Echo state network‐based feature extraction for efficient color image segmentation

Souahlia, Abdelkerim; Belatreche, Ammar; Benyettou, Abdelkader; Ahmed‐Foitih, Zoubir; Benkhelifa, Elhadj; Curran, Kevin

doi:10.1002/cpe.5719

Cited by 17 publications

(8 citation statements)

References 60 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Similar works dealing with ESN features extraction for image segmentation were reported in [12,17,18,19]. Other works propose to train ESN to classify image pixels based on their preliminary extracted features [2,8,9,10,13,14].…”

Section: Introductionmentioning

confidence: 82%

Reservoir Computing Approach for Gray Images Segmentation

Koprinkova-Hristova

2021

Preprint

View full text Add to dashboard Cite

The paper proposes a novel approach for gray scale images segmentation. It is based on multiple features extraction from single feature per image pixel, namely its intensity value, using Echo state network. The newly extracted features -reservoir equilibrium statesreveal hidden image characteristics that improve its segmentation via a clustering algorithm. Moreover, it was demonstrated that the intrinsic plasticity tuning of reservoir fits its equilibrium states to the original image intensity distribution thus allowing for its better segmentation. The proposed approach is tested on the benchmark image Lena.

show abstract

Section: Introductionmentioning

confidence: 82%

Reservoir Computing Approach for Gray Images Segmentation

Koprinkova-Hristova

2021

Preprint

View full text Add to dashboard Cite

show abstract

“…In the field of computer vision (CV), ESN have been used in image processing, such as image segmentation [277,278,279,280,281], image restoration [204], facial expression recognition [203]. Many methods also could process radio audio data [282,113,261,283], like video traffic [284], video annotation [285], audio Classification [115], speech recognition [286,287] and emotion recognition [288,289,290,291].…”

Section: Real-world Tasks Orientatedmentioning

confidence: 99%

A Review of Designs and Applications of Echo State Networks

Sun¹,

Song²,

Hong³

et al. 2020

Preprint

View full text Add to dashboard Cite

Recurrent Neural Networks (RNNs) have demonstrated their outstanding ability in sequence tasks and have achieved state-of-the-art in wide range of applications, such as industrial, medical, economic and linguistic. Echo State Network (ESN) is simple type of RNNs and has emerged in the last decade as an alternative to gradient descent training based RNNs. ESN, with a strong theoretical ground, is practical, conceptually simple, easy to implement. It avoids non-converging and computationally expensive in the gradient descent methods. Since ESN was put forward in 2002, abundant existing works have promoted the progress of ESN, and the recently introduced Deep ESN model opened the way to uniting the merits of deep learning and ESNs. Besides, the combinations of ESNs with other machine learning models have also overperformed baselines in some applications. However, the apparent simplicity of ESNs can sometimes be deceptive and successfully applying ESNs needs some experience. Thus, in this paper, we categorize the ESN-based methods to basic ESNs, DeepESNs and combinations, then analyze them from the perspective of theoretical studies, network designs and specific applications. Finally, we discuss the challenges and opportunities of ESNs by summarizing the open questions and proposing possible future works.

show abstract

“…Its properties make it interesting for the biomedical domain 24 , 25 , but, so far, applications are predominantly described for other fields 22 , 26 , 27 , mainly focusing on signal processing tasks. In turn, RC application to (biomedical) image processing can only rarely be found 28 , 29 ; and we are not aware of previous work on RC-based processing of spatio-temporal image data.…”

Section: Introductionmentioning

confidence: 99%

Spatio-temporal feature learning with reservoir computing for T-cell segmentation in live-cell $$\hbox {Ca}^{2+}$$ fluorescence microscopy

Hadaeghi

Diercks

Schetelig

et al. 2021

Sci Rep

View full text Add to dashboard Cite

Advances in high-resolution live-cell $$\hbox {Ca}^{2+}$$ Ca 2 + imaging enabled subcellular localization of early $$\hbox {Ca}^{2+}$$ Ca 2 + signaling events in T-cells and paved the way to investigate the interplay between receptors and potential target channels in $$\hbox {Ca}^{2+}$$ Ca 2 + release events. The huge amount of acquired data requires efficient, ideally automated image processing pipelines, with cell localization/segmentation as central tasks. Automated segmentation in live-cell cytosolic $$\hbox {Ca}^{2+}$$ Ca 2 + imaging data is, however, challenging due to temporal image intensity fluctuations, low signal-to-noise ratio, and photo-bleaching. Here, we propose a reservoir computing (RC) framework for efficient and temporally consistent segmentation. Experiments were conducted with Jurkat T-cells and anti-CD3 coated beads used for T-cell activation. We compared the RC performance with a standard U-Net and a convolutional long short-term memory (LSTM) model. The RC-based models (1) perform on par in terms of segmentation accuracy with the deep learning models for cell-only segmentation, but show improved temporal segmentation consistency compared to the U-Net; (2) outperform the U-Net for two-emission wavelengths image segmentation and differentiation of T-cells and beads; and (3) perform on par with the convolutional LSTM for single-emission wavelength T-cell/bead segmentation and differentiation. In turn, RC models contain only a fraction of the parameters of the baseline models and reduce the training time considerably.

show abstract

Echo state network‐based feature extraction for efficient color image segmentation

Cited by 17 publications

References 60 publications

Reservoir Computing Approach for Gray Images Segmentation

Reservoir Computing Approach for Gray Images Segmentation

A Review of Designs and Applications of Echo State Networks

Spatio-temporal feature learning with reservoir computing for T-cell segmentation in live-cell $$\hbox {Ca}^{2+}$$ fluorescence microscopy

Contact Info

Product

Resources

About