Pi-Hsia Hung

Publications

1 Visual Thing Recognition with Binary Scale-Invariant Feature Transform and Support Vector Machine Classifiers Using Color Information

Authors: Wei-Jong Yang, Jar-Ferr Yang, Wei-Hau Du, Pau-Choo Chang, Pi-Hsia Hung

Abstract:

The demands of smart visual thing recognition in various devices have been increased rapidly for daily smart production, living and learning systems in recent years. This paper proposed a visual thing recognition system, which combines binary scale-invariant feature transform (SIFT), bag of words model (BoW), and support vector machine (SVM) by using color information. Since the traditional SIFT features and SVM classifiers only use the gray information, color information is still an important feature for visual thing recognition. With color-based SIFT features and SVM, we can discard unreliable matching pairs and increase the robustness of matching tasks. The experimental results show that the proposed object recognition system with color-assistant SIFT SVM classifier achieves higher recognition rate than that with the traditional gray SIFT and SVM classification in various situations.

Keywords: SIFT, color moments, visual thing recognition system, color SIFT

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 433

Abstracts

2 The Characteristics of Quantity Operation for 2nd and 3rd Grade Mathematics Slow Learners

Authors: Pi-Hsia Hung

Abstract:

The development of mathematical competency has individual benefits as well as benefits to the wider society. Children who begin school behind their peers in their understanding of number, counting, and simple arithmetic are at high risk of staying behind throughout their schooling. The development of effective strategies for improving the educational trajectory of these individuals will be contingent on identifying areas of early quantitative knowledge that influence later mathematics achievement. A computer-based quantity assessment was developed in this study to investigate the characteristics of 2nd and 3rd grade slow learners in quantity. The concept of quantification involves understanding measurements, counts, magnitudes, units, indicators, relative size, and numerical trends and patterns. Fifty-five tasks of quantitative reasoning—such as number sense, mental calculation, estimation and assessment of reasonableness of results—are included as quantity problem solving. Thus, quantity is defined in this study as applying knowledge of number and number operations in a wide variety of authentic settings. Around 1000 students were tested and categorized into 4 different performance levels. Students’ quantity ability correlated higher with their school math grade than other subjects. Around 20% students are below basic level. The intervention design implications of the preliminary item map constructed are discussed.

Keywords: quantity, Validity, Mathematical Cognition, number sense, mathematics assessment

Procedia PDF Downloads 135
1 Visual Thing Recognition with Binary Scale-Invariant Feature Transform and Support Vector Machine Classifiers Using Color Information

Authors: Wei-Jong Yang, Jar-Ferr Yang, Wei-Hau Du, Pau-Choo Chang, Pi-Hsia Hung

Abstract:

The demands of smart visual thing recognition in various devices have been increased rapidly for daily smart production, living and learning systems in recent years. This paper proposed a visual thing recognition system, which combines binary scale-invariant feature transform (SIFT), bag of words model (BoW), and support vector machine (SVM) by using color information. Since the traditional SIFT features and SVM classifiers only use the gray information, color information is still an important feature for visual thing recognition. With color-based SIFT features and SVM, we can discard unreliable matching pairs and increase the robustness of matching tasks. The experimental results show that the proposed object recognition system with color-assistant SIFT SVM classifier achieves higher recognition rate than that with the traditional gray SIFT and SVM classification in various situations.

Keywords: SIFT, color moments, visual thing recognition system, color SIFT

Procedia PDF Downloads 259