Image Copy Research Articles

Automatic Digitization of Engineering Diagrams using Intelligent Algorithms

At present usage of computational intelligence became the ultimate need of the heavy engineering industries. Digitization can be achieved in these sectors by scanning the hard copy images. When older documents are digitized are not of very high fidelity and therefore the accuracy, reliability of the estimates of components such as equipment and materials after digitization are remarkably low since (Piping and Instrumentation Diagrams) P&IDs come in various shapes and sizes, with varying levels of quality along with myriad smaller challenges such as low resolution of images, high intra project diagram variation along with no standardization in the engineering sector for diagram representation to name a few, digitizing P&IDs remains a challenging problem. In this study an end to end pipeline is proposed for automatically digitizing engineering diagrams which would involve automatic recognition, classification and extraction of diagram components from images and scans of engineering drawings such as P&IDs and automatically generating digitized drawings automatically from this obtained data. This would be done using image processing algorithms such as template matching, canny edge detection and the sliding window method. Then the lines would be obtained from the P&ID using canny edge detection and sliding window approach, the text would be recognized using an aspect ratio calculation. Finally, all the extracted components of the P&ID are associated with the closest texts present and the components mapped to each other. By the way of using such pipelines as proposed the diagrams are consistently of high quality, other smaller problems such as mis-spelling and valuable time churn are solved or minimized to large extent and paving the way for application of big data technologies such as machine learning analytics on these diagrams resulting in further efficiencies in operational processes.

Image and report quality in non-obstetric ultrasound examinations undertaken by sonographers: Results of an audit of 3731 cases.

This paper reports the results of a rolling audit of sonographer-performed non-obstetric ultrasound examinations undertaken between 2010 and 2020 in a large University Teaching Hospital Ultrasound Department in the United Kingdom. We believe that this represents the largest published audit of sonographer non-obstetric ultrasound examination quality. Random samples of sonographer ultrasound examinations were regularly and systematically audited by consultant-level ultrasound practitioners through review of soft copy images and reports. Examination and report quality were assessed against an internal audit standard in 3731 patients over an 11-year period and also against externally set audit standards in 3186 patients over a nine-year period. Both image and report quality exceeded externally set audit standards in all nine years of audit. In the internal audit, the quality standard just failed to be met for the first five years of audit but was achieved in all the subsequent six years. This audit provides further information on the quality and safety of sonographer-led ultrasound service delivery within a service that has quality safeguards, readily available support and an active education programme. It is used not only to provide assurance to patients, clinicians, managers and commissioners of this service but also to direct individual professional development and drive an iterative process of quality improvement.

A Study of Copy Image Detection Using the Model of Raspberry Pi Machine Learning Process

Many physical gadgets are associated to the internet shaping. They are known as the Internet of Things. These gadgets are developing a calculation of useless and useful data. The preparing and transmission of this data was an investigating task. Various internets of things are talked in the current research work. A sector of security was the unmistakable application in the internet of things system. It was efficient to reduce the crime and provides security to individuals form business, home, military and so on. This research paper consumes the Raspberry Pi Internet of things basic characteristics the representation for the machine learning process. It ends the request of customer employments for instance, to transmit information safely by the process of internet of things engineering. It analyzes for normal internet of things, very low manageable and finance process depend upon the structure of security learning process that aids detectable 97% proof, nearby identification and outsiders verification. These arrangements develops by using the USB web camera as an image capturing unit, door hit through electrical way that provides APIs, to accumulate preparations that was perfect along with the structure of internet of things for improving the video graph arrangement for Raspberry Pi app.

Residual visualization-guided explainable copy-relationship learning for image copy detection in social networks

In modern times, people are used to sharing individual works, giving opinions, and expressing emotions and sentiments by transmitting a variety of images such as personal photographs, pictures of purchased products, and emoticons on social networks. However, this results in a lot of illegal image copies being distributed, which causes the issues of misleading the public and copyright infringement. To identify such images, recent copy detection approaches learn similarity functions by directly exploring neural networks such as Siamese and pseudo-Siamese networks and thereby determine the copy relationships between images. Unfortunately, since such approaches directly use these networks as black boxes, without considering the essential differences between image copies and similar images, handling the prevalent need for copy detection, i.e., identifying image copies from similar images, remains a challenge. To address the above issue, first, a residual visualization scheme is proposed in this study to visualize the residual image by subtracting the original image from the geometrically aligned test image, thereby revealing the relationship between the two images. Then, guided by the observations on the residual visualization results, a newly designed explainable copy-relationship learning network (ECRLN) architecture is presented. In this network architecture, the characteristics of residual images, ranging from local details to global appearances and from low-level visual patterns to high-level semantics, are sufficiently utilized to learn a measurement function for computing the copy-relationship confidence between images. The extensive experiments conducted on the publicly available datasets as well as our own challenging dataset demonstrate that the proposed approach can accurately identify image copies from similar images with good interpretability and achieve state-of-the-art performance in detection accuracy and training efficiency.

Single-shot phase-shifting radial-shearing digital holography with Fibonacci-sieve array irrespective of initial phases

We present a kind of single-shot common-path phase-shifting digital holography realized by both a holographic grating and a Fibonacci-sieve array. A test object wave is first split into multiple image copies by the holographic grating placed at the Fourier plane in a Kepler telescope. Then, the image copies are directed onto their own sieves, which can generate a pair of axial foci with customized phase shifts. This way, three phase-shifting sub-holograms are captured by a single exposure and extracted to reconstruct the original complex amplitude of the test object. The proposed method is free of pre-calibration and mechanical movement during the measuring process, so the imaging speed and robustness are significantly improved. The common path is less sensitive to external interference than an in-line system. A measurement of a USAF1951 test target is presented, and the results show the high resolution and stability over 24 h at least, which gives it potential in application to high-resolution dynamic measurements, such as biological microscopy.

Makerspaces: Combining Information Literacy with Pattern Design for Fiber Art through Digital Images

This article shares a practice of using digital images to create patterns for embroidery with aspects of information literacy within makerspaces. The author shares her enjoyment of a fiber art practice born of using digital images to create fiber art patterns from rare illuminated manuscripts. The author suggests that because images from these manuscripts are often considered hidden images, offering a program for pattern design will increase research discovery and opportunities for scholarship. This autoethnography offers one librarian’s experience using digital images from rare illuminated manuscripts and the ancient embroidery technique laid work to create colorful fiber copies of musical instruments and musicians for her art piece “The Medieval Screen.” Laid work is documented from the famous Bayeux Tapestry, and fiber copies of hidden images are one vibrant way to display forgotten images. The author asks if there will be a significant increase in downloads and views of historical digital images from offering makerspaces to teach this method combined with information literacy.

Analysis of Perceptual Hashing Algorithms in Image Manipulation Detection

Perceptual image hashing is a family of algorithms that generate content-based image hashes. Unlike cryptographic hashes, perceptual hashes are designed to not change much when an image undergoes minor modifications such as compression, color-correction, and brightness. Therefore, these algorithms have been found useful in detecting redundant images, perform reverse image search, and flagging/filtering inappropriate imagery by comparing image hashes with a dataset of known perceptual hashes. While these applications have been served well, there is a new threat to visual information in the form of misinformation through content changing image manipulation using techniques such as object addition/removal and copy-move. While perceptual hashing algorithms can very accurately identify copies of similar images and their legitimately modified versions, their effectiveness in detecting content changing manipulations is not studied well. Existing research and advanced image libraries propose various image hashing algorithms, and several datasets of manipulated images are available in the public domain. In this work, a review and performance analysis of several perceptual hashing algorithms against two such datasets is presented. Results show less than ideal performance in distinguishing maliciously manipulated images from legitimate ones. Based on the results, limitations of perceptual hashing algorithms are identified, and future research directions are proposed.

Local Features-Based Watermarking for Image Security in Social Media

The last decade shows an explosion of using social media, which raises several challenges related to the security of personal files including images. These challenges include modifying, illegal copying, identity fraud, copyright protection and ownership of images. Traditional digital watermarking techniques embed digital information inside another digital information without affecting the visual quality for security purposes. In this paper, we propose a hybrid digital watermarking and image processing approach to improve the image security level. Specifically, variants of the widely used Least-Significant Bit (LSB) watermarking technique are merged with a blob detection algorithm to embed information into the boundary pixels of the largest blob of a digital image. The proposed algorithms are tested using several experiments and techniques, which are followed by uploading the watermarked images into a social media site to evaluate the probability of extracting the embedding watermarks. The results show that the proposed approaches outperform the traditional LSB algorithm in terms of time, evaluation criteria and the percentage of pixels that have changed.

A Self Supervised Defending Mechanism Against Adversarial Iris Attacks based on Wavelet Transform

In biometric applications, deep neural networks have presented significant improvements. However, when presenting carefully designed input training data known as adversarial examples, their output is severely reduced. These types of attacks are termed as adversarial attacks, and any biometric security system is greatly affected by these attacks. In the proposed work, an effective defensive mechanism has been developed against adversarial attacks which are introduced in iris images. The proposed defensive mechanism is following the concept of wavelet domain processing and it investigates the mid and high frequency components of wavelet domain components. Based on this, the model reproduces the various denoised copies of input iris images. The proposed strategies are intended to denoise each sub-band of the wavelet domain and assess the sub-bands most likely to be affected by the adversary using the reconstruction error measured for each sub-band. We test the effectiveness of the proposed adversarial protection mechanism against various attack methods and analyzed the results with other state of the art defense approaches.

ONTOGENETIC ASPECTS OF MELATONIN RECEPTORS 1A DENSITY IN THE LATERAL PREOPTIC NUCLEUS OF THE HYPOTHALAMUS UNDER VARIOUS LIGHT CONDITIONS

The aim: To find out density of melatonin receptors 1A in the neurons of the lateral preoptic nucleus of the hypothalamus in mature and old rats under various light conditions. Materials and methods: The study was carried out on 72 albino mature and old rats with light conditions appropriate for the experiment. To find out circadian differences of melatonin receptors 1A the material for the study was taken at 2 p.m. and 2 a.m. Visualization of primary antibodies against melatonin receptors 1A (Abcam) was conducted by means of the polymeric system Dako and diaminobenzidine staining under the microscope Delta Optical Evolution 100. The intensity of staining was assessed on the digital copies of images according to computer microdensitometry method. Results: Immunohistochemical examinations conducted enable to suggest that melatonin receptors 1A in the neurons of the lateral preoptic nucleus of the hypothalamus respond to different light conditions. In particular, intensity of immunohistochemical staining to melatonin receptors 1A under conditions of light deprivation increases both in mature and old rats, but it decreases under conditions of light stimulation. The parameter is higher at 2 a.m. as compared with 2 p.m. Intensity of immunohistochemical staining to melatonin receptors 1A in the neurons of the lateral preoptic nucleus of the hypothalamus is always lower in old rats than in mature ones. Conclusions: Density of melatonin receptors 1A in the neurons of the lateral preoptic nucleus of the hypothalamus is subordinated to the circadian rhythm: it increases at night and decreases in the daytime. At the same time, light stimulation results in disorders of the rhythm and development of desynchronization.

A comparative study on diagnosis of silicosis by digital and high kV film-screen chest radiography

Objective: To explore the feasibility of soft copy image of chest digital radiography (DR(SC)) in pneumoconiosis diagnosis by observing the reading effect of DR(SC) and comparing it with high kV film-screen chest radiography (FSR(HkV)) . To provide a basis for the establishment of a regional information network platform for pneumoconiosis diagnosis. Methods: A total of 119 miners who were exposed to silica dust and engaged in copper or lead-zinc mining were collected by the Third People's Hospital of Yunnan from October 2017 to August 2019. The pulmonary X-ray findings of DRSC and FSR(HkV) in each case were independently judged and diagnosed by three experienced diagnostic physicians, and the final decision was made according to the consensus of most doctors. The consistency of the two imaging methods was analyzed by observing the main small opacity shape, the overall density, the range of distribution, the aggregation of the small opacity and the big opacity, and the diagnosis stage. Results: there were 118 males and 1 female with an average age of 46.21 years. Average exposure time 7.38 years. The pulmonary X-ray findings of 113 cases with two imaging methods were mainly the round small opacity of p,q and r,but the reticular irregular opacity were not prominent, Only one case of FSR(HkV) judgment p shape and DR(SC) judgment q shape were observed, The coincidence rate was 99.12% (112/113) . huger opacity were observed in the other 6 cases. The coincidence rate was 95.51% (4/89) between pneumoconiosis-free and stageⅠ, and there was no significant difference between stageⅡand stage Ⅲ (30/30, P>0.05) . Conclusion: The x-ray findings of Silicosis with small round shadow in lung can be used by DR(SC) in diagnosis and staging.

Content-Based Image Copy Detection Using Convolutional Neural Network

With the rapid development of network technology, concerns pertaining to the enhancement of security and protection against violations of digital images have become critical over the past decade. In this paper, an image copy detection scheme based on the Inception convolutional neural network (CNN) model in deep learning is proposed. The image dataset is transferred by a number of image processing manipulations and the feature values in images are automatically extracted for learning and detecting the suspected unauthorized digital images. The experimental results show that the proposed scheme takes on an extraordinary role in the process of detecting duplicated images with rotation, scaling, and other content manipulations. Moreover, the mechanism of detecting duplicate images via a convolutional neural network model with different combinations of original images and manipulated images can improve the accuracy and efficiency of image copy detection compared with existing schemes.

Robust and fast image hashing with two-dimensional PCA

Image hashing is a useful technology of many multimedia systems, such as image retrieval, image copy detection, multimedia forensics and image authentication. Most of the existing hashing algorithms do not reach a good classification between robustness and discrimination and some hashing algorithms based on dimensionality reduction have high computational cost. To solve these problems, we propose a robust and fast image hashing based on two-dimensional (2D) principal component analysis (PCA) and saliency map. The saliency map determined by a visual attention model called LC (luminance contrast) method can ensure good robustness of our hashing. Since 2D PCA is a fast and efficient technique of dimensionality reduction, the use of 2D PCA helps to learn a compact and discriminative code and provide a fast speed of our hashing. Extensive experiments are carried out to validate the performances of our hashing. Classification comparison shows that our hashing is better than some state-of-the-art algorithms. Computational time comparison illustrates that our hashing outperforms some compared algorithms based on dimensionality reduction.

An Automatic Centroid Image Selection Method Based on Fuzzy Logic Reasoning in Image Deduplication

Centroid selection plays a key role in image deduplication. It means selecting an optimal solution as a centroid image in a duplicate image set. Meanwhile, it will delete other image copies and establish pointers to point to the centroid image in the original position. At present, there is not a mature centroid selection scheme. Centroid selection mainly relies on users to manually complete according to experience. In a massive data environment, it will consume a lot of human resources, and it is easy to make mistakes by subjective judgment. Therefore, in order to solve this problem, this article proposes an automatic centroid image selection method based on fuzzy logic reasoning. In a duplicate image set, the image attribute information is used to automatically infer comprehensive quantized values to represent images, and the centroid image is selected by comparing the quantized values. The experimental results showed that the scheme not only could meet the visual perception characteristics, but also meet the purpose of image deduplication.

CNN Feature-Based Image Copy Detection with Contextual Hash Embedding

As one of the important techniques for protecting the copyrights of digital images, content-based image copy detection has attracted a lot of attention in the past few decades. The traditional content-based copy detection methods usually extract local hand-crafted features and then quantize these features to visual words by the bag-of-visual-words (BOW) model to build an inverted index file for rapid image matching. Recently, deep learning features, such as the features derived from convolutional neural networks (CNN), have been proven to outperform the hand-crafted features in many applications of computer vision. However, it is not feasible to directly apply the existing global CNN features for copy detection, since they are usually sensitive to partial content-discarded attacks, such as copping and occlusion. Thus, we propose a local CNN feature-based image copy detection method with contextual hash embedding. We first extract the local CNN features from images and then quantize them to visual words to construct an index file. Then, as the BOW quantization process decreases the discriminability of these features to some extent, a contextual hash sequence is captured from a relatively large region surrounding each CNN feature and then is embedded into the index file to improve the feature’s discriminability. Extensive experimental results demonstrate that the proposed method achieves a superior performance compared to the related works in the copy detection task.

Robust image hashing with compressed sensing and ordinal measures

Image hashing is an efficient technology for processing digital images and has been successfully used in image copy detection, image retrieval, image authentication, image quality assessment, and so on. In this paper, we design a new image hashing with compressed sensing (CS) and ordinal measures. This hashing method uses a visual attention model called Itti model and Canny operator to construct an image representation, and exploits CS to extract compact features from the representation. Finally, the CS-based compact features are quantized via ordinal measures. L2 norm is used to judge similarity of hashes produced by the proposed hashing method. Experiments about robustness validation, discrimination test, block size discussion, selection of visual attention model, selection of quantization scheme, and effectiveness of the use of ordinal measures are conducted to verify performances of the proposed hashing method. Comparisons with some state-of-the-art algorithms are also carried out. The results illustrate that the proposed hashing method outperforms some compared algorithms in classification according to ROC (receiver operating characteristic) graph.

Perceptual Image Hashing With Texture and Invariant Vector Distance for Copy Detection

Content-based image copy detection has become one of the important technologies in copyright protection, where two major processes, content-based feature extraction and matching are included. However, it is certainly true that enough storage space is required to establish feature database for matching, which greatly increases time and storage consumption, as well as lacks flexibility. Fortunately, perceptual image hashing is a good strategy to address these problems, in which content-based features are extracted and further encoded to hash codes. On the one hand, content-based features provide and ensure higher copy detection accuracy, while on the other hand, hash codes instead of feature database reduce storage space and improve time efficiency. Meanwhile, a better balance between robustness and discrimination is one of the most objectives of image hashing, which is conducive to its application in multimedia management and security. Consequently, we present an effective image hashing method for copy detection. Specifically, to obtain perceptual robustness against to copy attacks, we extract the global statistical characteristics in gray-level co-occurrence matrix (GLCM) to reveal texture changes. Then, to make up the discrimination limitation, we leverage the local dominant DCT coefficients from the first row/column in each sub-image to calculate vector distance. Finally, two kinds of complementary information (global feature via texture and local feature via vector distance) are simultaneously preserved to generate hash codes. Various experiments performed on benchmark database indicate that our proposed perceptual image hashing provides higher detection accuracy and better balance between robustness and discrimination than the state-of-the-art algorithms.

Image Copy Detection Based on Local Binary Pattern and SVM Classifier

Abstract Due to the availability of a large number of image editing software, it is very easy to find duplicate copies of original images. In such a situation, there is a need to develop a robust technique that can be used for the identification of duplicate copies apart from differentiating it from different images. In this paper, we have proposed an image hashing technique based on uniform Local Binary Pattern (LBP). Here, the input image is initially pre-processed before calculating the Local Binary Pattern (LBP) which is used for image identification. Experiments show that proposed hashing gives excellent performance against the Histogram equalization attack. The Receiver Operating Curve (ROC) indicates that the proposed hashing also performs better in terms of robustness and discrimination. Support Vector Machine (SVM) classifier shows that generated features can easily classify images into a set of similar and different images, and can classify new data with a high level of accuracy.

The botanical illustrations of Franz Scheidl (fl. 1770–1795)

Franz Scheidl created approximately 800 botanical illustrations published in Vienna as coloured copperplate engravings in Nikolaus Joseph von Jacquin's Hortus botanicus Vindobonensis and Florae Austriacae icones between 1770 and 1778. Johann Jacob von Well's unpublished “Phytanthologia eikonike”, an illustrated manuscript intended as a kind of florilegium and produced between 1768 and 1780, consists in part of plant illustrations based on living specimens, in part of incomplete copies of pre-existing images; these are also by Scheidl's hand. Another manuscript, “Pflanzen Blumen und Früchte”, held by the Oak Spring Garden Foundation Library in Upperville, Virginia, to date incompletely known and possessing similar characteristics, has also been ascribed to Scheidl; its contents are analysed here. These materials help to understand the work of a botanical illustrator and copyist working in the late eighteenth century.

Robust image hashing with visual attention model and invariant moments

Image hashing is an efficient technique of multimedia processing for many applications, such as image copy detection, image authentication, and social event detection. In this study, the authors propose a novel image hashing with visual attention model and invariant moments. An important contribution is the weighted DWT (discrete wavelet transform) representation by incorporating a visual attention model called Itti saliency model into LL sub-band. Since the Itti saliency model can efficiently extract saliency map reflecting regions of attention focus, perceptual robustness of the proposed hashing is achieved. In addition, as invariant moments are robust and discriminative features, hash construction with invariant moments extracted from the weighted DWT representation ensures good classification performance between robustness and discrimination. Extensive experiments with open image datasets are done to validate the performances of the proposed hashing. The results demonstrate that the proposed hashing is robust and discriminative. Performance comparisons with some hashing algorithms are also conducted, and the receiver operating characteristic results illustrate that the proposed hashing outperforms the compared hashing algorithms in classification performance between robustness and discrimination.