6221143
Analysis of traditional Chinese seals and synthesis of personalized seals
People have long used seals for various purposes. One practical use of seals is for authentication and a lot of research has been focused on this area. In ancient China, seals revealed one's identity and were commonly used by various classes of people. Traditional Chinese seals can also be considered as a form of art, for people to appreciate and to learn about the culture at different dynasties. Nowadays, at this digital age, we would like not only be able to represent the seals in digital format, but also we would like to use image processing techniques to help us better understand them so that although we are not experts we will still be able to produce good designs. In this paper, we propose two things: 1) an analysis method to better understand traditional Chinese seals so that we can describe them in a more semantic way rather than just representing each one as a bitmap image; and 2) a synthesis method to generate a new seal image given a handwritten character, while considering the information obtained from the analysis of the traditional Chinese seals
18854339
Brush Writing Style Classification from Individual Chinese Characters
Chinese calligraphic artwork usually contains a few or even a single character. In order to perform automatic brush writing style classification on these kinds of Chinese calligraphic images, existing approaches need to be modified because they assume that many characters of the same style exist in the input image. A novel approach is proposed to address the brush writing style classification problem for single-character Chinese calligraphic images by combining the texture analysis and the structural information through a set of parameterised ellipses
37235208
Model-Based Analysis of Chinese Calligraphy Images
206606389
e-Learning Martial Arts
Traditionally, when people want to learn martial arts, they have to go to training clubs and learn under the coach together with the other students. To reduce the tuition fees, there are usually a lot of students under a single coach and hence, it is difficult for the students to get enough suggestions in the class. It would be far easier if the students could practice themselves at home and ask for suggestions from a virtual coach in the computer. Occasionally, in case they find difficulties going to the next step, they may then approach the real coach for suggestions and training. In this paper, we propose such a training system based on the motion capture system. The system automatically analyzes the motions of the player and gives suggestions. The students can also view the martial art techniques stored in the system or their own techniques captured by the motion capture system from various points of view in order to gain objective ideas of the techniques.
8282971
Protein Subcellular Localization Based on Psi-blast and Machine Learning
Subcellular location is an important functional annotation of proteins. An automatic, reliable and efficient prediction system for protein subcellular localization is necessary for large-scale genome analysis. This paper describes a protein subcellular localization method which extracts features from protein profiles rather than from amino acid sequences. The protein profile represents a protein family, discards part of the sequence information that is not conserved throughout the family and therefore is more sensitive than the amino acid sequence. The amino acid compositions of whole profile and the N-terminus of the profile are extracted, respectively, to train and test the probabilistic neural network classifiers. On two benchmark datasets, the overall accuracies of the proposed method reach 89.1% and 68.9%, respectively. The prediction results show that the proposed method perform better than those methods based on amino acid sequences. The prediction results of the proposed method are also compared with Subloc on two redundance-reduced datasets.
6040271
Web-Based Handwriting Education with Animated Virtual Teacher
Traditionally, in teaching handwriting, the teacher writes a character on the blackboard, and then students try to follow the teacher in order to write the same character. The teacher can check whether a student’s handwriting is spatially correct by looking at its shape but he/she is not able to verify if the student is writing in the correct stroke order with the correct number of strokes. As a result, we design a web-based handwriting education system. With our proposed system, students can stay at home to do the exercises for learning the handwriting of characters. A student’s handwriting can be captured in digital format with the information in both temporal and spatial domain and the results can be sent to the teacher for evaluation. In addition, we propose to animate a virtual teacher on the student’s terminal for demonstrating the handwriting of the characters to give students a sense of presence.
1007735
Structurally Unambiguous Finite Automata
We define a structurally unambiguous finite automaton (SUFA) to be a nondeterministic finite automaton (NFA) with one starting state q0 such that for all input strings w and for any state q, there is at most one path from q0 to q that consumes w. The definition of SUFA differs from the usual definition of an unambiguous finite automaton (UFA) in that the new definition is defined in terms of the transition logic of the finite automaton, and is independent of the choice of final states. We show that SUFA can be exponentially more succinct in the number of states than UFA and MDFA (deterministic finite automata with multiple initial states). Some interesting examples of SUFA are given. We argue that SUFA is a meaningful concept, and can have practical importance as it can implemented efficiently on synchronous models of parallel computation.
1397724
Descriptional complexity of nfa of different ambiguity
In this paper, we study the tradeoffs in descriptional complexity of NFA (nondeterministic finite automata) of various amounts of ambiguity. We say that two classes of NFA are separated if one class can be exponentially more succinct in descriptional sizes than the other. New results are given for separating DFA (deterministic finite automata) from UFA (unambiguous finite automata), UFA from MDFA (DFA with multiple initial states) and UFA from FNA (finitely ambiguous NFA). We present a family of regular languages that we conjecture to be a good candidate for separating FNA from LNA (linearly ambiguous NFA).
32788724
Tight Lower Bounds on the Size of Sweeping Automata
A sweeping automaton is a two-way deterministic finite automaton which makes turns only at the endmarkers. We say that a sweeping automaton is degenerate if the automaton has no left-moving transitions. We show that for each positive integer n, there is a nondeterministic finite automaton An over a two-letter alphabet such that An has n states, whereas the smallest equivalent nondegenerate sweeping automaton has 2n states.
17124839
Separating Exponentially Ambiguous Finite Automata from Polynomially Ambiguous Finite Automata
We resolve an open problem raised by Ravikumar and Ibarra [SIAM J. Comput., 18 (1989), pp. 1263--1282] on the succinctness of representations relating to the types of ambiguity of finite automata. We show that there exists a family of nondeterministic finite automata {An} over a two-letter alphabet such that, for any positive integer n, An is exponentially ambiguous and has n states, whereas the smallest equivalent deterministic finite automaton has 2n states, and any smallest equivalent polynomially ambiguous finite automaton has 2n -1 states.
16283263
Robust classification of blurred imagery
In this paper, we present two novel approaches for the classification of blurry images. It is assumed that the blur is linear and space invariant, but that the exact blurring function is unknown. The proposed fusion-based approaches attempt to perform the simultaneous tasks of blind image restoration and classification. We call such a problem blind image fusion. The techniques are implemented using the nonnegativity and support constraints recursive inverse filtering (NAS-RIF) algorithm for blind image restoration and the Markov random field (RIRF)-based fusion method for classification by Schistad-Solberg et al. Simulation results on synthetic and real photographic data demonstrate the potential of the approaches. The algorithms are compared with one another and to situations in which blind blur removal is not attempted.