Modeling spatial and temporal textures

Thesis (Ph. D.)--Massachusetts Institute of Technology, Program in Media Arts & Sciences, 1997.Includes bibliographical references (leaves 155-161).by Fang Liu.Ph.D

Introducing Type-2 Fuzzy Sets for Image Texture Modelling

In this paper, the texture property ”coarseness” is modeled by means of type-2 fuzzy sets, relating representative coarseness measures (our reference set) with the human perception of this texture property. The type-2 approach allows to face both the imprecision in the interpretation of the measure value and the uncertainty about the coarseness degree associated to a measure value. In our study, a wide variety of measures is analyzed, and assessments about coarseness perception are collected from pools. This information is used to obtain type-2 fuzzy sets where the secondary fuzzy sets are modelled by means of triangular membership functions fitted to the collected data

Fuzzy sets on 2D spaces for fineness representation

The analysis of the perceptual properties of texture plays a fundamental role in tasks like semantic description of images, content-based image retrieval using linguistic queries, or expert systems design based on low level visual features. In this paper, we propose a methodology to model texture properties by means of fuzzy sets defined on bidimensional spaces. In particular, we have focused our study on the fineness property that is considered as the most important feature for human visual interpretation. In our approach, pairwise combinations of fineness measures are used as a reference set, which allows to improve the ability to capture the presence of this property. To obtain the membership functions, we propose to learn the relationship between the computational values given by the measures and the human perception of fineness. The performance of each fuzzy set is analyzed and tested with the human assessments, allowing us to evaluate the goodness of each model and to identify the most suitable combination of measures for representing the fineness presence

Pilot/vehicle model analysis of visual and motion cue requirements in flight simulation

The optimal control model (OCM) of the human operator is used to predict the effect of simulator characteristics on pilot performance and workload. The piloting task studied is helicopter hover. Among the simulator characteristics considered were (computer generated) visual display resolution, field of view and time delay

An adaptive fuzzy approach for modelling visual texture properties

The analysis of the perceptual properties of texture plays a fundamental role in tasks like semantic description of images, content-based image retrieval using linguistic queries, or expert systems design based on low level visual features. The presence of these properties in images is very difficult to characterize due to their imprecision, and, moreover, because their perception may change depending on the user or the image context. In this paper, texture properties are modeled by means of an adaptive fuzzy approach that takes into account the subjectivity of the human perception. For this purpose, a methodology in two phases has been proposed. First, non-adaptive fuzzy models, that represent the average human perception about the presence of the texture properties, are obtained. For this modeling, we propose to learn a relationship between representative measures of the properties and the assessments given by human subjects. In a second phase, the obtained fuzzy sets are adapted in order to model the particular perception of the properties that a user may have, as well as the changes in perception influenced by the image context. For this purpose, the membership functions are automatically transformed on the basic of the information given by the user or extracted from the image context, respectively

Perception-based fuzzy partitions for visual texture modelling

Visual textures in images are usually described by humans using linguistic terms related to their perceptual properties, like “very coarse”, “low directional”, or “high contrasted”. Computational models with the ability of providing a perceptual texture characterization on the basis of these terms can be very useful in tasks like semantic description of images, content-based image retrieval using linguistic queries, or expert systems design based on low level visual features. In this paper, we address the problem of simulating the human perception of texture, obtaining linguistic labels to describe it in natural language. For this modeling, fuzzy partitions defined on the domain of some of the most representative measures of each property are employed. In order to define the fuzzy partitions, the number of linguistic labels and the parameters of the membership functions are calculated taking into account the relationship between the computational values given by the measures and the human perception of the corresponding property. The performance of each fuzzy partition is analyzed and tested using the human assessments, and a ranking of measures is obtained according to their ability to represent the perception of the property, allowing to identify the most suitable measure

Taste This Score

Digital scores were introduced in contemporary music some decades ago, as well as extra-musical images and visual signs in music writing. What is not yet explored and seems to be a very fertile field of research is musical writing based on research on transmodality. This article presents an investigation on some of the possibilities of musical and sonic writing through the incorporation of images of food textures. This is part of more extensive work that includes research on sonic relationships with visual textures (not only food) and that also investigates the compositional and performative possibilities of transmodal digital dynamic scores (video scores)

Taste This Score

Digital scores were introduced in contemporary music some decades ago, as well as extra-musical images and visual signs in music writing. What is not yet explored and seems to be a very fertile field of research is musical writing based on research on transmodality. This article presents an investigation on some of the possibilities of musical and sonic writing through the incorporation of images of food textures. This is part of more extensive work that includes research on sonic relationships with visual textures (not only food) and that also investigates the compositional and performative possibilities of transmodal digital dynamic scores (video scores).Las partituras digitales han arribado a la música contemporánea hace ya algunas décadas, así como la incorporación de imágenes y signos visuales extra musicales en la escritura musical. Lo que aún no está muy explorado y parecería ser un terreno muy fértil es la escritura musical a partir de investigaciones basadas en transmodalidad. Este artículo indaga en algunas de las posibilidades de escritura musical y sonora a partir de la incorporación de imágenes de texturas de alimentos. Se enmarca dentro de un trabajo más extenso que abarca una investigación sobre posibles asociaciones sonoras con texturas visuales (no solo de alimentos) y que también indaga en las posibilidades compositivas e interpretativas de partituras digitales dinámicas (video partituras) transmodales.Las partituras digitales han arribado a la música contemporánea hace ya algunas décadas, así como la incorporación de imágenes y signos visuales extra musicales en la escritura musical. Lo que aún no está muy explorado y parecería ser un terreno muy fértil es la escritura musical a partir de investigaciones basadas en transmodalidad. Este artículo indaga en algunas de las posibilidades de escritura musical y sonora a partir de la incorporación de imágenes de texturas de alimentos. Se enmarca dentro de un trabajo más extenso que abarca una investigación sobre posibles asociaciones sonoras con texturas visuales (no solo de alimentos) y que también indaga en las posibilidades compositivas e interpretativas de partituras digitales dinámicas (video partituras) transmodales

Scene Segmentation and Object Classification for Place Recognition

This dissertation tries to solve the place recognition and loop closing problem in a way similar to human visual system. First, a novel image segmentation algorithm is developed. The image segmentation algorithm is based on a Perceptual Organization model, which allows the image segmentation algorithm to ‘perceive’ the special structural relations among the constituent parts of an unknown object and hence to group them together without object-specific knowledge. Then a new object recognition method is developed. Based on the fairly accurate segmentations generated by the image segmentation algorithm, an informative object description that includes not only the appearance (colors and textures), but also the parts layout and shape information is built. Then a novel feature selection algorithm is developed. The feature selection method can select a subset of features that best describes the characteristics of an object class. Classifiers trained with the selected features can classify objects with high accuracy. In next step, a subset of the salient objects in a scene is selected as landmark objects to label the place. The landmark objects are highly distinctive and widely visible. Each landmark object is represented by a list of SIFT descriptors extracted from the object surface. This object representation allows us to reliably recognize an object under certain viewpoint changes. To achieve efficient scene-matching, an indexing structure is developed. Both texture feature and color feature of objects are used as indexing features. The texture feature and the color feature are viewpoint-invariant and hence can be used to effectively find the candidate objects with similar surface characteristics to a query object. Experimental results show that the object-based place recognition and loop detection method can efficiently recognize a place in a large complex outdoor environment