An MLP for Binarizing Images of Old Manuscripts

Sari, Toufik; Kefali, Abderrahmane; Bahi, Halima

doi:10.1109/icfhr.2012.176

Cited by 5 publications

(2 citation statements)

References 8 publications

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“…In this study, we employed a modified version of the MLP network used by Sari and Kefari [ 49 ]. This network was trained on the principle of back propagation algorithm [ 50 ].…”

Section: Methodsmentioning

confidence: 99%

Machine Learning Methods for Automatic Segmentation of Images of Field- and Glasshouse-Based Plants for High-Throughput Phenotyping

Okyere

Cudjoe

Sadeghi‐Tehran

et al. 2023

Plants

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Image segmentation is a fundamental but critical step for achieving automated high- throughput phenotyping. While conventional segmentation methods perform well in homogenous environments, the performance decreases when used in more complex environments. This study aimed to develop a fast and robust neural-network-based segmentation tool to phenotype plants in both field and glasshouse environments in a high-throughput manner. Digital images of cowpea (from glasshouse) and wheat (from field) with different nutrient supplies across their full growth cycle were acquired. Image patches from 20 randomly selected images from the acquired dataset were transformed from their original RGB format to multiple color spaces. The pixels in the patches were annotated as foreground and background with a pixel having a feature vector of 24 color properties. A feature selection technique was applied to choose the sensitive features, which were used to train a multilayer perceptron network (MLP) and two other traditional machine learning models: support vector machines (SVMs) and random forest (RF). The performance of these models, together with two standard color-index segmentation techniques (excess green (ExG) and excess green–red (ExGR)), was compared. The proposed method outperformed the other methods in producing quality segmented images with over 98%-pixel classification accuracy. Regression models developed from the different segmentation methods to predict Soil Plant Analysis Development (SPAD) values of cowpea and wheat showed that images from the proposed MLP method produced models with high predictive power and accuracy comparably. This method will be an essential tool for the development of a data analysis pipeline for high-throughput plant phenotyping. The proposed technique is capable of learning from different environmental conditions, with a high level of robustness.

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“…In this study, we employed a modified version of the MLP network used by Sari and Kefari [ 49 ]. This network was trained on the principle of back propagation algorithm [ 50 ].…”

Section: Methodsmentioning

confidence: 99%

Machine Learning Methods for Automatic Segmentation of Images of Field- and Glasshouse-Based Plants for High-Throughput Phenotyping

Okyere

Cudjoe

Sadeghi‐Tehran

et al. 2023

Plants

View full text Add to dashboard Cite

show abstract

“…This method uses an artificial neuron network of multilayer perceptron (MLP) type to classify the image pixels into two classes: foreground and background [12]. The MLP has one hidden layer, 25 inputs, and one single output.…”

Section: Sari Et Al 'S Methodmentioning

confidence: 99%

Text Extraction from Historical Document Images by the Combination of Several Thresholding Techniques

Sari

Kefali

Bahi

2014

Advances in Multimedia

Self Cite

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This paper presents a new technique for the binarization of historical document images characterized by deteriorations and damages making their automatic processing difficult at several levels. The proposed method is based on hybrid thresholding combining the advantages of global and local methods and on the mixture of several binarization techniques. Two stages have been included. In the first stage, global thresholding is applied on the entire image and two different thresholds are determined from which the most of image pixels are classified into foreground or background. In the second stage, the remaining pixels are assigned to foreground or background classes based on local analysis. In this stage, several local thresholding methods are combined and the final binary value of each remaining pixel is chosen as the most probable one. The proposed technique has been tested on a large collection of standard and synthetic documents and compared with well-known methods using standard measures and was shown to be more powerful.

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