OVIE : object-oriented and vector-based image editing

A powerful image editing system called OVIE is described, which provides fast and accurate creation, composition, rendering and other manipulation of image contents. Flexibility and convenience of the system are achieved by including two modules: image decomposition and image vectorization to understand and represent an image respectively. To understand an image comprehensively, we propose to integrate image segmentation, shape completion and image completion techniques to ensure a seamless image editing. An array of pixels is replaced by vector data with geometric edit ability for image representation since the geometrically-based editing has physical meanings and thus it is more natural or intuitive for users to edit. Compared to the existing works, our system is more convenient and can generate effects with higher quality. © 2012 IEEE.

Localisation of an object in motion in 3D space via angle only measurements

In this paper, we discuss an approach to localisation of a moving object in 3D space. The method used to track this object is angle only measurements obtained via radar elements strategically placed within the X, Y and Z planes. In addition, computer simulations are conducted to verify the theoretical assertions presented with respect to the application employing an Extended Kalman Filter.

Looking for meaning: the found object in animation

This paper will focus upon the use of found objects in stop-motion animation. It will survey a number of found-object animated films, exploring how the viewer might closely identify with such objects in motion, as well as attributing to them multiple meanings. This analysis will be furthered through the consideration of an object-orientated phenomenological perspective, referencing Graham Harman and Martin Heidegger. It will also consider how the cinema studies concept of star studies might be applied to the use of found objects in animation as a means of detecting additional layers of meaning.

Object categorization via sparse representation of local features

Sparse representation has been introduced to address many recognition problems in computer vision. In this paper, we propose a new framework for object categorization based on sparse representation of local features. Unlike most of previous sparse coding based methods in object classification that only use sparse coding to extract high-level features, the proposed method incorporates sparse representation and classification into a unified framework. Therefore, it does not need a further classifier. Experimental results show that the proposed method achieved better or comparable accuracy than the well known bag-of-features representation with various classifiers.

The mutual cultivation of subject and object in architecture

The relationship of the migrant figure and the house has been explored in previous research on the migrant house. In addition, the relationship between the migrant house and 1ts perception and representation within the local and national communities within the immigrant-receiving city has been analyzed. The approach and theoretical frameworks for this research involved an understanding of the relationship between the human subject and the object of architecture. Given that much poststructuralist theory has attended to the rethinking of human subjectivity and to rethinking the humanbody, traditional conceptualizations of the relationship between human subject and Object m architecture also need to be re-examined. This paper thus presents an exammatlon of one approach to this relationship, focusing on the exploratory and creative work of the late John Hejduk. Hejduk's Vladivostok presents an assembly of figure-objects that together are a literary metaphor for both society and the city. The paper Will argue that the human subject is imagined within an already ordered architectural sett1ng, and that the human subject cannot be thought prior to and outside the stage of architecture. Rather than searching for a causal or determining relat1onsh1p between subject and object this conceptual underpinning between the subject and object asks for an understanding of the dynamic and animated cultivation between them. In examining Hejduk's entry on the 'subjecUobject' in Vladivostok, the paper Will present an account of the mutual responsiveness that arises between human beings and architecture. It will argue that culture and subjectivity is internal rather than external or marginal to architecture.

Object matching using boundary descriptors

The problem of object recognition is of immense practical importance and potential, and the last decade has witnessed a number of breakthroughs in the state of the art. Most of the past object recognition work focuses on textured objects and local appearance descriptors extracted around salient points in an image. These methods fail in the matching of smooth, untextured objects for which salient point detection does not produce robust results. The recently proposed bag of boundaries (BoB) method is the first to directly address this problem. Since the texture of smooth objects is largely uninformative, BoB focuses on describing and matching objects based on their post-segmentation boundaries. Herein we address three major weaknesses of this work. The first of these is the uniform treatment of all boundary segments. Instead, we describe a method for detecting the locations and scales of salient boundary segments. Secondly, while the BoB method uses an image based elementary descriptor (HoGs + occupancy matrix), we propose a more compact descriptor based on the local profile of boundary normals’ directions. Lastly, we conduct a far more systematic evaluation, both of the bag of boundaries method and the method proposed here. Using a large public database, we demonstrate that our method exhibits greater robustness while at the same time achieving a major computational saving – object representation is extracted from an image in only 6% of the time needed to extract a bag of boundaries, and the storage requirement is similarly reduced to less than 8%.

Multiple-object tracking in cluttered and crowded public spaces

This paper addresses the problem of tracking moving objects of variable appearance in challenging scenes rich with features and texture. Reliable tracking is of pivotal importance in surveillance applications. It is made particularly difficult by the nature of objects encountered in such scenes: these too change in appearance and scale, and are often articulated (e.g. humans). We propose a method which uses fast motion detection and segmentation as a constraint for both building appearance models and their robust propagation (matching) in time. The appearance model is based on sets of local appearances automatically clustered using spatio-kinetic similarity, and is updated with each new appearance seen. This integration of all seen appearances of a tracked object makes it extremely resilient to errors caused by occlusion and the lack of permanence of due to low data quality, appearance change or background clutter. These theoretical strengths of our algorithm are empirically demonstrated on two hour long video footage of a busy city marketplace.

Robotic object grasping in context of human grasping and manipulation

This paper presents experimental and deductive findings that shed new light on grasp force estimation, which improves robot’s chances to grasp and manipulate the object close to optimum conditions on the first attempt, which in turn improves robot’s object manipulation dexterity.
This paper proposes that object slippage detection in the human hand is not detected based purely on microvibrations sensed by the human skin during incipient slippage but also on load sensing at each finger and movement of fingers relative to each other while holding an object.