scholarly journals Reconhecimento de Grafos Fino de Precedência

2019 ◽  
Author(s):  
Flavia Bonomo ◽  
Fabiano Oliveira ◽  
Moysés Sampaio Jr ◽  
Jayme Szwarcfiter
Keyword(s):  
Recognition Algorithm ◽  
Interval Graphs ◽  
Efficient Recognition

The class of k-thin graphs have recently been introduced generalizing interval graphs. The complexity of the recognition of k-thin is open, even for fixed k 1. We introduce a subclass of the k-thin graphs, called precedence k-thin graphs, presenting an efficient recognition algorithm based on PQ trees.

scholarly journals Sobre Finura Própria de Grafos

2018 ◽  
Author(s):  
Moysés S. Sampaio Jr. ◽  
Fabiano S. Oliveira ◽  
Jayme L. Szwarcfiter
Keyword(s):  
Recognition Algorithm ◽  
Unit Interval ◽  
Interval Graphs ◽  
Graph Classes ◽  
Efficient Recognition

Both graph classes of k-thin and proper k-thin graphs have recently been introduced generalizing interval and unit interval graphs, respectively. The complexity of the recognition of k-thin and proper k-thin are open, even for fixed k 2. In this work, we introduce a subclass of the proper 2-thin graphs, called proper 2-thin of precedence. For this class, we present a characterization and an efficient recognition algorithm.

scholarly journals Recognition and Characterization of Chronological Interval Digraphs

10.37236/2497 ◽  
2013 ◽  
Vol 20 (3) ◽  
Author(s):  
Sandip Das ◽  
Mathew Francis ◽  
Pavol Hell ◽  
Jing Huang
Keyword(s):  
Linear Time ◽  
Interval Graph ◽  
Recognition Algorithm ◽  
Interval Graphs ◽  
Structure Characterization ◽  
Left Endpoint ◽  
New Class ◽  
Closed Intervals ◽  
Novel Structure ◽  
Definition Of

Interval graphs admit elegant structural characterizations and linear time recognition algorithms; on the other hand, the usual interval digraphs lack a forbidden structure characterization as well as a low-degree polynomial time recognition algorithm. In this paper we identify another natural digraph analogue of interval graphs that we call ”chronological interval digraphs”. By contrast, the new class admits both a forbidden structure characterization and a linear time recognition algorithm. Chronological interval digraphs arise by interpreting the standard definition of an interval graph with a natural orientation of its edges. Specifically, $G$ is a chronological interval digraph if there exists a family of closed intervals $I_v$, $v \in V(G)$, such that $uv$ is an arc of $G$ if and only if $I_u$ intersects $I_v$ and the left endpoint of $I_u$ is not greater than the left endpoint of $I_v$. (Equivalently, if and only if $I_u$ contains the left endpoint of $I_v$.)We characterize chronological interval digraphs in terms of vertex orderings, in terms of forbidden substructures, and in terms of a novel structure of so-called $Q$-paths. The first two characterizations exhibit strong similarity with the corresponding characterizations of interval graphs. The last characterization leads to a linear time recognition algorithm.

SVM-based global vision system of sports competition and action recognition

2020 ◽  
pp. 1-12
Author(s):  
Changxin Sun ◽  
Di Ma
Keyword(s):  
Target Recognition ◽  
Vision System ◽  
Task Assignment ◽  
Recognition Algorithm ◽  
Control Factor ◽  
Training Time ◽  
Adaboost Algorithm ◽  
Advantages And Disadvantages ◽  
Time Control ◽  
Correlation Degree

In the research of intelligent sports vision systems, the stability and accuracy of vision system target recognition, the reasonable effectiveness of task assignment, and the advantages and disadvantages of path planning are the key factors for the vision system to successfully perform tasks. Aiming at the problem of target recognition errors caused by uneven brightness and mutations in sports competition, a dynamic template mechanism is proposed. In the target recognition algorithm, the correlation degree of data feature changes is fully considered, and the time control factor is introduced when using SVM for classification,At the same time, this study uses an unsupervised clustering method to design a classification strategy to achieve rapid target discrimination when the environmental brightness changes, which improves the accuracy of recognition. In addition, the Adaboost algorithm is selected as the machine learning method, and the algorithm is optimized from the aspects of fast feature selection and double threshold decision, which effectively improves the training time of the classifier. Finally, for complex human poses and partially occluded human targets, this paper proposes to express the entire human body through multiple parts. The experimental results show that this method can be used to detect sports players with multiple poses and partial occlusions in complex backgrounds and provides an effective technical means for detecting sports competition action characteristics in complex backgrounds.

Recognition of classroom student state features based on deep learning algorithms and machine learning

2020 ◽  
pp. 1-12
Author(s):  
Hu Jingchao ◽  
Haiying Zhang
Keyword(s):  
Deep Learning ◽  
Feature Recognition ◽  
Subjective Evaluation ◽  
Recognition Algorithm ◽  
Video Data ◽  
State Recognition ◽  
Detection Model ◽  
State Classification ◽  
Intelligent Models ◽  
Model Recognition

The difficulty in class student state recognition is how to make feature judgments based on student facial expressions and movement state. At present, some intelligent models are not accurate in class student state recognition. In order to improve the model recognition effect, this study builds a two-level state detection framework based on deep learning and HMM feature recognition algorithm, and expands it as a multi-level detection model through a reasonable state classification method. In addition, this study selects continuous HMM or deep learning to reflect the dynamic generation characteristics of fatigue, and designs random human fatigue recognition experiments to complete the collection and preprocessing of EEG data, facial video data, and subjective evaluation data of classroom students. In addition to this, this study discretizes the feature indicators and builds a student state recognition model. Finally, the performance of the algorithm proposed in this paper is analyzed through experiments. The research results show that the algorithm proposed in this paper has certain advantages over the traditional algorithm in the recognition of classroom student state features.

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