Theoretical Analysis of Image Processing Using Parameter-Tuning Stochastic Resonance Technique

2006 ◽  
Author(s):  
Bohou Xu ◽  
Xingxing Wu ◽  
Zhong-Ping Jiang ◽  
Daniel W. Repperger
Keyword(s):  
Image Processing ◽  
Theoretical Analysis ◽  
Stochastic Resonance ◽  
Parameter Tuning ◽  
Resonance Technique

scholarly journals InstantDL: an easy-to-use deep learning pipeline for image segmentation and classification

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Dominik Jens Elias Waibel ◽  
Sayedali Shetab Boushehri ◽  
Carsten Marr
Keyword(s):  
Image Processing ◽  
Deep Learning ◽  
Specific Problem ◽  
State Of The Art ◽  
Image Data ◽  
Semantic Segmentation ◽  
Parameter Tuning ◽  
Cellular Processes ◽  
Minimal Effort ◽  
Instance Segmentation

Abstract Background Deep learning contributes to uncovering molecular and cellular processes with highly performant algorithms. Convolutional neural networks have become the state-of-the-art tool to provide accurate and fast image data processing. However, published algorithms mostly solve only one specific problem and they typically require a considerable coding effort and machine learning background for their application. Results We have thus developed InstantDL, a deep learning pipeline for four common image processing tasks: semantic segmentation, instance segmentation, pixel-wise regression and classification. InstantDL enables researchers with a basic computational background to apply debugged and benchmarked state-of-the-art deep learning algorithms to their own data with minimal effort. To make the pipeline robust, we have automated and standardized workflows and extensively tested it in different scenarios. Moreover, it allows assessing the uncertainty of predictions. We have benchmarked InstantDL on seven publicly available datasets achieving competitive performance without any parameter tuning. For customization of the pipeline to specific tasks, all code is easily accessible and well documented. Conclusions With InstantDL, we hope to empower biomedical researchers to conduct reproducible image processing with a convenient and easy-to-use pipeline.

TUNING THE NOISE IN MAGNETIC RESONANCE IMAGING TO MAXIMIZE NONLINEAR INFORMATION TRANSMISSION

2013 ◽  
Vol 12 (01) ◽  
pp. 1350005 ◽  
Author(s):  
AGNÈS DELAHAIES ◽  
FRANÇOIS CHAPEAU-BLONDEAU ◽  
DAVID ROUSSEAU ◽  
FLORENCE FRANCONI
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Theoretical Analysis ◽  
Stochastic Resonance ◽  
Post Processing ◽  
Resonance Imaging ◽  
Noise Effect ◽  
Signal Noise ◽  
Noise Coupling ◽  
Magnetic Resonance Imaging Mri

We demonstrate a new instance of useful-noise effect or stochastic resonance, occurring in magnetic resonance imaging (MRI). Based on the physics of signal–noise coupling specific to MRI, we establish the possibility of regimes where nonlinear post-processing can benefit from an increase in the level of the noise present in the MRI apparatus. The validation is obtained by both theoretical analysis and experimental observations. We especially show that the beneficial tuning of the noise can be practically achieved by controlling the bandwidth of the sampling receiver of the MRI apparatus. These results constitute a nontrivial extension of stochastic resonance in the domain of images, arising here with a signal–noise coupling in MRI which is distinct from the purely additive or multiplicative couplings previously investigated in the framework of useful-noise effect.

scholarly journals Learning Adaptive Parameter Tuning for Image Processing

2018 ◽  
Vol 2018 (13) ◽  
pp. 196-1-196-8 ◽  
Author(s):  
Jingming Dong ◽  
Iuri Frosio ◽  
Jan Kautz
Keyword(s):  
Image Processing ◽  
Parameter Tuning ◽  
Adaptive Parameter

Structured Sign for Guidance of Mobile Robot

1991 ◽  
Vol 3 (5) ◽  
pp. 379-386
Author(s):  
Hesin Sai ◽  
◽  
Yoshikuni Okawa
Keyword(s):  
Image Processing ◽  
Theoretical Analysis ◽  
Mobile Robot ◽  
Mobile Robots ◽  
Personal Computer ◽  
The Other ◽  
Guidance System ◽  
Fluorescent Light ◽  
Trajectory Following ◽  
Good Coincidence

As part of a guidance system for mobile robots operating on a wide and flat floor, such as an ordinary factory or a gymnasium, we have proposed a special-purpose sign. It consists of a cylinder, with four slits, and a fluorescent light, which is placed on the axis of the cylinder. Two of the slits are parallel to each other, and the other two are angled. A robot obtains an image of the sign with a TV camera. After thresholding, we have four bright sets of pixels which correspond to the four slits of the cylinder. We compute by measuring the relative distances between the four points, the distance and the angle to the direction of the sign can be computed using simple geometrical equations. Using a personal computer with an image processing capability, we have investigated the accuracy of the proposed position identification method and compared the experimental results against the theoretical analysis of measured error. The data shows good coincidence between the analysis and the experiments. Finally, we have built a movable robot, which has three microprocessors and a TV camera, and performed several control experiments for trajectory following.

Parameter Tuning in the Application of Stochastic Resonance to Redundant Sensor Systems

2015 ◽  
Vol 27 (3) ◽  
pp. 251-258
Author(s):  
Nagisa Koyama ◽  
◽  
Shuhei Ikemoto ◽  
Koh Hosoda
Keyword(s):  
Stochastic Resonance ◽  
Random Noise ◽  
Parameter Tuning ◽  
Underlying Mechanism ◽  
Optimization Method ◽  
Tactile Sensing ◽  
Engineering Research ◽  
Sensing System ◽  
Actual Application ◽  
Higher Sensitivity

<div class=""abs_img""> <img src=""[disp_template_path]/JRM/abst-image/00270003/04.jpg"" width=""340"" />Basic concept of proposed method</div> Stochastic resonance (SR) is a phenomenon by which the addition of random noise improves the detection of weak signals. Thus far, this phenomenon has been extensively studied with the aim of improving sensor sensitivity in various fields of engineering research. However, the possibility of actual application of SR has not been explored because the target signal has to be known in order to confirm the occurrence of SR. In this paper, we propose an optimization method for making SR usable in engineering applications. The underlying mechanism of the proposed method is investigated using information theory and numerical simulation. We developed a tactile sensing system based on the simulation results. The proposed method is applied to this system in order to optimize its parameters for exploiting SR. Results of the experiment show that the developed tactile sensing system successfully achieved higher sensitivity than a conventional system.

Classification-Based Character Segmentation of Image

2014 ◽  
Vol 519-520 ◽  
pp. 572-576
Author(s):  
Yuan Chun Hu ◽  
Jian Sun ◽  
Wei Liu
Keyword(s):  
Image Processing ◽  
Cross Validation ◽  
Parameter Tuning ◽  
Training Model ◽  
Character Segmentation ◽  
Basic Training ◽  
Image Transformation ◽  
Boundary Area ◽  
Comparison Results ◽  
Simple Technology

In traditional way, the segmentation of image is conducted by simple technology of image processing, which cannot be operated automatically. In this paper, we present a kind of classification method to find the boundary area to segment character image. Referring to sample points and sample areas, the essential segmentation information is extracted. By merging different formats of image transformation, including rotation, erosion and dilation, more features are used to train and test the segmentation model. Parameter tuning is also proposed to optimize the model for promotion. By the means of cross validation, the basic training model and parameter tuning are integrated in iteration way. The comparison results show that the best precision and recall can up to 97.84% in precision and 94.09% in recall.

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