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Figure 3: Pre-processing of the infrared image and its segmentation into separate blobs.

Figure 3 Pre-processing of the infrared image and its segmentation into separate blobs.

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Figure 1: On the left-hand-side: reticule mask and on the right-hand-side: rosette scan use Initiall<z an infrarnd canlarc Figure 1: On the left-hand-side: reticule mask and on the right-hand-side: rosette scan used
Figure 1: On the left-hand-side: reticule mask and on the right-hand-side: rosette scan use Initiall<z an infrarnd canlarc Figure 1: On the left-hand-side: reticule mask and on the right-hand-side: rosette scan used
Figure 2: Basic configuration of the infrared seeker and tracker.
Figure 2: Basic configuration of the infrared seeker and tracker.
Figure 4a: EZB 624 PCI board. The eight ZISC78 chips that make the 624 neurons neura network are visible to the right of the board. Figure 4b: The Cognimem chip with 1024 neuron
Figure 4a: EZB 624 PCI board. The eight ZISC78 chips that make the 624 neurons neura network are visible to the right of the board. Figure 4b: The Cognimem chip with 1024 neuron
Figure 5: Radial distance, from the centroid to the perimeter of the blob.
Figure 5: Radial distance, from the centroid to the perimeter of the blob.
number we will use hereafter when referring to these characteristics. Table 4: The numbers in parenthesis is an identification number we use for that characteristic.
number we will use hereafter when referring to these characteristics. Table 4: The numbers in parenthesis is an identification number we use for that characteristic.
Figure 6: Histograms of the invariant features of the blobs that correspond to aircrafts and flares. lhe aircraft and flare histograms are respectively represented by solid and dashed lines. In these
Figure 6: Histograms of the invariant features of the blobs that correspond to aircrafts and flares. lhe aircraft and flare histograms are respectively represented by solid and dashed lines. In these
Figure 7: Structure of the RCE neural network.
Figure 7: Structure of the RCE neural network.
will be y2 =0. Figure 8: Illustration of the spherical regions of influence of two RCE neurons in 2-dimensional
will be y2 =0. Figure 8: Illustration of the spherical regions of influence of two RCE neurons in 2-dimensional
Figure 9: Three domains of the pattern space P* that are the union of the spherical influence fields
Figure 9: Three domains of the pattern space P* that are the union of the spherical influence fields
Figure 10a: Geometrical situation in the feature space P* that corresponds to Case 1 in training the
Figure 10a: Geometrical situation in the feature space P* that corresponds to Case 1 in training the
Figure 11a: Geometrical situation in the vector space P* that corresponds to Case 2, when the radius of the sphere of influence of the new neuron centered on X has its radius set to Rmax-
Figure 11a: Geometrical situation in the vector space P* that corresponds to Case 2, when the radius of the sphere of influence of the new neuron centered on X has its radius set to Rmax-
Figure 12a: The geometrical situation where the training vector X lies in a region in which the
Figure 12a: The geometrical situation where the training vector X lies in a region in which the
Figure 13a: The geometrical situation in which the feature vector X lies only in the influence
Figure 13a: The geometrical situation in which the feature vector X lies only in the influence
lable 5: Identification errors made by the ZISC neural network on the validation data set in the 10 experiments required for its 10-fold cross-validation. The left-most column shows the number
lable 5: Identification errors made by the ZISC neural network on the validation data set in the 10 experiments required for its 10-fold cross-validation. The left-most column shows the number
Kohavi and Provost [41]). Table 6: Two versions of the confusion matrix for our tests are shown in the white areas of this able. The one on the left-hand side shows the number of patterns that the ZISC classified as
Kohavi and Provost [41]). Table 6: Two versions of the confusion matrix for our tests are shown in the white areas of this able. The one on the left-hand side shows the number of patterns that the ZISC classified as
(b) Aircraft mistaken for a flare. (d) Flare to the right mistaken for an aircraft. Aircraft (f) Flare mistaken for aircraft exhaust. (h) Flare mistaken for an aircraft. Figure 14: Images of aircrafts and flares on which the RCE neural network made mistakes.
(b) Aircraft mistaken for a flare. (d) Flare to the right mistaken for an aircraft. Aircraft (f) Flare mistaken for aircraft exhaust. (h) Flare mistaken for an aircraft. Figure 14: Images of aircrafts and flares on which the RCE neural network made mistakes.
(b) Aircraft mistaken for a flare.
(b) Aircraft mistaken for a flare.
(f) Flare mistaken for aircraft exhaust. (h) Flare mistaken for an aircraft. Figure 14: Images of aircrafts and flares on which the RCE neural network made mistakes.
(f) Flare mistaken for aircraft exhaust. (h) Flare mistaken for an aircraft. Figure 14: Images of aircrafts and flares on which the RCE neural network made mistakes.
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