Advances in understanding the biology of vision show that humans use not only bottom-up, feature-based information in
visual analysis, but also top-down contextual information. To reflect this method of processing, we developed a
technology called CASSIE for Science Applications International Corporation (SAIC) that uses low-level image features
and contextual cues to determine the likelihood that a certain target will be found in a given area.
CASSIE is a tool by which information from various data layers can be probabilistically combined to determine spatial
and informational context within and across different types of data. It is built on a spatial foundation consisting of a two-dimensional
hexagonal, hierarchical grid structure for data storage and access. This same structure facilitates very fast
computation of information throughout the hierarchy for all data layers, as well as fast propagation of probabilistic
information derived from those layers.
Our research with CASSIE investigates the effectiveness of generated probability maps to reflect a human interpretation,
potential benefits in terms of accuracy and processing speed for subsequent target detection, and methods for
incorporating feedback from target detection algorithms to apply additional contextual constraints (for example,
allowable or expected target groupings). We discuss further developments such as learning in CASSIE and how to
incorporate additional data modalities.
The motion imagery community would benefit from standard measures for assessing image interpretability. The National Imagery Interpretability Rating Scale (NIIRS) has served as a community standard for still imagery, but no comparable scale exists for motion imagery. Several considerations unique to motion imagery indicate that the standard methodology employed in the past for NIIRS development may not be applicable or, at a minimum, requires modifications. The dynamic nature of motion imagery introduces a number of factors that do not affect the perceived interpretability of still imagery—namely target motion and camera motion. We conducted a series of evaluations to understand and quantify the effects of critical factors. This paper presents key findings about the relationship of perceived interpretability to ground sample distance, target motion, camera motion, and frame rate. Based on these findings, we modified the scale development methodology and validated the approach. The methodology adapts the standard NIIRS development procedures to the softcopy exploitation environment and focuses on image interpretation tasks that target the dynamic nature of motion imagery. This paper describes the proposed methodology, presents the findings from a methodology assessment evaluation, and offers recommendations for the full development of a scale for motion imagery.
The development of a motion imagery (MI) quality scale, akin to the National Image Interpretibility Rating Scale (NIIRS) for still imagery, would have great value to designers and users of surveillance and other MI systems. A multiphase study has adopted a perceptual approach to identifying the main MI attributes that affect interpretibility. The current perceptual study measured frame rate effects for simple motion imagery interpretation tasks of detecting and identifying a known target. By using synthetic imagery, there was full control of the contrast and speed of moving objects, motion complexity, the number of confusers, and the noise structure. To explore the detectibility threshold, the contrast between the darker moving objects and the background was set at 5%, 2%, and 1%. Nine viewers were to detect or identify a moving synthetic "bug" in each of 288 10-second clip. We found that frame rate, contrast, and confusers had a statistically significant effect on image interpretibility (at the 95% level), while the speed and background showed no significant effect. Generally, there was a significant loss in correct detection and identification for frame rates below 10 F/s. Increasing the contrast improved detection and at high contrast, confusers did not affect detection. Confusers reduced detection of higher speed objects. Higher speed improved detection, but complicated identification, although this effect was small. Higher speed made detection harder at 1 Frame/s, but improved detection at 30 F/s. The low loss of quality at moderately lower frame rates may have implications for bandwidth limited systems. A study is underway to confirm, with live action imagery, the results reported here with synthetic.
The motion imagery community would benefit from the availability of standard measures for assessing image interpretability. The National Imagery Interpretability Rating Scale (NIIRS) has served as a community standard for still imagery, but no comparable scale exists for motion imagery. Several considerations unique to motion imagery indicate that the standard methodology employed in the past for NIIRS development may not be applicable or, at a minimum, require modifications. Traditional methods for NIIRS development rely on a close linkage between perceived image quality, as captured by specific image interpretation tasks, and the sensor parameters associated with image acquisition. The dynamic nature of motion imagery suggests that this type of linkage may not exist or may be modulated by other factors. An initial study was conducted to understand the effects target motion, camera motion, and scene complexity have on perceived image interpretability for motion imagery. This paper summarizes the findings from this evaluation. In addition, several issues emerged that require further investigation:
- The effect of frame rate on the perceived interpretability of motion imagery
- Interactions between color and target motion which could affect perceived interpretability
- The relationships among resolution, viewing geometry, and image interpretability
- The ability of an analyst to satisfy specific image exploitation tasks relative to different types of motion imagery clips
Plans are being developed to address each of these issues through direct evaluations. This paper discusses each of these concerns, presents the plans for evaluations, and explores the implications for development of a motion imagery quality metric.
We present both semi-automated and automated methods for road extraction using IKONOS imagery. The automated method extracts straight-line, gridded road networks by inferring a local grid structure from initial information and then filling in missing pieces using hypothesization and verification. This can be followed by the semi-automated road tracker tool to approximate curvilinear roads and to fill in some of the remaining missing road structure. After a panchromatic texture analysis, our automated method incorporates an object-level processing phase which enables the algorithm to avoid problems arising from interference such as crosswalks and vehicles. It is limited, however, in that the logic is designed for reasoning concerning intersecting grid patterns of straight road segments. Many suburban areas are characterized by curving streets which may not be well-approximated using this automatic method. In these areas, missing content can be filled in using a semi-automated tool which tracks between user-supplied points. The semi-automated algorithm is based on measures derived from both the panchromatic and multispectral bands of IKONOS. We will discuss both of these algorithms in detail and how they fit into our overall solution strategy for road extraction. A presentation of current experimentation and test results will be followed by a discussion of advantages, shortcomings, and directions for future research and improvements.