We report on an evaluation study of a ship classifier based on the Principal Components Analysis (PCA). A set of ship profiles are used to build a covariance matrix which is diagonalized using the Karhunen-Loève transform. A subset of the principal components corresponding to the highest eigenvalues are selected as the ship features space. The recognition process consists in projecting a profile on this eigen-subspace and performing a similarity measure (herein a standard Eucidean distance). We have measured the recognition performance of the classifier using various sets of range-proffle signatures of ship silhouette images and simulated synthetic aperture radar images of ships under various aspect angles. It is found that the PCA-based ship classifier design offers good class discriminacy when trained with a limited number of ship classes (< 10) under an aspect angle range of 60 degrees about the ship side view. Additional tests are however necessary to validate the classifier on larger data sets and real images.

In recent years, new harmonic analysis tools providing sparse representation in high dimension space have been proposed. In particular, ridgelets and curvelets bases are similar to the sparse components of naturally occurring image data derived empirically by computational neuroscience researchers. Ridgelets take the form of basis elements which exhibit very high directional sensitivity and are highly anisotropic. The ridgelet transform have been shown to provide a sparse representation for smooth objects with straight edges. Independently, for the purpose of scene description, the shape of the Fourier energy spectra has been used as an efficient way to provide a "holistic" description of the scene picture and its semantic category. Similarly, we focus on a simple binary semantic classification (artificial vs. natural) based on various ridgelet features. The learning stage is performed on a large image database using different state of the art Linear Discriminant techniques. Classification results are compared with those resulting from the Gabor representation. Additionally, ridgelet representation provides us with a way to accurately reconstruct the original signal. Using this synthesis step, we filter the ridgelet coefficients with the discriminant vector. The resulting image identifies the elements within the scene contributing to the different perceptual dimensions.

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Deaf and hearing-impaired people capture information in video through visual content and captions. Those activities require different visual attention strategies and up to now, little is known on how caption readers balance these two visual attention demands. Understanding these strategies could suggest more efficient ways of producing captions. Eye tracking and attention overload detections are used to study these strategies. Eye tracking is monitored using a pupilcenter-corneal-reflection apparatus. Afterward, gaze fixation is analyzed for each region of interest such as caption area, high motion areas and faces location. This data is also used to identify the scanpaths. The collected data is used to establish specifications for caption adaptation approach based on the location of visual action and presence of character faces. This approach is implemented in a computer-assisted captioning software which uses a face detector and a motion detection algorithm based on the Lukas-Kanade optical flow algorithm. The different scanpaths obtained among the subjects provide us with alternatives for conflicting caption positioning. This implementation is now undergoing a user evaluation with hearing impaired participants to validate the efficiency of our approach.