfactory plant measurement quality, as the averaged color cannot characterize the different leaf parts. Meanwhile, the nutrients and stress distribution patterns contain unique features which might provide valuable signals for phenotyping. There is great potential to develop a finer level of image processing algorithm which analyzes the nutrients and stress distributions across the leaf for improved quality of phenotyping measurements. In this paper, a new leaf image processing algorithm based on Random Forest and leaf region rescaling was developed in order to analyze the distribution patterns on the corn leaf. The normalized difference vegetation index (NDVI) was used as an example to demonstrate the improvements of the new algorithm in differentiating between different nitrogen stress levels. With the Random Forest method integrated into the algorithm, the distribution patterns along the corn leaf’s mid-rib direction were successfully modeled and utilized for improved phenotyping quality. The algorithm was tested in a field corn plant phenotyping assay with different genotypes and nitrogen treatments. Compared with the traditional image processing algorithms which average the NDVI (for example) throughout the whole leaf, the new algorithm more clearly differentiates the leaves from different nitrogen treatments and genotypes. We expect that, besides NDVI, the new distribution analysis algorithm could improve the quality of other plant feature measurements in similar ways.

Download Full-text

Comparative analysis of image processing techniques for obstacle avoidance and path deduction

Journal of Physics Conference Series ◽

10.1088/1742-6596/2070/1/012121 ◽

2021 ◽

Vol 2070 (1) ◽

pp. 012121

Author(s):

R Rajavarshini ◽

S Shruthi ◽

P Mahanth ◽

Boddu Chaitanya Kumar ◽

A Suyampulingam

Keyword(s):

Machine Learning ◽

Image Processing ◽

Obstacle Avoidance ◽

Transportation System ◽

Image Processing Algorithm ◽

Image Processing Techniques ◽

Haar Cascade ◽

Image Processing Algorithms ◽

Processing Algorithms ◽

Processing Techniques

Abstract The growing need for automation has a significant impact on our daily lives. Automating the essentials of our society like transportation system has plenty of applications like unmanned ground vehicles in military, wheel chair for disabled, domestic robots, etc., There are driving, braking, obstacle tackling etc., to a transportation system that can be automated. This paper particularly focuses on automating the obstacle avoidance which provides intelligence to the vehicle and ensures a high degree of safety and is performed using image processing algorithms. Edge based detection, image segmentation, and Machine Learning based method are the three image processing techniques used to detect and avoid obstacles. Haar cascade classifier is the machine learning method where Haar cascade analysis is performed for better accurate results with justifying graphs and parametric values obtained. A comparison of the three image processing algorithms is also tabulated considering obstacle size, colour, familiarities and environmental lightings and the best image processing algorithm is inferred.

Download Full-text

Convolutional neural network in the images colorization problem

Information Technology and Nanotechnology ◽

10.18287/1613-0073-2019-2416-340-353 ◽

2019 ◽

pp. 340-353

Author(s):

M V Bulygin ◽

M M Gayanova ◽

A M Vulfin ◽

A D Kirillova ◽

R Ch Gayanov

Keyword(s):

Neural Network ◽

Neural Networks ◽

Image Processing ◽

Processing System ◽

Black And White ◽

Convolutional Networks ◽

Advantages And Disadvantages ◽

The Neural Network ◽

Image Processing Algorithms ◽

Processing Algorithms

Object of the research are modern structures and architectures of neural networks for image processing. Goal of the work is improving the existing image processing algorithms based on the extraction and compression of features using neural networks using the colorization of black and white images as an example. The subject of the work is the algorithms of neural network image processing using heterogeneous convolutional networks in the colorization problem. The analysis of image processing algorithms with the help of neural networks is carried out, the structure of the neural network processing system for image colorization is developed, colorization algorithms are developed and implemented. To analyze the proposed algorithms, a computational experiment was conducted and conclusions were drawn about the advantages and disadvantages of each of the algorithms.

Download Full-text

The Image Processing Algorithm of Surface Topography Based on Laser Interference Measurement

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.719-720.959 ◽

2015 ◽

Vol 719-720 ◽

pp. 959-963 ◽

Cited By ~ 1

Author(s):

Lian Gen Yang ◽

Lang He ◽

Xuan Ze Wang

Keyword(s):

Image Processing ◽

Surface Topography ◽

Laser Interferometry ◽

Measurement Range ◽

Interference Microscopy ◽

Processing Algorithm ◽

Laser Interference ◽

Image Processing Algorithm ◽

Depth Measurement ◽

Processing Algorithms

In the measurement methods of microcosmic surface topography, laser interference microscopy with high precision and non-contact advantages, has been widely used. Based on the current processing algorithms of interference image, this paper studies principally the processing algorithms of single-wavelength laser interferometry and analyses the image processing algorithm of two-wavelength laser interferometry. On the basis of the original measurement precision, the two-wavelength measurement method can extend the depth measurement range and restore effectively the tested surface topography.

Download Full-text

Otoconia perfect/imperfect crystals

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100167949 ◽

1994 ◽

Vol 52 ◽

pp. 42-43

Author(s):

César D. Fermin ◽

Dale Martin

Keyword(s):

Image Processing ◽

Room Temperature ◽

Phosphotungstic Acid ◽

Gallus Domesticus ◽

Crystal Matrix ◽

Organic Templates ◽

Image Processing Algorithms ◽

Processing Algorithms ◽

Diffraction Patterns

Otoconia of higher vertebrates are interesting biological crystals that display the diffraction patterns of perfect crystals (e.g., calcite for birds and mammal) when intact, but fail to produce a regular crystallographic pattern when fixed. Image processing of the fixed crystal matrix, which resembles the organic templates of teeth and bone, failed to clarify a paradox of biomineralization described by Mann. Recently, we suggested that inner ear otoconia crystals contain growth plates that run in different directions, and that the arrangement of the plates may contribute to the turning angles seen at the hexagonal faces of the crystals.Using image processing algorithms described earlier, and Fourier Transform function (2FFT) of BioScan Optimas®, we evaluated the patterns in the packing of the otoconia fibrils of newly hatched chicks (Gallus domesticus) inner ears. Animals were fixed in situ by perfusion of 1% phosphotungstic acid (PTA) at room temperature through the left ventricle, after intraperitoneal Nembutal (35mg/Kg) deep anesthesia. Negatives were made with a Hitachi H-7100 TEM at 50K-400K magnifications. The negatives were then placed on a light box, where images were filtered and transferred to a 35 mm camera as described.

Download Full-text