Efficient algorithms for subwindow search in object detection and localization

Recently, a simple yet powerful branch-and-bound method called Efficient Subwindow Search (ESS) was developed to speed up sliding window search in object detection. A major drawback of ESS is that its computational complexity varies widely from O(n²) to O(n⁴) for n × n matrices. Our experimental experience shows that the ESS's performance is highly related to the optimal confidence levels which indicate the probability of the object's presence. In particular, when the object is not in the image, the optimal subwindow scores low and ESS may take a large amount of iterations to converge to the optimal solution and so perform very slow. Addressing this problem, we present two significantly faster methods based on the linear-time Kadane's Algorithm for 1D maximum subarray search. The first algorithm is a novel, computationally superior branchand- bound method where the worst case complexity is reduced to O(n³). Experiments on the PASCAL VOC 2006 data set demonstrate that this method is significantly and consistently faster (approximately 30 times faster on average) than the original ESS. Our second algorithm is an approximate algorithm based on alternating search, whose computational complexity is typically O(n²). Experiments shows that (on average) it is 30 times faster again than our first algorithm, or 900 times faster than ESS. It is thus wellsuited for real time object detection.

Combining image regions and human activity for indirect object recognition in indoor wide-angle views

Traditional methods of object recognition are reliant on shape and so are very difficult to apply in cluttered, wideangle and low-detail views such as surveillance scenes. To address this, a method of indirect object recognition is proposed, where human activity is used to infer both the location and identity of objects. No shape analysis is necessary. The concept is dubbed 'interaction signatures', since the premise is that a human will interact with objects in ways characteristic of the function of that object - for example, a person sits in a chair and drinks from a cup. The human-centred approach means that recognition is possible in low-detail views and is largely invariant to the shape of objects within the same functional class. This paper implements a Bayesian network for classifying region patches with object labels, building upon our previous work in automatically segmenting and recognising a human's interactions with the objects. Experiments show that interaction signatures can successfully find and label objects in low-detail views and are equally effective at recognising test objects that differ markedly in appearance from the training objects.

Literary praxis : engaging with texts in classroom settings

This article focuses on a small group of teachers as they reflect on the strategies they use to support their students in their efforts to interpret literary texts. We argue that the interpretation of literary texts within classroom settings is mediated in complex ways: by the social context of the classroom, the insti-tutional setting of the school (including its curriculum and organization), as well as mandated educa-tional policies. These dimensions shape the relationships between teachers and students as they engage in the ‘social exchange of meanings’ (Reid, 1984) that is prompted by the texts chosen for study. Stu-dents bring their own biographies to this exchange, drawing on their experiences outside school in order to make meanings from the texts they are required to read. Teachers, on the other hand, also bring their biographies with them into classrooms, including their beliefs about the value of a literary educa-tion. By exploring the reflections in which a small group of teachers of literature engage about their work, we ask questions about the value of a literary education, reaffirming its significance in the con-temporary world.

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.