Computational Batik Motif Generation: Innovation of Traditional Heritage by Fractal Computation\ud

Human-computer interaction has been the cause of the emerging innovations in many fields, including in design and art, architectural, technological artifacts, and even traditional heritage. In the case of Indonesian traditional heritages, the computation of fractal designs has been introduced to develop batik design – the genuine textile art and skill that becomes a symbol of Indonesian culture. The uniqueness of Batik, which depicted in the richness of its motifs, is regarded as one of interesting aspect to be researched and innovated using computational techniques. Recent studies of batik motifs have discovered conjecture to the existence of fractal geometry in batik designs. This finding has given some inspiration of implementing certain fractal concepts, such escape-time fractal (complex plane) and iterated function system to generate batik motifs. We develop motif generator based upon the Collage Theorem by using Java TM platform. This software is equipped by interface that can be used by user to generate basic patterns, which could be interpreted and painted as batik motif. Experimentally, we found that computationally generated fractal motifs are appropriated to be implemented as batik motif. However, human made batik motifs are less detail and some of them differ significantly with the computationally generated ones for tools used to draw batik and human aesthetic constraints

Human and Ideal Observers for Detecting Image Curves

This paper compares the ability of human observers to detect target image curves with that of an ideal observer. The target curves are sampled from a generative model which specifies (probabilistically) the geometry and local intensity properties of the curve. The ideal observer performs Bayesian inference on the generative model using MAP estimation. Varying the probability model for the curve geometry enables us investigate whether human performance is best for target curves that obey specific shape statistics, in particular those observed on natural shapes. Experiments are performed with data on both rectangular and hexagonal lattices. Our results show that human observers' performance approaches that of the ideal observer and are, in general, closest to the ideal for conditions where the target curve tends to be straight or similar to natural statistics on curves. This suggests a bias of human observers towards straight curves and natural statistics

Human and Ideal Observers for Detecting Image Curves

This paper compares the ability of human observers to detect target image curves with that of an ideal observer. The target curves are sampled from a generative model which specifies (probabilistically) the geometry and local intensity properties of the curve. The ideal observer performs Bayesian inference on the generative model using MAP estimation. Varying the probability model for the curve geometry enables us investigate whether human performance is best for target curves that obey specific shape statistics, in particular those observed on natural shapes. Experiments are performed with data on both rectangular and hexagonal lattices. Our results show that human observers' performance approaches that of the ideal observer and are, in general, closest to the ideal for conditions where the target curve tends to be straight or similar to natural statistics on curves. This suggests a bias of human observers towards straight curves and natural statistics

Human and ideal observers for detecting image curves

This paper compares the ability of human observers to detect target image curves with that of an ideal observer. The target curves are sampled from a generative model which specifies (probabilistically) the geometry and local intensity properties of the curve. The ideal observer performs Bayesian inference on the generative model using MAP estimation. Varying the probability model for the curve geometry enables us investigate whether human performance is best for target curves that obey specific shape statistics, in particular those observed on natural shapes. Experiments are performed with data on both rectangular and hexagonal lattices. Our results show that human observers ’ performance approaches that of the ideal observer and are, in general, closest to the ideal for conditions where the target curve tends to be straight or similar to natural statistics on curves. This suggests a bias of human observers towards straight curves and natural statistics.

Human and ideal observers for detecting image curves

This paper compares the ability of human observers to detect target image curves with that of an ideal observer. The target curves are sampled from a generative model which specifies (probabilistically) the geometry and local intensity properties of the curve. The ideal observer performs Bayesian inference on the generative model using MAP estimation. Varying the probability model for the curve geometry enables us investigate whether human performance is best for target curves that obey specific shape statistics, in particular those observed on natural shapes. Experiments are performed with data on both rectangular and hexagonal lattices. Our results show that human observers ’ performance approaches that of the ideal observer and are, in general, closest to the ideal for conditions where the target curve tends to be straight or similar to natural statistics on curves. This suggests a bias of human observers towards straight curves and natural statistics.