The knowledge of the source printer can help in printed text document authentication, copyright ownership, and provide important clues about the author of a fraudulent document along with his/her potential means and motives. The development of automated systems for classifying printed documents based on their source printer, using image processing techniques, is gaining a lot of attention in multimedia forensics. Currently, the state-of-the-art systems require that the font of letters present in the test documents of unknown origin must be available in those used for training the classifier. In this paper, we attempt to take the first step toward overcoming this limitation. Specifically, we introduce a novel printer specific local texture descriptor. The highlight of our technique is the use of encoding and regrouping strategy based on small linear-shaped structures composed of pixels having similar intensity and gradient. The results of experiments performed on two separate datasets show that: 1) on a publicly available dataset, the proposed method outperforms state-of-the-art algorithms for characters printed in the same font and reduces the confusion between the printers of same brand and model on another dataset having documents printed in four different fonts, the proposed method outperforms state-of-the-art methods for cross font experiments.

With the classification revolution driven by convolutional neural networks (CNN), plenty of suitable CNN architectures are readily available. However, a limiting aspect of CNN is their hunger for data. Training data can be scarce in many forensics applications, particularly for printer forensics where hard copy pages need to be printed and then digitized using scanners. This paper aims to tackle this problem by using: 1) complementary representations of data and 2) augmented data variations. We derive a simple yet effective noise residual representation of a character image that complements the information contained in the original image. Further, rotated character variations and their monotonic grayscale transformations are used as augmented data. Since such variations are directly linked to printer mechanism, they help improve the overall classification accuracy. Finally, spatial pyramid pooling is used to accommodate characters of all sizes without compromising on a character's spatial information. The proposed method outperforms state-of-the-art CNN based method on a publicly available dataset, and its generic nature allows it to be combined with any other CNN architecture as well. Experiments indicate that for the case of very limited training data availability, the proposed method can achieve 2.5% increase in printer classification accuracy.

An important aspect of examining printed documents for potential forgeries and copyright infringement is the identification of the source printer as it can be helpful for detecting forged documents and ascertaining the leak. This paper proposes a system for classification of source printer from scanned images of printed documents using all the printed letters simultaneously. The proposed system uses local texture patterns-based features and a single classifier for classifying all the printed letters. Letters are extracted from scanned images using connected component analysis followed by morphological filtering without the need of using an optical character recognition. Each letter is sub-divided into a flat region and an edge region, and local tetra patterns are estimated separately for these two regions. A strategically constructed pooling technique is used to extract the final feature vectors. The proposed method has been tested on both a publicly available data set of ten printers, and a new data set of 18 printers scanned at a resolution of 600 as well as 300 dpi printed in four different fonts. The results indicate that the proposed system is capable of simultaneously dealing with all the printed letters and using a single classifier outperforms existing handcrafted feature-based methods. To achieve accuracies similar to that of state-of-art methods, it needs a much smaller number of training pages by using all the printed letters.

We propose a method for dietary assessment to automatically identify and locate food in a variety of images captured during controlled and natural eating events. Two concepts are combined to achieve this: a set of segmented objects can be partitioned into perceptually similar object classes based on global and local features; and perceptually similar object classes can be used to assess the accuracy of image segmentation. These ideas are implemented by generating multiple segmentations of an image to select stable segmentations based on the classifier's confidence score assigned to each segmented image region. Automatic segmented regions are classified using a multichannel feature classification system. For each segmented region, multiple feature spaces are formed. Feature vectors in each of the feature spaces are individually classified. The final decision is obtained by combining class decisions from individual feature spaces using decision rules. We show improved accuracy of segmenting food images with classifier feedback.

In this paper we investigate features and their combinations for food image analysis and a classification approach based on k-nearest neighbors and vocabulary trees. The system is evaluated on a food image dataset consisting of 1453 images of eating occasions in 42 food categories which were acquired by 45 participants in natural eating conditions. The same image dataset is used to test the classification system proposed in the previously reported work [1]. Experimental results indicate that using our combination of features and vocabulary trees for classification improves the food classification performance about 22% for the Top 1 classification accuracy and 10% for the Top 4 classification accuracy.

We are developing a dietary assessment system that records daily food intake through the use of food images. Recognizing food in an image is difficult due to large visual variance with respect to eating or preparation conditions. This task becomes even more challenging when different foods have similar visual appearance. In this paper we propose to incorporate two types of contextual dietary information, food co-occurrence patterns and personalized learning models, in food image analysis to reduce ambiguity in food visual appearance and improve food recognition accuracy. We evaluate our model on 1453 food images acquired by 45 participants in natural eating conditions. The result shows that incorporating contextual dietary information improves the food categorization accuracy by about 10%.

We are developing a dietary assessment system to automatically identify and quantify foods and beverages consumed by analyzing meal images captured with a mobile device. After food items are segmented and identified, accurately estimating the volume of the food in the image is important for determining the nutrient content of the food. In this paper, we proposed a novel food portion size estimation method for rigid food items using a single image. First, we create a 3D graphical model during the training step using 3D reconstruction from multiple views. Then, for each food image, we determine the translation and elevation parameters of each of the food items, which are relative to the camera coordinate through camera calibration. Using these geometric parameters we project the pre-built 3D model of each food item back to the image plane. Subsequently, the remaining degrees-of-freedom (DOF) for the final pose is estimated by image similarity measure. The experimental results of our volume estimation method for four food categories validate the accuracy and reliability of our model-based approach.

We are developing a dietary assessment system that records daily food intake through the use of food images taken at a meal. The food images are then analyzed to extract the nutrient content in the food. In this paper, we describe the image analysis tools to determine the regions where a particular food is located (image segmentation), identify the food type (feature classification) and estimate the weight of the food item (weight estimation). An image segmentation and classification system is proposed to improve the food segmentation and identification accuracy. We then estimate the weight of food to extract the nutrient content from a single image using a shape template for foods with regular shapes and area-based weight estimation for foods with irregular shapes.

In this paper we describe an image segmentation method for segmenting food items in images used for dietary assessment. Dietary assessment methods used to determine the foods and beverages consumed at a meal are essential for understanding the link between diet and health. Snakes, or active contours, are used extensively to locate object boundaries and segment images. Experimental results using classical snakes on food images show the problems associated with contour initialization and poor detection performance for food images. In this paper, we explore various methods of contour initialization and integrate a background removal method to improve the performance of food image segmentation. We describe the details of the proposed food image segmentation method and also evaluate our segmentation approach on food images.

Many chronic diseases, such as heart diseases, diabetes, and obesity, can be related to diet. Hence, the need to accurately measure diet becomes imperative. We are developing methods to use image analysis tools for the identification and quantification of food consumed at a meal. In this paper we describe a new approach to food identification using several features based on local and global measures and a "voting" based late decision fusion classifier to identify the food items. Experimental results on a wide variety of food items are presented.

Of the 10 leading causes of death in the US, 6 are related to diet. Unfortunately, methods for real-time assessment and proactive health management of diet do not currently exist. There are only minimally successful tools for historical analysis of diet and food consumption available. In this paper, we present an integrated database system that provides a unique perspective on how dietary assessment can be accomplished. We have designed three interconnected databases: an image database that contains data generated by food images, an experiments database that contains data related to nutritional studies and results from the image analysis, and finally an enhanced version of a nutritional database by including both nutritional and visual descriptions of each food. We believe that these databases provide tools to the healthcare community and can be used for data mining to extract diet patterns of individuals and/or entire social groups.

Chronic diseases, such as heart disease, diabetes, and obesity, have been linked with diet. Nutrient intake is also associated with diet. However, much of the research completed to elucidate these associations has not incorporated the concept of time. This paper introduces the concept of temporal dietary patterns and demonstrates a novel construct of 24-hour temporal dietary patterns for energy intake, present in a sample of the adult U.S. population 20 years and older (NHANES 1999-2004 dataset). An appropriate distance metric is proposed for comparing 24-hour diet records and is used with kernel k-means clustering to identify the temporal dietary patterns.

Many chronic diseases, such as heart disease, diabetes, and obesity, can be related to diet. Hence, the need to accurately measure diet becomes imperative. We are developing image analysis tools for the identification and quantification of foods consumed at a meal. Our system relies on a single meal image from the user for doing food identification and quantity estimation. Therefore, it is very important to assist the user in acquiring a good quality image by providing immediate feedback about the image quality. This paper presents low complexity image quality measures which are deployed on handheld mobile devices.

Recently there has been a great deal of interest in using features intrinsic to a data-generating sensor for the purpose of source identification. Numerous methods have been proposed for different problems related to sensor forensics, such as source camera identification using sensor noise as intrinsic signatures, printer identification using banding artifacts as intrinsic signatures and so on. The goal of our work is to identify the scanner used for generating a scanned (digital) version of a printed (hard-copy) document. In this paper we do extensive analysis of the effect of font shape and size on our recently proposed, texture analysis based intrinsic signatures for scanned documents. Some improvements in these intrinsic signatures are proposed to make them robust to font shapes and sizes.

In this paper, we describe the Technology Assisted Dietary Assessment (TADA) project at Purdue University. Dietary intake, what someone eats during the course of a day, provides valuable insights for mounting intervention programs for prevention of many chronic diseases such as obesity and cancer. Accurate methods and tools to assess food and nutrient intake are essential for research on the association between diet and health. An overview of our methods used in the TADA project is presented. Our approach includes the use of image analysis tools for identification and quantification of food that is consumed at a meal. Images obtained before and after foods are eaten are used to estimate the amount and type of food consumed.

Digital images can be obtained through a variety of sources including digital cameras and scanners. In many cases, the ability to determine the source of a digital image is important. This paper presents methods for authenticating images that have been acquired using flatbed desktop scanners. These methods use scanner fingerprints based on statistics of imaging sensor pattern noise. To capture different types of sensor noise, a denoising filterbank consisting four different denoising filters is used for obtaining the noise patterns. To identify the source scanner, a support vector machine classifier based on these fingerprints is used. These features are shown to achieve high classification accuracy. Furthermore, the selected fingerprints based on statistical properties of the sensor noise are shown to be robust under postprocessing operations, such as JPEG compression, contrast stretching, and sharpening.

Digital images can be captured or generated by a variety of sources including digital cameras, scanners and computer graphics softwares. In many cases it is important to be able to determine the source of a digital image such as for criminal and forensic investigation. This paper presents methods for distinguishing between an image captured using a digital camera, a computer generated image and an image captured using a scanner. The method proposed here is based on the differences in the image generation processes used in these devices and is independent of the image content. The method is based on using features of the residual pattern noise that exist in images obtained from digital cameras and scanners. The residual noise present in computer generated images does not have structures similar to the pattern noise of cameras and scanners. The experiments show that a feature based approach using an SVM classifier gives high accuracy.

Forensic characterization of devices is important in many situations such as establishing the trust and verifying authenticity of data and the device that created it. Current forensic identification techniques for digital cameras, scanners and printers are highly reliable due to the fact that each of these devices cannot escape inherent electro-mechanical properties which add "signatures" to the data they produce. In this paper we will describe the sensor forensics work going on at Purdue University.

In this paper, we have proposed a novel scheme for the extraction of textual areas of an image using globally matched wavelet filters. A clustering-based technique has been devised for estim ating globally matched wavelet filters using a collection of groundtruth images. We have extended our text extraction scheme for the segmentation of document images into text, background, and picture components (which include graphics and continuous tone images). Multiple, two-class Fisher classifiers have been used for this purpose. We also exploit contextual information by using a Markov random field formulation-based pixel labeling scheme for refinement of the segmentation results. Experimental results have established effectiveness of our approach.

In this paper we have proposed a novel scheme for locating text regions in an image. The method is based on multiresolution wavelet analysis. We used matched wavelets to capture textural characteristics of image regions. A clustering based approach has been proposed for estimating globally matched wavelets (GMWs) for a given collection of images. Using these GMWs, we generate feature vectors for segmentation and identification of text regions in an image. Our method, unlike most of the other methods, does not require any a priori information about the font, font size, scripts, geometric transformation, distortion or background texture. We have tested our method on various categories of images like license plates, posters, hand written documents and document images etc. The results show proposed method to be a robust, versatile and effective tool for text extraction from images.