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- Image fusion for the detection of camouflaged peoplePublication . Bento, Nádia Alexandra FerreiraThe use of thermal imaging is a benefit for the Armed Forces. Due to their advantages, they have a large number of applications, including the detection of camouflaged people. For better results, the thermal information can be merged with the color information which allows a greater detail, resulting in a greater degree of security. The present study implemented as pixel level image fusion methods: Principal Components Analysis; Laplacian Pyramid; and Discrete Wavelet Transform. A qualitative analysis concluded that the method which performs better is the one that uses Wavelets, followed by the Laplacian Pyramid and finally the PCA. A quantitative analysis was made using as performance metrics: Standard Deviation, Entropy, Spatial Frequency, Mutual Information, Fusion Quality Index and Structural Similarity Index. The values obtained support the conclusions drawn from the qualitative analysis. The Mutual Information, Fusion Quality Index and Structural Similarity Index are the appropriate metrics to measure the quality of image fusion as they take into account the relationship between the fused image and the input images.
- Identification of landmines in thermal infrared imagesPublication . Pimenta, Jorge LeitãoThis paper explores the detection of landmines using thermal images acquired in military context. The conditions in which the images are obtained have a direct influence on the methods used to perform the automatic detection of landmines through image processing techniques. The proposed methodology follows two main phases: acquisition of thermal images and its processing. In the first phase, four different experiences were prepared to analyze the factors that influence the quality of the detection. In the second phase was conducted the image processing on a set of images based on classification techniques using the K-Nearest Neighbor (KNN) and Support Vector Machine (SVM) algorithms. The classification was performed on a set of features extracted from ROI’s obtained by a sliding window. A second approach was also implemented based on segmentation using thresholds. The results achieved allow to identify factors that influence the detection of the mines: the burial depth, the presence of vegetation on the surface and the time of the day at which images were obtained. The optimal classification was obtained with the KNN classifier with 40 features selected with Sequential Backward Selection (SBS), and using the distance metric of correlation.