Mr. Philippe Simard Profile

Philippe Simard

President & Co-founder at SimActive Inc

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Publications (3)

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Proceedings Article | 16 July 2002 Paper

Database verification using an imaging sensor

Philippe Simard, Frank Ferrie

Proceedings Volume 4713, (2002) https://doi.org/10.1117/12.474431

KEYWORDS: Databases, Sensors, Cameras, Image sensors, Data modeling, Motion models, Synthetic vision, Navigation systems, Optical sensors, Buildings

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In aviation, synthetic vision systems produce artificial views of the world to support navigation and situational awareness in poor visibility conditions. Synthetic images of local terrain are rendered from a database and registered through the aircraft navigation system. Because the database reflects, at best, a nominal state of the environment, it needs to be verified to ensure its consistency with reality. This paper presents a technique for real-time verification of databases using a single imaging device, of any type. It is differential and as such, requires motion of the sensor. The geometric information of the database is used to predict how the sensor image should change. If the measured change is different from the predicted change, the database geometry is assumed to be incorrect. Geometric anomalies are localized and their severity is estimated in absolute terms using a minimization process. The technique is tested against real flight data acquired by an helicopter to verify a database consisting of a digital elevation map. Results show that geometric anomalies can be detected and that their location and importance can be evaluated.

Proceedings Article | 28 August 2001 Paper

Online database updating by change detection

Philippe Simard, Frank Ferrie

Proceedings Volume 4363, (2001) https://doi.org/10.1117/12.438014

KEYWORDS: Databases, Sensors, Image sensors, Algorithm development, Synthetic vision, Computer simulations, Visibility, Cameras, Error analysis, Buildings

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Synthetic vision systems render artificial images of the world based on a database and position/attitude information of the aircraft. Due to both its static nature and inherent modelling errors, the database introduces anomalies in the synthetic imagery. Since it reflects at best a nominal state of the environment, it often requires updating via online measurements. The latter can vary from correction of pose and geometry to more complex operations such as marking the locations of detected obstacles. This paper presents an approach for detecting database geometric anomalies online. Since range sensors have a low update rate, they cannot be used for quick validation. Instead of range data, the proposed technique employs an imaging sensor, which can be of any type. It takes advantage of the fact that given a geometric model of the scene and known motion of the observer, the sensor image warping can be exactly predicted. If the geometry of the database is incorrect, the sensor image will not be correctly predicted and geometric differences will thus be detected. The algorithm is tested against simulated imagery and results show that it can correctly identify geometric anomalies.

Proceedings Article | 23 June 2000 Paper

Evaluation of algorithms for fusing infrared and synthetic imagery

Philippe Simard, Norah Link, Ronald Kruk

Proceedings Volume 4023, (2000) https://doi.org/10.1117/12.389336

KEYWORDS: Image sensors, Image fusion, Sensors, Image quality, Image enhancement, Algorithm development, Infrared imaging, Infrared radiation, Image registration, Databases

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Algorithms for image fusion were evaluated as part of the development of an airborne Enhanced/Synthetic Vision System (ESVS) for helicopter Search and Rescue operations. The ESVS will be displayed on a high- resolution, wide field-of-view helmet-mounted display (HMD). The HMD full field-of-view (FOV) will consist of a synthetic image to support navigation and situational awareness, and an infrared image inset will be fused into the center of the FOV to provide real-world feedback and support flight operations at low altitudes. Three fusion algorithms were selected for evaluation against the ESVS requirements. In particular, algorithms were modified and tested against the unique problem of presenting a useful fusion of varying quality. A pixel averaging algorithm was selected as the simplest way to fuse two difference sources of imagery. Two other algorithms, originally developed for real- time fusion of low-light visible images with infrared images, (one at the TNO Human Factors Institute and the other at the MIT Lincoln Laboratory) were adapted and implemented. To evaluate the algorithms' performance, artificially generated infrared images were fused with synthetic images and viewed in a sequence corresponding to a search and rescue scenario for a descent to hover. Application of all three fusion algorithms improved the raw infrared image, but the MIT-based algorithm generated some undesirable effects such as contrast reversals. This algorithm was also computationally intensive and relatively difficult to tun. The pixel averaging problem was simplest in terms of per-pixel operations and provided good results. The TNO-based algorithm was superior in that while it was slightly more complex than pixel averaging, it demonstrated similar results, was more flexible, and had the advantage of predictably preserving certain synthetic features which could be used to support obstacle detection.

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