Geometric and Algebraic Aspects of 3D Affine and Projective Structures from Perspective 2D Views

We investigate the differences --- conceptually and algorithmically --- between affine and projective frameworks for the tasks of visual recognition and reconstruction from perspective views. It is shown that an affine invariant exists between any view and a fixed view chosen as a reference view. This implies that for tasks for which a reference view can be chosen, such as in alignment schemes for visual recognition, projective invariants are not really necessary. We then use the affine invariant to derive new algebraic connections between perspective views. It is shown that three perspective views of an object are connected by certain algebraic functions of image coordinates alone (no structure or camera geometry needs to be involved)

Identifying Problems Associated with Focus and Context Awareness in 3D Modelling Tasks

Creating complex 3D models is a challenging process. One of the main reasons for this is that 3D models are usually created using software developed for conventional 2D displays which lack true depth perspective, and therefore do not support correct perception of spatial placement and depth-ordering of displayed content. As a result, modellers often have to deal with many overlapping components of 3D models (e.g. vertices, edges, faces, etc.) on a 2D display surface. This in turn causes them to have difficulties in distinguishing distances, maintaining position and orientation awareness, etc. To better understand the nature of these problems, which can collectively be defined as ‘focus and context awareness’ problems, we have conducted a pilot study with a group of novice 3D modellers, and a series of interviews with a group of professional 3D modellers. This article presents these two studies, and their findings, which have resulted in identifying a set of focus and context awareness problems that modellers face in creating 3D models using conventional modelling software. The article also provides a review of potential solutions to these problems in the related literature

Motion sequence analysis in the presence of figural cues

Published in final edited form as: Neurocomputing. 2015 January 5, 147: 485–491The perception of 3-D structure in dynamic sequences is believed to be subserved primarily through the use of motion cues. However, real-world sequences contain many figural shape cues besides the dynamic ones. We hypothesize that if figural cues are perceptually significant during sequence analysis, then inconsistencies in these cues over time would lead to percepts of non-rigidity in sequences showing physically rigid objects in motion. We develop an experimental paradigm to test this hypothesis and present results with two patients with impairments in motion perception due to focal neurological damage, as well as two control subjects. Consistent with our hypothesis, the data suggest that figural cues strongly influence the perception of structure in motion sequences, even to the extent of inducing non-rigid percepts in sequences where motion information alone would yield rigid structures. Beyond helping to probe the issue of shape perception, our experimental paradigm might also serve as a possible perceptual assessment tool in a clinical setting.The authors wish to thank all observers who participated in the experiments reported here. This research and the preparation of this manuscript was supported by the National Institutes of Health RO1 NS064100 grant to LMV. (RO1 NS064100 - National Institutes of Health)Accepted manuscrip

Exploring perceptions and attitudes towards teaching and learning manual technical drawing in a digital age

This paper examines the place of manual technical drawing in the 21st century by discussing the perceived value and relevance of teaching school students how to draw using traditional instruments, in a world of computer aided drafting (CAD). Views were obtained through an e-survey, questionnaires and structured interviews. The sample groups represent professional CAD users (e.g. engineers, architects); university lecturers; Technology Education teachers and student teachers; and school students taking Scottish Qualification Authority (SQA) Graphic Communication courses. An analysis of these personal views and attitudes indicates some common values between the various groups canvassed of what instruction in traditional manual technical drafting contributes towards learning. Themes emerge such as problem solving, visualisation, accuracy, co-ordination, use of standard conventions, personal discipline and artistry. In contrast to the assumptions of Prensky's thesis (2001a&b) of digital natives, the study reported in this paper indicate that the school students apparently appreciate the experience of traditional drafting. In conclusion, the paper illustrates the perceived value of such learning in terms of transferable skills, personal achievement and enjoyment

Supporting Focus and Context Awareness in 3D Modeling Using Multi-Layered Displays

Although advances in computer technology over the past few decades have made it possible to create and render highly realistic 3D models these days, the process of creating these models has remained largely unchanged over the years. Modern 3D modeling software provide a range of tools to assist users with creating 3D models, but the process of creating models in virtual 3D space is nevertheless still challenging and cumbersome. This thesis, therefore, aims to investigate whether it is possible to support modelers more effectively by providing them with alternative combinations of hardware and software tools to improve their 3D modeling tasks. The first step towards achieving this goal has been to better understand the type of problems modelers face in using conventional 3D modeling software. To achieve this, a pilot study of novice 3D modelers, and a more comprehensive study of professional modelers were conducted. These studies resulted in identifying a range of focus and context awareness problems that modelers face in creating complex 3D models using conventional modeling software. These problems can be divided into four categories: maintaining position awareness, identifying and selecting objects or components of interest, recognizing the distance between objects or components, and realizing the relative position of objects or components. Based on the above categorization, five focus and context awareness techniques were developed for a multi-layer computer display to enable modelers to better maintain their focus and context awareness while performing 3D modeling tasks. These techniques are: object isolation, component segregation, peeling focus, slicing, and peeling focus and context. A user study was then conducted to compare the effectiveness of these focus and context awareness techniques with other tools provided by conventional 3D modeling software. The results of this study were used to further improve, and evaluate through a second study, the five focus and context awareness techniques. The two studies have demonstrated that some of these techniques are more effective in supporting 3D modeling tasks than other existing software tools

Simultaneous Multi-View Object Recognition and Grasping in Open-Ended Domains

A robot working in human-centric environments needs to know which kind of objects exist in the scene, where they are, and how to grasp and manipulate various objects in different situations to help humans in everyday tasks. Therefore, object recognition and grasping are two key functionalities for such robots. Most state-of-the-art tackles object recognition and grasping as two separate problems while both use visual input. Furthermore, the knowledge of the robot is fixed after the training phase. In such cases, if the robot faces new object categories, it must retrain from scratch to incorporate new information without catastrophic interference. To address this problem, we propose a deep learning architecture with augmented memory capacities to handle open-ended object recognition and grasping simultaneously. In particular, our approach takes multi-views of an object as input and jointly estimates pixel-wise grasp configuration as well as a deep scale- and rotation-invariant representation as outputs. The obtained representation is then used for open-ended object recognition through a meta-active learning technique. We demonstrate the ability of our approach to grasp never-seen-before objects and to rapidly learn new object categories using very few examples on-site in both simulation and real-world settings.Comment: arXiv admin note: text overlap with arXiv:2103.1099

Vehicle Detection and Tracking Techniques: A Concise Review

Vehicle detection and tracking applications play an important role for civilian and military applications such as in highway traffic surveillance control, management and urban traffic planning. Vehicle detection process on road are used for vehicle tracking, counts, average speed of each individual vehicle, traffic analysis and vehicle categorizing objectives and may be implemented under different environments changes. In this review, we present a concise overview of image processing methods and analysis tools which used in building these previous mentioned applications that involved developing traffic surveillance systems. More precisely and in contrast with other reviews, we classified the processing methods under three categories for more clarification to explain the traffic systems