scholarly journals Image Processing Techniques Based Crowd Size Estimation

2020 ◽  
Vol 12 (3) ◽  
pp. 407-414
Author(s):  
Mohanad Abdulhamid ◽  
◽  
Lwanga Wanjira ◽  
Keyword(s):  
Image Processing ◽  
Theoretical Foundation ◽  
Visual Basic ◽  
Size Estimation ◽  
Image Processing Techniques ◽  
Development Tools ◽  
Image Processing Algorithms ◽  
Processing Algorithms ◽  
High Level ◽  
Processing Techniques

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.

Method for Estimating the Manual Nesting Process Efficiency for Profiling Machines, Based upon Image Processing Techniques

2015 ◽  
Vol 808 ◽  
pp. 86-91
Author(s):  
Radu Eugen Breaz ◽  
Octavian Bologa ◽  
Sever Gabriel Racz
Keyword(s):  
Image Processing ◽  
Software Package ◽  
Process Efficiency ◽  
Matlab Software ◽  
Image Processing Techniques ◽  
Graphic File ◽  
Image Processing Algorithms ◽  
Processing Algorithms ◽  
Processing Techniques

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.

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
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.

scholarly journals An Exploration of the Image Processing Techniques for the Detection of Leukemia

2018 ◽  
Vol 7 (2) ◽  
pp. 96-99
Author(s):  
A. Premnath ◽  
V. S. Meenakshi
Keyword(s):  
Image Processing ◽  
Bone Marrow ◽  
Blood Cell ◽  
Diagnostic Method ◽  
Cell Classification ◽  
Image Processing Techniques ◽  
Image Processing Algorithms ◽  
Processing Algorithms ◽  
Processing Techniques

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.

Development of a Hybrid Technique Utilising Image Processing and Fuzzy Logic for Dynamically Detecting a Movable Colour Object

2013 ◽  
Vol 389 ◽  
pp. 734-739
Author(s):  
Ayman Abbas ◽  
Khaled El-Geneidy
Keyword(s):  
Image Processing ◽  
Hybrid System ◽  
Mobile Object ◽  
Hybrid Technique ◽  
Autonomous Mobile Robot ◽  
Complex Environment ◽  
Image Processing Techniques ◽  
Image Processing Algorithms ◽  
Processing Algorithms ◽  
Processing Techniques

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.

Image Enhancement Techniques Using Particle Swarm Optimization Technique

2015 ◽  
pp. 327-347 ◽  
Author(s):  
V. Santhi ◽  
B. K. Tripathy
Keyword(s):  
Image Processing ◽  
Pattern Recognition ◽  
Particle Swarm Optimization ◽  
Real Time ◽  
Quality Enhancement ◽  
Swarm Optimization ◽  
Image Processing Techniques ◽  
Real Time Applications ◽  
High Level ◽  
Processing Techniques

The image quality enhancement process is considered as one of the basic requirement for high-level image processing techniques that demand good quality in images. High-level image processing techniques include feature extraction, morphological processing, pattern recognition, automation engineering, and many more. Many classical enhancement methods are available for enhancing the quality of images and they can be carried out either in spatial domain or in frequency domain. But in real time applications, the quality enhancement process carried out by classical approaches may not serve the purpose. It is required to combine the concept of computational intelligence with the classical approaches to meet the requirements of real-time applications. In recent days, Particle Swarm Optimization (PSO) technique is considered one of the new approaches in optimization techniques and it is used extensively in image processing and pattern recognition applications. In this chapter, image enhancement is considered an optimization problem, and different methods to solve it through PSO are discussed in detail.

Image Enhancement Techniques Using Particle Swarm Optimization Technique

2015 ◽  
pp. 860-878
Author(s):  
V. Santhi ◽  
B. K. Tripathy
Keyword(s):  
Image Processing ◽  
Pattern Recognition ◽  
Particle Swarm Optimization ◽  
Real Time ◽  
Quality Enhancement ◽  
Swarm Optimization ◽  
Image Processing Techniques ◽  
Real Time Applications ◽  
High Level ◽  
Processing Techniques

The image quality enhancement process is considered as one of the basic requirement for high-level image processing techniques that demand good quality in images. High-level image processing techniques include feature extraction, morphological processing, pattern recognition, automation engineering, and many more. Many classical enhancement methods are available for enhancing the quality of images and they can be carried out either in spatial domain or in frequency domain. But in real time applications, the quality enhancement process carried out by classical approaches may not serve the purpose. It is required to combine the concept of computational intelligence with the classical approaches to meet the requirements of real-time applications. In recent days, Particle Swarm Optimization (PSO) technique is considered one of the new approaches in optimization techniques and it is used extensively in image processing and pattern recognition applications. In this chapter, image enhancement is considered an optimization problem, and different methods to solve it through PSO are discussed in detail.

Application of Image Processing and Fuzzy Logic to Mobile Robots Providing Assistance to Fire Fighters

2013 ◽  
Vol 389 ◽  
pp. 740-746
Author(s):  
Ayman Abbas ◽  
Khaled El-Geneidy
Keyword(s):  
Image Processing ◽  
Feature Detection ◽  
Robot Vision ◽  
Region Of Interest ◽  
Influential Factors ◽  
Fire Fighting ◽  
Fire Fighters ◽  
Image Processing Techniques ◽  
Image Processing Algorithms ◽  
Processing Techniques

The motive behind this research project is to devise a method for overcoming some of the challenges faced by fire fighters in Egypt while accomplishing their duties. This is achieved by utilizing robot vision technology as one of the approaches used for task automation. Based on a study of different methods of automation in human tracking and fire fighting applications, image processing techniques with the highest potential in a fire fighting environment were identified. A system has been developed which fusses the selected image processing algorithms with fuzzified readings from distance sensors, to extract the major blue areas in acquired images that is more likely to correspond to the uniform worn by fire fighters in Egypt. Subsequently the extracted blue area is used to identify a region of interest within the image in order to reduce the computations. The feature detection process constrains its search for a feature found on the back of the target fire fighter to the identified region of interest. Based on the location and area of this feature, the system will calculate the required velocity components to control the motion of the robot and the camera pan and tilt mechanism, in order to continue tracking the target along its path. The system has been validated by conducting an experiment which simulates the key influential factors in a fire fighting environment.

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