In this paper, we propose a new method to improve the image registration accuracy in feedforward neural networks (FNN) based scheme. In the proposed method, Bayesian regularization is applied to improve the generaliza...
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India has a large population of spinach eaters. Despite this fact most people and young generation have difficulty in distinguishing the spinach species because of the structure similarity of many plant species. So, a...
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Independent Component Analysis (ICA) feature extraction is an efficient sparse coding method for noise suppression. However, single channel signal can not be directly applied in ICA feature extraction. In this paper, ...
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In view of the wide variety of plants on the earth, the plant species identification is particularly necessary to protect and preserve biodiversity. In this work, we propose a plant image classification method based o...
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By introducing the velocity difference between the preceding car and the car before the preceding one into the optimal velocity model (OVM), we present an extended dynamical model which takes into account the next-nea...
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In the study of object recognition, image texture segmentation has being a hot and difficult aspect in computer vision. Feature extraction and texture segmentation algorithm are two key steps in texture segmentation. ...
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image quality evaluation is becoming essential in many imageprocessing problems. This paper proposes a new image quality evaluation approach based on decision fusion method of canonical correlation analysis (CCA). By...
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In recent years, different Artificial Intelligence methods have been applied to pulsar search, such as Artificial Neural Network method, PEACE Sorting Algorithm, Real-time Classification method. In this paper, Weighti...
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We propose a new algorithm to find minimal rough set reducts by using Particle Swarm Optimization (PSO). Like Genetic Algorithm, PSO is also a type of evolutionary algorithm. But compared with GA, PSO does not need co...
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Huge image databases require the automatic analysis of image content in order to retrieve information. Especially the detection and localization of visual object class members is an important issue. In this work, we d...
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ISBN:
(纸本)9784901122078
Huge image databases require the automatic analysis of image content in order to retrieve information. Especially the detection and localization of visual object class members is an important issue. In this work, we deal with the localization of visual object class members in a patch based object recognition framework. In particular, we show how not only the location and scale of an object can be determined, but also the orientation, a parameter typically neglected in current localization systems. Our method uses features computed at Difference of Gaussian interest points and remembers the orientation of the local patches relative to the reference object. Using a general Hough transform like voting scheme, the position and orientation of query objects can be retrieved. Tests on two different leaf databases show the capabilities of the approach.
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