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For the detection of product color uniformity and seam texture defects in the production process of test paper, detection methods are proposed respectively. For the detection of the color uniformity, a uniformity detection method based on dynamic light correction is proposed. The illumination correction image is used for color dynamic compensation of the test paper image to solve the influence of uneven illumination on the test paper. For the detection of texture defects such as seams, a texture defect detection method based on gray gradient aggregation intensity is proposed. This method uses one-way Gaussian convolution to strengthen features, and then calculates the gradient, divides the region and calculates the gradient extremum aggregation strength to judge texture defects, which can quickly and efficiently find seam defects. The experimental results show that the above methods have obvious effects on the color uniformity and seam detection of the test paper. The color uniformity accuracy rate is 96%, the seam detection accuracy rate is 97%. The total detection time of a single test paper is about 90ms. The algorithm is real-time and accurate, and can be used in actual production detection.

Because the traditional insulation paper state detection method has some shortcomings, this paper proposes a non-destructive detection method, which uses the texture feature value of insulation paper image to establish a relationship with the degree of polymerization (DP) of insulation paper state, and then indirectly estimates the aging state of insulation paper. Firstly, the insulation paper images in different aging states are collected, and the texture feature values of the images are extracted by gray histogram, gray level co-occurrence matrix and gray level run-length matrix; then, the texture feature values are selected by correlation; then, the validity of the selected texture feature values to represent the insulation paper state is verified by support vector machine (SVM); finally, the multiple linear regression method is used. The relationship between the degree of polymerization and the texture feature value is established and verified by the actual results. The results show that the texture feature value has a good fitting relationship with the degree of polymerization, which verifies the effectiveness of this method in evaluating the state of insulating paper.

In this work, we describe the application of the ‘steerable pyramid’ (Portilla and Simoncelli, 2000), a high-dimensional texture descriptor, to the discrimination and classification of a large collection of photographic paper textures. This work is part of a broader effort to bring precise physical measurements to the characterization of photographic papers, and our intended audience includes photograph conservators, curators, and collectors. For our purposes, it is important both to enable fine-grained local comparisons of very similar textures and to identify salient groupings across the entire landscape of photographic papers. The steerable pyramid was developed with the explicit goal of synthesizing textures; as such, it is an overcomplete representation. Its high native dimensionality is an advantage for highly precise local comparisons of texture, as in nearest neighbor search; it is, however, a disadvantage for grouping tasks, which require a well-structured global topology. For grouping tasks, we describe a number of compression and grouping approaches designed to meet a variety of analytical needs. Finally, we validate the steerable pyramid descriptor numerically using ‘one versus all’ classification on an augmented data set that includes multiple samples per paper type.

Paper surfaces under the microscopic view are observed to be formed by intertwisted wood fibers. Such structures of paper surfaces are unique from one location to another and are almost impossible to duplicate. Previous work used microscopic surface normals to characterize such intrinsic structures as a “fingerprint” of paper for security and forensic applications. In this work, we examine several key research questions of feature extraction in both scientific and engineering aspects to facilitate the deployment of paper surface-based authentication when flatbed scanners are used as the acquisition device. We analytically show that, under the unique optical setup of flatbed scanners, the specular reflection does not play a role in norm map estimation. We verify, using a larger dataset than prior work, that the scanner-acquired norm maps, although blurred, are consistent with those measured by confocal microscopes. We confirm that, when choosing an authentication feature, high spatial-frequency subbands of the heightmap are more powerful than the norm map. Finally, we show that it is possible to empirically calculate the physical dimensions of the paper patch needed to achieve a certain authentication performance in equal error rate (EER). We analytically show that log(EER) is decreasing linearly in the edge length of a paper patch.

With the increasing use of surveillance cameras, face recognition is being studied by many researchers for security purposes. Although high accuracy has been achieved for frontal faces, the existing methods have shown poor performance for occluded and corrupt images. Recently, sparse representation based classification (SRC) has shown the state‐of‐the‐art result in face recognition on corrupt and occluded face images. Several researchers have developed extended SRC methods in the last decade. This paper mainly focuses on SRC and its extended methods of face recognition. SRC methods have been compared on the basis of five issues of face recognition such as linear variation, non‐linear variation, undersampled, pose variation, and low resolution. Detailed analysis of SRC methods for issues of face recognition have been discussed based on experimental results and execution time. Finally, the limitation of SRC methods have been listed to help the researchers to extend the work of existing methods to resolve the unsolved issues.

Currently, racial oil blotting papers are counted by hand. However, it takes a long time and this is hard work. Hence, an automatic counting system is necessary. In order to achieve this, we developed a cheap system using a webcam. This system firstly captures one image of arranged papers by shifting. Secondly, the paper boundaries are obtained as straight lines by utilizing the proposed image processing. By counting the acquired lines, the system outputs the number of papers. However, the captured image has noises such as the change of illumination and shadows. They make the extraction of the accurate boundaries difficult. For the accurate extraction, optimal parameters of the image processing must be set. Since the prediction of the optimal parameters in advance is difficult, the proposed method adopts genetic algorithm (GA). Also, Hough transformation for the straight line detection is improved. By applying the proposed method, the accurate paper counting is possible. For the experiment, 14 images were captured in various environments using a general webcam. Then, the proposed method was compared to a latest related work. As a result, F-measure of each method was 0.995 and 0.945.

In this paper, we propose a novel paper currency defect detection algorithm using quaternion uniform strength. We first build paper currency image preprocessing integration framework which includes intensity balancing, paper currency location, and geometric correction. We then propose a global–local paper currency image registration algorithm by moving key areas within certain range which can eliminate the false difference effectively. Finally, the quaternion uniform strength is calculated by using quaternion convolution edge detector. The defect degree of paper currency is determined by using the quaternion uniform color difference. The proposed algorithm is tested using different datasets from five countries: CNY, USD, EUR, VND, and RUB. The experimental results demonstrate that the proposed algorithm yields better results than the existing state-of-the-art paper currency defect detection techniques. The demo of the proposed paper currency defect detection algorithm will be publicly available.

Number plate recognition brings a drastic improvement for the city traffic enhancement. It provides the direction in which the steps should be taken for working of an effective intelligent transportation system. ANPR have become necessity for traffic control management due to rapid increment of vehicles. The main aim of ANPR is to monitor traffic and for security purpose. Recognition of number plate uses image processing techniques and latest technology in detecting characters on vehicle license plates automatically. In recent years, there are many technological developments in recognizing license plate area of research. Image processing protocols like OCR technology allow the traffic surveillance to deal with several problems that occurs in criminal investigation, toll collection, monitoring traffic, controlling speed, parking management etc. For efficient management of traffic and mass surveillance in transportation system, an ANPR system is essential. With the aid of image processing algorithms and vehicle images dataset, it becomes possible to monitor traffic at a large scale. Vehicle images are helpful for recognizing characters on license plates by performing image segmentation, feature extraction and character recognition. Data collected through captured images are utilized in the commercial applications, law enforcement, traffic applications etc. The software examines the vehicle picture as an input image that results in displaying the plate numbers. The system with image processing used reliably for traffic detection where modification of the technologies enables an accurate acknowledgement of vehicle number plates. There are several ANPR systems developed, working on character recognition of LP by the help of image processing technique. This paper reviews the performance by researchers in this particular area towards meeting goals of transportation system. It also provides major issues and challenges in this field.

The data hiding techniques have attracted a lot of attention in recent years and mainly with the intensive growth of multimedia and its possibility for covert communication. Steganography is one of the information hiding methods to confirm the ability of a multimedia carrier to exchange secret information between two end-points so that it is imperceptible, thus avoiding the detection of hidden information. The secret information can be embedded in several multimedia carriers, such as image or audio or video files. It works by embedding the message in a source cover which may make the observer feel it is the source cover itself. The type of multimedia carrier here is an image. However, this technique suffers from the problem of the carrier distortion. In this paper, we investigate the impact of some distortion types on the carrier images and discuss the possibility of using distraction images in steganography to protect the stego-image. Furthermore, we highlight the current challenges of image steganography. The experimentations show very interesting results.

Forgery of paper invoices and certificates negatively causes huge economic loss every year. In the molding process of paper, plant fibres inside will distribute randomly and form the unique microscopic surface. Accordingly, forensic researchers have presented many techniques to build its feature descriptor for paper anti-counterfeiting. Among those methods, normal vector field and reconstructed surface based on multi-view photos captured by mobile camera have indicated incredibly effectiveness and practicality. However, the inefficiency of existing models using mobile camera is the neck in real time detection scenarios. In this paper, we focus on the efficient microstructure orientation estimation of paper surface for authentication. To address this problem, we investigate the reflection characteristics of rough surface and quantify the light attenuation accurately. Then, we model the entire propagation path of light emitted by flash, and exploit single photo captured by mobile camera to reconstruct microscopic inclination based on beam energy loss. Reduction of required pictures significantly enhances forensics efficiency. Through comparison experiments, we demonstrate that our proposed method can be used for paper discrimination effectively with speedy feature extraction.

The authors present a comprehensive overview of image processing and analysis work done to support research into the model flowering plant Arabidopsis thaliana. Beside the plant's importance in biological research, using image analysis to obtain experimental measurements of it is an interesting vision problem in its own right, involving the segmentation and analysis of sequences of images of objects whose shape varies between individual specimens and also changes over time. While useful measurements can be obtained by segmenting a whole plant from the background, they suggest that the increased range and precision of measurements made available by leaf-level segmentation makes this a problem well worth solving. A variety of approaches have been tried by biologists as well as computer vision researchers. This is an interdisciplinary area and the computer vision community has an important contribution to make. They suggest that there is a need for publicly available datasets with ground truth annotations to enable the evaluation of new approaches and to support the building of training data for modern data-driven computer vision approaches, which are those most likely to result in the kind of fully automated systems that will be of use to biologists.

In order to better solve the problem of automatic identification of quality control line and detection line in the detection of gold standard test strip, this paper proposes to collect the image information of gold standard test strip after color rendering through CMOS sensor, preprocess the obtained information, transform RGB image into gray image, build cloud model in the CIELAB/HSV/HSL space, and apply the improved AdaBoost algorithm to determine the position of detection line and quality control line Place. Compared with the traditional template matching method, it improves the accuracy and accuracy of recognition.

With technological advancements many small to large, simple to complex activities are automated. Growth of Artificial Intelligent techniques has eased the way we would look to solve the real-world problems. One such area which has recently gained lot of attention is the biometric analytics like Face Recognition, Fingerprint, voice etc. It involves extracting features such as face expressions, gender, age etc. Gender information plays a vital role in areas such as human computer interaction, crime detection, gender preferences, facial biometrics for digital payments etc. This paper proposes gender recognition using facial images and fingerprints with different algorithm like Visual Geometry Group-VGGNet16?, Segmentation based Fractal Texture Analysis (SFTA) etc.

Colorimetric detection using microfluidic paper-based analytical devices (µPADs) and smartphones enable lowcost mobile chemical analysis solutions. However, variable illumination conditions and phone characteristics (i.e. camera hardware and software capabilities) limit the accurate interpretation and reproducibility of quantitative results. In this paper, we describe a method to automatically compensate an image captured by a smartphone camera under variable illumination conditions. By incorporating a two-step algorithm, we approximate the mobile camera picture color distribution to resemble a laboratory-grade measurement under reference illumination conditions. For every test image, the algorithm first applies a color mapping step that performs histogram matching of a set of color reference spots printed on the device to a laboratory reference measurement. After this initial correction step, a transformation matrix is computed via a least-square fit to minimize the differences between the device and the laboratory references. This matrix is then applied to the RGB channel values obtained from a µPAD to correct for illumination variations. The methodology was tested by correcting a test dataset captured using a smartphone to approximate to a calibration dataset acquired using a lab-grade camera. After correction, the relative error between the datasets fell to 10-20%, leading to an increase in classification accuracy between 12-33%. This approach enables colorimetric chemical analysis with smartphones outside the lab, removing the need to control external lighting conditions.

People re-identification is a fundamental stage in video surveillance systems. This stage is used for determining the labels of the people’s images usually based on their visual similarities. The appearance salience caused by wearing the clothes and carrying objects, with specific colors can be used for improving the performance of re-identification systems. To address the mentioned point, in this paper, we propose a color-based re-ranking process. In the proposed approach first, the input images are semantically segmented as: person’s body, possible carried objects, and background. Then the most frequent colors of person’s body and possible carried objects are used to re-order the ranked results obtained from two commonly used re-identification approaches i.e., Gaussian of Gaussian (GOG) and Hierarchical Gaussian Descriptors (HGDs). Experimental results on various people re-identification datasets show that our proposed re-ranking approach can improve the performance of re-identification systems.

We explored the paper augmentation technique where a tablet computer is placed under the paper to modify its content appearance and create the illusion of a static object appearing perceptually dynamic. Our approach is based on the Deformation Lamps (DL) technique commonly used with front projection. However, we applied it to rear projection, which removes the need for a fixed hardware setup or handling and wearing dedicated hardware. The key challenge was to ensure the appropriate mixing of light reflecting from the paper (to see what is printed on the paper) and the light passing though the paper (to see what the tablet screen is projecting) since (i) a tablet screen is a relatively weak light source when being used in rear projection and (ii) commonly, paper has high opacity so the content from one side is not visible on the other side. In order to achieve the appropriate mixing of light, we reduced the opacity of the paper by perforating it (thus allowing more light to pass through), and modified the algorithm for generating dynamic luminance animation by adding gamma and brightness correction. The outcomes provide a guide on how to modify the paper and the algorithm in order to achieve the DL effect with rear projection using a tablet computer.

Analysis of images with deep learning and machine learning has recently show more accuracy in medical image processing. The paper review the major machine learning methods is used for the extraction of retinal fundus image. It describes how hardware has been modified according to time for retinal eye segmentation that device is helpful for the environment and also useful for humans The algorithms used for image extraction, clear and noise-free Images of different color scales are used to improve image quality. The paper gives a review of how researchers and scientists have been trying different methods to improve vessel segmentation in the future.

This paper is review on image fusion for different technique i.e. Non-subsampled Contourlet Transform (NSCT) and Neural Network (NN). NSCT is the pyramid structure and gives the multiscale feature of the image. NSCT used to directional filtering structure and used to filter bank. Neural network is a progression of calculations that tries to perceive fundamental connections in a bunch of information through an interaction that impersonates the manner in which the human mind works. The implemented algorithm will applied to MRI image and with the help of NN to calculated accuracy. The previous algorithm was work of different technique i.e. principal component analysis, discrete wavelet transorm and curvelet transform. The implemented algorithm will implement MATLAB software and calculate mean square error and peak signal to noise ratio.

In the context of papers used in the graphic arts, including silver gelatin, inkjet, and wove papers, prior work has studied measures of texture similarity for purposes of classifying such papers. The majority of prior work has been based on classical image processing approaches such as Fourier, wavelet, and fractal analysis. In this work, recent advances in deep learning are used to develop a texture similarity approach for measuring paper texture similarity. Since the available datasets generally lack labels, the convolutional neural network is trained using triplet loss to minimize the feature distance of tiles from the same image while simultaneously maximizing the feature distance of tiles drawn from different images. The approach is tested on three paper texture image databases considered in prior works and the results suggest the proposed approach achieves state-of-the-art performance.

The optical model for reflectance lightness of stereo paper product surface is proposed, which could promote color reproduction study of paper model products and improving color reproduction quality. The reflectance lightness was measured using non-contact measurement method with spectroradiometer, CS2000A, that can measure stereo paper product surface lightness and tristimulus. Once inputting the paper product parameters of surface angle and tone, the model will predict the reflectance lightness for stereo paper product surface. After analyzing statistical information of light transmission, paper product surface reflectance lightness can be obtained. The results showed that prediction model is versatile and easy to use, besides the model quite approached to realistic paper product surface. The optical model simulation system could sufficiently accomplish reflectance lightness, which provides a novel way to predict and reproduce printing quality.

This paper reviews the performance analysis based on image security for digital watermarked images using different techniques in transform domains. Digital watermarking is a technique of embedding data like text or image which is called watermark into digital data. It can be done by using different methods depending upon the digital data. It is examined that the most robust method in providing the security for watermarked images can be achieved by combining DWT with SVD when compared to other methods. Though DWT itself maintains good authenticity, when SVD is added to DWT, it increases the robustness in extracting watermark image. Firstly, DWT is applied to the original image and then SVD is applied to the resultant of DWT image. The steps involved in the algorithm to embed and extract the watermark are explained. Finally, the performance analysis of DWT and DWT with SVD is compared and quality of the image is examined.

Prior work on texture analysis of historic, photographic papers has focused primarily on measures of texture similarity. However, automated grouping or clustering of photographic paper textures in a way that is meaningful to art conservators remains an open problem. In this work a deep learning approach to automated classification is presented, for clusters derived from a human sorting experiment conducted by 19 art conservators and paper experts and subsequently extended through crowd-sourcing. The proposed approach uses a deep convolutional neural network, and results are presented on the performance in automatically classifying images when compared to human experts.

The purpose of this paper is to automate the process of the billing system of vegetables. Raspberry PI is the heart of this project which monitors all the components in the system. Initially, we capture the different vegetable images and train the AI model with images. These images are used to recognize the vegetables taken by the customer. The camera captures the selected item image and AI model based on convolution neural network recognizes this image using an image recognition technique. Image recognition is not only the technique to find the vegetable, but also using the resistance of the vegetable we can recognize the vegetable. Resistance method of recognizing the vegetable is not suitable because it may cause damage to the vegetables. So we choose an image recognition method. The weight of the item is also measured using the sensor which removes the human intervention in weighing the item. Recognized item and its weight are going to display on the screen. Customer can add or delete the items using user interface. Finally, a bill is created based on the selected items of the customer.

The electrophoretic display (EPD) has been widely applied on electronic readers (E-Readers) for displaying image/text contents. With a touch panel, the user can put annotations on the contents at will. However, none of the available E-Readers support the function of paintbrush, which allows the user to plot gray tone figures. This is because the EPD is a bistable display whose driving method is quite complicated. In this paper, we first discuss the issues of realizing paintbrush function on the EPD display controller and then propose a new signal processing engine for the paintbrush function. The engine contains four stages: 1) screening the detected pixel coordinates; 2) interpolating pixels; 3) performing image halftoning to generate more visual gray levels; and 4) driving the pixels to the target states. The experimental results show that the engine meets the real-time requirement to complete the pixel interpolation as well as produce the gray-tone traces.

A secure medical image watermarking framework using SVD, DE, step space filling curve and IntDCT is proposed. The proposed algorithm starts with partition of the host medical image into 8×8 blocks and then IntDCT is applied in each block. By collecting integer DC coefficients, a feeble resolution medical image is obtained. SVD is applied on this feeble resolution image. For maintaining a strong relationship between imperceptibility and robustness, the popular differential evolution (DE) technique is chosen to examine the most suitable scaling factors. The singular values of the host image are modified with the singular values of the watermark to insert the watermark in the host image. Here, DE is playing a very important role to determine the most suitable scaling factors to the embedded process for maintaining the dilemma between robustness and imperceptibility without degrading the nobility of medical images. To increase the security of the watermark image, first it is scuffled with the help of step space filling curve before the embedding process. The experimental observations and results proved the efficiency of the proposed framework. It maintains the quality of the watermarked images and the watermark can be extracted from the seriously malformed images.

Steganography is the art of hiding a secret message inside a publicly visible carrier message. Ideally, it is done without modifying the carrier, and with minimal loss of information in the secret message. Recently, various deep learning based approaches to steganography have been applied to different message types. We propose a deep learning based technique to hide a source RGB image message inside finite length speech segments without perceptual loss. To achieve this, we train three neural networks; an encoding network to hide the message in the carrier, a decoding network to reconstruct the message from the carrier and an additional image enhancer network to further improve the reconstructed message. We also discuss future improvements to the algorithm proposed.

This paper studies the authentication problem of specific pieces of paper using mobile imaging devices. Prior work showing high matching accuracy has used the normal vector field , which serves as a unique, microscopic, physically unclonable feature of paper surfaces, estimated by consumer grade scanners. Industrial cameras were also used to capture the appearance of the surface rendered after the normal vector field based on the laws of optics under a semi-controlled lighting condition. In comparison, past explorations based on mobile cameras were very limited and have not had substantial success in obtaining consistent appearance images due to the uncontrolled nature of the ambient light. We show in this paper that images captured by mobile cameras can be used for authentication when the camera flashlight is exploited for creating a semi-controlled lighting condition. We have proposed new algorithms to demonstrate that the normal vector field of paper surface can be estimated by using multiple camera-captured images of different viewpoints. Perturbation analysis shows that the proposed method is robust to inaccurate estimates of camera locations, and a matching accuracy of $10^{-\textrm {4}}$ in equal error rate can be achieved using 6 to 8 images under a lab-controlled ambient light environment. Our findings can relax the restricted imaging setups and enable paper authentication under a more casual, ubiquitous setting with a mobile imaging device, which may facilitate duplicate detection of paper documents and counterfeit mitigation of merchandise packaging.

This paper is intended as a road map for the literature on Synthetic Aperture Radar (SAR) image processing and analysis with Information-Theoretic and Information-Geometric Statistical tools. We do this by commenting upon seemingly disjoint problems, and showing that they can be tackled successfully within the same framework: tools that arise from using Information Theory (entropies and divergences) and Information Geometry (geodesic distances) enhanced with Statistics.

We apply image analysis methods based on micro-computed tomography (μCT) to extract the parameters that characterize the structure and bonding parameters in the fiber network of paper. The scaling and variational properties of μCT images are examined by analyzing paper structural properties of two 1 × 1 mm2 test pieces, which have been cut out from a low-grammage handsheet. We demonstrate the applicability of the methods for extracting the free fiber length, fiber cross-sectional data, the distances between the fibers, and the number of fiber-to-fiber bonds, which are the key properties required for the adequate representation of the network in numerical models. We compare the extracted connectivity data with the early reported analytical estimations and conclude that the number of contacts in three-dimensional networks is controlled by the fiber aspect ratio. In addition, we compare the cross-sectional data with those measured by the fiber morphology characterization tools and estimate the fiber shrinkage from completely wet to dry state to be nearly 20%. QC 20160613

We have quantified the image quality of color electronic paper (e-paper) by subjective evaluations and clarified the requirements for improving the image quality. In the color e-paper field, various display types have been developed with the focus on improving the display performances or manufacture costs. However, color e-papers are still under development, and the image quality is not sufficient when compared with printed papers, photographs and conventional displays. With respect to the image quality of color e-paper in the future, it is very significant to clarify how reflectance, contrast ratio, National Television System Committee (NTSC) ratio, and other factors affect the image quality. Therefore, we have quantified the image quality factors such as color reproduction, tone reproduction, and surface texture. From our experimental results, we could clarify the significant factors and the practical range of image quality of color e-paper. For instance, both the reflectance of approximately 30% or higher, contrast ratio of approximately 8:1 or higher, and NTSC ratio of 20% or higher are the requirements for obtaining subjective evaluation score that is equivalent to that of newspapers, which are read very widely.

The ability to recognize different types of photographic paper can be useful when attributing photographs to specific photographers or eras. This paper uses the recently developed texture feature, Local Radius Index (LRI), and the similarity metrics based on it, to classify photographic papers, from raking light photos of such. The results show that LRI leads to better classification of photographic papers than previous methods. They also suggest the ability to distinguish photographic papers by brand/style and reflectance.

Digital images are easy to manipulate and edit due to availability of powerful image processing and editing software. Nowadays, it is possible to add or remove important features from an image without leaving any obvious traces of tampering. As digital cameras and video cameras replace their analog counterparts, the need for authenticating digital images, validating their content and detecting forgeries will only increase. This paper reviews the detection techniques for copy move forgery tampering attacks. The copy-move attack in which a part of the image is copied and pasted somewhere else in the image with the intent to cover an important image feature is discussed in particular. It is done for hiding some image object, or adding more details resulting in atleast some part being cloned.

Convolutional Neural Network (CNN) architecture is a class of deep learning or deep neural networks, used for analyzing visual information. Traditional CNN architecture has large datasets useful for image classification and recognition. But the datasets in CNN are limited and take time for scene recognition tasks. The time taken for recognizing images plays a crucial role in deep learning. As the datasets are large in number, the object should be compared with millions of images from the dataset, and the time for object recognition goes on increasing. Scene-centric data collection, semantic segmentation, feature selection, and use of residual learning framework to improve the object detection and recognition with respect to time and improve the dataset collection.

Image mining is a method of searching and discovering the valuable information and knowledge from a set of huge image data. Image mining essentially depends on the data mining, digital image processing, machine learning, image retrieval, and artificial intelligence. Image mining is a process which is conducted to extract the hidden information such as image data and the additional pattern that could not be observed from the image. The main problems could face the mining of the collected images can be summarized in two main points: first is the image must be suitable for the mining process and second is the image’s chosen objects and features in order to be treated to extract the most effective route to save the time, and to save the effort. This paper is a survey presents the steps of the image mining process and represented an intensive view on using the image mining to the classify the brain tumors. In addition, it’s proposed a general scheme to accomplish the processes and to analysis the latest techniques which have been used to classify the brain tumors with comparison to the training groups and the amount of accuracy that obtained from the analysis. In addition, the paper compares the relevant and most recent published literature. The high published accuracy claim to be 98% which was obtained using the Deep convolutional neural network (DCNN).

Prior work has shown that visual cues are more dominant than tactile cues for static objects. When texture and material properties are added, the reverse is true. However, it is not well understood which cues play a role in perceiving these textures when objects are moving. In this paper, we present an evaluation investigating speed and angular direction mismatch between visual and tactile information while objects are moving. Our results show that when objects are stationary or moving slowly, the sense of touch dominates visual cues; but as the speed increases, visual cues dominate tactile cues. When tactile and visual cues were not matching for moving virtual objects, visual cues were more dominant when the virtual objects moved at angles less than 30 degrees different than the tactile feedback in either direction. Therefore, potentially a 1D directional haptic device with physical texture substitutions may be able to convey sufficient realism when touching virtual objects that are moving faster than 4-8cm/s and with a difference in angular direction of 30 degrees or less. In this paper, we also present the limitations of this work and outline a research agenda for future investigation of visual and tactile cue dominance for perceiving textures of virtual objects moving in a virtual environment.

Paper formation (the distribution and intermixing of fibres in a paper sheet), plays a central role in paper products, and is usually evaluated off-line, with a significant delay relative to the high production rates achieved in modern paper machines. In this paper, we address an approach for evaluating and monitor paper formation using images acquired with an especially designed sensor, in-line, in-situ and in real time. The methodology essentially consists of applying wavelet texture analysis to raw images, in order to compute a wavelet signature for each image, based on which their discrimination, according to the formation quality level, can be made. A PCA analysis of such features confirms the different formation quality levels defined a priori after visual inspection, and, furthermore, suggests a new subclass for abnormal samples, related to the bulkiness of fibre flocks. A multivariate statistical process control framework, based on such PCA description (PCA-MSPC), is proposed to monitor formation quality, which provides quite good results when applied to the available images, as analyzed with the ROC curve for the method and confirmed with a Monte Carlo simulation study using subimages with 1/4 of the size of the original ones.

This paper introduces a method of filling information recognition for scanned image of paper form. The cross point coordinates are extracted by Strip-like corrosion and expansion of the binary images. The form image base frame is constructed using a rectangular fit based on the Hough transform. Using the image pan, rotate, and scale algorithms, the template form image base frame to the scanned form image base frame can be matched. The filling grid area is determined based on the structural similarity algorithm (SSIM) and the filling information is thus obtained. The experimental results show that this method can accurately, quickly and efficiently read the batch paper form filling information digitally, and has strong robustness.

Gesture recognition is one of the most popular tasks in computer vision, where convolutional neural networks (CNNs) based method has obtained the state-of-the-art performance. However, it is generally acknowledged that CNNs need a large amount of data to achieve such performance. Event Camera is a kind of biologically inspired event-based camera, which can keep the information of moving objects and remove the redundant background data. In this paper, we set up a rock-paper-scissor hand gesture recognition system based on FlowNet and Event Camera. Event camera is used to acquire event data. Then we propose an algorithm for the proposed gesture recognition. Specifically, FlowNet2.0 is employed to extract the motion representation of the pre-processed visual data, and a CNN classification network is applied to recognize the symbols extracted according to the motion representation. As a comparison, we also apply the rock-paper-scissor gesture recognition algorithm on traditional camera. The experimental results show that the proposed system based on Event Camera gets better performance, and to some extent, weaken the dependence on the training data. The whole system achieves 94.0\(\%\) out-of-sample accuracy and allows computation at up to 30 fps.

Application of image frequency filtering to elimination of the noise in scanned photographs caused by the embossing of the photographic paper is considered.

Paper checks may have complex background features including fine lines and patterns, which make forgery more difficult. Also, halftoning techniques are used to produce continuous tones and to prevent copies with void pantograph features. When these kinds of checks are scanned, Moire patterns may occur. These patterns make it difficult for customers to examine the scanned check images on ATM (Automated Teller Machine) displays. They also can decrease the classification accuracy of check recognition systems. In this paper, we propose an algorithm to enhance the perceptual quality of scanned check images by reducing the Moire patterns. The proposed algorithm consists of foreground extraction, Moire detection and Moire removal. Subjective image quality assessment was performed to evaluate the degree of improvement. Experimental results show that the proposed algorithm improves perceptual quality while maintaining check recognition accuracy.

International audience; Texture characterization of photographic papers is likely to provide scholars with valuable information regarding artistic practices. Currently, texture assessment remains mostly based on visual and manual inspections, implying long repetitive tasks prone to inter- and even intra-observer variability. Automated texture characterization and classification procedures are thus important tasks in historical studies of large databases of photographic papers, likely to provide quantitative and reproducible assessments of texture matches. Such procedures may, for instance, produce vital information on photographic prints of uncertain origins. The hyperbolic wavelet transform, because it relies on the use of different dilation factor along the horizontal and vertical axes, permits to construct robust and meaningful multiscale and anisotropic representation of textures. In the present contribution, we explore how unsupervised clustering strategies can be complemented both to assess the significance of extracted clusters and the strength of the contribution of each texture to its associated cluster. Graph based filterbank strategies are notably investigated with the aim to produce small size significant clusters. These tools are illustrated at work on a large database of about 2500 exposed and non exposed photographic papers carefully assembled and documented by the MoMA and P. Messier's foundation. Results are commented and interpreted.

The human visual system uses cast shadows to judge the three-dimensional layout of an object. The purpose of this installation is to demonstrate novel visual illusions of depth and transparency for paper materials, which is induced by the conventional light projection of cast shadow patterns. Thus, this installation focuses on perceptual rather than technical aspects. Illuminating a target object, the spatial vicinity of the object is darkened to produce the visual impression of a shadow of the object. By controlling the blurriness of the cast shadow and/or spatial distance between the object and its shadow, a perceptual change in the layout of the object is induced. The audience can interactively enjoy visual experiences wherein objects and letters on a paper perceptually float in the air. We also demonstrate that it is possible to edit the material appearance of a real object by manipulating the shape of the shadow; an opaque colored paper appear to be a transparent color film floating in the air.

Paper under the microscopic view has a rough surface formed by intertwisted wood fibers. Such roughness is unique on a specific location of the paper and is almost impossible to duplicate. Previous work has shown that commodity scanners and cameras are capable of capturing such intrinsic roughness in term of surface normal vectors for security and forensics applications. In this paper, we examine several candidate mathematical models for camera captured images of paper surfaces and compare the modeling accuracies with reference to the measurement by the confocal microscopy. Experimental results show that the model with distinct intensity bias for images captured from different viewpoints can provide the closest result to the confocal measurement. We discover that high-frequency subbands of reconstructed 3D surfaces are more powerful than the norm map in describing the uniqueness of a physical surface. We show through a practical paper surface based authentication system that incorporating these findings can improve the discrimination performance.

Currency detection falls into the field of computer vision technology. Detection of currency is a helping hand for visually impaired people. Moreover it is also useful in surveillance system. In this paper we presented a paper currency detection system which can detect paper currency from image. Detection is based on training different currencies. At first we extracted SURF and LBP features of currencies respectively. Later we combined both features. Then trained them with SVM classifier. SVM as a classifier performs very well in training image datasets. After that applying sliding window technique on input image, we detected currency from an image. In this currency detection system we focused on only paper currencies of Bangladesh. Along with currency detection, this system shows number of currencies and also the total amount of currencies exists in an image. The proposed system is able to detect paper currencies in rotated positions also and it achieves an average accuracy of 92.6% in detection.

International audience; Photographic paper texture characterization constitutes a challenging image processing task and an important stake both for manufacturers and art museums. The present contribution shows how the Hyperbolic Wavelet Transform, thanks to its joint multiscale and anisotropie nature, permits to achieve an accurate photographic paper texture analysis and characterization. A cepstral-type distance, constructed on the coefficients of the Hyperbolic Wavelet Transform, is then used to measure similarity between pairs of paper textures. Spectral clustering followed by Ascendant Hierarchical Clustering applied to the similarity matrix enables an unsupervised classification of photographic paper sheets. This methodology is applied to a test dataset made available in the framework of the Historic Photographic Paper Classification Challenge. The relevance of the proposed texture characterization and classification procedure is assessed by comparisons against the database documentation provided by experts.

At crime scenes, fingerprints are often found on paper. The visualization of invisible fingerprints, so-called latent fingerprints (LFPs), is critical in forensic investigation. Laser-induced fluorescence technology for fingerprint detection has the advantages of sensibility, high-resolution, non-destructiveness and non-contact. In this paper, the characteristics of commonly used paper and the interaction between ultraviolet light and porous paper are briefly analyzed firstly. Next the fingerprint detection device is introduced. Then the output energy of the laser is optimized and a series of experiments are carried out. Local gray value stretching and power enhancement algorithms are used to process the fingerprint fluorescence data, and the high contrast fingerprint images are obtained. The experimental results show that the fingerprint substance and paper are degraded in different degrees under the excitation of ultraviolet light, and the second or third detection can improve the signal-to-noise ratio of detection. By the method of this paper, clear fingerprint images can be obtained quickly, and more than 10 secondary fingerprint structures can be extracted from these fingerprint images, which provides quantitative evidences for criminal investigation.

It is known that a virtual static object on a display is perceived as moving when the object, to which the particular pattern of luminance contours is given, is presented against the background with dynamic luminance change. The present study reports a novel technique in which printed objects having particular luminance contours apparently move against a background with dynamic luminance modulations. Our technique can illusorily express not only horizontal translations but also the expansion-compression and rotation of the objects. We believe that our technique can be used to highlight static objects in paper advertisements by placing the objects against a background with dynamic luminance changes.

The regularly expanding measures of patient’s information as medicinal image, forces new difficulties to clinical routine, for example, diagnosis, treatment and checking. Image mining is the way toward seeking and finding profitable data learning of information. It is connected on image preparing and machine learning. Picture handling is having essentially for ailment recognition on restorative pictures. These sickness acknowledgment and arrangement are particular to human organ and image nature. With help of image processing and machine learning strategies it is conceivable to computerize as well as help doctors in clinical analysis. In this paper depicts the utilization of different image processing and machine learning methods for identification of eye diseases.

Field-sequential color displays are used as an alternative to the standard displays where energy consumption can be reduced or peak luminance can be increased. Its spatial resolution is potentially three times higher compared to a conventional color filter LCD. The concept of field sequential color displays shows the problem of color breakup artifact. This paper reviews the cause and the factors for the perception of color breakup, discusses the approaches for the reduction of color breakup. It is suggested that, with field-sequential color algorithm, 180Hz or 240Hz LC panel could have potential market in the high luminance and high resolution display applications.

Video content is used extensively in many fields. However, in some fields, video manipulation techniques are required to improve the human-friendliness of such content. In this paper, we propose a method that automatically generates animations in the style of colored paper mosaics, to create human-friendly, artistic imagery. To enhance temporal coherence while maintaining the characteristics of colored paper mosaics, we also propose a particle video-based method that determines coherent locations for tiles in animations. The proposed method generates evenly distributed particles, which are used to produce animated tiles via our tile modeling process.