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In this paper, we demonstrate an automatic image analysis for Micro Total Analysis Systems (μTAS) or Lab-on-Paper (LoP). By placing fiducial markers on μTAS, application can crop image and remove bending distortions, also it can perform colorimetric analysis. With previously taken photo of μTAS device with applied reagents, user can determine ie. illness presence by uploading photo into the program. In the paper was prepared μTAS sheet, algorithm for finding fiducial points and software for colorimetric analysis written in Matlab.

In this paper, we present an industrial application of multispectral imaging, for density measurement of colorants in photographic paper. We designed and developed a 9-band LED illumination based multispectral imaging system specifically for this application in collaboration with FUJIFILM Manufacturing Europe B.V., Tilburg, Netherlands. Unlike a densitometer, which is a spot density measurement device, the proposed system enables fast density measurement in a large area of a photo paper. Densities of the four colorants (CMYK) at every surface point in an image are calculated from the spectral reflectance image. Fast density measurements facilitate automatic monitoring of density changes (which is proportional to thickness changes), which helps control the manufacturing process for quality and consistent output. Experimental results confirm the effectiveness of the proposed system.

There are several paradigms for color generation in reflective displays (e-Paper) including RGBW color filtering or stacked RGB or CMY. Theoretically, the highest white state reflectance and best color gamut are achieved by stacking three layers of pixels. However, stacking 3 layers induces significant optical losses especially at high resolution and typically does not allow for video operation. Therefore RGBW color filtering is currently preferred for higher resolution and single-layer e-Paper, but only provides color at 25% of the area and a maximum theoretical white reflectance of 50%. Presented herein is a new bi-primary color-system that cooperatively displays two complimentary colors inside a single sub-pixel, and therefore doubles the white state reflectance and color gamut for single-layer e-Paper. Also discussed are candidate e-Paper technologies that may be able to adopt the bi-primary color system, including possible advantages and challenges for each technology.

This report describes a method for the creation and automatic content verification of low-cost self-verifiable paper documents. The image of an original document is decomposed to symbol templates and their corresponding locations. The resulting data is further compressed and encrypted, and encoded in custom designed high-capacity color barcodes. The original image and barcodes are printed on the same paper to form a self-verifiable authentic document. During content verification, the paper document is scanned to obtain the barcodes and target image. The original image is reconstructed from data extracted from the barcodes, which is then registered with and compared to the target image. The verification is carried out hierarchically from the entire image down to word and symbol levels. For symbol level comparison, multiple types of features and shape matching are utilized in a cascade. The proposed verification method is inexpensive, robust and fast. Evaluation on 216 character tables and 102 real documents achieved greater than 99% alteration detection rate and less than 1% false positives at the word/symbol level.

Wet paper code is a complex model which mainly used in the field of image coding. This paper is based on the wet paper code model and human visual system, and constructs a new wet paper code steganographic method. According to the regional complexity and other characteristics of the host image, the secret bits are adaptively embedded into wavelet coefficients of image subbands with wet paper code. Secret information receivers do not need to know the specific method of secret writing, just do a simple matrix multiplication operation and can extract the secret information, which in many ways to improve the security of the steganographic algorithm. The experiments show that the method has good visual invisibility and resistance of active steganalysis attacks.

An important print quality attribute of digital printing equipment deals with the absolute position of the printed image relative to the page. Historically, the most precise method of measuring image to paper (IOP) registration is by scanning a printed sheet on a flatbed scanner. These measurements have been limited to sheets smaller than the full capacity of the printer. In addition, the precision of the measurement has been limited by the accuracy of the scanner itself and the measurement of a few (~4) points on the page have limited the information that can be gathered. The new method proposed in this paper measures IOP registration throughout the sheet in a more precise manner. In a similar fashion, the relative position of the image on both the simplex and duplex side of the print can be determined. In addition, the new method helps link the source of registration errors to individual sub-systems. By generating the individual error sources from a printed sheet enables the understanding of the percentage contribution of each sub-system, prioritizes efforts to obtain better IOP performance, finds initial IOP setup errors of a printing engine, compares different technologies affecting IOP registration in sub-systems and potentially acts as a diagnostic tool for individual sub-systems.

Based on the unique characteristic, the paper currency numbers can be put into record and the automatic identification equipment for paper currency numbers is supplied to currency circulation market in order to provide convenience for financial sectors to trace the fiduciary circulation socially and provide effective supervision on paper currency. Simultaneously it is favorable for identifying forged notes, blacklisting the forged notes numbers and solving the major social problems, such as armor cash carrier robbery, money laundering. For the purpose of recognizing the paper currency numbers, a recognition algorithm based on neural network is presented in the paper. Number lines in original paper currency images can be draw out through image processing, such as image de-noising, skew correction, segmentation, and image normalization. According to the different characteristics between digits and letters in serial number, two kinds of classifiers are designed. With the characteristics of associative memory, optimization-compute and rapid convergence, the Discrete Hopfield Neural Network (DHNN) is utilized to recognize the letters; with the characteristics of simple structure, quick learning and global optimum, the Radial-Basis Function Neural Network (RBFNN) is adopted to identify the digits. Then the final recognition results are obtained by combining the two kinds of recognition results in regular sequence. Through the simulation tests, it is confirmed by simulation results that the recognition algorithm of combination of two kinds of recognition methods has such advantages as high recognition rate and faster recognition simultaneously, which is worthy of broad application prospect.

Due to the rise in performance of digital printing, image-based applications are gaining popularity. This creates needs for specifying the quality potential of printers and materials in more detail than before. Both production and end-use standpoints are relevant. This paper gives an overview of an on-going study which has the goal of determining a framework model for the visual quality potential of paper in color image printing. The approach is top-down and it is founded on the concept of a layered network model. The model and its subjective, objective and instrumental measurement layers are discussed. Some preliminary findings are presented. These are based on data from samples obtained by printing natural image contents and simple test fields on a wide range of paper grades by ink-jet in a color managed process. Color profiles were paper specific. Visual mean opinion score data by human observers could be accounted for by two or three dimensions. In the first place these are related to brightness and color brightness. Image content has a marked effect on the dimensions. This underlines the challenges in designing the test images.

In this paper a new method is proposed to control a vision-based robot in large navigation spaces. In this case, visual features observed by an on-board camera can change drastically or even disappear completely between the initial image, as seen at the beginning of a task, and the final image, as seen at the desired position of the robot. These features are therefore not suffcient for controlling the entire motion of the robotic system from beginning to end. This problem requires a more complete definition and representation of the navigation space. This can be achieved by a topological representation, where the environment is directly defined in the sensor space by a data-base of images. In our approach, this data-base is acquired during an offline learning step. An image retrieval method then indexes and matches a request image, given by the camera, to the closest view within the data-base. In this way, an image path is extracted from the database to link the initial and desired images providing enough information to control the robot. The central point of this paper is focused on the closed-loop control law that drives the robot to its desired position using this image path. The method proposed does not require either a global reconstruction or a temporal planning step. Furthermore, the robot is not obliged to converge directly upon each image waypoint but chooses automatically a better trajectory. The visual servoing control law designed uses specific features which ensure that the robot navigates within the visibility path. Experimental simulations are given to show the effectiveness of this method for controlling the motion of a camera in three-dimensional environments (free-flying camera, or camera moving on a plane).

In this paper a model for auditory perception is introduced. This model is based on a network of integrate-and-fire and resonate-and-fire neurons and is aimed to control the phonotaxis behavior of a roving robot. The starting point is the model of phonotaxis in Gryllus Bimaculatus: the model consists of four integrate-and-fire neurons and is able of discriminating the calling song of male cricket and orienting the robot towards the sound source. This paper aims to extend the model to include an amplitude-frequency clustering. The proposed spiking network shows different behaviors associated with different characteristics of the input signals (amplitude and frequency). The behavior implemented on the robot is similar to the cricket behavior, where some frequencies are associated with the calling song of male crickets, while other ones indicate the presence of predators. Therefore, the whole model for auditory perception is devoted to control different responses (attractive or repulsive) depending on the input characteristics. The performance of the control system has been evaluated with several experiments carried out on a roving robot.