Spatial Prior Fuzziness Pool-Based Interactive Classification of Hyperspectral Images

Acquisition of labeled data for supervised Hyperspectral Image (HSI) classification is expensive in terms of both time and costs. Moreover, manual selection and labeling are often subjective and tend to induce redundancy into the classifier. Active learning (AL) can be a suitable approach for HSI classification as it integrates data acquisition to the classifier design by ranking the unlabeled data to provide advice for the next query that has the highest training utility. However, multiclass AL techniques tend to include redundant samples into the classifier to some extent. This paper addresses such a problem by introducing an AL pipeline which preserves the most representative and spatially heterogeneous samples. The adopted strategy for sample selection utilizes fuzziness to assess the mapping between actual output and the approximated a-posteriori probabilities, computed by a marginal probability distribution based on discriminative random fields. The samples selected in each iteration are then provided to the spectral angle mapper-based objective function to reduce the inter-class redundancy. Experiments on five HSI benchmark datasets confirmed that the proposed Fuzziness and Spectral Angle Mapper (FSAM)-AL pipeline presents competitive results compared to the state-of-the-art sample selection techniques, leading to lower computational requirements

FPGA-Based Discrete Wavelet Transforms System

Abstract. Although FPGA technology offers the potential of designing high performance systems at low cost, its programming model is prohibitively low level. To allow a novice signal/image processing end-user to benefit from this kind of devices, the level of design abstraction needs to be raised. This approach will help the application developer to focus on signal/image processing algorithms rather than on low-level designs and implementations. This paper presents a framework for an FPGA-based Discrete Wavelet Transform system. The approach helps the end-user to generate FPGA configurations for DWT at a high level without any knowledge of the low-level design styles and architectures.

Analysis of performance of palmprint matching with enforced sparsity

In this paper, a new and simple palmprint recognition solution based on sparse representation is suggested. It is shown that when the aim is to recover a palmprint from a limited number of observations as a linear combination of measurements of the same palmprint class, the ensuing representation in intrinsically very sparse. It can be efficiently computed by solving an l1 norm convex minimisation problem. When combined with well known subspace feature selection techniques such as PCA and LDA as well as with downsampled images, our tests, which have been carried out on 250 classes of the widely used PolyU database, have yielded an EER as low as 0.11% depending on the palmprints selected during the enrolment phase. Coupled with an execution time as short as 8.4 ms, the obtained results outperform similar work in the literature including EigenPalms, FisherPalms and Gabor based palmprintmatching algorithms, which shows the effectiveness of the new solution