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Object recognition is one of oldest applications of automatic pattern recognition. The object recognition has generated lot of interest among researchers for a variety of applications like face detection, people counting, vehicle detection, manufacturing industry, online images, security etc. The main objective of this work is to recognize aeroplanes, even if they are of different size (different scale) or even if they are Oriented with different skew angles. This work would be useful in tracking an aircraft for navigation applications. Further the identification of aeroplanes can be used in military applications to detect the enemy aircrafts.
To achieve these objectives, the main challenge is the different shape, size and orientation of aircrafts which pose difficulty in the object recognition. In this work, an aeroplane is identified by extracting and comparing features between the test and training database images. This task is difficult for computers, however for humans; object recognition is effortless and instantaneous. In the first stage, the images of different aeroplanes and helicopters are selected and these images are downloaded from the web page “www.grabcad.com”. These images are grouped into two sets. The first set comprises database images which are used for training the system, whereas the second set is used for testing and obtaining the recognition accuracy for different algorithms. All these images are normalized and binarized using the thresholding concept.
In the second stage, 2D- Transforms (2D-FFT and 2D-Hough Transform) are applied to all the pixels of these binarised images (both testing and training database). After applying the transform, the pixel intensity value will have both, the real and also the imaginary values. Since the imaginary values of the pixel, has only “phase information”, which is not useful in the recognition of aeroplanes, this imaginary value of all the pixels in all the images are neglected.
The real part of the pixel intensity values (after applying the transform) is only considered for recognition. In this work, all the images are normalized to 50 X 50 size and hence the total number of pixels becomes 2500 for every image. The size of the matrix of each image (both test and database) is converted to (2500 X 1) column matrix from 50 X 50 matrix size. Hence after applying 2D transforms each image is of matrix size (2500 X 1).This matrix of (2500 X 1) size for each image (both testing and training), becomes the feature vector for that particular image. This process is applied to all the images and the features are extracted for all the test and database images.
In the third and the last stage, k-NN (k Nearest Neighbourhood) classifier is used in the identification of an aeroplane. The k-NN classifier with k=1 is the Euclidean distance. Hence, the recognition is achieved by calculating and identifying a database image which has minimum Euclidean distance to the given test image. The test image is shown on the left side of the result image, whereas the identified image of the database is shown on the right side of the result image. The cross validation of the results is also performed in this work. The Recognition accuracy with 2D-FFT is obtained as 88% and the Recognition accuracy with 2D-Hough transform is found to be 82%.
The reason for this difference can be because of the reason that, Hough transforms works on the principle of detection of straight lines in any image. Hence it can be concluded that 2D-FFT has higher Recognition accuracy compared to 2D-Hough transform..