Robust lip detection based on histogram of oriented gradient features and convolutional neural network under effects of light and background

Lee, Wooyoung; Ko, Kwang-Eun; Sim, Kwee-Bo

doi:10.1016/j.ijleo.2017.02.017

Cited by 7 publications

(5 citation statements)

References 12 publications

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“…Then, a kmeans initial segmentation is used to obtain the initial curve and label the lesions as the object region and the rest of the leaf as a background region. Lee et al [93] presented a method that can find the lip area using its shape feature, regardless of the influences from the light and background with over 94% accuracy and over 98% precision. The method finds the face area from an input image, divides the face image in half, and applies sliding window detection to the bottom half of the image.…”

Section: Region Based Methodsmentioning

confidence: 99%

Segmentation of images by color features: A survey

et al. 2018

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Image segmentation is an important stage for object recognition. Many methods have been proposed in the last few years for grayscale and color images. In this paper, we present a deep review of the state of the art on color image segmentation methods; through this paper, we explain the techniques based on edge detection, thresholding, histogram-thresholding, region, feature clustering and neural networks. Because color spaces play a key role in the methods reviewed, we also explain in detail the most commonly color spaces to represent and process colors. In addition, we present some important applications that use the methods of image segmentation reviewed. Finally, a set of metrics frequently used to evaluate quantitatively the segmented images is shown.

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Section: Region Based Methodsmentioning

confidence: 99%

Segmentation of images by color features: A survey

et al. 2018

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“…32 The HOG identifier focuses on the structure or shape of an object. 33 It is better than any edge descriptor as it uses magnitude as well as gradient angle to calculate features. Generates histograms for regions of the image using the gradient size and orientations.…”

Section: Histogram Of Oriented Gradientsmentioning

confidence: 99%

“…The histogram of directed gradients (HOG) is a feature descriptor used in machine vision and image processing for object detection and image classification processes 32 . The HOG identifier focuses on the structure or shape of an object 33 . It is better than any edge descriptor as it uses magnitude as well as gradient angle to calculate features.…”

Section: Feature Extractionmentioning

confidence: 99%

Covid‐19 detection from radiographs by feature‐reinforced ensemble learning

Elen

2022

Concurrency and Computation

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Summary The coronavirus (Covid‐19) epidemic continues to have a negative influence on the global population's well‐being and health. Scientists in many fields around the world are working non‐stop to find a solution to the prevention of this epidemic. In the field of computer science, this struggle is supported by studies on especially the analysis of X‐ray and CT images with artificial intelligence. In this study, two different ensemble learning models, including deep learning and a combination of machine learning methods, are presented for the detection of SARS‐CoV‐2 infection from X‐ray images. The main purpose of this study is to increase the classification ability of Residual Convolutional Neural Network (ResCNN), which is used as a deep learning method, with the assist of machine learning algorithms and extracted features from images. The proposed models were validated on a total of 5228 chest X‐ray images categorized as Normal, Pneumonia, and Covid‐19. The images in the dataset were sized in four different ways, 32 × 32, 64 × 64, 128 × 128, and 256 × 256, in order to analyze the validity of the proposed models in more detail. These four datasets were partitioned with the 10‐fold cross‐validation technique and converted into a total of 40 training and test data. Both proposed models use features derived from the ResCNN as the basis and test a certain number of machine learning algorithms with a majority voting technique by dividing them into subsets. In the architecture of the second model, it combines the features extracted from the Local Binary Patterns (LBP) and Histogram of Oriented Gradients (HOG) methods in addition to the features obtained from the ResCNN. It has been seen that the classification ability of both proposed models is better than the ResCNN in the experiments. In particular, the second model gives a similar classification score even though it is tested with images four‐times smaller (e.g., 32 × 32 vs. 128 × 128) than those used in the ResCNN. This shows that the model can give ideal results with lower computational cost.

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“…The third post-processing feature is HOG. It is a shape-based feature [45] and has been used in various computer vision applications [46,47]. The target image is pre-processed and resized to a ratio of 1:2 because the image is required to be divided into 8 × 8 or 16 × 16 patches.…”

Section: False-positive Mitotic-cells Removal Via Post-processingmentioning

confidence: 99%

Artificial Intelligence-Based Mitosis Detection in Breast Cancer Histopathology Images Using Faster R-CNN and Deep CNNs

Mahmood

Arsalan

Owais

et al. 2020

JCM

123

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Breast cancer is the leading cause of mortality in women. Early diagnosis of breast cancer can reduce the mortality rate. In the diagnosis, the mitotic cell count is an important biomarker for predicting the aggressiveness, prognosis, and grade of breast cancer. In general, pathologists manually examine histopathology images under high-resolution microscopes for the detection of mitotic cells. However, because of the minute differences between the mitotic and normal cells, this process is tiresome, time-consuming, and subjective. To overcome these challenges, artificial-intelligence-based (AI-based) techniques have been developed which automatically detect mitotic cells in the histopathology images. Such AI techniques accelerate the diagnosis and can be used as a second-opinion system for a medical doctor. Previously, conventional image-processing techniques were used for the detection of mitotic cells, which have low accuracy and high computational cost. Therefore, a number of deep-learning techniques that demonstrate outstanding performance and low computational cost were recently developed; however, they still require improvement in terms of accuracy and reliability. Therefore, we present a multistage mitotic-cell-detection method based on Faster region convolutional neural network (Faster R-CNN) and deep CNNs. Two open datasets (international conference on pattern recognition (ICPR) 2012 and ICPR 2014 (MITOS-ATYPIA-14)) of breast cancer histopathology were used in our experiments. The experimental results showed that our method achieves the state-of-the-art results of 0.876 precision, 0.841 recall, and 0.858 F1-measure for the ICPR 2012 dataset, and 0.848 precision, 0.583 recall, and 0.691 F1-measure for the ICPR 2014 dataset, which were higher than those obtained using previous methods. Moreover, we tested the generalization capability of our technique by testing on the tumor proliferation assessment challenge 2016 (TUPAC16) dataset and found that our technique also performs well in a cross-dataset experiment which proved the generalization capability of our proposed technique.

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Robust lip detection based on histogram of oriented gradient features and convolutional neural network under effects of light and background

Cited by 7 publications

References 12 publications

Segmentation of images by color features: A survey

Segmentation of images by color features: A survey

Covid‐19 detection from radiographs by feature‐reinforced ensemble learning

Artificial Intelligence-Based Mitosis Detection in Breast Cancer Histopathology Images Using Faster R-CNN and Deep CNNs

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