A pan-sharpening method using joint and dual bilateral filters (DBFs) has been proposed. This approach is based on a consistent combination of large- and small-scale features obtained from the decomposition of high spectral resolution multispectral (MS) and high spatial resolution panchromatic (PAN) images. In the decomposition process, MS and PAN images are used to extract the features using joint and DBFs, respectively. These filters accommodate the relationship between MS and PAN images and decompose them into a base layer (large-scale) and a detail layer (small-scale). Since the joint bilateral filter (JBF) preserves the edges of an auxiliary image, it is used for decomposition of MS images where different layers are estimated using the PAN image as an auxiliary image. Similarly, different layers of the PAN image are obtained from a DBF which preserves the edges of both (MS and PAN) input images. This process is further extended to multistage decomposition to obtain a bilateral image pyramid. The base and detail layers of both MS and PAN images obtained at various stages are combined using a weighted sum. Finally, the estimated weighted sum of detail layer (small-scale) of the PAN image is fused separately to the weighted base layers (large-scale) of the MS images. Performance of the proposed method is evaluated by conducting the experiments on degraded as well as undegraded datasets, captured using different satellites such as Quickbird, Ikonos-2, and Worldview-2. The noise rejection capabilities of the proposed method are also tested by conducting experiments on the noisy data. The results are compared with the widely popular methods and the recently proposed fusion approaches based on a bilateral filter. Along with qualitative evaluation, the quantitative performance of the proposed fusion technique has also been verified by estimating different measures for degraded and undegraded experiments. The experimental results and quantitative measures demonstrate that the proposed method performs better in degraded and undegraded conditions along with noisy situations when compared to other state-of-art methods.
Many chronic diseases, including obesity and cancer, are related to diet. Such diseases may be prevented and/or successfully treated by accurately monitoring and assessing food and beverage intakes. Existing dietary assessment methods such as the 24-hour dietary recall and the food frequency questionnaire, are burdensome and not generally accurate. In this paper, we present a user interface for a mobile telephone food record that relies on taking images, using the built-in camera, as the primary method of recording. We describe the design and implementation of this user interface while stressing the solutions we devised to meet the requirements imposed by the image analysis process, yet keeping the user interface easy to use.
Measuring accurate dietary intake is considered to be an open research problem in the nutrition and health fields. We
are developing a system, known as the mobile device food record (mdFR), to automatically identify and quantify foods
and beverages consumed based on analyzing meal images captured with a mobile device. The mdFR makes use of a
fiducial marker and other contextual information to calibrate the imaging system so that accurate amounts of food can be
estimated from the scene. Food identification is a difficult problem since foods can dramatically vary in appearance. Such
variations may arise not only from non-rigid deformations and intra-class variability in shape, texture, color and other
visual properties, but also from changes in illumination and viewpoint. To address the color consistency problem, this
paper describes illumination quality assessment methods implemented on a mobile device and three post color correction
methods.
Several methods exist for printer identification from a printed document. We have developed a system that
performs printer identification using intrinsic signatures of the printers. Because an intrinsic signature is tied
directly to the electromechanical properties of the printer, it is difficult to forge or remove. In previous work we
have shown that intrinsic signatures are capable of solving the problem of printer classification on a restricted set
of printers. In this paper we extend our previous work to address the problem of forensic printer identification,
in which a document may or may not belong to a known set of printers. We propose to use a Euclidean distance
based metric in a reduced feature space. The reduced feature space is obtained by using sequential feature
selection and linear discriminant analysis.
Accurate methods and tools to assess food and nutrient intake are essential for the association between diet
and health. Preliminary studies have indicated that the use of a mobile device with a built-in camera to obtain
images of the food consumed may provide a less burdensome and more accurate method for dietary assessment.
We are developing methods to identify food items using a single image acquired from the mobile device. Our
goal is to automatically determine the regions in an image where a particular food is located (segmentation)
and correctly identify the food type based on its features (classification or food labeling). Images of foods are
segmented using Normalized Cuts based on intensity and color. Color and texture features are extracted from
each segmented food region. Classification decisions for each segmented region are made using support vector
machine methods. The segmentation of each food region is refined based on feedback from the output of classifier
to provide more accurate estimation of the quantity of food consumed.
When traveling in a region where the local language is not written using a "Roman alphabet," translating
written text (e.g., documents, road signs, or placards) is a particularly difficult problem since the text cannot
be easily entered into a translation device or searched using a dictionary. To address this problem, we are
developing the "Rosetta Phone," a handheld device (e.g., PDA or mobile telephone) capable of acquiring an
image of the text, locating the region (word) of interest within the image, and producing both an audio and a
visual English interpretation of the text. This paper presents a system targeted for interpreting words written in
Arabic script. The goal of this work is to develop an autonomous, segmentation-free Arabic phrase recognizer,
with computational complexity low enough to deploy on a mobile device. A prototype of the proposed system
has been deployed on an iPhone with a suitable user interface. The system was tested on a number of noisy
images, in addition to the images acquired from the iPhone's camera. It identifies Arabic words or phrases by
extracting appropriate features and assigning "codewords" to each word or phrase. On a dictionary of 5,000
words, the system uniquely mapped (word-image to codeword) 99.9% of the words. The system has a 82%
recognition accuracy on images of words captured using the iPhone's built-in camera.
KEYWORDS: Printing, Information security, Optical proximity correction, Digital watermarking, System identification, Particles, Security printing, Manufacturing, Image analysis, Signal analyzers
Several methods exist for printer identification from a printed document. We have developed a system that
performs printer identification using intrinsic signatures of the printers. Because an intrinsic signature is tied
directly to the electromechanical properties of the printer, it is difficult to forge or remove. There are many
instances where existance of the intrinsic signature in the printed document is undesireable. In this work we
explore texture based attacks on intrinsic printer identification from text documents. An updated intrinsic printer
identification system is presented that merges both texture and banding features. It is shown that this system
is scable and robust against several types of attacks that one may use in an attempt to obscure the intrinsic
signature.
This paper investigates the performance and proposes modifications to earlier methods for image authentication
using distributed source coding. This approach works well on images that have undergone affine geometric
transformations such as rotation and resizing and intensity transformations such as contrast and brightness
adjustment. The results show that the improvements proposed here can be used to make the original scheme for
image authentication robust to affine geometric and intensity transformations. The modifications are of much
lesser computational complexity when compared with other schemes for estimation of channel parameters.
Digital images can be obtained through a variety of sources including digital cameras and scanners. With rapidly
increasing functionality and ease of use of image editing software, determining authenticity and identifying forged
regions, if any, is becoming crucial for many applications. This paper presents methods for authenticating and
identifying forged regions in images that have been acquired using flatbed scanners. The methods are based on
using statistical features of imaging sensor pattern noise as a fingerprint for the scanner. An anisotropic local
polynomial estimator is used for obtaining the noise patterns. A SVM classifier is trained for using statistical
features of pattern noise for classifying smaller blocks of an image. This feature vector based approach is shown
to identify the forged regions with high accuracy.
Digital images can be captured or generated by a variety of sources including digital cameras and scanners. In
many cases it is important to be able to determine the source of a digital image. This paper presents methods for
authenticating images that have been acquired using flatbed desktop scanners. The method is based on using the
pattern noise of the imaging sensor as a fingerprint for the scanner, similar to methods that have been reported
for identifying digital cameras. To identify the source scanner of an image a reference pattern is estimated for
each scanner and is treated as a unique fingerprint of the scanner. An anisotropic local polynomial estimator is
used for obtaining the reference patterns. To further improve the classification accuracy a feature vector based
approach using an SVM classifier is used to classify the pattern noise. This feature vector based approach is
shown to achieve a high classification accuracy.
Digital images can be captured or generated by a variety of sources including digital cameras and scanners. In
many cases it is important to be able to determine the source of a digital image. Methods exist to authenticate
images generated by digital cameras or scanners, however they rely on prior knowledge of the image source
(camera or scanner). This paper presents methods for determining the class of the image source (camera or
scanner). The method is based on using the differences in pattern noise correlations that exist between digital
cameras and scanners. To improve the classification accuracy a feature vector based approach using an SVM
classifier is used to classify the pattern noise.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.