KEYWORDS SPECIFICATION FOR IMAGES USING SANDGLASS-TYPE NEURAL NETWORKS

2004 ◽  
Vol 04 (02) ◽  
pp. 143-152 ◽  
Author(s):  
SEIJI ITO ◽  
SIGERU OMATU
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Factor Analysis ◽  
Image Segmentation ◽  
Dimensional Space ◽  
Training Dataset ◽  
Two Dimensional ◽  
Segmentation Method ◽  
Testing Dataset ◽  
Specification Method

We propose a keyword specification method for images, which can be used to retrieve an image by a keyword. In order to specify a keyword for a sub-region of the image, images are segmented in some regions. Here, we consider ten keywords to specify the regions. The image segmentation method consists of the maximum-distance algorithm, labeling, and merging the small regions. We provide training regions for each keyword. Important features of the keyword are selected using the Factor Analysis (FA). The features are compressed into a two-dimensional space using a Sandglass-type Neural Network (SNN). We train the SNN using the training dataset. Then 60 samples of the testing dataset are evaluated by keywords extraction.

scholarly journals Hybrid Iris Segmentation Method Based on CNN and Principal Curvatures

2020 ◽  
pp. paper31-1-paper31-10
Author(s):  
Varvara Tikhonova ◽  
Elena Pavelyeva
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Image Segmentation ◽  
Convolutional Neural Networks ◽  
Segmentation Method ◽  
Iris Segmentation ◽  
Principal Curvatures ◽  
Mathematical Methods ◽  
Iris Image ◽  
Segmentation Approach

In this article the new hybrid iris image segmentation method based on convolutional neural networks and mathematical methods is proposed. Iris boundaries are found using modified Daugman’s method. Two UNet-based convolutional neural networks are used for iris mask detection. The first one is used to predict the preliminary iris mask including the areas of the pupil, eyelids and some eyelashes. The second neural network is applied to the enlarged image to specify thin ends of eyelashes. Then the principal curvatures method is used to combine the predicted by neural networks masks and to detect eyelashes correctly. The pro- posed segmentation algorithm is tested using images from CASIA IrisV4 Interval database. The results of the proposed method are evaluated by the Intersection over Union, Recall and Precision metrics. The average metrics values are 0.922, 0.957 and 0.962, respectively. The proposed hy- brid iris image segmentation approach demonstrates an improvement in comparison with the methods that use only neural networks.

scholarly journals Active Learning with Bayesian UNet for Efficient Semantic Image Segmentation

2021 ◽  
Vol 7 (2) ◽  
pp. 37
Author(s):  
Isah Charles Saidu ◽  
Lehel Csató
Keyword(s):  
Neural Network ◽  
Image Segmentation ◽  
Active Learning ◽  
Convolutional Neural Network ◽  
Medical Image ◽  
Segmentation Method ◽  
Semantic Image Segmentation ◽  
Batch Normalization ◽  
Set Up ◽  
Image Datasets

We present a sample-efficient image segmentation method using active learning, we call it Active Bayesian UNet, or AB-UNet. This is a convolutional neural network using batch normalization and max-pool dropout. The Bayesian setup is achieved by exploiting the probabilistic extension of the dropout mechanism, leading to the possibility to use the uncertainty inherently present in the system. We set up our experiments on various medical image datasets and highlight that with a smaller annotation effort our AB-UNet leads to stable training and better generalization. Added to this, we can efficiently choose from an unlabelled dataset.

scholarly journals Evaluation of Power Insulator Detection Efficiency with the Use of Limited Training Dataset

2020 ◽  
Vol 10 (6) ◽  
pp. 2104
Author(s):  
Michał Tomaszewski ◽  
Paweł Michalski ◽  
Jakub Osuchowski
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Object Detection ◽  
Convolutional Neural Network ◽  
Deep Neural Networks ◽  
Detection Efficiency ◽  
Training Data ◽  
Training Dataset ◽  
Training Set ◽  
Convolutional Network

This article presents an analysis of the effectiveness of object detection in digital images with the application of a limited quantity of input. The possibility of using a limited set of learning data was achieved by developing a detailed scenario of the task, which strictly defined the conditions of detector operation in the considered case of a convolutional neural network. The described solution utilizes known architectures of deep neural networks in the process of learning and object detection. The article presents comparisons of results from detecting the most popular deep neural networks while maintaining a limited training set composed of a specific number of selected images from diagnostic video. The analyzed input material was recorded during an inspection flight conducted along high-voltage lines. The object detector was built for a power insulator. The main contribution of the presented papier is the evidence that a limited training set (in our case, just 60 training frames) could be used for object detection, assuming an outdoor scenario with low variability of environmental conditions. The decision of which network will generate the best result for such a limited training set is not a trivial task. Conducted research suggests that the deep neural networks will achieve different levels of effectiveness depending on the amount of training data. The most beneficial results were obtained for two convolutional neural networks: the faster region-convolutional neural network (faster R-CNN) and the region-based fully convolutional network (R-FCN). Faster R-CNN reached the highest AP (average precision) at a level of 0.8 for 60 frames. The R-FCN model gained a worse AP result; however, it can be noted that the relationship between the number of input samples and the obtained results has a significantly lower influence than in the case of other CNN models, which, in the authors’ assessment, is a desired feature in the case of a limited training set.

scholarly journals Automatic differentiation of thyroid scintigram by deep convolutional neural network: a dual center study

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Pei Yang ◽  
Yong Pi ◽  
Tao He ◽  
Jiangming Sun ◽  
Jianan Wei ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Convolutional Neural Network ◽  
Predictive Value ◽  
Automatic Differentiation ◽  
External Validation ◽  
Deep Convolutional Neural Network ◽  
Image Feature ◽  
Training Dataset ◽  
Internal Validation

Abstract Background 99mTc-pertechnetate thyroid scintigraphy is a valid complementary avenue for evaluating thyroid disease in the clinic, the image feature of thyroid scintigram is relatively simple but the interpretation still has a moderate consistency among physicians. Thus, we aimed to develop an artificial intelligence (AI) system to automatically classify the four patterns of thyroid scintigram. Methods We collected 3087 thyroid scintigrams from center 1 to construct the training dataset (n = 2468) and internal validating dataset (n = 619), and another 302 cases from center 2 as external validating datasets. Four pre-trained neural networks that included ResNet50, DenseNet169, InceptionV3, and InceptionResNetV2 were implemented to construct AI models. The models were trained separately with transfer learning. We evaluated each model’s performance with metrics as following: accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), recall, precision, and F1-score. Results The overall accuracy of four pre-trained neural networks in classifying four common uptake patterns of thyroid scintigrams all exceeded 90%, and the InceptionV3 stands out from others. It reached the highest performance with an overall accuracy of 92.73% for internal validation and 87.75% for external validation, respectively. As for each category of thyroid scintigrams, the area under the receiver operator characteristic curve (AUC) was 0.986 for ‘diffusely increased,’ 0.997 for ‘diffusely decreased,’ 0.998 for ‘focal increased,’ and 0.945 for ‘heterogeneous uptake’ in internal validation, respectively. Accordingly, the corresponding performances also obtained an ideal result of 0.939, 1.000, 0.974, and 0.915 in external validation, respectively. Conclusions Deep convolutional neural network-based AI model represented considerable performance in the classification of thyroid scintigrams, which may help physicians improve the interpretation of thyroid scintigrams more consistently and efficiently.

Dermoscopic image segmentation method based on convolutional neural networks

2021 ◽  
Vol 66 (2) ◽  
pp. 89
Author(s):  
Ugur Erkan ◽  
Dang Ngoc Hoang Thanh ◽  
Le Thi Thanh ◽  
V.B. Surya Prasath ◽  
Aditya Khamparia
Keyword(s):  
Neural Networks ◽  
Image Segmentation ◽  
Convolutional Neural Networks ◽  
Segmentation Method ◽  
Dermoscopic Image

Detecting Eyes and Lips Using Neural Networks and SURF Features

2012 ◽  
pp. 338-354 ◽  
Author(s):  
Artem A. Lenskiy ◽  
Jong-Soo Lee
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Image Segmentation ◽  
Success Rate ◽  
Detection Rate ◽  
Facial Feature ◽  
Speed Up ◽  
Surf Features ◽  
Left And Right ◽  
The Right

In this chapter, the authors elaborate on the facial image segmentation and the detection of eyes and lips using two neural networks. The first neural network is applied to segment skin-colors and the second to detect facial features. As for input vectors, for the second network the authors apply speed-up robust features (SURF) that are not subject to scale and brightness variations. The authors carried out the detection of eyes and lips on two well-known facial feature databases, Caltech. and PICS. Caltech gave a success rate of 92.4% and 92.2% for left and right eyes and 85% for lips, whereas the PCIS database gave 96.9% and 95.3% for left and right eyes and 97.3% for lips. Using videos captured in real environment, among all videos, the authors achieved an average detection rate of 94.7% for the right eye and 95.5% for the left eye with a 86.9% rate for the lips

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