A Color Error Correcting Model for Scanning Input Image

2006 ◽  
Author(s):  
Xinwu Li
Keyword(s):  
Input Image ◽  
Color Error

A Comparative Study of Ant and Genetic Algorithms in Digital Mammography

2018 ◽  
pp. 194-200
Author(s):  
J. Magelin Mary ◽  
Chitra K. ◽  
Y. Arockia Suganthi
Keyword(s):  
Genetic Algorithm ◽  
Image Processing ◽  
Medical Image ◽  
Medical Image Processing ◽  
Processing Technique ◽  
Input Image ◽  
Image Processing Technique ◽  
Computational Time ◽  
Image Mining ◽  
The Individual

Image processing technique in general, involves the application of signal processing on the input image for isolating the individual color plane of an image. It plays an important role in the image analysis and computer version. This paper compares the efficiency of two approaches in the area of finding breast cancer in medical image processing. The fundamental target is to apply an image mining in the area of medical image handling utilizing grouping guideline created by genetic algorithm. The parameter using extracted border, the border pixels are considered as population strings to genetic algorithm and Ant Colony Optimization, to find out the optimum value from the border pixels. We likewise look at cost of ACO and GA also, endeavors to discover which one gives the better solution to identify an affected area in medical image based on computational time.

Document Image Quality Assessment with Relaying Reference to Determine Minimum Readable Resolution for Compression

2020 ◽  
Vol 2020 (9) ◽  
pp. 323-1-323-8
Author(s):  
Litao Hu ◽  
Zhenhua Hu ◽  
Peter Bauer ◽  
Todd J. Harris ◽  
Jan P. Allebach
Keyword(s):  
Image Quality ◽  
Quality Assessment ◽  
Image Quality Assessment ◽  
Research Area ◽  
Input Image ◽  
Quality Score ◽  
Document Image ◽  
Digital Cameras ◽  
Active Research ◽  
Traditional Approaches

Image quality assessment has been a very active research area in the field of image processing, and there have been numerous methods proposed. However, most of the existing methods focus on digital images that only or mainly contain pictures or photos taken by digital cameras. Traditional approaches evaluate an input image as a whole and try to estimate a quality score for the image, in order to give viewers an idea of how “good” the image looks. In this paper, we mainly focus on the quality evaluation of contents of symbols like texts, bar-codes, QR-codes, lines, and hand-writings in target images. Estimating a quality score for this kind of information can be based on whether or not it is readable by a human, or recognizable by a decoder. Moreover, we mainly study the viewing quality of the scanned document of a printed image. For this purpose, we propose a novel image quality assessment algorithm that is able to determine the readability of a scanned document or regions in a scanned document. Experimental results on some testing images demonstrate the effectiveness of our method.

Segmentasi Citra Mri Menggunakan Deteksi Tepi Untuk Identifikasi Kanker Payudara

2017 ◽  
Vol 15 (2) ◽  
pp. 17 ◽  
Author(s):  
Ervina Varijki ◽  
Bambang Krismono Triwijoyo
Keyword(s):  
Breast Cancer ◽  
Edge Detection ◽  
Object Segmentation ◽  
Computation Time ◽  
Input Image ◽  
Boundary Line ◽  
Cancer Breast ◽  
Average Computation Time ◽  
Breast Cancer Mri ◽  
Image Format

One type of cancer that is capable identified using MRI technology is breast cancer. Breast cancer is still the leading cause of death world. therefore early detection of this disease is needed. In identifying breast cancer, a doctor or radiologist analyzing the results of magnetic resonance image that is stored in the format of the Digital Imaging Communication In Medicine (DICOM). It takes skill and experience sufficient for diagnosis is appropriate, andaccurate, so it is necessary to create a digital image processing applications by utilizing the process of object segmentation and edge detection to assist the physician or radiologist in identifying breast cancer. MRI image segmentation using edge detection to identification of breast cancer using a method stages gryascale change the image format, then the binary image thresholding and edge detection process using the latest Robert operator. Of the20 tested the input image to produce images with the appearance of the boundary line of each region or object that is visible and there are no edges are cut off, with the average computation time less than one minute.

Face detection in single and multiple images using different skin color models

2020 ◽  
Vol 13 ◽  
Author(s):  
Manpreet Kaur ◽  
Jasdev Bhatti ◽  
Mohit Kumar Kakkar ◽  
Arun Upmanyu
Keyword(s):  
Face Detection ◽  
Skin Color ◽  
Input Image ◽  
Morphological Operations ◽  
Reliability Model ◽  
Multiple Images ◽  
Face Region ◽  
Bounding Box ◽  
The Face ◽  
Precision And Accuracy

Introduction: Face Detection is used in many different steams like video conferencing, human-computer interface, in face detection, and in the database management of image. Therefore, the aim of our paper is to apply Red Green Blue ( Methods: The morphological operations are performed in the face region to a number of pixels as the proposed parameter to check either an input image contains face region or not. Canny edge detection is also used to show the boundaries of a candidate face region, in the end, the face can be shown detected by using bounding box around the face. Results: The reliability model has also been proposed for detecting the faces in single and multiple images. The results of the experiments reflect that the algorithm been proposed performs very well in each model for detecting the faces in single and multiple images and the reliability model provides the best fit by analyzing the precision and accuracy. Moreover Discussion: The calculated results show that HSV model works best for single faced images whereas YCbCr and TSL models work best for multiple faced images. Also, the evaluated results by this paper provides the better testing strategies that helps to develop new techniques which leads to an increase in research effectiveness. Conclusion: The calculated value of all parameters is helpful for proving that the proposed algorithm has been performed very well in each model for detecting the face by using a bounding box around the face in single as well as multiple images. The precision and accuracy of all three models are analyzed through the reliability model. The comparison calculated in this paper reflects that HSV model works best for single faced images whereas YCbCr and TSL models work best for multiple faced images.

scholarly journals CoCoX: Generating Conceptual and Counterfactual Explanations via Fault-Lines

2020 ◽  
Vol 34 (03) ◽  
pp. 2594-2601
Author(s):  
Arjun Akula ◽  
Shuai Wang ◽  
Song-Chun Zhu
Keyword(s):  
Neural Network ◽  
State Of The Art ◽  
Input Image ◽  
Classification Model ◽  
Learning Models ◽  
Fault Line ◽  
Semantic Level ◽  
Explainable Ai ◽  
Fault Lines ◽  
Classification Category

We present CoCoX (short for Conceptual and Counterfactual Explanations), a model for explaining decisions made by a deep convolutional neural network (CNN). In Cognitive Psychology, the factors (or semantic-level features) that humans zoom in on when they imagine an alternative to a model prediction are often referred to as fault-lines. Motivated by this, our CoCoX model explains decisions made by a CNN using fault-lines. Specifically, given an input image I for which a CNN classification model M predicts class cpred, our fault-line based explanation identifies the minimal semantic-level features (e.g., stripes on zebra, pointed ears of dog), referred to as explainable concepts, that need to be added to or deleted from I in order to alter the classification category of I by M to another specified class calt. We argue that, due to the conceptual and counterfactual nature of fault-lines, our CoCoX explanations are practical and more natural for both expert and non-expert users to understand the internal workings of complex deep learning models. Extensive quantitative and qualitative experiments verify our hypotheses, showing that CoCoX significantly outperforms the state-of-the-art explainable AI models. Our implementation is available at https://github.com/arjunakula/CoCoX

scholarly journals Image Statistics Preserving Encrypt-then-Compress Scheme Dedicated for JPEG Compression Standard

Entropy ◽  
2021 ◽  
Vol 23 (4) ◽  
pp. 421
Author(s):  
Dariusz Puchala ◽  
Kamil Stokfiszewski ◽  
Mykhaylo Yatsymirskyy
Keyword(s):  
Statistical Analysis ◽  
Image Encryption ◽  
Experimental Studies ◽  
Quality Measures ◽  
Input Image ◽  
Jpeg Compression ◽  
Image Statistics ◽  
Compression Stage ◽  
Wide Range ◽  
The Impact

In this paper, the authors analyze in more details an image encryption scheme, proposed by the authors in their earlier work, which preserves input image statistics and can be used in connection with the JPEG compression standard. The image encryption process takes advantage of fast linear transforms parametrized with private keys and is carried out prior to the compression stage in a way that does not alter those statistical characteristics of the input image that are crucial from the point of view of the subsequent compression. This feature makes the encryption process transparent to the compression stage and enables the JPEG algorithm to maintain its full compression capabilities even though it operates on the encrypted image data. The main advantage of the considered approach is the fact that the JPEG algorithm can be used without any modifications as a part of the encrypt-then-compress image processing framework. The paper includes a detailed mathematical model of the examined scheme allowing for theoretical analysis of the impact of the image encryption step on the effectiveness of the compression process. The combinatorial and statistical analysis of the encryption process is also included and it allows to evaluate its cryptographic strength. In addition, the paper considers several practical use-case scenarios with different characteristics of the compression and encryption stages. The final part of the paper contains the additional results of the experimental studies regarding general effectiveness of the presented scheme. The results show that for a wide range of compression ratios the considered scheme performs comparably to the JPEG algorithm alone, that is, without the encryption stage, in terms of the quality measures of reconstructed images. Moreover, the results of statistical analysis as well as those obtained with generally approved quality measures of image cryptographic systems, prove high strength and efficiency of the scheme’s encryption stage.

scholarly journals Weighted statistical binary patterns for facial feature representation

2021 ◽  
Author(s):  
Hung Phuoc Truong ◽  
Thanh Phuong Nguyen ◽  
Yong-Guk Kim
Keyword(s):  
Comprehensive Evaluation ◽  
Facial Feature ◽  
Input Image ◽  
Feature Representation ◽  
Illumination Variation ◽  
Straight Line ◽  
Mean And Variance ◽  
The Mean ◽  
Face Datasets ◽  
Degraded Images

AbstractWe present a novel framework for efficient and robust facial feature representation based upon Local Binary Pattern (LBP), called Weighted Statistical Binary Pattern, wherein the descriptors utilize the straight-line topology along with different directions. The input image is initially divided into mean and variance moments. A new variance moment, which contains distinctive facial features, is prepared by extracting root k-th. Then, when Sign and Magnitude components along four different directions using the mean moment are constructed, a weighting approach according to the new variance is applied to each component. Finally, the weighted histograms of Sign and Magnitude components are concatenated to build a novel histogram of Complementary LBP along with different directions. A comprehensive evaluation using six public face datasets suggests that the present framework outperforms the state-of-the-art methods and achieves 98.51% for ORL, 98.72% for YALE, 98.83% for Caltech, 99.52% for AR, 94.78% for FERET, and 99.07% for KDEF in terms of accuracy, respectively. The influence of color spaces and the issue of degraded images are also analyzed with our descriptors. Such a result with theoretical underpinning confirms that our descriptors are robust against noise, illumination variation, diverse facial expressions, and head poses.

scholarly journals A Stochastic Model for Block Segmentation of Images Based on the Quadtree and the Bayes Code for It

Entropy ◽  
2021 ◽  
Vol 23 (8) ◽  
pp. 991
Author(s):  
Yuta Nakahara ◽  
Toshiyasu Matsushima
Keyword(s):  
Computational Cost ◽  
Block Size ◽  
Input Image ◽  
Generative Model ◽  
Image Size ◽  
Variable Block ◽  
General Data ◽  
The Difference ◽  
Segmentation Of Images ◽  
Target Data

In information theory, lossless compression of general data is based on an explicit assumption of a stochastic generative model on target data. However, in lossless image compression, researchers have mainly focused on the coding procedure that outputs the coded sequence from the input image, and the assumption of the stochastic generative model is implicit. In these studies, there is a difficulty in discussing the difference between the expected code length and the entropy of the stochastic generative model. We solve this difficulty for a class of images, in which they have non-stationarity among segments. In this paper, we propose a novel stochastic generative model of images by redefining the implicit stochastic generative model in a previous coding procedure. Our model is based on the quadtree so that it effectively represents the variable block size segmentation of images. Then, we construct the Bayes code optimal for the proposed stochastic generative model. It requires the summation of all possible quadtrees weighted by their posterior. In general, its computational cost increases exponentially for the image size. However, we introduce an efficient algorithm to calculate it in the polynomial order of the image size without loss of optimality. As a result, the derived algorithm has a better average coding rate than that of JBIG.

scholarly journals A generalised approach for high-throughput instance segmentation of stomata in microscope images

Plant Methods ◽  
2021 ◽  
Vol 17 (1) ◽  
Author(s):  
Hiranya Jayakody ◽  
Paul Petrie ◽  
Hugo Jan de Boer ◽  
Mark Whitty
Keyword(s):  
High Throughput ◽  
Imaging Techniques ◽  
Detection Algorithm ◽  
Input Image ◽  
Detection Methods ◽  
Sample Collection ◽  
High Throughput Analysis ◽  
General Applicability ◽  
Additional Image ◽  
Bounding Boxes

Abstract Background Stomata analysis using microscope imagery provides important insight into plant physiology, health and the surrounding environmental conditions. Plant scientists are now able to conduct automated high-throughput analysis of stomata in microscope data, however, existing detection methods are sensitive to the appearance of stomata in the training images, thereby limiting general applicability. In addition, existing methods only generate bounding-boxes around detected stomata, which require users to implement additional image processing steps to study stomata morphology. In this paper, we develop a fully automated, robust stomata detection algorithm which can also identify individual stomata boundaries regardless of the plant species, sample collection method, imaging technique and magnification level. Results The proposed solution consists of three stages. First, the input image is pre-processed to remove any colour space biases occurring from different sample collection and imaging techniques. Then, a Mask R-CNN is applied to estimate individual stomata boundaries. The feature pyramid network embedded in the Mask R-CNN is utilised to identify stomata at different scales. Finally, a statistical filter is implemented at the Mask R-CNN output to reduce the number of false positive generated by the network. The algorithm was tested using 16 datasets from 12 sources, containing over 60,000 stomata. For the first time in this domain, the proposed solution was tested against 7 microscope datasets never seen by the algorithm to show the generalisability of the solution. Results indicated that the proposed approach can detect stomata with a precision, recall, and F-score of 95.10%, 83.34%, and 88.61%, respectively. A separate test conducted by comparing estimated stomata boundary values with manually measured data showed that the proposed method has an IoU score of 0.70; a 7% improvement over the bounding-box approach. Conclusions The proposed method shows robust performance across multiple microscope image datasets of different quality and scale. This generalised stomata detection algorithm allows plant scientists to conduct stomata analysis whilst eliminating the need to re-label and re-train for each new dataset. The open-source code shared with this project can be directly deployed in Google Colab or any other Tensorflow environment.

Sparse-FCM and Deep Convolutional Neural Network for the segmentation and classification of acute lymphoblastic leukaemia

2020 ◽  
Vol 65 (6) ◽  
pp. 759-773
Author(s):  
Segu Praveena ◽  
Sohan Pal Singh
Keyword(s):  
Neural Network ◽  
Acute Lymphoblastic Leukaemia ◽  
Convolutional Neural Network ◽  
Optimization Algorithm ◽  
Lymphoblastic Leukaemia ◽  
Input Image ◽  
Deep Convolutional Neural Network ◽  
Grey Wolf ◽  
Deep Cnn

AbstractLeukaemia detection and diagnosis in advance is the trending topic in the medical applications for reducing the death toll of patients with acute lymphoblastic leukaemia (ALL). For the detection of ALL, it is essential to analyse the white blood cells (WBCs) for which the blood smear images are employed. This paper proposes a new technique for the segmentation and classification of the acute lymphoblastic leukaemia. The proposed method of automatic leukaemia detection is based on the Deep Convolutional Neural Network (Deep CNN) that is trained using an optimization algorithm, named Grey wolf-based Jaya Optimization Algorithm (GreyJOA), which is developed using the Grey Wolf Optimizer (GWO) and Jaya Optimization Algorithm (JOA) that improves the global convergence. Initially, the input image is applied to pre-processing and the segmentation is performed using the Sparse Fuzzy C-Means (Sparse FCM) clustering algorithm. Then, the features, such as Local Directional Patterns (LDP) and colour histogram-based features, are extracted from the segments of the pre-processed input image. Finally, the extracted features are applied to the Deep CNN for the classification. The experimentation evaluation of the method using the images of the ALL IDB2 database reveals that the proposed method acquired a maximal accuracy, sensitivity, and specificity of 0.9350, 0.9528, and 0.9389, respectively.

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