Advances in Neural Networks – ISNN 2013
10th International Symposium on Neural Networks, Dalian, China, July 4-6, 2013, Proceedings, Part I
Page
%P
-
Chapter and Conference Paper
CardiacRT-NN: Real-Time Detection of Cardiovascular Disease Using Self-attention CNN-LSTM for Embedded Systems
Deep learning (DL) has emerged as a critical technology in the advancement of non-invasive cardiac monitoring by analyzing electrocardiogram (ECG) data. Although traditional approaches utilizing up to 12-lead ...
-
Chapter and Conference Paper
Robust Object Tracking Based on Deep Feature and Dual Classifier Trained with Hard Samples
Visual tracking has attracted more and more attention in recent years. In this paper, we proposed a novel tracker that is composed of a feature network, a dual classifier, a target location module, and a sampl...
-
Chapter and Conference Paper
Bird Species Recognition Based on Deep Learning and Decision Fusion
Bird species recognition is one of the most challenging tasks in fine-grained visual categorizations (FGVC) and has attracted wide attention in recent years. In this paper, we develop a bird recognition system...
-
Article
Open AccessIn situ biomarker discovery and label-free molecular histopathological diagnosis of lung cancer by ambient mass spectrometry imaging
Sensitive and spatial exploration of the metabolism of tumors at the metabolome level is highly challenging. In this study, we developed an in situ metabolomics method based on ambient mass spectrometry imaging u...
-
Chapter and Conference Paper
A Novel Method for Barcode Detection and Recognition in Complex Scenes
This paper presents a novel method for barcode detection and recognition in the scenes consisting of multiple barcodes. In order to effectively detect the locations of the barcodes, we construct a new feature ...
-
Article
An image segmentation method based on the fusion of vector quantization and edge detection with applications to medical image processing
In the existing segmentation algorithms, most of them take single pixel as processing unit and segment an image mainly based on the gray value information of the image pixels. However, the spatially structural...
-
Chapter and Conference Paper
A Parallel Image Segmentation Method Based on SOM and GPU with Application to MRI Image Processing
This paper presents a parallel image segmentation method based on self-organizing map (SOM) neural network by extending the authors’ former work from serial computation to parallel processing in order to accel...
-
Book and Conference Proceedings
-
Book and Conference Proceedings
Advances in Neural Networks – ISNN 2013
10th International Symposium on Neural Networks, Dalian, China, July 4-6, 2013, Proceedings, Part II
-
Chapter and Conference Paper
A Vector Quantization Approach for Image Segmentation Based on SOM Neural Network
In the existing segmentation algorithms, most of them take single pixel as processing unit and segment an image mainly based on the gray value information of the image pixels. However, the spatially structural...
-
Chapter and Conference Paper
Face Recognition Based on Gabor Enhanced Marginal Fisher Model and Error Correction SVM
In previous work, we proposed the Gabor manifold learning method for feature extraction in face recognition, which combines Gabor filtering with Marginal Fisher Analysis (MFA), and obtained better classificati...
-
Chapter and Conference Paper
Face Recognition Based on Gabor-Enhanced Manifold Learning and SVM
Recently proposed Marginal Fisher Analysis (MFA), as one of the manifold learning methods, has obtained better classification results than the conventional subspace analysis methods and other manifold learning...
-
Chapter and Conference Paper
A Parallel Implementation of Error Correction SVM with Applications to Face Recognition
The Error Correction SVM method is an excellent multiclass classification approach and has been applied to face recognition successfully. Yet, it suffers from the computational complexity. To reduce the comput...
-
Chapter and Conference Paper
A Two-Pass Classification Method Based on Hyper-Ellipsoid Neural Networks and SVM’s with Applications to Face Recognition
In this paper we propose a two-pass classification method and apply it to face recognitions. The method is obtained by integrating together two approaches, the hyper-ellipsoid neural networks (HENN’s) and the ...
-
Chapter and Conference Paper
An SVM Classification Algorithm with Error Correction Ability Applied to Face Recognition
This paper presents an SVM classification algorithm with predesigned error correction ability by incorporating the error control coding schemes used in digital communications into the classification algorithm....
-
Book and Conference Proceedings
Advances in Neural Networks – ISNN 2004
International Symposium on Neural Networks, Dalian, China, August 2004, Proceedings, Part I
-
Book and Conference Proceedings
Advances in Neural Networks - ISNN 2004
International Symposium on Neural Networks, Dalian, China, August 19-21, 2004, Proceedings, Part II
-
Chapter and Conference Paper
An Optimal Neural-Network Model for Learning Posterior Probability Functions from Observations
This paper presents the further results of the authors’ former work [1] in which a neural-network method was proposed for sequential detection with similar performance as the optimal sequential probability rat...
