In order to realize the rapid and stable recognition and automatic tracking of various complex roads by the intelligent vehicles, this paper proposes imageprocessing and cascade Proportion Integration Differentiation...
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ISBN:
(纸本)9781665462198
In order to realize the rapid and stable recognition and automatic tracking of various complex roads by the intelligent vehicles, this paper proposes imageprocessing and cascade Proportion Integration Differentiation (PID) steering and speed control algorithms based on CMOS grayscale cameras in the context of the national college student intelligent vehicle competition. First, the grayscale image of the track is acquired by the grayscale camera. Then, the Otsu method is used to binarize the image, and the information of black boundary guide line is extracted. In order to improve the speed of the race, various track elements in the image are identified and classified, and the deviation between the actual centerline position and the ideal centerline position of the intelligent vehicle is calculated. Third, the discrete incremental cascade PID control algorithm is used to calculate the pulse width modulation (PWM) signal corresponding to the deviation. And the PWM signal is acted on the steering motor through the driving circuit, driving the intelligent vehicle to always drive along the middle road, so as to achieve the purpose of automatic tracking guidance. Experiments prove that the intelligent vehicle of this design can identify complex roads quickly and in a stable way, accurately complete automatic tracking, and obtain higher speed performance.
Preclinical research, clinical diagnosis, and treatment can all benefit from the information that a medical image can offer. Due to the increased usage of digital medical imaging, numerous researchers are actively cre...
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We propose the development of an Adaptive Vehicle Control (AVC) system using readily available components such as a Raspberry Pi microcontroller, a motor, a motor driver, and a Raspberry Pi camera. This system aims to...
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There are many challenges for a Deep Learning application project. Many problems need to be solved in an optimal way to improve the performance of the system. We can focus on a specific step or on the whole process. T...
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The phase information of multi-source hybrid images is prone to confusion during image propagation. Since traditional phase retrieval algorithms require a large number of assumptions that cannot be executed by obtaini...
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In country such as India due to monsoon and frequent climatic changes leads to natural disasters such as floods, drought, landslides, cyclones, forest fire, earthquake and so on. The post disaster badly impacts on hum...
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image segmentation in imageprocessing is the method by which images are divided into different segments. Many methods are available and one of the commonly used method are fuzzy-based systems. This paper ignites ligh...
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The paper presents a comparative analysis of nature inspired Artificial Bee Colony and Particle Swarm Optimization algorithms for the enhancement of images. The paper briefly reviews the swarm-based algorithms. It des...
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ISBN:
(纸本)9781665424615
The paper presents a comparative analysis of nature inspired Artificial Bee Colony and Particle Swarm Optimization algorithms for the enhancement of images. The paper briefly reviews the swarm-based algorithms. It describes the dynamics of ABC and Particle Swarm Optimization algorithm and mapping for image enhancement. The key purpose of imageprocessing is to extract essential features from images. During an imageprocessing operation, the input given is an image, and its output is an enhanced high-quality image based on some characteristics/features related to that image. The mapping of the ABC and PSO algorithms for image enhancement was implemented using the Matlab imageprocessing toolbox. For both algorithms ABC and PSO, the parameters of Contrast Enhancement alpha was set in the range of 0-255 and beta was set in the range of 1-109. Finally, the Comparative Analysis of ABC and PSO algorithms was done using the metrics of Entropy, MSE and PSNR values for the images. It is found that ABC yields a better performance as compared to PSO.
Visual informations is very rich to control and pursue mobile robots, like wheeled mobile robots, underwater robots and aerospace mobile robots, etc. These are considered as mobile robots that move in different spaces...
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ISBN:
(纸本)9781665427357
Visual informations is very rich to control and pursue mobile robots, like wheeled mobile robots, underwater robots and aerospace mobile robots, etc. These are considered as mobile robots that move in different spaces, their main problem in this case is navigation especially in unknown environments. In effect, this navigation is possible only by the localisation and orientation of the robot by using different embedded sensors. We have the camera which is a necessary sensor in this work, it is an embedded instrument that gives very rich visual information as a sensor complementing the other sensors. In this context, the recognition of objects from visual informations is a main function among the functions very useful in imageprocessing tasks due to its varied applications in the field of robotics. Based on the analysis of this informations and the determination of image features like color or shape or object primitives (points, lines, edges, etc.) or some other features. What interest us in this paper, various feature extraction techniques and classification of point and edge detection are discussed which are required for object recognition showing advantages and disadvantages of the selected algorithms. So, points and edge detection refers to the process of identifying and detecting sharp discontinuities in an image. In this work, we try to develop a novel algorithm using the work on the existing to create a novel detector.
Multimodal biometric systems unite data from multiple biometric sources to surmount the limitations of individual systems. This paper presents an efficient algorithm for extracting details based on the image represent...
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ISBN:
(数字)9798350309249
ISBN:
(纸本)9798350309256
Multimodal biometric systems unite data from multiple biometric sources to surmount the limitations of individual systems. This paper presents an efficient algorithm for extracting details based on the image representation of fingerprints. Additionally, we conduct an in-depth investigation of T-Norm algorithms and offer a comprehensive score-level merging analysis. This analysis combines matching outcomes derived from the left and right fingerprints using the Hammacher, Schweizer-Sklar, Dombi, and Yager algorithms. The latter approaches are compared based on the receiver operating characteristic curve. The experimental evaluation conducted on the NIST fingerprint database validates the efficacy of fusion at the score level. Furthermore, it demonstrates the effectiveness of the proposed algorithm when compared to other T-Norms.
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