Knowledge-based target recognition system evolution

Gilmore, John F.

doi:10.1117/12.55829

Cited by 11 publications

(3 citation statements)

References 11 publications

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“…For expression (2) or (3), it requires 6 ALU operations, hence for expression (1) that requires 30 ALU operations. It requires totally 60 ALU operations for expression (7) and is expected 14 cycles by arranging the operations in a pipeline.…”

Section: Pseudo Median Filtrationmentioning

confidence: 99%

<title>Hardware scheme for plankton recognition</title>

Liu

Tang

Yao

et al. 2001

Image Matching and Analysis

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For the plankton recognition system, we proposed the new generation system which is a parallel high perfoimance DSP system, the reasons are easily to modify the algorithms and to be developed to another application system. We estimate the performance of low level, intermediate level, and high level algorithm based on DSP. This paper focuses on the new architecture concepts of plankton recognition system architecture and some algorithm optimization on the TMS320C6201 DSP.

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Section: Pseudo Median Filtrationmentioning

confidence: 99%

<title>Hardware scheme for plankton recognition</title>

Liu

Tang

Yao

et al. 2001

Image Matching and Analysis

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show abstract

“…This trade-off can be quantified in terms of a Receiver Operating Characteristic (ROC) curve, which plots the achievable probability of detection as a function of the probability of false alarm. Most ATR systems use either pixel, feature or decision level fusion of information [7,8,9]. Some systems may use a combination of the above mentioned optimization of the tasks assigned to each UAV in accordance with its preferences, capabilities and constraints, managing the formation and maintenance of the UAVs [12].…”

Section: Images Showing the Computation Of The First Metric C(x T(ω)mentioning

confidence: 99%

“…9 shows one such case where there are two different target types identified. We see that target type 1 (sphere) appears thrice and target type 2 (truck) appears once in the image.…”

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confidence: 99%

Optical-based ATR algorithms for applications in swarmed UAVs

Rangammagari¹

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Optical-Based ATR Algorithms for Applications in Swarmed UAVs by Vasavi Rangammagari Swarmed Unmanned Aerial Vehicles (UAVs) with Automatic Target Recognition (ATR) technology are becoming an important element of electronic warfare. The advantages of UAVs over piloted vehicles have increased the need to develop robust and reliable ATR algorithms. Various issues like changing weather, camouflage, low contrast and resolution, clutter, inadequate databases place a limit on the performance capabilities of a typical ATR algorithm. In an effort to deal with these issues, a correlation-based algorithm is proposed in this thesis. This algorithm calculates the correlation between the input image and a target template which is created by projecting a 3-D model from the perspective of the UAV. The locations of correlation peaks are then declared to be the locations of the targets. We apply this algorithm to images with one object of a known class and move on to the more general case of images with an unknown number of targets from one or more classes. We provide an analysis of the performance of this correlation-based algorithm. We compare the performance of the proposed correlation-based approach with that of a training-based approach. To provide a concrete example of an "off-the-shelf" training-based ATR algorithm, the open source IntelCV library was used. In the training-based method, a sample set is created and trained (Haar-like features are used for training) to produce results for comparison purpose. We further develop and analyze a method of correlating across multiple frames that have been preprocessed using the correlation-based approach. This method is shown to be useful in detecting true targets and suppressing false alarms in cases where a single image is not sufficient for classification.

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A Blackboard System for Analysing Brain Ct-Scans

Cuyper

Bister

et al. 1992

Signal Processing

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Knowledge-based target recognition system evolution

Cited by 11 publications

References 11 publications

<title>Hardware scheme for plankton recognition</title>

<title>Hardware scheme for plankton recognition</title>

Optical-based ATR algorithms for applications in swarmed UAVs

A Blackboard System for Analysing Brain Ct-Scans

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