Improving Unsupervised Learning With Exemplar CNNS

Most recent unsupervised learning methods explore alternative objectives, often referred to as self-supervised tasks, to train convolutional neural networks without the supervision of human annotated labels. This paper explores the generation of surrogate classes as a self-supervised alternative to learn discriminative features, and proposes a clustering algorithm to overcome one of the main limitations of this kind of approach. Our clustering technique improves the initial implementation and achieves 76.4% accuracy in the STL-10 test set, surpassing the current state-ofthe- art for the STL-10 unsupervised benchmark. We also explore several issues with the unlabeled set from STL-10 that should be considered in future research using this dataset

Sensor node localisation using a stereo camera rig

In this paper, we use stereo vision processing techniques to detect and localise sensors used for monitoring simulated environmental events within an experimental sensor network testbed. Our sensor nodes communicate to the camera through patterns emitted by light emitting diodes (LEDs). Ultimately, we envisage the use of very low-cost, low-power, compact microcontroller-based sensing nodes that employ LED communication rather than power hungry RF to transmit data that is gathered via existing CCTV infrastructure. To facilitate our research, we have constructed a controlled environment where nodes and cameras can be deployed and potentially hazardous chemical or physical plumes can be introduced to simulate environmental pollution events in a controlled manner. In this paper we show how 3D spatial localisation of sensors becomes a straightforward task when a stereo camera rig is used rather than a more usual 2D CCTV camera

Remote real-time monitoring of subsurface landfill gas migration

The cost of monitoring greenhouse gas emissions from landfill sites is of major concern for regulatory authorities. The current monitoring procedure is recognised as labour intensive, requiring agency inspectors to physically travel to perimeter borehole wells in rough terrain and manually measure gas concentration levels with expensive hand-held instrumentation. In this article we present a cost-effective and efficient system for remotely monitoring landfill subsurface migration of methane and carbon dioxide concentration levels. Based purely on an autonomous sensing architecture, the proposed sensing platform was capable of performing complex analytical measurements in situ and successfully communicating the data remotely to a cloud database. A web tool was developed to present the sensed data to relevant stakeholders. We report our experiences in deploying such an approach in the field over a period of approximately 16 months. Copyright 2011 by the authors; licensee MDPI, Basel, Switzerland

Conformational stability of heme proteins in vacuo

NRC publication: Ye

An intuitive user interface for visual sports coaching

This paper describes a dynamic multi-video user interface for sports coaching. It is intended that sports coaches could use this split screen to minimise and maximise multiple video streams of an athlete on one side of the split screen, while playing an additional video source on the other side of the split screen, such as a clip from a professional athlete. This split screen approach allows users to contrast movements in the athletes videos, with that of a professional. Users can also avail of the ability to use video overlays, text input and can also use screen capture technology to record the application display, so that an athlete can review a coaching session at later date

TREAT: Terse Rapid Edge-Anchored Tracklets

Fast computation, efficient memory storage, and performance on par with standard state-of-the-art descriptors make binary descriptors a convenient tool for many computer vision applications. However their development is mostly tailored for static images. To respond to this limitation, we introduce TREAT (Terse Rapid Edge-Anchored Tracklets), a new binary detector and descriptor, based on tracklets. It harnesses moving edge maps to perform efficient feature detection, tracking, and description at low computational cost. Experimental results on 3 different public datasets demonstrate improved performance over other popular binary features. These experiments also provide a basis for benchmarking the performance of binary descriptors in video-based applications

Evaluating the Evaluator: Modelling Systematic Data Analysis Strategies for Software Selection

Developers of interactive software are confronted by an increasing variety of software tools to help engineer the interactive aspects of software applications. Not only do these tools fall into different categories in terms of functionality, but within each category there is a growing number of competing tools with similar, although not identical, features. Choice of user interface development tool (UIDT) is therefore becoming increasingly complex.Les d\ue9veloppeurs de logiciels interactifs disposent d'un \ue9ventail de plus en plus vaste d'outils logiciels pour les aider \ue0 concevoir les aspects interactifs des applications logicielles. Non seulement ces outils sont-ils class\ue9s dans des cat\ue9gories diff\ue9rentes selon leurs fonctionnalit\ue9s, mais on retrouve aussi \ue0 l'int\ue9rieur de chacune de ces cat\ue9gories un nombre croissant d'outils dot\ue9s de caract\ue9ristiques semblables bien que non identiques. Il devient donc de plus en plus difficile de choisir un outil de d\ue9veloppement d'interfaces utilisateurs (UIDT).NRC publication: Ye

3D estimation and visualization of motion in a multicamera network for sports

In this work, we develop image processing and computer vision techniques for visually tracking a tennis ball, in 3D, on a court instrumented with multiple low cost IP cameras. The technique first extracts 2D ball track data from each camera view, using object tracking methods. Next, an automatic feature- based video synchronization method is applied. This technique uses both the extracted 2D ball information from two or more camera views, plus camera calibration information. Then, in order to find 3D trajectory, the temporal 3D locations of the ball is estimated using triangulation of correspondent 2D locations obtained from automatically synchronized videos. Furthermore, we also incorporate a physics-based trajectory model into the system to improve the continuity of the tracked 3D ball during times when no two cameras have overlapping views of the ball location. The resultant 3D ball tracks are then visualized in a virtual 3D graphical environment. Finally, we quantify the accuracy of our system in terms of reprojection error

A multiscale representation method for nonrigid shapes with a single closed contour

In this paper, we discuss the criteria that should be satisfied by a descriptor for nonrigid shapes with a single closed contour. We then propose a shape representation method that fulfills these criteria. In the proposed approach, contour convexities and concavities at different scale levels are represented using a two-dimensional (2-D) matrix. The representation can be visualized as a 2-D surface, where "hills" and "valleys" represent contour convexities and concavities, respectively. The optimal matching of two shape representations is achieved using dynamic programming and a dissimilarity measure is defined based on this matching. The proposed algorithm is very efficient and invariant to several kinds of transformations including some articulations and modest occlusions. The retrieval performance of the approach is illustrated using the MPEG-7 shape database, which is one of the most complete shape databases currently available. Our experiments indicate that the proposed representation is well suited for object indexing and retrieval in large databases. Furthermore, the representation can be used as a starting point to obtain more compact descriptors

Evaluating a dancer\u27s performance using Kinect-based skeleton tracking

In this work, we describe a novel system that automatically evaluates dance performances against a gold-standard performance and provides visual feedback to the performer in a 3D virtual environment. The system acquires the motion of a performer via Kinect-based human skeleton tracking, making the approach viable for a large range of users, including home enthusiasts. Unlike traditional gaming scenarios, when the motion of a user must by kept in synch with a pre-recorded avatar that is displayed on screen, the technique described in this paper targets online interactive scenarios where dance choreographies can be set, altered, practiced and refined by users. In this work, we have addressed some areas of this application scenario. In particular, a set of appropriate signal processing and soft computing methodologies is proposed for temporally aligning dance movements from two different users and quantitatively evaluating one performance against another