It is critical in military applications to be able to extract features in imagery that may be of interest to
the viewer at any time of the day or night. Infrared (IR) imagery is ideally suited for producing
these types of images. However, even under the best of circumstances, the traditional approach of
applying a global automatic gain control (AGC) to the digital image may not provide the user with
local area details that may be of interest. Processing the imagery locally can enhance additional
features and characteristics in the image which provide the viewer with an improved understanding
of the scene being observed. This paper describes a multi-resolution pyramid approach for
decomposing an image, enhancing its contrast by remapping the histograms to desired pdfs, filtering
them and recombining them to create an output image with much more visible detail than the input
image. The technique improves the local area image contrast in light and dark areas providing the
warfighter with significantly improved situational awareness.
Super resolution reconstruction (SRR) improves resolution by increasing the effective sampling frequency. Target acquisition range increases but the amount of increase depends upon the relationship between the optical blur diameter and the detector size. Range improvement of up to 52% is possible.
Modern systems digitize the scene into 12 or more bits but the display typically presents only 8 bits. Gray scale compression forces scene detail to fall into a gray level and thereby "disappear." Local area processing (LAP) readjusts the gray scale so that scene detail becomes discernible. Without LAP the target signature is small compared to the global scene dynamic range and this results in poor range performance. With LAP, the target contrast is large compared to the local background. The combination of SRR and LAP significantly increases range performance.
Through the trade-off temporal information, a significant increase in spatial resolution is obtainable. This improvement
is quantifiable by using Airy's disc analysis against camera sensor pitch. Integrate the use of Airy's disc to quantify the
image improvement in resolvability and ultimately system range. It this comparison that sets the ground works for
realistic expectation. Our SR system is a natural tracker of moving vehicles with the addition of improved target
resolvability. Super Resolution can capitalize on camera platforms instability. A by product of SR is digitally stabilize
imagery to a fraction of a sub-pixel. Investigation in the sub-pixel remapping has lead to the developed of improved
super resolve images. Another, approach has lead to the development of a window management scheme for further
improvement. The cleaner, from a noise and structural point-of-view, the composite SR image is the more favorable it is
to high-sharpening. Mapping into a transform space greatly reduces the correlation complexity which makes it easier to
realize the complete algorithm into hardware. We have implemented this system into a real-time architecture. The
hardware configuration is composed of an FPGA and supporting processor.
It is critical in surveillance applications to be able to extract features in imagery that may be of interest to the viewer at
any time of the day or night. Infrared (IR) imagery is ideally suited for producing these types of images. However, even
this imagery is not always optimal. Processing the imagery with a local area image operator can enhance additional
features and characteristics in the image that provide the viewer with an improved understanding of the scene being
observed. This paper discusses the development of two algorithms for image enhancement for infrared imagery using
local area processing. The enhancement algorithm extends theory previously developed for medical applications.
Algorithm differences addressed include application to IR imagery and to a panning camera rather than still imagery. It
also discusses the obstacles encountered and overcome for insertion of this algorithm into a 10" gimbaled midwave
infrared imaging system for a variety of real-time processing applications. This technology is directly applicable to
driver's vision enhancement systems as well as other night visions systems such as night vision goggles.
Infrared imagery scenes change continuously with environmental conditions. Strategic targets embedded in them are often difficult to be identified with the naked eye. An IR sensor-based mine detector must include Automatic Target Recognition (ATR) to detect and extract land mines from IR scenes. In the course of the ATR development process, mine signature data were collected using a commercial 8-12 (mu) spectral range FLIR, model Inframetrics 445L, and a commercial 3-5 (mu) starting focal planar array FLIR, model Infracam. These sensors were customized to the required field-of-view for short range operation. These baseline data were then input into a specialized parallel processor on which the mine detection algorithm is developed and trained. The ATR is feature-based and consists of several subprocesses to progress from raw input IR imagery to a neural network classifier for final nomination of the targets. Initially, image enhancement is used to remove noise and sensor artifact. Three preprocessing techniques, namely model-based segmentation, multi-element prescreener, and geon detector are then applied to extract specific features of the targets and to reject all objects that do not resemble mines. Finally, to further reduce the false alarm rate, the extracted features are presented to the neural network classifier. Depending on the operational circumstances, one of three neural network techniques will be adopted; back propagation, supervised real-time learning, or unsupervised real-time learning. The Close Range IR Mine Detection System is an Army program currently being experimentally developed to be demonstrated in the Army's Advanced Technology Demonstration in FY95. The ATR resulting from this program will be integrated in the 21st Century Land Warrior program in which the mine avoidance capability is its primary interest.
Alternative algorithms for detecting and classifying mines and minelike objects must be evaluated against common image sets to assess performance. The Khoros CantataTM environment provides a standard interface that is both powerful and user friendly. It provides the image algorithmist with an object oriented graphical programming interface (GPI. A Khoros user can import 'toolboxes' of specialized image processing primitives for development of high order algorithms. When Khoros is coupled with a high speed single instruction multiple data (SIMD) algorithms. When Khoros is coupled with a high speed single instruction multiple (SIMD) processor, that operates as a co-processor to a Unix workstation, multiple algorithms and images can be rapidly analyzed at high speeds. The Khoros system and toolboxes with SIMD extensions permit rapid description of the algorithm and allow display and evaluation of the intermediate results. The SIMD toolbox extensions mirror the original serial processor's code results with a SIMD drop in replacement routine which is highly accelerated. This allows an algorithmist to develop identical programs/workspace which run on the host workstation without the use of SIMD coprocessor, but of course with a severe speed performance lost. Since a majority of mine detection componenets are extremely 'CPU intensive', it becomes impractical to process a large number of video frames without SIMD assistance. Development of additional SIMD primitives for customized user toolboxes has been greatly simplified in recent years with the advancement of higher order languages for SIMD processors (e.g.: C + +, Ada). The results is a tool that should greatly enhance the scientific productivity of the mine detection community.
We are building automatic target recognizer (ATR) systems. These systems are being applied to many different target detection scenarios. Our work has been in the military application field, but the problems are the same for most commercial applications as well. The measures of performance are the same. How well can a human perform the same target detection task? What is the probability of detecting (Pd) the target versus the false alarm rate (FAR)? The community has evolved comparative performance techniques that present the merits of alternative system approaches. In this paper, we present the results of a comparative study of alternative algorithms for detecting and classifying buried and surface land mines from an airborne platform in infrared imagery. The results show that for low signal-to-clutter ratios, more complex algorithms produce higher Pd for a given FAR. More complex algorithms signify the need for a high performance, high throughput processor to meet typical time lines. An update on the geometric arithmetic parallel processor (GAPPTM) high performance/throughput machine is therefore provided.
Land mine detection and extraction from infra-red (IR) scenes using real-time parallel processing is of significant interest to ground based infantry. The mine detection algorithms consist of several sub-processes to progress from raw input IR imagery to feature based mine nominations. Image enhancement is first applied; this consists of noise and sensor artifact removal. Edge grouping is used to determine the boundary of the objects. The generalized Hough Transform tuned to the land mine signature acts as a model based matched nomination filter. Once the object is found, the model is used to guide the labeling of each pixel as background, object, or object boundary. Using these labels to identify object regions, feature primitives are extracted in a high speed parallel processor. A feature based screener then compares each object's feature primitives to acceptable values and rejects all objects that do not resemble mines. This operation greatly reduces the number of objects that must be passed from a real-time parallel processor to the classifier. We will discuss details of this model- based approach, including results from actual IR field test imagery.
This paper describes the work completed by Martin Marietta in support of the U.S. Army's standoff minefield detection system, advanced technology transition demonstration. This paper discusses the high priority and urgent need for the standoff mine detection system within the Army Combat Engineers, it presents the results of the successful application of non developmental technology/hardware in an airborne mine/minefield detection system, and it discusses the significant payoff of applying advanced ATR and high speed parallel processing. The technologies discussed include the IR imager as the source of mine imagery, advanced image processing algorithms including neural nets, and a high speed parallel processor unique to Martin Marietta called GAPP (geometric arithmetic parallel processor).