Calina Ciuhu

Automatic Imaging System With Decision Support for Inspection of Pigmented Skin Lesions and Melanoma Diagnosis

In this paper, we describe an automatic system for inspection of pigmented skin lesions and melanoma diagnosis, which supports images of skin lesions acquired using a conventional (consumer level) digital camera. More importantly, our system includes a decision support component, which combines the outcome of the image classification with context knowledge such as skin type, age, gender, and affected body part. This allows the estimation of the personal risk of melanoma, so as to add confidence to the classification. We found that our system classified images with an accuracy of 86%, with a sensitivity of 94%, and specificity of 68%. The addition of context knowledge was indeed able to point to images that were erroneously classified as benign, albeit not to all of them.

A framework for robust feature selection for real-time fashion style recommendation

In this paper, we present the Smart Mirror system for fashion recommendation. The system uses intelligent vision technology to recognize clothing styles and supports real-time fashion recommendation. An important design challenge is to achieve sufficiently high style recognition accuracy while simultaneously offering robustness to input variations occurring in practice. We propose a framework for the selection of features that offer robust performance by assessing various evaluation measures under realistic deviations of optimal input data. The process is applied to a variety of low level features for clothing style description, including color histograms, local binary pattern (LBP) features and histogram of oriented gradient (HOG) features. We conclude the paper with an illustration of our results for web camera data and with a number of recommendations on how to move forward towards automatic fashion style perception.

A two-dimensional generalized sampling theory and application to de-interlacing

Yens generalisation of the sampling theorem has been proposed as the theoretical solution for de-interlacing by Delogne and Vandendorpe. The solution results in a vertical interpolation filter with coefficients that depend on the motion vector value, which uses samples that exist in the current field and additional samples from a neighbouring field shifted over (part of) a motion vector. We propose a further generalisation, where we design vector-adaptive inseparable 2D filters, which use samples from the current and the motion compensated previous field that are not available for all vectors on a vertical line. The resulting inseparable filters give a better interpolation quality at a given number of input pixels. We will show that the algorithm can be made robust against the sensitivity to inaccurate motion vectors.

Motion estimation on interlaced video

Motion compensated de-interlacing and motion estimation based on Yens generalisation of the sampling theorem (GST) have been proposed by Delogne and Vandendorpe. Motion estimation methods using three-fields have been designed on a block-by-block basis, minimising the difference between two GST predictions. We will show that this criterion degenerates into a two-fields criterion, leading to erroneous motion vectors, when the vertical displacement per field period is an even number of pixels. We provide a solution for this problem, by adding a term to the matching criterion.