Per-colorant-channel color barcodes for mobile applications: an interference cancellation framework.

We propose a color barcode framework for mobile phone applications by exploiting the spectral diversity afforded by the cyan (C), magenta (M), and yellow (Y) print colorant channels commonly used for color printing and the complementary red (R), green (G), and blue (B) channels, respectively, used for capturing color images. Specifically, we exploit this spectral diversity to realize a three-fold increase in the data rate by encoding independent data in the C, M, and Y print colorant channels and decoding the data from the complementary R, G, and B channels captured via a mobile phone camera. To mitigate the effect of cross-channel interference among the print-colorant and capture color channels, we develop an algorithm for interference cancellation based on a physically-motivated mathematical model for the print and capture processes. To estimate the model parameters required for cross-channel interference cancellation, we propose two alternative methodologies: a pilot block approach that uses suitable selections of colors for the synchronization blocks and an expectation maximization approach that estimates the parameters from regions encoding the data itself. We evaluate the performance of the proposed framework using specific implementations of the framework for two of the most commonly used barcodes in mobile applications, QR and Aztec codes. Experimental results show that the proposed framework successfully overcomes the impact of the color interference, providing a low bit error rate and a high decoding rate for each of the colorant channels when used with a corresponding error correction scheme.

Capacity analysis for orthogonal halftone orientation modulation channels.

Halftone dot orientation modulation has recently been proposed as a method for data hiding in printed images. Extraction of data embedded with halftone orientation modulation is accomplished by computing, from the scanned hardcopy image, detection statistics that uniquely identify the embedded orientation. From a communications perspective, this data hiding setup forms an interesting class of channels with dot orientation as input and a vector of statistics as the output. This paper derives capacity expressions for these channels that allow for numerical evaluation of the capacity. Results provide significant insight for orientation modulation based print-scan resilient data hiding: the capacity varies significantly as a function of the image graylevel and experimentally observed error free data rates closely mirror the variation in capacity.

High capacity color barcodes: per channel data encoding via orientation modulation in elliptical dot arrays.

We present a new high capacity color barcode. The barcode we propose uses the cyan, magenta, and yellow (C,M,Y) colorant separations available in color printers and enables high capacity by independently encoding data in each of these separations. In each colorant channel, payload data is conveyed by using a periodic array of elliptically shaped dots whose individual orientations are modulated to encode the data. The orientation based data encoding provides beneficial robustness against printer and scanner tone variations. The overall color barcode is obtained when these color separations are printed in overlay as is common in color printing. A reader recovers the barcode data from a conventional color scan of the barcode, using red, green, and blue (R,G,B) channels complementary, respectively, to the print C, M, and Y channels. For each channel, first the periodic arrangement of dots is exploited at the reader to enable synchronization by compensating for both global rotation/scaling in scanning and local distortion in printing. To overcome the color interference resulting from colorant absorptions in noncomplementary scanner channels, we propose a novel interference minimizing data encoding approach and a statistical channel model (at the reader) that captures the characteristics of the interference, enabling more accurate data recovery. We also employ an error correction methodology that effectively utilizes the channel model. The experimental results show that the proposed method works well, offering (error-free) operational rates that are comparable to or better than the highest capacity barcodes known in the literature.

Orientation modulation for data hiding in clustered-dot halftone prints.

We present a new framework for data hiding in images printed with clustered dot halftones. Our application scenario, like other hardcopy embedding methods, encounters fundamental challenges due to extreme bilevel quantization inherent in halftoning, the stringent requirements of image fidelity, and other unavoidable printing and scanning distortions. To overcome these challenges, while still allowing for automated extraction of the embedded data and a high embedding capacity, we propose a number of innovations. First, we perform the embedding jointly with the halftoning by employing an analytical halftone threshold function that allows steering of the halftone spot orientation within each halftone cell based upon embedded data. In this process, image fidelity is emphasized and, if necessary, the capability to recover individual data values is sacrificed resulting in unavoidable erasures and errors. To overcome these and other sources of errors, we propose a suitable data detection and error control methodology based upon a statistical representation for the print-scan channel that effectively models the channel dependence upon the cover image gray-level. To combat the geometric distortion inherent in the print-scan process, we exploit the periodic halftone structure to recover from global scaling and rotation and propose a novel decision directed synchronization technique that counters locally varying printing distortion. Experimental results demonstrate the power of the proposed framework: we achieve high operational rates while preserving halftone image quality.

Malignant-lesion segmentation using 4D co-occurrence texture analysis applied to dynamic contrast-enhanced magnetic resonance breast image data.

PURPOSE: To investigate the use of four-dimensional (4D) co-occurrence-based texture analysis to distinguish between nonmalignant and malignant tissues in dynamic contrast-enhanced (DCE) MR images. MATERIALS AND METHODS: 4D texture analysis was performed on DCE-MRI data sets of breast lesions. A model-free neural network-based classification system assigned each voxel a "nonmalignant" or "malignant" label based on the textural features. The classification results were compared via receiver operating characteristic (ROC) curve analysis with the manual lesion segmentation produced by two radiologists (observers 1 and 2). RESULTS: The mean sensitivity and specificity of the classifier agreed with the mean observer 2 performance when compared with segmentations by observer 1 for a 95% confidence interval, using a two-sided t-test with alpha = 0.05. The results show that an area under the ROC curve (A(z)) of 0.99948, 0.99867, and 0.99957 can be achieved by comparing the classifier vs. observer 1, classifier vs. union of both observers, and classifier vs. intersection of both observers, respectively. CONCLUSION: This study shows that a neural network classifier based on 4D texture analysis inputs can achieve a performance comparable to that achieved by human observers, and that further research in this area is warranted.