Rib segmentation algorithm for X-ray image based on unpaired sample augmentation and multi-scale network

Wang, Hongyu; Zhang, Dandan; Ding, Songtao; Gao, Zhanyi; Feng, Jun; Wan, Shaohua

doi:10.1007/s00521-021-06546-x

Cited by 31 publications

(14 citation statements)

References 36 publications

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“…The experiment results show that the proposed method can effectively solve the problem of overenhancement and noise amplification, highlighting the image texture and improving the brightness, and hence significantly improve the subjective visual effect. In recent years, image enhancement methods based on deep neural networks have attracted many research interests [43][44][45]. These data-driven approaches have achieved some good results under complex situations.…”

Section: Discussionmentioning

confidence: 99%

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A novel detail weighted histogram equalization method for brightness preserving image enhancement based on partial statistic and global mapping model

Yuan

Zheng

et al. 2022

IET Image Processing

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Histogram equalization (HE) is a classic and widely used image contrast enhancement algorithm for its good performance and high efficiency. However, over‐enhancement caused by high peak in the histogram affects the subjective quality of the image processed by HE to a large extent. In this paper, a detail weighted histogram equalization (DWHE) method is proposed based on a novel histogram modification (HM) model named partial statistic and global mapping (PSGM) to alleviate high peak and suppress over‐enhancement. Moreover, the authors implement a refined version of gamma correction (GC) named texture enhancement function (TEF) on high‐frequency images to reduce the noise amplification effect. At last, the authors propose a novel adaptively weighted pixel‐level image fusion method to further reduce the phenomenon of over‐enhancement and improve brightness distribution. Both subjective and quantitative evaluations are conducted on images containing a variety of scenes. Compared with several state‐of‐the‐art image enhancement methods, the proposed framework obtained generally the best performance in aspects of both subjective appearance and objective evaluation indices. Therefore, it is proved that the proposed methods can effectively alleviate the over‐enhancement, enrich image details, and efficiently improve the visual quality while preserving the brightness of the image.

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Section: Discussionmentioning

confidence: 99%

“…In recent years, image enhancement methods based on deep neural networks have attracted many research interests [43–45]. These data‐driven approaches have achieved some good results under complex situations.…”

Section: Discussionmentioning

confidence: 99%

A novel detail weighted histogram equalization method for brightness preserving image enhancement based on partial statistic and global mapping model

Yuan

Zheng

et al. 2022

IET Image Processing

View full text Add to dashboard Cite

show abstract

“…The mini-batch gradient descent is carried out on the training set of 8500 images with a batch size set to 50 and a learning rate of 5.00E-06; the loss function is set to Binary Cross-Entropy Loss with Logits L ( x , y ) = − ( y ln σ ( x ) + (1 − y ) ln (1 − σ ( x )). The dev set is used to optimize the model in avoidance of overfitting, and the final model is chosen at the epoch when the maximum area under the receiver operating characteristic (AUROC) is obtained on the dev set [ 30 , 31 ]. After OSCAR+ is fine-tuned, its output is connected to a random forest classifier (RF).…”

Section: Methodsmentioning

confidence: 99%

Multimodal detection of hateful memes by applying a vision-language pre-training model

Chen¹,

Pan

2022

PLoS ONE

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Detrimental to individuals and society, online hateful messages have recently become a major social issue. Among them, one new type of hateful message, “hateful meme”, has emerged and brought difficulties in traditional deep learning-based detection. Because hateful memes were formatted with both text captions and images to express users’ intents, they cannot be accurately identified by singularly analyzing embedded text captions or images. In order to effectively detect a hateful meme, the algorithm must possess strong vision and language fusion capability. In this study, we move closer to this goal by feeding a triplet by stacking the visual features, object tags, and text features of memes generated by the object detection model named Visual features in Vision-Language (VinVl) and the optical character recognition (OCR) technology into a Transformer-based Vision-Language Pre-Training Model (VL-PTM) OSCAR+ to perform the cross-modal learning of memes. After fine-tuning and connecting to a random forest (RF) classifier, our model (OSCAR+RF) achieved an average accuracy and AUROC of 0.684 and 0.768, respectively, on the hateful meme detection task in a public test set, which was higher than the other eleven (11) published baselines. In conclusion, this study has demonstrated that VL-PTMs with the addition of anchor points can improve the performance of deep learning-based detection of hateful memes by involving a more substantial alignment between the text caption and visual information.

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“…With the development of artificial intelligence technology becoming more and more mature, there are more and more researches on applying artificial intelligence technology to character recognition. The traditional digital recognition of water meter images usually consists of the following parts: one is to denoise the image to reduce the noise that interferes with the recognized numbers in the image, so that the numbers are clearer; the other is to binarize the image to separate the numbers in the image from the background, convert the image with three channels (RGB) into a grayscale image, and then convert it into a binary image with only 0 and 1 2 ; the third is to segment the area where the character is located, and then perform tilt correction on the area to obtain a single character that is easy to identify Text image; Fourth, accurate recognition of a single character. However, the traditional solution is easily disturbed by the external environment, and the process is complicated.…”

Section: Introductionmentioning

confidence: 99%

Research on water meter reading recognition based on deep learning

Liang

Liao

et al. 2022

Sci Rep

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At present, there are still many old-fashioned water meters in the society, and the water department needs to send staff to read the water meter after arriving at the scene with a handheld all-in-one machine. However, there are many problems in this manual meter reading method. First, a large number of meter reading work leads to low efficiency of the entire water department, consuming a lot of time and energy, and high labor costs; second, the water meters in natural scenes have problems such as serious dial contamination and other environmental factors that interfere with the meter reading staff, and the results of the meter reader cannot be verified later. In response to these problems, this paper studies a deep learning method for automatic detection and recognition of water meter readings. This paper first introduces the existing in-depth learning models, such as Faster R-CNN, SSD, and YOLOv3. Then two datasets are sorted out, one is the original water table picture dataset, and the other is a dataset cut out from the water meter image with the black bounding box showing the water meter readings. Then two plans are proposed, one is the original water table image dataset, and the other is a dataset cut out from the water meter image with the black bounding box showing the water meter readings. Finally, by comparing the three models from different angles, it is determined that YOLOv3 in the second solution has the best recognition effect, and the accuracy rate reaches 90.61%, which can greatly improve work efficiency, save labor costs, and assist auditors in reviewing the read water meter readings.

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Rib segmentation algorithm for X-ray image based on unpaired sample augmentation and multi-scale network

Cited by 31 publications

References 36 publications

A novel detail weighted histogram equalization method for brightness preserving image enhancement based on partial statistic and global mapping model

A novel detail weighted histogram equalization method for brightness preserving image enhancement based on partial statistic and global mapping model

Multimodal detection of hateful memes by applying a vision-language pre-training model

Research on water meter reading recognition based on deep learning

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