程展緯. / 論文 (藝术碩士)--香港中文大學, 1998. / 參考文獻 (leaves 22-23). / 附中英文摘要. / Cheng Zhanwei. / Lun wen (yi shu shuo shi)-- Xianggang Zhong wen da xue, 1998. / Can kao wen xian (leaves 22-23). / Fu Zhong Ying wen zhai yao. / Chapter (一） --- 前言´ؤ´ؤ從物理性的分析開始 --- p.1 / Chapter (二） --- 繪畫中幻象界與物象界的對立關 係 --- p.3 / Chapter (三） --- 游離的概念 --- p.8 / Chapter (四） --- 參照模型槪念看繪畫的游 離 --- p.9 / Chapter (五） --- 參照視錯覺雕塑看繪畫的游離 性 --- p.15 / Chapter (六） --- 總結 --- p.19 / Chapter (七） --- 註釋 --- p.20 / Chapter (八） --- 參考書目 --- p.22

acase@tulane.edu / 1 / Blair Youmans

This paper discusses how sparse local measurements of positions and surface normals may be used to identify and locate overlapping objects. The objects are modeled as polyhedra (or polygons) having up to six degreed of positional freedom relative to the sensors. The approach operated by examining all hypotheses about pairings between sensed data and object surfaces and efficiently discarding inconsistent ones by using local constraints on: distances between faces, angles between face normals, and angles (relative to the surface normals) of vectors between sensed points. The method described here is an extension of a method for recognition and localization of non-overlapping parts previously described in [Grimson and Lozano-Perez 84] and [Gaston and Lozano-Perez 84].

Many object recognition systems use a small number of pairings of data and model features to compute the 3D transformation from a model coordinate frame into the sensor coordinate system. With perfect image data, these systems work well. With uncertain image data, however, their performance is less clear. We examine the effects of 2D sensor uncertainty on the computation of 3D model transformations. We use this analysis to bound the uncertainty in the transformation parameters, and the uncertainty associated with transforming other model features into the image. We also examine the impact of the such transformation uncertainty on recognition methods.

A common method for finding an object's pose is the generalized Hough transform, which accumulates evidence for possible coordinate transformations in a parameter space and takes large clusters of similar transformations as evidence of a correct solution. We analyze this approach by deriving theoretical bounds on the set of transformations consistent with each data-model feature pairing, and by deriving bounds on the likelihood of false peaks in the parameter space, as a function of noise, occlusion, and tessellation effects. We argue that blithely applying such methods to complex recognition tasks is a risky proposition, as the probability of false positives can be very high.

Determining the identity and pose of occluded objects from noisy data is a critical step in interacting intelligently with an unstructured environment. Previous work has shown that local measurements of position and surface orientation may be used in a constrained search process to solve this problem, for the case of rigid objects, either two-dimensional or three-dimensional. This paper considers the more general problem of recognizing and locating objects that can vary in parameterized ways. We consider objects with rotational, translational, or scaling degrees of freedom, and objects that undergo stretching transformations. We show that the constrained search method can be extended to handle the recognition and localization of such generalized classes of object families.

Determining the identity and pose of occluded objects from noisy data is a critical part of a system's intelligent interaction with an unstructured environment. Previous work has shown that local measurements of the position and surface orientation of small patches of an object's surface may be used in a constrained search process to solve this problem for the case of rigid polygonal objects using two-dimensional sensory data, or rigid polyhedral objects using three-dimensional data. This note extends the recognition system to deal with the problem of recognizing and locating curved objects. The extension is done in two dimensions, and applies to the recognition of two-dimensional objects from two-dimensional data, or to the recognition of three-dimensional objects in stable positions from two- dimensional data.

When we observe or use something, its property seems to change because of the way we establish relationships with it. Inspired by the Uncertainty Principle – a physical theory published by Heisenberg in the year of 1927 – I take both people and objects as something always in an uncertain status. We cannot fully define objects, but only try to understand and live with it in a complex and constantly changing context.Three pieces of furniture are created to visualize the idea about how the relationships between people and objects could be from this viewpoint, exploring how far away people could accept the imperfect but surprising experience of the world.

Recognition and tracking of multiple objects in video sequences is one of the main challenges in computer vision that currently deserves a lot of attention from researchers. Almost all the reported approaches are very application-dependent and there is a lack of a general methodology for dynamic object recognition and tracking that can be instantiated in particular cases. In this thesis, the work is oriented towards the definition and development of such a methodology which integrates object recognition and tracking from a general perspective using a probabilistic framework called PIORT (probabilistic integrated object recognition and tracking framework). It include some modules for which a variety of techniques and methods can be applied. Some of them are well-known but other methods have been designed, implemented and tested during the development of this thesis.The first step in the proposed framework is a static recognition module that provides class probabilities for each pixel of the image from a set of local features. These probabilities are updated dynamically and supplied to a tracking decision module capable of handling full and partial occlusions. The two specific methods presented use RGB colour features and differ in the classifier implemented: one is a Bayesian method based on maximum likelihood and the other one is based on a neural network. The experimental results obtained have shown that, on one hand, the neural net based approach performs similarly and sometimes better than the Bayesian approach when they are integrated within the tracking framework. And on the other hand, our PIORT methods have achieved better results when compared to other published tracking methods. All these methods have been tested experimentally in several test video sequences taken with still and moving cameras and including full and partial occlusions of the tracked object in indoor and outdoor scenarios in a variety of cases with different levels of task complexity. This allowed the evaluation of the general methodology and the alternative methods that compose these modules.A Probabilistic Integrated Object Recognition and Tracking Framework for Video Sequences / El reconocimiento y seguimiento de múltiples objetos en secuencias de vídeo es uno de los principales desafíos en visión por ordenador que actualmente merece mucha atención de los investigadores. Casi todos los enfoques reportados son muy dependientes de la aplicación y hay carencia de una metodología general para el reconocimiento y seguimiento dinámico de objetos, que pueda ser instanciada en casos particulares. En esta tesis, el trabajo esta orientado hacia la definición y desarrollo de tal metodología, la cual integra reconocimiento y seguimiento de objetos desde una perspectiva general usando un marco probabilístico de trabajo llamado PIORT (Probabilistic Integrated Object Recognition and Tracking). Este incluye algunos módulos para los que se puede aplicar una variedad de técnicas y métodos. Algunos de ellos son bien conocidos, pero otros métodos han sido diseñados, implementados y probados durante el desarrollo de esta tesis.El primer paso en el marco de trabajo propuesto es un módulo estático de reconocimiento que provee probabilidades de clase para cada píxel de la imagen desde un conjunto de características locales. Estas probabilidades son actualizadas dinámicamente y suministradas a un modulo decisión de seguimiento capaz de manejar oclusiones parciales o totales. Se presenta dos métodos específicos usando características de color RGB pero diferentes en la implementación del clasificador: uno es un método Bayesiano basado en la máxima verosimilitud y el otro método está basado en una red neuronal. Los resultados experimentales obtenidos han mostrado que, por una parte, el enfoque basado en la red neuronal funciona similarmente y algunas veces mejor que el enfoque bayesiano cuando son integrados dentro del marco probabilístico de seguimiento. Por otra parte, nuestro método PIORT ha alcanzado mejores resultados comparando con otros métodos de seguimiento publicados. Todos estos métodos han sido probados experimentalmente en varias secuencias de vídeo tomadas con cámaras fijas y móviles incluyendo oclusiones parciales y totales del objeto a seguir, en ambientes interiores y exteriores, en diferentes tareas y niveles de complejidad. Esto ha permitido evaluar tanto la metodología general como los métodos alternativos que componen sus módulos.

Object individuation, the capacity to determine whether two perceptual encounters belong to the same object or two different objects, is one of the most basic cognitive abilities and provides a foundation for infants’ understanding of the physical world. Yet very little work has been done to explore infants’ use of auditory information to individuate objects. The first research to investigate infants’ use of sound information to individuate objects was reported by Wilcox et al. (2006), who used a violation-ofexpectation task to examine the extent to which 4.5-month-olds use differences in sound to individuate objects. The results suggested that 4.5-month-olds use property-rich sounds (sounds intimately related to an objects’ physical, amodal properties) but not property-poor sounds (sounds that are more contrived) to distinguish the identity of objects involved in occlusion events. The current study investigated infants’ sensitivity to these two types of sounds within the context of a search task. Three experiments were conducted with infants aged 5 to 7 months. The outcome of these experiments builds and extends on the findings of Wilcox et al. in three ways. First, converging evidence was obtained, using a search task, that young infants are more sensitive to property-rich than property-poor sounds. Second, more detailed information was obtained on infants’ interpretation of samesounds events (two identical, rather than two different, sounds). Finally, possible explanations for infants’ greater sensitivity to property-rich sounds were assessed. The outcome of these studies, collectively, provides insight into the types of sounds that infants use to identify objects and the reasons why some sounds are more salient to infants than others.