From on-line sketching to 2D and 3D geometry: A fuzzy knowledge based system

The paper describes the development of a fuzzy knowledge based prototype system for conceptual design. This real time system is designed to infer user’s sketching intentions, to segment sketched input and generate corresponding geometric primitives: straight lines, circles, arcs, ellipses, elliptical arcs, and B-spline curves. Topology information (connectivity, unitary constraints and pairwise constraints) is received dynamically from 2D sketched input and primitives. From the 2D topology information, a more accurate 2D geometry can be built up by applying a 2D geometric constraint solver. Subsequently, 3D geometry can be received feature by feature incrementally. Each feature can be recognised by inference knowledge in terms of matching its 2D primitive configurations and connection relationships. The system accepts not only sketched input, working as an automatic design tools, but also accepts user’s interactive input of both 2D primitives and special positional 3D primitives. This makes it easy and friendly to use. The system has been tested with a number of sketched inputs of 2D and 3D geometry

Intelligent classification of sketch strokes

This paper presents an intelligent method for classifying pen strokes in an on-line sketching system. The method, based on adaptive threshold and fuzzy knowledge with respect to curve's linearity and convexity, can identify sketch strokes (curves) into lines, circles, arcs, ellipses, elliptical arcs, loop lines, spring lines and free-form B-spline curves. The proposed method has proven to be fast, suitable for real-time classification and identification

Accelerating Reinforcement Learning by Composing Solutions of Automatically Identified Subtasks

This paper discusses a system that accelerates reinforcement learning by using transfer from related tasks. Without such transfer, even if two tasks are very similar at some abstract level, an extensive re-learning effort is required. The system achieves much of its power by transferring parts of previously learned solutions rather than a single complete solution. The system exploits strong features in the multi-dimensional function produced by reinforcement learning in solving a particular task. These features are stable and easy to recognize early in the learning process. They generate a partitioning of the state space and thus the function. The partition is represented as a graph. This is used to index and compose functions stored in a case base to form a close approximation to the solution of the new task. Experiments demonstrate that function composition often produces more than an order of magnitude increase in learning rate compared to a basic reinforcement learning algorithm

Robust EM algorithm for model-based curve clustering

Model-based clustering approaches concern the paradigm of exploratory data analysis relying on the finite mixture model to automatically find a latent structure governing observed data. They are one of the most popular and successful approaches in cluster analysis. The mixture density estimation is generally performed by maximizing the observed-data log-likelihood by using the expectation-maximization (EM) algorithm. However, it is well-known that the EM algorithm initialization is crucial. In addition, the standard EM algorithm requires the number of clusters to be known a priori. Some solutions have been provided in [31, 12] for model-based clustering with Gaussian mixture models for multivariate data. In this paper we focus on model-based curve clustering approaches, when the data are curves rather than vectorial data, based on regression mixtures. We propose a new robust EM algorithm for clustering curves. We extend the model-based clustering approach presented in [31] for Gaussian mixture models, to the case of curve clustering by regression mixtures, including polynomial regression mixtures as well as spline or B-spline regressions mixtures. Our approach both handles the problem of initialization and the one of choosing the optimal number of clusters as the EM learning proceeds, rather than in a two-fold scheme. This is achieved by optimizing a penalized log-likelihood criterion. A simulation study confirms the potential benefit of the proposed algorithm in terms of robustness regarding initialization and funding the actual number of clusters.Comment: In Proceedings of the 2013 International Joint Conference on Neural Networks (IJCNN), 2013, Dallas, TX, US

HMM-based Offline Recognition of Handwritten Words Crossed Out with Different Kinds of Strokes

In this work, we investigate the recognition of words that have been crossed-out by the writers and are thus degraded. The degradation consists of one or more ink strokes that span the whole word length and simulate the signs that writers use to cross out the words. The simulated strokes are superimposed to the original clean word images. We considered two types of strokes: wave-trajectory strokes created with splines curves and line-trajectory strokes generated with the delta-lognormal model of rapid line movements. The experiments have been performed using a recognition system based on hidden Markov models and the results show that the performance decrease is moderate for single writer data and light strokes, but severe for multiple writer data

A conceptual design tool: Sketch and fuzzy logic based system

A real time sketch and fuzzy logic based prototype system for conceptual design has been developed. This system comprises four phases. In the first one, the system accepts the input of on-line free-hand sketches, and segments them into meaningful parts by using fuzzy knowledge to detect corners and inflection points on the sketched curves. The fuzzy knowledge is applied to capture user’s drawing intention in terms of sketching position, direction, speed and acceleration. During the second phase, each segmented sub-part (curve) can be classified and identified as one of the following 2D primitives: straight lines, circles, circular arcs, ellipses, elliptical arcs or B-spline curves. Then, 2D topology information (connectivity, unitary constraints and pairwise constraints) is extracted dynamically from the identified 2D primitives. From the extracted information, a more accurate 2D geometry can be built up by a 2D geometric constraint solver. The 2D topology and geometry information is then employed to further interpretation of a 3D geometry. The system can not only accept sketched input, but also users’ interactive input of 2D and 3D primitives. This makes it friendly and easier to use, in comparison with ‘sketched input only’, or ‘interactive input only’ systems. Finally, examples are given to illustrate the system