Self-adaptive SOM-CNN neural system for dynamic object detection in normal and complex scenarios

Ramírez-Quintana, Juan A.; Chacón-Murguía, Mario I.

doi:10.1016/j.patcog.2014.09.009

Cited by 41 publications

(14 citation statements)

References 31 publications

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“…Besides the approaches mentioned above, some neural network based foreground detection methods, which are closely related to the deep learning based methods, are also proposed [33,34,35,36,37,38]. Typical methods include Self-organizing Background Subtraction (SOBS) [33,34], and Background Neural Networks (BNN) [38].…”

Section: Related Workmentioning

confidence: 99%

Foreground Detection with Deeply Learned Multi-Scale Spatial-Temporal Features

Wang

Zhu

2018

Sensors

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Foreground detection, which extracts moving objects from videos, is an important and fundamental problem of video analysis. Classic methods often build background models based on some hand-craft features. Recent deep neural network (DNN) based methods can learn more effective image features by training, but most of them do not use temporal feature or use simple hand-craft temporal features. In this paper, we propose a new dual multi-scale 3D fully-convolutional neural network for foreground detection problems. It uses an encoder–decoder structure to establish a mapping from image sequences to pixel-wise classification results. We also propose a two-stage training procedure, which trains the encoder and decoder separately to improve the training results. With multi-scale architecture, the network can learning deep and hierarchical multi-scale features in both spatial and temporal domains, which is proved to have good invariance for both spatial and temporal scales. We used the CDnet dataset, which is currently the largest foreground detection dataset, to evaluate our method. The experiment results show that the proposed method achieves state-of-the-art results in most test scenes, comparing to current DNN based methods.

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Section: Related Workmentioning

confidence: 99%

Foreground Detection with Deeply Learned Multi-Scale Spatial-Temporal Features

Wang

Zhu

2018

Sensors

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“…With the rapid development of artificial intelligence, convolutional neural networks (CNNs) have been extensively applied for feature learning [4,5], image classification [6,7], and object detection [8,9]. In the field of medical image analysis tasks, CNNs have been widely utilized to classify skin diseases and even reach the level of professional dermatologists in some tasks, which is considered by most researches [10][11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

Assistant Diagnosis of Basal Cell Carcinoma and Seborrheic Keratosis in Chinese Population Using Convolutional Neural Network

Huang

Jin

et al. 2020

Journal of Healthcare Engineering

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Objectives. To evaluate CNN models’ performance of identifying the clinical images of basal cell carcinoma (BCC) and seborrheic keratosis (SK) and to compare their performance with that of dermatologists. Methods. We constructed a Chinese skin diseases dataset which includes 1456 BCC and 1843 SK clinical images and the corresponding medical history. We evaluated the performance using four mainstream CNN structures and transfer learning techniques. We explored the interpretability of the CNN model and compared its performance with that of 21 dermatologists. Results. The fine-tuned InceptionResNetV2 achieved the best performance, with an accuracy and area under the curve of 0.855 and 0.919, respectively. Further experimental results suggested that the CNN model was not only interpretable but also had a performance comparable to that of dermatologists. Conclusions. This study is the first on the assistant diagnosis of BCC and SK based on the proposed dataset. The promising results suggested that CNN model’s performance was comparable to that of expert dermatologists.

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“…The convolutional neural network (CNN), a type of deep neural network, has achieved ground-breaking results in different tasks related to pattern recognition over the last decade. Inspired by the visual cortex of humans [27], CNNs differentiate a large number of classes in image recognition problems [18] [28]. However, they require a large volume of training data to learn the better feature patterns [29], and cannot easily obtain a large labeled and balanced dataset for medical applications.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Pneumonia Identification from Chest X-Rays: A Systematic Literature Review

Khan

Zaki

Ali

2020

Preprint

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Chest radiography is an important diagnostic tool for chest-related diseases. Medical imaging research is currently embracing the automatic detection techniques used in computer vision. Over the past decade, Deep Learning techniques have shown an enormous breakthrough in the field of medical diagnostics. Various automated systems have been proposed for the rapid detection of pneumonia on chest x-rays images Although such detection algorithms are many and varied, they have not been summarized into a review that would assist practitioners in selecting the best methods from a real-time perspective, perceiving the available datasets, and understanding the currently achieved results in this domain. This paper overviews the current literature on pneumonia identification from chest x-ray images. After summarizing the topic, the review analyzes the usability, goodness factors, and computational complexities of the algorithms that implement these techniques. It also discusses the quality, usability, and size of the available datasets, and ways of coping with unbalanced datasets.

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Self-adaptive SOM-CNN neural system for dynamic object detection in normal and complex scenarios

Cited by 41 publications

References 31 publications

Foreground Detection with Deeply Learned Multi-Scale Spatial-Temporal Features

Foreground Detection with Deeply Learned Multi-Scale Spatial-Temporal Features

Assistant Diagnosis of Basal Cell Carcinoma and Seborrheic Keratosis in Chinese Population Using Convolutional Neural Network

Intelligent Pneumonia Identification from Chest X-Rays: A Systematic Literature Review

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