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

2021 ◽  
Vol 2070 (1) ◽  
pp. 012121
Author(s):  
R Rajavarshini ◽  
S Shruthi ◽  
P Mahanth ◽  
Boddu Chaitanya Kumar ◽  
A Suyampulingam

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.

2015 ◽  
Vol 808 ◽  
pp. 86-91
Author(s):  
Radu Eugen Breaz ◽  
Octavian Bologa ◽  
Sever Gabriel Racz

The paper presents a method for estimating the efficiency of the manual nesting process. By using the graphic file generated during the nesting process and using image processing techniques, the method allows the user to calculate the percentage of material used for manufacturing the parts. The method combines a manual approach - the user has to select some specific points on the graphic file with the mouse, with some image processing algorithms form Matlab software package.


2020 ◽  
Vol 12 (3) ◽  
pp. 407-414
Author(s):  
Mohanad Abdulhamid ◽  
◽  
Lwanga Wanjira ◽  

Image processing algorithms are the basis for image computer analysis and machine Vision. Employing a theoretical foundation, image algebra, and powerful development tools, Visual C++, Visual Fortran, Visual Basic, and Visual Java, high-level and efficient computer vision techniques have been developed. This paper analyzes different image processing algorithms by classifying them in logical groups. In addition, specific methods are presented illustrating the application of such techniques to the real world images. In most cases more than one method is used. This allows a basis for comparison of different methods as advantageous features as well as negative characteristics of each technique is delineated. The main objective of this paper is to use image processing techniques to estimate the size of a crowd from a still photograph. The simulation results show that the different images have different efficiencies.


2018 ◽  
Vol 7 (2) ◽  
pp. 96-99
Author(s):  
A. Premnath ◽  
V. S. Meenakshi

In the pathological diagnostic method, categorization of blood cell has more essential to detect and analyze the disease. The complications that are connected with blood can be distributed only after the blood cell classification. The illness that begins with the bone marrow is the Leukemia. Therefore, it must be handled at the beginning step and proceeds to death if continuing untreated. This present research elucidates an investigation of diagnosing leukemia from microscopic blood image exhausting various image processing algorithms.


2013 ◽  
Vol 389 ◽  
pp. 734-739
Author(s):  
Ayman Abbas ◽  
Khaled El-Geneidy

The drive behind this research is to devise an autonomous method for dynamically detecting a movable coloured object within ambiguous environments. Based on a study of different methods of automation using image processing techniques, those with the highest potential of operating effectively and efficiently in a complex environment of varying light intensity were identified. A hybrid system has been developed which utilises the selected image processing algorithms and fuzzified readings from distance sensors, to extract an identifiable colour area in the acquired image frames. Subsequently the identified colour is used to recognise the blob area within the frame containing the moving object to be tracked. Based on the location and area of this blob, the hybrid system will dynamically identify the exact location of the target mobile object. This system mounted on an autonomous mobile robot constantly monitors the detected object.


Author(s):  
Muhammad Nur Aiman Shapiee ◽  
Muhammad Ar Rahim Ibrahim ◽  
Mohd Azraai Mohd Razman ◽  
Muhammad Amirul Abdullah ◽  
Rabiu Muazu Musa ◽  
...  

2010 ◽  
Vol 426-427 ◽  
pp. 260-264 ◽  
Author(s):  
Yuan Yuan Liu ◽  
Z.F. Chi ◽  
J.W. Wang ◽  
Hai Guang Zhang ◽  
Qing Xi Hu

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.


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