-
Article
A Prompt Learning Based Intent Recognition Method on a Chinese Implicit Intent Dataset CIID
As one of the core modules of the dialogue system, intent recognition plays an important role in human–computer interaction. Most of the existing intent recognition research is limited to simple, direct, and e...
-
Article
Special issue on “Recent advances on computational intelligence techniques and applications in big data”
-
Chapter and Conference Paper
An Unpaired Cross-Modality Segmentation Framework Using Data Augmentation and Hybrid Convolutional Networks for Segmenting Vestibular Schwannoma and Cochlea
The crossMoDA challenge aims to automatically segment the vestibular schwannoma (VS) tumor and cochlea regions of unlabeled high-resolution T2 scans by leveraging labeled contrast-enhanced T1 scans. The 2022 e...
-
Article
3 s-STNet: three-stream spatial–temporal network with appearance and skeleton information learning for action recognition
Human action recognition (HAR) is one of the active research areas in computer vision. Although significant progress has been made in the field of action recognition in recent years, most research methods focu...
-
Article
Micro-expression recognition based on SqueezeNet and C3D
Micro-expression recognition has attracted extensive attention from psychological and computer vision communities due to its multiple real-life applications. Compared with macro-expression, the change of micro...
-
Article
Mixed graph convolution and residual transformation network for skeleton-based action recognition
Action recognition based on a human skeleton is an extremely challenging research problem. The temporal information contained in the human skeleton is more difficult to extract than the spatial information. Ma...
-
Chapter and Conference Paper
Software Anti-patterns Detection Under Uncertainty Using a Possibilistic Evolutionary Approach
Code smells (a.k.a. anti-patterns) are manifestations of poor design solutions that could deteriorate the software maintainability and evolution. Despite the high number of existing detection methods, the issu...
-
Article
Integrating Gaussian mixture model and dilated residual network for action recognition in videos
Action recognition in video is one of the important applications in computer vision. In recent years, the two-stream architecture has made significant progress in action recognition, but it has not systematica...
-
Article
Multi-model Ensemble Learning Architecture Based on 3D CNN for Lung Nodule Malignancy Suspiciousness Classification
Classification of benign and malignant in lung nodules using chest CT images is a key step in the diagnosis of early-stage lung cancer, as well as an effective way to improve the patients’ survival rate. Howev...
-
Article
Cascaded hybrid residual U-Net for glioma segmentation
Glioma segmentation is critical for making surgical plans. Recently, the traditional glioma segmentation method is less competitive with two deep learning segmentation strategies: the patch-based method which ...
-
Chapter and Conference Paper
Class-Dependent Weighted Feature Selection as a Bi-Level Optimization Problem
Feature selection aims at selecting relevant features from the original feature set, but these features do not have the same degree of importance. This can be achieved by feature weighting, which is a method f...
-
Article
Automatic Labeling of MR Brain Images Through the Hashing Retrieval Based Atlas Forest
The multi-atlas method is one of the efficient and common automatic labeling method, which uses the prior information provided by expert-labeled images to guide the labeling of the target. However, most multi-...
-
Article
A Multi-objective hybrid filter-wrapper evolutionary approach for feature selection
Feature selection is an important pre-processing data mining task, which can reduce the data dimensionality and improve not only the classification accuracy but also the classifier efficiency. Filters use stat...
-
Article
A target-oriented segmentation method for specific tissues in MRI images of the brain
The multi-atlas based segmentation method can achieve the accurate segmentation of specific tissues of the human brain in the magnetic resonance imaging (MRI). The correct image registration and fusion scheme ...
-
Chapter
Using Datagram in the K-views Model
The performance of the K-views template (K-views-T) algorithm is related to the size of a view template and the number of characteristic views in the set of characteristic views. If the size of a view template...
-
Chapter
Image Texture, Texture Features, and Image Texture Classification and Segmentation
In this chapter, we will discuss the basic concept of , texture features, and image and segmentation. These concepts will be the foundation to and algorithms used for . Once texture features are avai...
-
Chapter
Advanced K-views Algorithms
This chapter introduces the weighted K-views voting algorithm (K-views-V) and its fast version called the fast K-views-V algorithm. These methods are developed to improve K-views template (K-views-T) and K-vie...
-
Chapter
Algorithms for Image Texture Classification
Image texture classification utilizes either unsupervised or supervised algorithms as a classifier based on textural features extracted from images. Many of these algorithms are from early research in the stat...
-
Chapter
Basic Concept and Models of the K-views
In this chapter, we introduce the concepts of “view” and “characteristic view”. This view concept is quite different from those of gray-level co-occurrence matrix (GLCM) and local binary pattern (LBP). We emph...
-
Chapter
Features-Based K-views Model
This chapter describes a new K-views algorithm, the K-views rotation-invariant features (K-views-R) algorithm, for texture image classification using rotation-invariant features. These features are statistical...
