We present the current state of our work on a mathematical framework for identification and delineation of
histopathology images-local histograms and occlusion models. Local histograms are histograms computed over
defined spatial neighborhoods whose purpose is to characterize an image locally. This unit of description is
augmented by our occlusion models that describe a methodology for image formation. In the context of this
image formation model, the power of local histograms with respect to appropriate families of images will be shown
through various proved statements about expected performance. We conclude by presenting a preliminary study
to demonstrate the power of the framework in the context of histopathology image classification tasks that, while
differing greatly in application, both originate from what is considered an appropriate class of images for this
framework.
Facial recognition is fast becoming one of the more popular and effective modalities of biometrics when used in
controlled environments. Controlled environments are those in which factors such as facial expression, pose, camera
position, and in particular illumination effects are controlled to some degree with respect to better performance.
Regulation or normalization of such factors has effects on all facial recognition algorithms, and the factor of illumination
effects is one of significant importance. In this paper we describe a method to address illumination effects in the
biometric modality of face recognition using Empirical Mode Decomposition (EMD) to identify illumination modes that
compose the image. Following identification of intrinsic mode functions that correspond to the dominant illumination
factors, we reconstruct the facial image minus these negative factors to synthesize a more neutral facial image. We then
perform recognition and verification experiments using different algorithms such as Principal Component Analysis
(PCA), Fisher Linear Discriminant Analysis (FLDA), and Correlation Filters (CF's) to demonstrate the fundamental
effectiveness of EMD as an illumination compensation method. Results are reported on the Carnegie Mellon University
Pose-Illumination-Expression (CMU PIE) Database and the Yale Face Database B.
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