In forensic science the finger marks left unintentionally by people at a crime scene are referred to as latent fingerprints. Most existing techniques to detect and lift latent fingerprints require application of a certain material directly onto the exhibit. The chemical and physical processing applied to the fingerprint potentially degrades or prevents further forensic testing on the same evidence sample. Many existing methods also have deleterious side effects. We introduce a method to detect and extract latent fingerprint images without applying any powder or chemicals on the object. Our method is based on the optical phenomena of polarization and specular reflection together with the physiology of fingerprint formation. The recovered image quality is comparable to existing methods. In some cases, such as the sticky side of tape, our method shows unique advantages.
Cross-correlation based image subtraction for detection of targets within an urban structure from synthetic aperture radar images is presented. In surveillance operations requiring re-imaging of the same scene, small displacements in array element locations may cause large phase and amplitude offsets. Subsequently, clutter will not cancel out, but will rather persist when subtracting two images, one without the target and the other with the target present. We propose a correlation-based robust technique that mitigates image offsets and rotations and is applicable to synthetic aperture radar urban sensing where the statistics are highly non-Gaussian. Simulation results demonstrating the effectiveness of the proposed technique are also presented.
The polarization of light carries much useful information about the environment. Biological studies have shown that some animal species use polarization information for navigation and other purposes. It has been previously shown that a bioinspired polarization-difference imaging (PDI) technique can facilitate detection and feature extraction of targets in scattering media. It has also been established [J. Opt. Soc. Am. A 15, 359 (1998)] that polarization sum and polarization difference are the optimum pair of linear combinations of images taken through two orthogonally oriented linear polarizers of a scene having a uniform distribution of polarization directions. However, in many real environments the scene has a nonuniform distribution of polarization directions. Using principal component analysis of the polarization statistics of the scene, we develop a method to determine the two optimum information channels with unequal weighting coefficients that can be formed as linear combinations of the images of a scene taken through a pair of linear polarizers not constrained to the horizontal and vertical directions of the scene. We determine the optimal orientations of linear polarization filters that enhance separation of a target from the background, where the target is defined as an area with distinct polarization characteristics as compared to the background. Experimental results confirm that in most situations adaptive PDI outperforms conventional PDI with fixed channels.
Shadow is an inseparable aspect of all natural scenes. When there are multiple light sources or multiple reflections several different shadows may overlap at the same location and create complicated patterns. Shadows are a potentially good source of information about a scene if the shadow regions can be properly identified and segmented. However, shadow region identification and segmentation is a difficult task and improperly identified shadows often interfere with machine vision tasks like object recognition and tracking. We propose here a new shadow separation and contrast enhancement method based on the polarization of light. Polarization information of the scene captured by our polarization-sensitive camera is shown to separate shadows from different light sources effectively. Such shadow separation is almost impossible to realize with conventional, polarization-insensitive imaging. CommentsCopyright 2006 Abstract: Shadow is an inseparable aspect of all natural scenes. When there are multiple light sources or multiple reflections several different shadows may overlap at the same location and create complicated patterns. Shadows are a potentially good source of information about a scene if the shadow regions can be properly identified and segmented. However, shadow region identification and segmentation is a difficult task and improperly identified shadows often interfere with machine vision tasks like object recognition and tracking. We propose here a new shadow separation and contrast enhancement method based on the polarization of light. Polarization information of the scene captured by our polarization-sensitive camera is shown to separate shadows from different light sources effectively. Such shadow separation is almost impossible to realize with conventional, polarization-insensitive imaging.
This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of the University of Pennsylvania's products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to pubs-permissions@ieee.org. By choosing to view this document, you agree to all provisions of the copyright laws protecting it. Adaptive Polarization Contrast Techniques for Through-Wall Microwave Imaging Applications AbstractIn this paper, we describe and utilize polarization contrast techniques of the adaptive polarization difference imaging algorithm and its transient modification for through-wall microwave imaging (TWMI) applications. Originally developed for optical imaging and sensing of polarization information in nature, this algorithm is modified to serve for target detection purposes in a through-wall environment. The proposed techniques exploit the polarization statistics of the observed scene for the detection and identification of changes within the scene and are not only capable of mitigating and substantially removing the wall effects but also useful in detecting motion, when conventional Doppler techniques are not applicable. Applications of the techniques to several TWMI scenarios including both homogeneous and periodic wall cases are presented. This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of the University of Pennsylvania's products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to pubs-permissions@ieee.org. By choosing to view this document, you agree to all provisions of the copyright laws protecting it.This journal article is available at ScholarlyCommons: http://repository.upenn.edu/ese_papers/478 1362 IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, VOL. 47, NO. 5, MAY 2009 Adaptive Polarization Contrast Techniques for Through-Wall Microwave Imaging Applications Konstantin M. Yemelyanov, Member, IEEE, Nader Engheta, Fellow, IEEE, Ahmad Hoorfar, Senior Member, IEEE, and John A. McVay, Member, IEEE Abstract-In this paper, we describe and utilize polarization contrast techniques of the adaptive polarization difference imaging algorithm and its transient modification for through-wall microwave imaging (TWMI) applications. Originally developed for optical imaging and sensing of polarization information in nature, this algorithm is modified to serve for target detection purposes in a through-wall environment. The proposed techniques exploit the polarization statistics of the observed scene for the detection and identification of...
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