A Neuro-Fuzzy Approach for Compensating Color Backlight Images

Su, Mu-Chun; Yang, Yuan-Shao; Chen, Gwo-Dong

doi:10.1007/s11063-006-9002-0

Cited by 5 publications

(4 citation statements)

References 24 publications

(17 reference statements)

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“…The X-ray control system is not different in that respect. Some general examples of histogram-based compensation are given in: [2]] [3]. Instead of the above arithmetic averaging and highlight-excluding methods, we propose an alternative control signal norm such as geometric averaging:…”

Section: Improvement and Experimentsmentioning

confidence: 99%

Geometric averaging of X-ray signals in automatic exposure control

Snoeren

2010

2010 IEEE International Conference on Image Processing

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Improper dose control in X-ray cardio-vascular systems leads to a reduced Signal-to-Noise Ratio (SNR) in regions of interest of the X-ray image. We aim at reducing the influence of direct radiation, entering a measuring field for X-ray dose control in a Flat Detector which gives too bright areas (highlights) in the image. It is our desire to use a norm-like signal size that represents a minimal dose value while maximizing information transfer and thus image quality. In a dose control system, it is common practice to employ a special averaging technique for computing a representative signal level controlling the X-ray. We have found that the geometric averaging outperforms the existing techniques and significantly improves the image quality. Our approach reduces the highlight influence and guarantees an adequate Contrast-to-Noise ratio for decentered objects. We provide convincing experimental results showing a strongly improved image quality with respect to contrast and detail.Index Terms-X-ray, cone beam CT, dose control, signal size, image quality.

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Section: Improvement and Experimentsmentioning

confidence: 99%

Geometric averaging of X-ray signals in automatic exposure control

Snoeren

2010

2010 IEEE International Conference on Image Processing

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“…We name the proposed neuro-fuzzy system as the artificial immune system based neuro-fuzzy system (AISNFS). In fact, part of the properties of the early version of AISNFS has been presented in [19][20]. In this paper, we present a complete version of AISNFS and apply it in medical diagnosis and function approximation problems.…”

Section: The Immunity-based On-line Learning Neuro-fuzzy Systemsmentioning

confidence: 99%

A New Approach to Artificial Immune Systems and its Application in Constructing On-line Learning Neuro-Fuzzy Systems

Su¹,

Wang²,

Yang³

2008

TOAIJ

Self Cite

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In this paper, we present an on-line learning neuro-fuzzy system which was inspired by parts of the mechanisms in immune systems. It illustrates how an on-line learning neuro-fuzzy system can capture the basic elements of the immune system and exhibit some of its appealing properties. During the learning procedure, a neuro-fuzzy system can be incrementally constructed. We illustrate the potential of the on-line learning neuro-fuzzy system on several benchmark classification problems and function approximation problems.

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“…Machine learning is the response to this paradigm, several algorithms have arisen having as main objective; making computers learn as can be seen in [2], even they may have different particular objectives according to their taks. Machine learning algorithms have already demonstrated their competence at learning in different engineering and science problems [3][4][5]. However, there is still no algorithm that is more versatile and generalised, i.e., a "do-it-all" algorithm that can be used in several situations no matter the nature of the task itself.…”

Section: Introductionmentioning

confidence: 99%

CODA Algorithm: An Immune Algorithm for Reinforcement Learning Tasks

Rebollo¹,

Cruz²,

Molina³

2016

Recent Advances in Robotic Systems

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This document presents the design of an algorithm that takes on its basis: reinforcement learning, learning from demonstration and most importantly Artificial Immune Systems. The main advantage of this algorithm named CODA (Cognition from Data). Is; it can learn from limited data samples-that is given a single example and the algorithm will create its own knowledge. The algorithm imitates from the Natural Immune System the clonal procedure for obtaining a repertoire of antibodies from a single antigen. It also uses the self-organised memory in order to reduce searching time in the whole action-state space by searching in specific clusters. CODA algorithm is presented and explained in detail in order to understand how these three principles are used. The algorithm is explained with pseudocode, flowcharts and block diagrams. The clonal/mutation results are presented with a simple example. It can be seen graphically how new data that has a completely new probability distribution. Finally, the first application where CODA is used, a humanoid hand is presented. In this application the algorithm created affordable grasping postures from limited examples, creates its own knowledge and stores data in memory data in memory in order to recognise whether it has been on a similar situation.

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A Neuro-Fuzzy Approach for Compensating Color Backlight Images

Cited by 5 publications

References 24 publications

Geometric averaging of X-ray signals in automatic exposure control

Geometric averaging of X-ray signals in automatic exposure control

A New Approach to Artificial Immune Systems and its Application in Constructing On-line Learning Neuro-Fuzzy Systems

CODA Algorithm: An Immune Algorithm for Reinforcement Learning Tasks

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