A Reconfigurable Accelerator for Morphological Operations

Tekleyohannes, Menbere Kina; Weis, Christian; Wehn, Norbert; Klein, Martin; Siegrist, Michael

doi:10.1109/ipdpsw.2018.00035

Cited by 8 publications

(4 citation statements)

References 33 publications

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“…Hair artifacts in lesion images have a huge impact in building a computer-based melanoma classification system as hair structures tend to block the lesion region. Color, length, and thickness of hair are some factors that need to be considered while building an automated image preprocessing system [18]. In this study, the hair artifacts removal model is designed to efficiently remove thin and thick hair noise from dermoscopy images without impacting the quality of the image.…”

Section: Fig 1 Skin Lesion Images From the Isic Datasetmentioning

confidence: 99%

Skin Lesion Classification towards Melanoma Detection Using EfficientNetB3

Salian

Sawarkar

2023

Adv. technol. innov.

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The rise of incidences of melanoma skin cancer is a global health problem. Skin cancer, if diagnosed at an early stage, enhances the chances of a patient’s survival. Building an automated and effective melanoma classification system is the need of the hour. In this paper, an automated computer-based diagnostic system for melanoma skin lesion classification is presented using fine-tuned EfficientNetB3 model over ISIC 2017 dataset. To improve classification results, an automated image pre-processing phase is incorporated in this study, it can effectively remove noise artifacts such as hair structures and ink markers from dermoscopic images. Comparative analyses of various advanced models like ResNet50, InceptionV3, InceptionResNetV2, and EfficientNetB0-B2 are conducted to corroborate the performance of the proposed model. The proposed system also addressed the issue of model overfitting and achieved a precision of 88.00%, an accuracy of 88.13%, recall of 88%, and F1-score of 88%.

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Section: Fig 1 Skin Lesion Images From the Isic Datasetmentioning

confidence: 99%

Skin Lesion Classification towards Melanoma Detection Using EfficientNetB3

Salian

Sawarkar

2023

Adv. technol. innov.

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“…In this stage, highly-parallelized atomic morphological operations (erosion and dilation) are implemented based on [43]. In this Text and Image Segmentation pipeline, opening and dilation use 8-connective windows with 5x5 and 3x3 structuring elements, respectively.…”

Section: Morphological Operations (Opening and Dilation)mentioning

confidence: 99%

iDocChip: A Configurable Hardware Architecture for Historical Document Image Processing

Tekleyohannes

Rybalkin

Ghaffar

et al. 2021

Int J Parallel Prog

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In recent years, $$\hbox {optical character recognition (OCR)}$$ optical character recognition (OCR) systems have been used to digitally preserve historical archives. To transcribe historical archives into a machine-readable form, first, the documents are scanned, then an $$\hbox {OCR}$$ OCR is applied. In order to digitize documents without the need to remove them from where they are archived, it is valuable to have a portable device that combines scanning and $$\hbox {OCR}$$ OCR capabilities. Nowadays, there exist many commercial and open-source document digitization techniques, which are optimized for contemporary documents. However, they fail to give sufficient text recognition accuracy for transcribing historical documents due to the severe quality degradation of such documents. On the contrary, the anyOCR system, which is designed to mainly digitize historical documents, provides high accuracy. However, this comes at a cost of high computational complexity resulting in long runtime and high power consumption. To tackle these challenges, we propose a low power energy-efficient accelerator with real-time capabilities called iDocChip, which is a configurable hybrid hardware-software programmable $$\hbox {System-on-Chip (SoC)}$$ System-on-Chip (SoC) based on anyOCR for digitizing historical documents. In this paper, we focus on one of the most crucial processing steps in the anyOCR system: Text and Image Segmentation, which makes use of a multi-resolution morphology-based algorithm. Moreover, an optimized $$\hbox {FPGA}$$ FPGA -based hybrid architecture of this anyOCR step along with its optimized software implementations are presented. We demonstrate our results on multiple embedded and general-purpose platforms with respect to runtime and power consumption. The resulting hardware accelerator outperforms the existing anyOCR by 6.2$$\times$$ × , while achieving 207$$\times$$ × higher energy-efficiency and maintaining its high accuracy.

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“…The text and image segmentation pipeline of i DocChip contains a dilation and an opening operations with a square- and cross-connective SEs, respectively. The fundamental processing blocks of morphological operation are implemented based on [ 83 ]. These highly parallelizable hardware blocks are parameterizable to any kernel size.…”

Section: I Docchip Hardware Architecturementioning

confidence: 99%

iDocChip: A Configurable Hardware Accelerator for an End-to-End Historical Document Image Processing

et al. 2021

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In recent years, there has been an increasing demand to digitize and electronically access historical records. Optical character recognition (OCR) is typically applied to scanned historical archives to transcribe them from document images into machine-readable texts. Many libraries offer special stationary equipment for scanning historical documents. However, to digitize these records without removing them from where they are archived, portable devices that combine scanning and OCR capabilities are required. An existing end-to-end OCR software called anyOCR achieves high recognition accuracy for historical documents. However, it is unsuitable for portable devices, as it exhibits high computational complexity resulting in long runtime and high power consumption. Therefore, we have designed and implemented a configurable hardware-software programmable SoC called iDocChip that makes use of anyOCR techniques to achieve high accuracy. As a low-power and energy-efficient system with real-time capabilities, the iDocChip delivers the required portability. In this paper, we present the hybrid CPU-FPGA architecture of iDocChip along with the optimized software implementations of the anyOCR. We demonstrate our results on multiple platforms with respect to runtime and power consumption. The iDocChip system outperforms the existing anyOCR by 44× while achieving 2201× higher energy efficiency and a 3.8% increase in recognition accuracy.

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A Reconfigurable Accelerator for Morphological Operations

Cited by 8 publications

References 33 publications

Skin Lesion Classification towards Melanoma Detection Using EfficientNetB3

Skin Lesion Classification towards Melanoma Detection Using EfficientNetB3

iDocChip: A Configurable Hardware Architecture for Historical Document Image Processing

iDocChip: A Configurable Hardware Accelerator for an End-to-End Historical Document Image Processing

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