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

A conceptual design tool: a sketch and fuzzy logic based system

Abstract: A real-time sketch and fuzzy logic based prototype system for conceptual design has been developed. This system comprises four phases. In the ® rst one, the system accepts the input of online free-hand sketches, and segments them into meaningful parts by using fuzzy knowledge to detect corners and in¯ection 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 subpart (curve) can be classi® ed and identi® ed as one of the following two-dimensional primitives: straight lines, circles, circular arcs, ellipses, elliptical arcs or B-spline curves. Then, two-dimensional topology information (connectivity, unitary constraints and pairwise constraints) is extracted dynamically from the identi® ed two-dimensional primitives. From the extracted information, more accurate two-dimensional geometry can be built up by a two-dimensional geometric constraint solver. The two-dimensional topology and geometry information is then employed to further interpretation of a three-dimensional geometry. The system can not only accept sketched input but also users' interactive input of two-and three-dimensional 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

Angles of Arc-Polygons and Lombardi Drawings of Cacti

We characterize the triples of interior angles that are possible in non-self-crossing triangles with circular-arc sides, and we prove that a given cyclic sequence of angles can be realized by a non-self-crossing polygon with circular-arc sides whenever all angles are at most pi. As a consequence of these results, we prove that every cactus has a planar Lombardi drawing (a drawing with edges depicted as circular arcs, meeting at equal angles at each vertex) for its natural embedding in which every cycle of the cactus is a face of the drawing. However, there exist planar embeddings of cacti that do not have planar Lombardi drawings.Comment: 12 pages, 8 figures. To be published in Proc. 33rd Canadian Conference on Computational Geometry, 202

Ab initio methods for protein structure prediction

Recent breakthroughs in DNA and protein sequencing have unlocked many secrets of molecular biology. A complete understanding of gene function, however, requires a protein structure in addition to its sequence. Modern protein structure determination methods such as NMR, cryo-EM and X-ray crystallography are woefully unable to keep pace with automated sequencing techniques, creating a serious gap between available sequences and structures. This thesis describes several ab initio computational methods designed in the near-term to facilitate structure determination experiments, and in the long-term goal to predict protein structure completely and reliably. First, VecFold is a novel method for predicting the global tertiary structure topologies of proteins. VecFold applies fragment assembly to construct structural models from a target sequence by folding a chain of predicted secondary structure elements; these elements are represented either as Calpha-based rigid bodies or as vectors. The knowledge-based energy function OPUS-Ca or a knowledge-based geometric packing potential is used to guide the folding process. The newest version of VecFold is demonstrated to modestly outperform Rosetta, one of the leading ab initio predictors, on the CASP8 benchmark set. In our protein domain boundary prediction method OPUS-Dom, VecFold generates a large ensemble of folded structure models, and the domain boundaries of each model are labeled by a domain parsing algorithm. OPUS-Dom then derives consensus domain boundaries from the statistical distribution of the putative boundaries; the original version is also aided by three empirical sequence-based domain profiles. The latest version of OPUS-Dom outperformed, in terms of prediction sensitivity, several state-of-the-art domain prediction algorithms over various multi-domain protein sets. Even though many VecFold-generated structures contain large errors, collectively these structures provide a more robust delineation of domain boundaries. The success of OPUS-Dom suggests that the arrangement of protein domains is more a consequence of limited coordination patterns per domain arising from tertiary packing of secondary structure segments, rather than sequence-specific constraints. Finally, the knowledge-based energy function OPUS-Core was applied to the problem of protein folding core prediction, and it was shown to outpredict two leading computational methods on a benchmark set of 29 well-characterized protein targets

An investigation into the use of genetic algorithms for shape recognition

The use of the genetic algorithm for shape recognition has been investigated in relation to features along a shape boundary contour. Various methods for encoding chromosomes were investigated, the most successful of which led to the development of a new technique to input normalised 'perceptually important point' features from the contour into a genetic algorithm. Chromosomes evolve with genes defining various ways of 'observing' different parts of the contour. The normalisation process provides the capability for multi-scale spatial frequency filtering and fine/coarse resolution of the contour features. A standard genetic algorithm was chosen for this investigation because its performance can be analysed by applying schema analysis to the genes. A new method for measurement of gene diversity has been developed. It is shown that this diversity measure can be used to direct the genetic algorithm parameters to evolve a number of 'good' chromosomes. In this way a variety of sections along the contour can be observed. A new and effective recognition technique has been developed which makes use of these 'good' chromosomes and the same fitness calculation as used in the genetic algorithm. Correct recognition can be achieved by selecting chromosomes and adjusting two thresholds, the values of which are found not to be critical. Difficulties associated with the calculation of a shape's fitness were analysed and the structure of the genes in the chromosome investigated using schema and epistatic analysis. It was shown that the behaviour of the genetic algorithm is compatible with the schema theorem of J. H. Holland. Reasons are given to explain the minimum value for the mutation probability that is required for the evolution of a number of' good' chromosomes. Suggestions for future research are made and, in particular, it is recommended that the convergence properties of the standard genetic algorithm be investigated

Processing boundary and region features for perception

A fundamental task for any visual system is the accurate detection of objects from background information, for example, defining fruit from foliage or a predator in a forest. This is commonly referred to as figure-ground segregation, which occurs when the visual system locates differences in visual features across an image, such as colour or texture. Combinations of feature contrast define an object from its surrounds, though the exact nature of that combination is still debated. Two processes are likely to contribute to object conspicuity, the pooling of features within an object's bounds relative to those in the background ('region' contrast) and detecting feature contrast at the boundary itself ('boundary' contrast). Investigations of the relative contributions of these two processes to perception have produced sometimes contradictory findings, some of which can be explained by the methodology adopted in those studies. For example, results from several studies adopting search-based methodologies have advocated nonlinear interaction of the boundary and region processes, whereas results from more subjective methods have indicated a linear combination. This thesis aims to compare search and subjective methodologies to determine how visual features (region and boundary) interact, highlight limitations of these metrics, and then unpack the contributions of boundary and region processes in greater detail. The first and second experiments investigated the relative contributions of boundary strength, regional orientation, and regional spatial frequency to object conspicuity. This was achieved via a comparison of search and subjective methodologies, which, as mentioned, have previously produced conflicting results in this domain. The results advocated a relatively strong contribution of boundary features compared to region-based features, and replicated the apparent incongruence between findings from search-based and subjective metrics. Results from the search task suggest nonlinear interaction and those from the subjective task suggest linear interaction. A unifying model that reconciles these seemingly contradicting findings (and those in the literature) is then presented, which considers the effect of metric sensitivity and performance ceilings in the paradigms employed. In light of the findings from the first and second experiments that suggest a stronger contribution of boundary information to object conspicuity, the third and fourth experiments investigated boundary features in more detail. Anecdotal reports from observers in the earlier experiments suggest that the conspicuity of boundaries is modulated by information in the background, regardless of boundary structure. As such, the relative contributions of boundary-background contrast and boundary composition were investigated using a novel stimulus generation technique that enables their effective isolation. A novel metric for boundary composition that correlates well with perception is also outlined. Results for those experiments suggested a significant contribution of both sources of boundary information, though advocate a critical role for boundary-background contrast. The final experiment explored the contribution of region-based information to object conspicuity in more detail, specifically how higher-order image structure, such as the components of complex texture, contribute to conspicuity. A state-of-the-art texture synthesis model, which reproduces textures via mechanisms that mimic processes in the human visual system, is evaluated respect to its perceptual applicability. Previous evaluations of this synthesis model are extended via a novel approach that enables the isolation of the model's parameters (which simulate physiological mechanisms) for independent examination. An alternative metric for the efficacy of the model is also presented