Compact and Accurate Scene Text Detector

Jeon, Minjun; Jeong, Young-Seob

doi:10.3390/app10062096

Cited by 27 publications

(13 citation statements)

References 28 publications

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“…The highlights of the mixed method based on SR and CNN include: (1) The sorting treatment of image patches based on the CNN model reduces the computational complexity of SR [ 24 , 25 , 26 ]; (2) The pixel value of the decision map obtained by means of the CNN model is imposed on the norm of sparse vectors, which can more accurately measure the activity level of the source image patches, giving full play to the advantages of strong spatial correlation between patches; (3) SR can handle the in-focused and out-focused junction areas that CNNs with black boxes cannot properly handle, making the patches in the junction area interpretable; and (4) SR can perform the nonlinear fusion of the patches at the junction of in-focused and out-focused area.…”

Section: Related Work and Discussionmentioning

confidence: 99%

The Multi-Focus-Image-Fusion Method Based on Convolutional Neural Network and Sparse Representation

Feng

Wang

Gao

2021

Entropy

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Multi-focus-image-fusion is a crucial embranchment of image processing. Many methods have been developed from different perspectives to solve this problem. Among them, the sparse representation (SR)-based and convolutional neural network (CNN)-based fusion methods have been widely used. Fusing the source image patches, the SR-based model is essentially a local method with a nonlinear fusion rule. On the other hand, the direct mapping between the source images follows the decision map which is learned via CNN. The fusion is a global one with a linear fusion rule. Combining the advantages of the above two methods, a novel fusion method that applies CNN to assist SR is proposed for the purpose of gaining a fused image with more precise and abundant information. In the proposed method, source image patches were fused based on SR and the new weight obtained by CNN. Experimental results demonstrate that the proposed method clearly outperforms existing state-of-the-art methods in addition to SR and CNN in terms of both visual perception and objective evaluation metrics, and the computational complexity is greatly reduced. Experimental results demonstrate that the proposed method not only clearly outperforms the SR and CNN methods in terms of visual perception and objective evaluation indicators, but is also significantly better than other state-of-the-art methods since our computational complexity is greatly reduced.

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Section: Related Work and Discussionmentioning

confidence: 99%

The Multi-Focus-Image-Fusion Method Based on Convolutional Neural Network and Sparse Representation

Feng

Wang

Gao

2021

Entropy

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“…CNNs have been widely used in the field of medical imaging. They are known to be efficient and accurate, usually outperforming other machine learning, or more specifically, deep-learning-based approaches not only for medical imaging analysis [ 18 , 19 , 20 ]. CNNs are space invariant networks.…”

Section: Introductionmentioning

confidence: 99%

Detection of Degenerative Changes on MR Images of the Lumbar Spine with a Convolutional Neural Network: A Feasibility Study

et al. 2021

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Our objective was to evaluate the diagnostic performance of a convolutional neural network (CNN) trained on multiple MR imaging features of the lumbar spine, to detect a variety of different degenerative changes of the lumbar spine. One hundred and forty-six consecutive patients underwent routine clinical MRI of the lumbar spine including T2-weighted imaging and were retrospectively analyzed using a CNN for detection and labeling of vertebrae, disc segments, as well as presence of disc herniation, disc bulging, spinal canal stenosis, nerve root compression, and spondylolisthesis. The assessment of a radiologist served as the diagnostic reference standard. We assessed the CNN’s diagnostic accuracy and consistency using confusion matrices and McNemar’s test. In our data, 77 disc herniations (thereof 46 further classified as extrusions), 133 disc bulgings, 35 spinal canal stenoses, 59 nerve root compressions, and 20 segments with spondylolisthesis were present in a total of 888 lumbar spine segments. The CNN yielded a perfect accuracy score for intervertebral disc detection and labeling (100%), and moderate to high diagnostic accuracy for the detection of disc herniations (87%; 95% CI: 0.84, 0.89), extrusions (86%; 95% CI: 0.84, 0.89), bulgings (76%; 95% CI: 0.73, 0.78), spinal canal stenoses (98%; 95% CI: 0.97, 0.99), nerve root compressions (91%; 95% CI: 0.89, 0.92), and spondylolisthesis (87.61%; 95% CI: 85.26, 89.21), respectively. Our data suggest that automatic diagnosis of multiple different degenerative changes of the lumbar spine is feasible using a single comprehensive CNN. The CNN provides high diagnostic accuracy for intervertebral disc labeling and detection of clinically relevant degenerative changes such as spinal canal stenosis and disc extrusion of the lumbar spine.

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“…CNN is known to be effective to learn local patterns and capture promising semantic information. Furthermore, it is also known to be efficient compared with other networks [72,73]. In 2017, Kalantari [74] first introduced a supervised CNN framework for MEF research.…”

Section: Supervised Methodsmentioning

confidence: 99%

Multi-Exposure Image Fusion Techniques: A Comprehensive Review

Liu

Song

et al. 2022

Remote Sensing

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Multi-exposure image fusion (MEF) is emerging as a research hotspot in the fields of image processing and computer vision, which can integrate images with multiple exposure levels into a full exposure image of high quality. It is an economical and effective way to improve the dynamic range of the imaging system and has broad application prospects. In recent years, with the further development of image representation theories such as multi-scale analysis and deep learning, significant progress has been achieved in this field. This paper comprehensively investigates the current research status of MEF methods. The relevant theories and key technologies for constructing MEF models are analyzed and categorized. The representative MEF methods in each category are introduced and summarized. Then, based on the multi-exposure image sequences in static and dynamic scenes, we present a comparative study for 18 representative MEF approaches using nine commonly used objective fusion metrics. Finally, the key issues of current MEF research are discussed, and a development trend for future research is put forward.

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Compact and Accurate Scene Text Detector

Cited by 27 publications

References 28 publications

The Multi-Focus-Image-Fusion Method Based on Convolutional Neural Network and Sparse Representation

The Multi-Focus-Image-Fusion Method Based on Convolutional Neural Network and Sparse Representation

Detection of Degenerative Changes on MR Images of the Lumbar Spine with a Convolutional Neural Network: A Feasibility Study

Multi-Exposure Image Fusion Techniques: A Comprehensive Review

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