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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.

In order to consummate the management of bank paper, a new algorithm which can recognize the areas of paper tape in bundle of paper money is presented. Image segmentation is used to divide the image into several sub-blocks. Specifically, image entropy is computed in each sub-block. Experimental in different algorithms, including color training algorithm and the median filter and edge detection algorithm, demonstrates its real-time, effectiveness and robustness.

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.

In this work, two optical systems previously designed and implemented by our research team, were used to enable the surface and bulk inspection of the ink-paper interaction by image analysis. Basically, the first system works by ejecting micro-liter ink drops onto the papers surface while monitoring the event under three different views over time. The second system is used for sectioning the paper samples through their thickness and to simultaneously acquire images of the ink penetration of each section cut. In the performed experiments, three black inks of different brands and a common copy paper were chosen, used, and tested with the two developed optical systems. Both qualitative and quantitative analyses were carried out at the surface level and in the bulk of the paper. In terms of conclusions, it was shown that the three tested ink-paper combinations revealed very distinct characteristics.

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.

Artifact-metrics is an automated method of authenticating artifacts based on a measurable intrinsic characteristic. Intrinsic characters, such as microscopic random-patterns made during the manufacturing process, are very difficult to copy. A transmitted light image of the distribution can be used for artifact-metrics, since the fiber distribution of paper is random. Little is known about the individuality of the transmitted light image although it is an important requirement for intrinsic characteristic artifact-metrics. Measuring individuality requires that the intrinsic characteristic of each artifact significantly differs, so having sufficient individuality can make an artifact-metric system highly resistant to brute force attack. Here we investigate the influence of paper category, matching size of sample, and image-resolution on the individuality of a transmitted light image of paper through a matching test using those images. More concretely, we evaluate FMR/FNMR curves by calculating similarity scores with matches using correlation coefficients between pairs of scanner input images, and the individuality of paper by way of estimated EER with probabilistic measure through a matching method based on line segments, which can localize the influence of rotation gaps of a sample in the case of large matching size. As a result, we found that the transmitted light image of paper has a sufficient individuality.

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.

Annotation watermarking (sometimes also called caption or illustration watermarking) denotes a specific application of watermarks, which embeds supplementary information directly in the media, so that additional information is intrinsically linked to media content and does not get separated from the media by non-malicious processing steps such as image cropping or compression. Recently, nested object annotation watermarking (NOAWM) has been introduced as a specialized annotation watermarking domain, whereby hierarchical object information is embedded in photographic images. In earlier work, the Hierarchical Graph Concept (HGC) has been suggested as a first approach to model object relations, which are defined by users during editing processes, into a hierarchical tree structure. The original HGC method uses a code-book decomposition of the annotation tree and a block-luminance algorithm for embedding. In this article, two new approaches for embedding nested object annotations are presented and experimentally compared to the original HGC approach. The first one adopts the code-book scheme of HGC using an alternative embedding based on Wet Paper Codes in blue-channel LSB domain, whereas the second suggests a new method based on the concept of intrinsic signal inheritance by sub-band energy and phase modulation of image luminance blocks. A comparative experimental evaluation based on more than 100 test images is presented in the paper, whereby aspects of transparency and robustness with respect to the most relevant image modifications to annotations, cropping and JPEG compression, are discussed comparatively for the two code-book schemes and the novel inheritance approach.

The whiteness level of a printing paper is considered as an important quality measure. High paper whiteness improves the contrast to printed areas providing a more distinct appearance of printed text and colors and increases the number of reproducible colors. Its influence on perceived color rendering quality is however not completely explained. The intuitive interpretation of paper whiteness is a material with high light reflection for all wavelengths in the visual part of the color spectrum. However, a slightly bluish shade is perceived as being whiter than a neutral white. Accordingly, papers with high whiteness values incline toward bluish-white. In paper production, a high whiteness level is achieved by the use of highly bleached pulp together with high light scattering filler pigment. To further increase whiteness levels expensive additives such as Fluorescent Whitening Agents (FWA) and shading dyes are needed. During the last years, the CIE whiteness level of some commercial available office paper has exceeded 170 CIE units, a level that can only be reached by the addition of significant amounts of FWA. Although paper whiteness is considered as an important paper quality criterion, its influence on printed color images is complicated. The dynamic mechanisms of the human visual system strive to optimize the visual response to each particular viewing condition. One of these mechanisms is chromatic adaptation, where colored objects get the same appearance under different light sources, i.e. a white paper appears white under tungsten, fluorescent and day light. In the process of judging printed color images, paper whiteness will be part of the chromatic adaptation. This implies that variations in paper whiteness would be discounted by the human visual system. On the other hand, high paper whiteness improves the contrast as well as the color gamut, both important parameters for the perceived color reproduction quality. In order to quantify the influence of paper whiteness pilot papers with different amount of FWA but in all other respects similar were produced on a small scale experimental paper machine. The fact that only the FWA content changes reduces the influences of other properties separated from the paper whiteness in the evaluation process. A set of images, all having characteristics with the potential to reveal the influence of the varied whiteness level on color reproduction quality, were printed on the pilot papers in two different printers. Prior to printing the test images in the experiment, ICC-profiles were calculated for all the used printer-substrate combinations. A visual assessment study of the printed samples was carried out in order to relate the influence of the paper whiteness level to perceived color reproduction quality. The results show an improved color rendering quality with increased CIE whiteness value up to a certain level. Any further increase in paper whiteness does not contribute to an improved color reproduction quality. Furthermore, the fact that some printing inks are UV blocking while others are not will introduce a non uniform color shift in the printed image when the FWA activation changes. This non uniform color shift has been quantified both for variations in illuminant as well as variations of FWA content in the paper.

A methodology has been developed to digitize images from photographic color paper (reflection prints) and store the spectral information as a function of the three dye density basis functions inherent to the paper film type. The result is a highly accurate digitization of the spectral information at each spatial pixel that can be digitally stored with very little additional storage space over conventional RGB-type methodologies.© (1996) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

This study is focused on laser structuring processes for producing secure tactile features on paper-based security documents. The feasibility of using the developed technique allows users and/or machines to discriminate among genuine and counterfeited security documents and/or facilitate the tactile detection by visually impaired people.

High-resolving full-field OCT method is considered that provides increased resolution and interferometric data acquisition speed due to high optical magnification and electronic lateral scan provided by video camera. OCT data processing algorithm based on signal squaring with subsequent low-pass filtering is considered. Experimental results obtained when evaluating samples of paper material are presented and discussed.

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.

An automatic recognition system for Paper Currency Numbers was developed in this paper. The paper currency number can be recognized and recorded by this system at the same time of binding. At First, an image of 8 bits grey-scale was acquired by CCD camera, and then some output number clusters can be through process of segmentation by Grey Ridge-Valley algorithm, orientation by projection, and character recognition by structure-analyzing algorithm. Results of experiments demonstrated that the proposed algorithm of gray ridges and valleys can extract feature effectively, the algorithm of distance of traversing number body orientation acute, and this system achieves a high recognition rate and a fast recognition speed. It still reached the practical degree. The system has applied national patent.

This paper introduces tracking control system with visual feedback to a moving object by using the measurement device which we developed. In order to recognize the moving object, we use two method, using cross-shape mark and Orientation Code Matching (OCM). And the measurement device is constructed PID control system with Extended Kalman Filter in order to track to object. Through the several experiments, we verify the percormance of recognition and tracking.

In this paper we present our work analysing electroencephalographic (EEG) signals for the detection of seizure precursors in epilepsy. Volterra-systems and Cellular Nonlinear Networks are considered for a multidimensional signal analysis which is called the feature extraction problem throughout this contribution. Recent results obtained by applying a pattern detection algorithm and a nonlinear prediction of brain electrical activity will be discussed in detail. The aim of this interdisciplinary project is the realization of an implantable seizure warning and preventing system.

Canalization is a form of network robustness found in genetic regulatory networks that results in a reduction of the variation of phenotypic expression relative to the complexity of the genome. Recently, it was discovered that canalization can evolve in a complex network through a self-organization of node (gene) behavior resulting from a competition of a network's nodes that selects for a diversity of behavior [K.E. Bassler, C. Lee, and Y. Lee, Phys. Rev. Lett. 93, 038101 (2004)]. Previously, this "self-organized" mechanism for the evolution of canalization has been studied only in deterministic model systems. This paper considers the effects of stochastic noise in the signals exchanged between nodes on the self-organized evolution of canalization. We find that small levels of stochastic noise increase the amount of canalization produced. At higher levels of noise, the amount of canalization produced levels off and reaches a maximum value, before it reduces at large levels of noise.

Over the last two decades, researchers investigated various approaches for detection and classification of targets in forward looking infrared (FLIR) imagery using correlation based techniques. In this paper, a novel technique is proposed to recognize and track single as well as multiple identical and/or dissimilar targets in real life FLIR sequences using a combination of extended maximum average correlation height (EMACH) and polynomial distance classifier correlation filter (PDCCF). The EMACH filters are used for the detection stage and PDCCF filter is used for the classification stage for improving the detection and discrimination capability. The EMACH and PDCCF filters are trained a priori using target images with expected size and orientation variations. In the first step, the input scene is correlated with all the detection filters (one for each desired or expected target class) and the resulting correlation outputs are combined. The regions of interest (ROI) are selected from the input scene based on the regions with higher correlation peak values in the combined correlation output. In the second step, PDCCF filter is applied to these ROIs to identify target types and reject clutters/backgrounds based on a distance measure and a threshold. Moving target detection and tracking is accomplished by applying this technique independently to all incoming image frames. Independent tracking of target(s) from one frame to the other allows the system to handle complicated situations such as a target disappearing in few frames and then reappearing in later frames. This method has been found to yield robust performance for challenging FLIR imagery in terms of faster and accurate detection and classification as well as tracking of the targets.

A device has been designed for off-line optical paper inspection and quality control of stripes and holes in the cigarette paper. Hardware description is first presented including main paper characteristics to be measured. Typical paper stripe and holes structures are then discussed with image processing and analysis considerations to discriminate these structures, focusing in the problems derived from the small area of holes and of their internal structure that is analyzed with a confocal microscope. Algorithms for image processing and analysis are described. These algorithms involve equalization, binarization, stripes structure detection, holes distribution and statistics.

A non-supervised clustering based method for classifying paper according to its quality is presented. The method is simple to train, requiring minimal human involvement. The approach is based on Self-Organizing Maps and texture features that discriminate the texture of effectively. Multidimensional texture feature vectors are first extracted from paper images. The dimensionality of the data is then reduced by a Self-Organizing Map (SOM). In dimensionality reduction, the feature data are projected to a two-dimensional space and clustered according to their similarity. The clusters represent different paper qualities and can be labeled according to the quality information of the training samples. After that, it is easy to find the quality class of the inspected paper by checking where a sample is placed in the low-dimensional space. Tests based on images taken in a laboratory environment from four different paper quality classes provided very promising results. Local Binary Pattern (LBP) texture features combined with a SOM-based approach classified the test data almost perfectly: the error percentage was only 0.2% with the multiresolution version of LBP and 1.6% with the regular LBP. The improvement to the previously used texture features in paper inspection is huge: the classification error is reduced over 40 times. In addition to the excellent classification accuracy, the method also offers a self-intuitive user interface and a synthetic view to the inspected data.

The human visual system has an amazing sensitivity-even a single photon catch can trigger the release of a signal in a rod photoreceptor cell under certain circumstances. However, behaviorally it requires on an average 5-8 photons for a human to "see" a flash of light. This discrepancy is due to the intrinsic "dark noise" in the visual system. Various aspects of human visual sensitivity to single photons are reviewed and discussed.

A 128(H) x 64(V) x RGB CMOS imager is integrated with region-of-interest selection, RGB-to-HSI transformation, HSI-based pixel segmentation, 36-bins x 12bits HSI histogramming and sum-of-absolute-difference (SAD) template matching. 32 learned color templates are stored and compared to each image. The chip captures the R, G and B images using in-pixel storage before passing the pixel content to a multiplying digital-to-analog converter (DAC) for white balancing. The DAC can also be used to pipe in images for a PC. The color processing uses a biologically inspired color opponent representation and an analog look-up table to determine the Hue (H) of each pixel. Saturation (S) is computed using a loser-take-all circuit. Intensity (I) is given by the sum color components. A histogram of the segments of the image, constructed by counting the number of pixels falling into 36 Hue intervals of 10 degrees, is stored on chip and compared against the histograms of new segments using SAD comparisons. We demonstrate color-based image segmentation and object recognition with this chip. Running at 30fps, it uses 1mW. To our knowledge, this is the first chip that integrates imaging, color segmentation and color-based object recognition at the focal plane.

We present an overview of signal and image processing techniques developed for the concealed weapon detection (CWD) application. The signal/image processing chain is described and the tasks include image denoising and enhancement, image registration and fusion, object segmentation, shape description, and weapon recognition. Finally, a complete CWD example is presented for illustration.© (2005) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Bioterrorism is no longer a hypothetical construct but a reality. Nevertheless, disease detection and intervention currently remain largely reliant on clinical assessment. Technology providing early detection of disease could impact the care of individual patients and the evolution of epidemic spread. Hyperspectral Imaging (HSI) is a remote sensing technology developed originally by the Department of Defense that combines high-resolution imaging with chemical spectroscopy. In other medical applications HSI is emerging as a new means of early or more sensitive detection of changes in tissue that can be used to define pathology, predict clinical outcomes and adapt therapy. As a small, robust, camera based, non-invasive device, HSI may be well suited to aid in defense against biological warfare or epidemic disease by providing early detection or confirmation of disease and by monitoring the efficacy of vaccination or therapy. Crossover applications exist in the evaluation and treatment of emerging diseases. HSI is well suited to be a screening tool to provide earlier or more accurate detection of disease in an at risk population to better treat and contain disease.

This paper discusses the relationship between the sequential hard c-means (SHCM), learning vector quantization (LVQ), and fuzzy c-means (FCM) clustering algorithms. LVQ and SHCM suffer from several major problems. For example, they depend heavily on initialization. If the initial values of the cluster centers are outside the convex hull of the input data, such algorithms, even if they terminate, may not produce meaningful results in terms of prototypes for cluster representation. This is due in part to the fact that they update only the winning prototype for every input vector. We also discuss the impact and interaction of these two families with Kohonen's self-organizing feature mapping (SOFM), which is not a clustering method, but which often lends itself to clustering algorithms. Then we present two generalizations of LVQ that are explicitly designed as clustering algorithms: we refer to these algorithms as generalized LVQ equals GLVQ; and fuzzy LVQ equals FLVQ. Learning rules are derived to optimize an objective function whose goal is to produce 'good clusters'. GLVQ/FLVQ (may) update every node in the clustering net for each input vector. We use Anderson's IRIS data to compare the performance of GLVQ/FLVQ with a standard version of LVQ. Experiments show that the final centroids produced by GLVQ are independent of node initialization and learning coefficients. Neither GLVQ nor FLVQ depends upon a choice for the update neighborhood or learning rate distribution--these are taken care of automatically.

Our innate ability to process and interpret large volumes of poorly defined visual data, in essence to perceive visual information, enables us to function effectively in a continually changing complex world. As knowledge engineers, it would be highly desirable to incorporate such flexibility into artificial systems. Fuzzy logic is a mathematical tool created to help synthesize complex systems and decision processes that must deal with imprecise or ambiguous information. In terms of vision, this ambiguity arises from the meanings attached to the sensor inputs and the rules used to describe the relationship between the various informative visual attributes. Notions that pertain to vision perception such as fuzzy images, fuzzy mathematical operators and fuzzy inference procedures are outlined in this paper.© (1992) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

People do pattern recognition differently from current optical computers, but we can learn from them. This is one sense of `personal.'' The other is that I have sacrificed thoroughness and completeness to cut to the heart of what one person (I) believes to be the key current research issues. It is the kind of paper this person likes to read from others

Earlier studies of the human vision system suggest that a complex object may be recognized from the qualitative features associated with its boundary. Since the human vision system uses qualitative features for object recognition, it is extremely robust to deformation caused by noise, obstruction, scale change, or optical distortion. We considered various qualitative descriptors for digital images. The descriptors developed in this research are based on the estimated curvature function from digital boundaries and can discriminate features such as straightness of a contour segment, perpendicularity of two contour segments, parallelness of two straight contour segments, parallelness of two curved contour segments in a global sense, and the direction of convergence if two straight contour segments are not parallel. We demonstrated that the qualitative descriptors developed in this paper can be applied for identification of elementary shapes, such as cylinders, bricks, cones, etc. We also discussed the recognition of more complex shapes after decomposing them into simpler components.

This paper will review the needs of prepress proofing, then, point out some limitations of current proofing methods. After this, DuPont's new technology aimed at providing a modular "ink-on-paper" proofing system, will be described first, the concept, then the overall process and components of this system. And finally, the capabilities of this technology will be summarized

We describe a two-layer neural network using holographic optical disks as the interconnection weights. Such a system can be used to implement one two-layer with a large number of hidden units, or several two-layer networks with a smaller number of hidden units© (1992) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

The goal of the research described in this paper is the design of an intelligent computer vision system to automatically extract information from paper-based maps and answer queries related to spatial features and structure a geographical data. The foundation to such a system is a set. of powerful image analysis algorithms to operate on digitized images of paper-based maps. Efficient algorithms to orient map images, detect symbols, identify and track various types of lines, follow closed contours, compute distances, find shortest paths, etc. have been developed. An intelligent query processor analyzes the queries presented by the user in a predefined syntax, controls the operation of the image processing algorithms, and interacts with the user. The query processor is written in LISP and calls image analysis routines written in FORTRAN. In this paper we describe the image analysis algorithms and present their effectiveness in extracting information from a simplified map image of 2,048 x 2,048 pixels.© (1988) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Rotation-invariant pattern recognition is shown to have intrinsic limitations determined by the set of patterns to be recognized and by the specific optical setup. Within these limitations, a general procedure is proposed for the generation of bipolar filters that do not require the sensitive alignment procedures involved in holographic filters and are suitable for superposition synthesis to achieve rotation invariance.

The properties of unstable resonators with moderate gain per pass and multipass resonators with various folding mirrors are reviewed with respect to matching the resonator geometry to the discharge volume for small and light weight lasers required for integration into robotics and manufacturing systems.

A model has been developed to compare the system sharpness of continuous and discrete imaging systems. The continuous system consists of the traditional photographic process including the camera lens the film the printer lens and the photographic paper. The discrete system is an electronic still camera (ESC) and is built around a CCD imaging sensor and a laser printer the camera lens and the photographic paper are the same for both systems. The model uses CMT acutance to measure sharpness and introduces the concept of using the ratio of the aliased signal to the non-aliased signal as a measure of the probability of system aliasing. The model allows the user to study the trade offs between system sharpness and aliasing as a function of sensor resolution pixel size and the nature of the optical anti-aliasing pre-filter used in the ESC. The results of the model indicate that when a well-defined optical anti-aliasing pre-filter is made from a birefringent filter then when the pre-filter introduces a shift of about one-half the pixel pitch of the sensor then the potential for aliasing is minimized. For a given sensor resolution as the pixel size is reduced the potential for aliasing increases as does the system sharpness and when the potential for aliasing is minimized by use of the pre-filter the smaller pixel size still results in a greater potential for aliasing than for the pixels. For a given resolution it is possible to move from the Frame Transfer format to an Interline Transfer format and have the same system sharpness and potential for aliasing thus allowing flexibility in system design but the potential for aliasing is greater than the minimum value associated with the Frame Transfer format. As the system resolution increases the minimum in the potential for aliasing is more sharply defined as a function of the image shift introduced by the pre-filter. Also the potential for aliasing drops off sharply as the sensor resolution increases. In the case of the ESC system the number of lines used to form the final print can have a very large impact on the system sharpness and the amount of aliasing seen by the observer. 1.© (1991) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

An experimental high resolution cathode ray tube (CRT) printer was developed to produce high quality 3. 5in. x 5 in. and 4 in. x 6 in. photographic prints. A monochrome CRT utilizing red green and blue filtration was used to sequenlially expose continuous-tone color images directly onto conventional photographic paper. A summary of the design and implementation of the printer at the systems level will be presented. The design approach taken to meet the system quality requirements is described from the electrical mechanical and optical points of view. In addition the presentation will emphasize the design challenges of implementing a CRT imager and the solutions to the unique problems it presents. Other concerns discussed in detail include spatial resolution and bit resolution image size and magnification colorimetry and paper response matching. Sample prints of test patterns and scanned images will be shown. 1.

The paper presents approaches for functional integration of automated system databases by means of artificial intelligence. The peculiarities of turning to account the database in the systems with the usage of a fuzzy implementation of functions were analyzed. Requirements for the normalization of such databases were defined. The question of data equivalence in conditions of uncertainty and collisions in the presence of the databases functional integration is considered and the model to reveal their possible occurrence is devised. The paper also presents evaluation method of standardization of integrated database normalization.

In this paper the idea of an autonomic drone landing system which bases on different markers detection, is presented. The issue of safe autonomic drone landing is one of the major aspects connected with drone missions. The idea of the proposed system is to detect the landing place, marked with an image called marker, using one of the image recognition algorithms, and heading during the landing procedure to this place. Choosing the proper marker, which allows the greatest quality of the recognition system, is the main problem faced in this paper. Seven markers are tested and compared. The achieved results are described and discussed.

In response to the slow running speed of the Deformable Part Model algorithm in the process of the pedestrian detection, this paper incorporated Cascade Detection algorithm and Branch-and-Bound algorithm into a fast pedestrian detection algorithm which is based on Deformable Part Model. In a pedestrian detection process, a sequence model evaluates individual parts sequentially to quickly prune most of the smaller possible objects. This aims to accelerate the process of object positioning, and to optimize global classification results in all possible image regions. Meanwhile, the boundaries of the maximum are adopted to search the clipping operation of the window. In order to improve detection speed without compromising the accuracy of the detection, this paper increase the number of the part models involved. According to the experimental results on INRIA data set, the proposed algorithm successfully improved the accuracy and the speed of detection.

With the progress of smartphones hardware, it is simple on smartphone using image recognition technique such as face detection. In addition, indoor navigation system development is much slower than outdoor navigation system. Hence, this research proves a usage of image recognition technique for navigation in indoor environment. In this paper, we introduced an indoor navigation application that uses the indoor environment features to locate user’s location and a route calculating algorithm to generate an appropriate path for user. The application is implemented on Android smartphone rather than iPhone. Yet, the application design can also be applied on iOS because the design is implemented without using special features only for Android. We found that digital navigation system provides better and clearer location information than paper map. Also, the indoor environment is ideal for Image recognition processing. Hence, the results motivate us to design an indoor navigation system using image recognition.

According to the sequence of license plate image has low resolution, in order to improve the image resolution, this paper proposes a multiframe super-resolution image reconstruction algorithm based on overcome registration error and Gauss weight interpolation. First, multiframe image sub-pixel image registration, through extracting the SIFT keypoints of the image, rough matching with BBF algorithm, accurate matching with RANSAC algorithm, establishing the homography matrix between matching points. In order to overcome the registration error, this paper proposes a method based on threshold to overcome the registration error. Finally, the multiframe low resolution image is projected into the high resolution image grid by image registration. The value of the high resolution grid is reconstructed with a non-uniform interpolation algorithm based on Gauss weight. The experiments show that both on the subjective evaluation and objective evaluation can obtain satisfactory reconstruction results, and the registration error is robust.

A Gibbs Sampler based topic model for image annotation, which takes into account the interaction between visual geometric context and related topic, is presented. Most of the existing topic models for scene annotation use segmentation-based algorithm. However, topic models using segmentation algorithm alone sometimes can produce erroneous results when used to annotate real-life scene pictures. Therefore, our algorithm makes use of peaks of image surface instead of segmentation regions. Existing approaches use SIFT algorithm and treat the peaks as round blob features. In this paper, the peaks are treated as anisotropic blob features, which models low level visual elements more precisely. In order to better utilize visual features, our model not only takes into consideration visual codeword, but also considers influence of visual properties to topic formation, such as orientation, width, length and color. The basic idea is based on the assumption that different topics will produce distinct visual appearance, and different visual appearance is helpful to distinguish topics. During the learning stage, each topic will be associated with a set of distributions of visual properties, which depicts appearance of the topic. This paper considers more geometric properties, which will reduce topic uncertainty and learn the images better. Tested with Corel5K, SAIAPR-TC12 and Espgame100k Datasets, our method performs moderately better than some state of the arts methods.

One of the most difficult problem in the use of artificial neural networks is the computational capacity. Although large search engine companies own specially developed hardware to provide the necessary computing power, for the conventional user only remains the state of the art method, which is the use of a graphic processing unit (GPU) as a computational basis. Although these processors are well suited for large matrix computations, they need massive energy. Therefore a new processor on the basis of a field programmable gate array (FPGA) has been developed and is optimized for the application of deep learning. This processor is presented in this paper. The processor can be adapted for a particular application (in this paper to an organic farming application). The power consumption is only a fraction of a GPU application and should therefore be well suited for energy-saving applications.

This paper proposed a single image super-resolution algorithm based on image patch classification and sparse representation where gradient information is used to classify image patches into three different classes in order to reflect the difference between the different types of image patches. Compared with other classification algorithms, gradient information based algorithm is simpler and more effective. In this paper, each class is learned to get a corresponding sub-dictionary. High-resolution image patch can be reconstructed by the dictionary and sparse representation coefficients of corresponding class of image patches. The result of the experiments demonstrated that the proposed algorithm has a better effect compared with the other algorithms.

Face recognition has been widely studied recently while video-based face recognition still remains a challenging task because of the low quality and large intra-class variation of video captured face images. In this paper, we focus on two scenarios of video-based face recognition: 1)Still-to-Video(S2V) face recognition, i.e., querying a still face image against a gallery of video sequences; 2)Video-to-Still(V2S) face recognition, in contrast to S2V scenario. A novel method was proposed in this paper to transfer still and video face images to an Euclidean space by a carefully designed convolutional neural network, then Euclidean metrics are used to measure the distance between still and video images. Identities of still and video images that group as pairs are used as supervision. In the training stage, a joint loss function that measures the Euclidean distance between the predicted features of training pairs and expanding vectors of still images is optimized to minimize the intra-class variation while the inter-class variation is guaranteed due to the large margin of still images. Transferred features are finally learned via the designed convolutional neural network. Experiments are performed on COX face dataset. Experimental results show that our method achieves reliable performance compared with other state-of-the-art methods.

Due to the improvement of the picture quality of closed-circuit television (CCTV), the demand for CCTV has increased rapidly and its market size has also increased. The current system structure of CCTV transfers compressed images without analysis received from CCTV to a control center. The compressed images are suitable for the evidence required for a criminal arrest, but they cannot prevent crime in real time, which has been considered a limitation. Thus, the present paper proposes a system implementation that can prevent crimes by applying a situation awareness system at the back end of the CCTV cameras for image acquisition to prevent crimes efficiently. In the system implemented in the present paper, the region of interest (ROI) is set virtually within the image data when a barrier, such as fence, cannot be installed in actual sites and unauthorized intruders are tracked constantly through data analysis and recognized in the ROI via the developed algorithm. Additionally, a searchlight or alarm sound is activated to prevent crime in real time and the urgent information is transferred to the control center. The system was implemented in the Raspberry Pi 2 board to be run in real time. The experiment results showed that the recognition success rate was 85% or higher and the track accuracy was 90% or higher. By utilizing the system, crime prevention can be achieved by implementing a social safety network.