An EyeTap video-based featureless projective motion estimation assisted by gyroscopic tracking for wearable computer mediated reality

In this paper we present a computationally economical method of recovering the projective motion of head mounted cameras or EyeTap devices, for use in wearable computer-mediated reality. The tracking system combines featureless vision and inertial methods in a closed loop system to achieve accurate robust head tracking using inexpensive sensors. The combination of inertial and vision techniques provides the high accuracy visual registration needed for fitting computer graphics onto real images and the robustness to large interframe camera motion due to fast head rotations. Operating on a 1.2 GHz Pentium III wearable computer with graphics accelerated hardware, the system is able to register live video images with less than 2 pixels of error (0.3 degrees) at 12 frames per second. Fast image registration is achieved by offloading computer vision computation onto the graphics hardware, which is readily available on many wearable computer systems. As an application of this tracking approach, we present a system which allows wearable computer users to share views of their current environments that have been stabilised to another viewer's head position.

Joint estimation of motion and radiometry of rotating camera for HDR global mosaic

In this paper, we present a global approach for constructing high dynamic range mosaics from multiple images with large exposure differences. To minimize registration errors caused by intensity mismatches in the image intensity space with low dynamic range, we propose the use of a scene radiance space with high dynamic range. By relating image intensities to scene radiances with a convenient distortion model, we robustly estimate registration parameters for the high dynamic range global mosaic, simultaneously estimating scene radiances and distortion parameters in a single framework using a computationally optimized Levenberg–Marquardt approach.

An FPGA-based architecture for a local tone-mapping operator

This paper presents an FPGA-based architecture for local tone mapping of gray scale high dynamic range images. The architecture is described in VHDL and has been synthesized using Altera Quartus tools. It achieves an operating frequency consistent with a video rate of 60 frames per second for a frame of 1,024 × 768 pixels. The proposed architecture is a modification of the nine-scale Reinhard operator. Approximations to the original Reinhard operator ensure that the operator is amenable to implementation in hardware. A peak signal-to-noise ratio study shows that our fixed-point hardware approximation produces results similar to a floating-point original.

Impact of platform heterogeneity on the design of parallel algorithms for morphological processing of high-dimensional image data

The main objective of this paper is to describe a realistic framework to understand parallel performance of high-dimensional image processing algorithms in the context of heterogeneous networks of workstations (NOWs). As a case study, this paper explores techniques for mapping hyperspectral image analysis techniques onto fully heterogeneous NOWs. Hyperspectral imaging is a new technique in remote sensing that has gained tremendous popularity in many research areas, including satellite imaging and aerial reconnaissance. The automation of techniques able to transform massive amounts of hyperspectral data into scientific understanding in valid response times is critical for space-based Earth science and planetary exploration. Using an evaluation strategy which is based on comparing the efficiency achieved by an heterogeneous algorithm on a fully heterogeneous NOW with that evidenced by its homogeneous version on a homogeneous NOW with the same aggregate performance as the heterogeneous one, we develop a detailed analysis of parallel algorithms that integrate the spatial and spectral information in the image data through mathematical morphology concepts. For comparative purposes, performance data for the tested algorithms on Thunderhead (a large-scale Beowulf cluster at NASA’s Goddard Space Flight Center) are also provided. Our detailed investigation of the parallel properties of the proposed morphological algorithms provides several intriguing findings that may help image analysts in selection of parallel techniques and strategies for specific applications.

Convex regularization-based hardware/software co-design for real-time enhancement of remote sensing imagery

In this paper, we address a new approach for high-resolution reconstruction and enhancement of remote sensing (RS) imagery in near-real computational time based on the aggregated hardware/software (HW/SW) co-design paradigm. The software design is aimed at the algorithmic-level decrease of the computational load of the large-scale RS image enhancement tasks via incorporating into the fixed-point iterative reconstruction/enhancement procedures the convex convergence enforcement regularization by constructing the proper projectors onto convex sets (POCS) in the solution domain. The established POCS-regularized iterative techniques are performed separately along the range and azimuth directions over the RS scene frame making an optimal use of the sparseness properties of the employed sensor system modulation format. The hardware design is oriented on employing the Xilinx Field Programmable Gate Array XC4VSX35-10ff668 and performing the image enhancement/reconstruction tasks in a computationally efficient parallel fashion that meets the near-real time imaging system requirements. Finally, we report some simulation results and discuss the implementation performance issues related to enhancement of the real-world RS imagery indicative of the significantly increased performance efficiency gained with the developed approach.

FPGA-based real-time 3D image preprocessing for image-guided medical interventions

Minimally invasive image-guided interventions (IGIs) are time and cost efficient, minimize unintended damage to healthy tissue, and lead to faster patient recovery. One emerging trend in IGI workflow is to use volumetric imaging modalities such as low-dose computed tomography (CT) and 3D ultrasound to provide real-time, accurate anatomical information intraoperatively. These intraoperative images, however, are often characterized by quantum (in low-dose CT) or speckle (in ultrasound) noise and must be enhanced prior to any advanced image processing. Anisotropic diffusion filtering and median filtering have been shown to be effective in enhancing and improving the visual quality of these images. However, achieving real-time performance, as required by IGIs, using software-only implementations is challenging because of the sheer size of the images and the arithmetic complexity of the filtering operations. We present a field-programmable gate array-based reconfigurable architecture for real-time preprocessing of intraoperative 3D images. The proposed architecture provides programmable kernels for 3D anisotropic diffusion filtering and 3D median filtering within the same framework. The implementation of this architecture using an Altera Stratix-II device achieved a voxel processing rate close to 200 MHz, which enables the use of these processing techniques in the IGI workflow prior to advanced operations such as segmentation, registration, and visualization.

Object Extraction Using a Stochastic Birth-and-Death Dynamics in Continuum

We define a new birth and death dynamics dealing with configurations of disks in the plane. We prove the convergence of the continuous process and propose a discrete scheme converging to the continuous case. This framework is developed to address image processing problems consisting in detecting a configuration of objects from a digital image. The derived algorithm is applied for tree crown extraction and bird detection from aerial images. The performance of this approach is shown on real data.

Designing a novel high-performance FPGA architecture for data intensive applications

A wide variety of real-time applications (e.g. multimedia, communication, etc.) require implementations that meet tight timing constraints. This work introduces novel high-performance FPGA architecture capable of implementing efficiently any time critical application. The fundamental contribution of the proposed reconfigurable architecture is the design of a highly efficient (performance and power consumption) interconnection structure, taking into consideration the statistical and spatial data extracted from applications, which are implemented on Virtex FPGAs. The derived architecture is software-supported by the MEANDER design framework. Using a number of real-time applications, extensive comparison study in terms of several design parameters proves the effectiveness of the proposed architecture against to Virtex one. More specifically, the proposed architecture achieves performance improvement and power savings up to 20 and 16%, respectively. Moreover, compared to a Virtex architecture with same power budget, our architecture achieves performance improvement by 42%.

Methods for quantitative usability requirements: a case study on the development of the user interface of a mobile phone

Quantitative usability requirements are a critical but challenging, and hence an often neglected aspect of a usability engineering process. A case study is described where quantitative usability requirements played a key role in the development of a new user interface of a mobile phone. Within the practical constraints of the project, existing methods for determining usability requirements and evaluating the extent to which these are met, could not be applied as such, therefore tailored methods had to be developed. These methods and their applications are discussed.

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Evolution of 3D mobile games development

3D computer graphics have been an important feature in games development since it was first introduced in the early 80s and there is no doubt that 3D based content is often viewed as more attractive in games than the more abstract 2D graphics. Many games publishers are keen to leverage their success in the console market into the mobile phone platform. However, the resource constraints of mobile phones and the fragmented nature of the environment present considerable challenges for games developers. In this paper we consider some of the current constraints together with current and, probable, future developments both in the software and hardware of mobile phones. As part of this process we benchmark some of the latest and most prevalent software and hardware devices to ascertain both the quality of the graphics produced and the effects upon battery life. Whilst our test results highlight that the current market does indeed present challenges, our research into the future developments highlights the fact we are approaching greater standardization, which will be an important factor for the successful development of 3D mobile games.

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LuxTrace: indoor positioning using building illumination

Tracking location is challenging due to the numerous constraints of practical systems including, but not limited to global cost, device volume and weight, scalability and accuracy; these constraints are typically more severe for systems that should be wearable and used indoors. We investigate the use of wearable solar cells to track changing light conditions (a concept that we named LuxTrace) as a source of user displacement and activity data. We evaluate constraints of this approach and present results from an experimental validation of displacement and activity estimation. The results indicate that a distance estimation accuracy of 21 cm (80% quantile) can be achieved. A simple method to combine LuxTrace with complementary absolute location estimation methods is also presented. We apply carpet-like distributed RFID tags to demonstrate online learning of new lighting environments.

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Questionnaire-based variability modeling for system configuration

Variability management is a recurrent issue in systems engineering. It arises for example in enterprise systems, where modules are configured and composed to meet the requirements of individual customers based on modifications to a reference model. It also manifests itself in the context of software product families, where variants of a system are built from a common code base. This paper proposes an approach to capture system variability based on questionnaire models that include order dependencies and domain constraints. The paper presents analysis techniques to detect circular dependencies and contradictory constraints in questionnaire models, as well as techniques to incrementally prevent invalid configurations by restricting the space of allowed answers to a question based on previous answers. The approach has been implemented as a toolset and has been used in practice to capture configurable process models for film post-production.

A multi-agent platform for content-based image retrieval

Efficient and possibly intelligent image retrieval is an important task, often required in many fields of human activity. While traditional database indexing techniques exhibit a remarkable performance in textual information retrieval current research in content-based image retrieval is focused on developing novel techniques that are biologically motivated and efficient. It is well known that humans have a remarkable ability to process visual information and to handle the volume and complexity of such information quite efficiently. In this paper, we present a content-based image retrieval platform that is based on a multi-agent architecture. Each agent is responsible for assessing the similarity of the query image to each candidate image contained in a collection based on a specific primitive feature and a corresponding similarity criterion. The outputs of various agents are integrated using one of several voting schemes supported by the system. The system’s performance has been evaluated using various collections of images, as well as images obtained in specific application domains such as medical imaging. The initial evaluation has yielded very promising results.

Legal safeguards for privacy and data protection in ambient intelligence

To get the maximum benefit from ambient intelligence (AmI), we need to anticipate and react to possible drawbacks and threats emerging from the new technologies in order to devise appropriate safeguards. The SWAMI project took a precautionary approach in its exploration of the privacy risks in AmI and sought ways to reduce them. It constructed four “dark scenarios” showing possible negative implications of AmI, notably for privacy protection. Legal analysis of the depicted futures showed the shortcomings of the current legal framework in being able to provide adequate privacy protection in the AmI environment. In this paper, the authors, building upon their involvement in SWAMI research as well as the further advancement of EU privacy analysis, identify various outstanding issues regarding the legal framework that still need to be resolved in order to deal with AmI in an equitable and efficacious way. This article points out some of the lacunae in the legal framework and postulates several privacy-specific safeguards aimed at overcoming them.

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The 2007 IEEE CEC simulated car racing competition

This paper describes the simulated car racing competition that was arranged as part of the 2007 IEEE Congress on Evolutionary Computation. Both the game that was used as the domain for the competition, the controllers submitted as entries to the competition and its results are presented. With this paper, we hope to provide some insight into the efficacy of various computational intelligence methods on a well-defined game task, as well as an example of one way of running a competition. In the process, we provide a set of reference results for those who wish to use the simplerace game to benchmark their own algorithms. The paper is co-authored by the organizers and participants of the competition.

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Multimodal support to group dynamics

The complexity of group dynamics occurring in small group interactions often hinders the performance of teams. The availability of rich multimodal information about what is going on during the meeting makes it possible to explore the possibility of providing support to dysfunctional teams from facilitation to training sessions addressing both the individuals and the group as a whole. A necessary step in this direction is that of capturing and understanding group dynamics. In this paper, we discuss a particular scenario, in which meeting participants receive multimedia feedback on their relational behaviour, as a first step towards increasing self-awareness. We describe the background and the motivation for a coding scheme for annotating meeting recordings partially inspired by the Bales’ Interaction Process Analysis. This coding scheme was aimed at identifying suitable observable behavioural sequences. The study is complemented with an experimental investigation on the acceptability of such a service.

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A low-power wireless video sensor node for distributed object detection

In this paper we propose MicrelEye, a wireless video node for cooperative distributed video processing applications that involve image classification. The node is equipped with a low-cost VGA CMOS image sensor, a reconfigurable processing engine (FPGA, Microcontroller, SRAM) and a Bluetooth 100-m transceiver. It has a size of few cubic centimeters and its typical power consumption is approximately ten times less than that of typical commercial DSP-based solutions. As regards classification, a highly optimized hardware-oriented support vector machine-like (SVM-like) algorithm called ERSVM is proposed and implemented. We describe our hardware and software architecture, its performance and power characteristics. The case study considered in this paper is people detection. The obtained results suggest that the present technology allows for the design of simple intelligent video nodes capable of performing classification tasks locally.