Filtering Random Valued Impulse Noise from Grayscale Images through Support Vector Machine and Markov Chain

This paper presents a context-based filter to denoise grayscale images affected by random valued impulse noise. A support vector machine classifier is used for noise detection and two Markov filter variants are evaluated for their denoising capacity. The classifier needs to be trained on a set of training images. The experiments performed on another set of test images have shown that the support vector machine with the radial basis function kernel combined with the Markov+ filter is the best configuration, providing the highest noise detection accuracy. Our filter was compared with existing denoising methods, it being better on some images and comparable with them on others.

Image Colorization Using Autoencoders

Modification of art may be viewed as enhancement or vandalization. Even though for a long time many were opposed to the idea of colorizing images, they now have finally viewed it for what it is - an enhancement of the art form. Grayscale image colorization has since been a long-standing artistic division. It has been used to revive or modify images taken prior to the invention of colour photography. This paper explores one method to reinvigorate grayscale images by colorizing them. We propose the use of deep learning, specifically the use of convolution neural networks. The obtained results show the ability of our model to realistically colorize grayscale images. Keywords: Deep Learning, Convolutional Neural Network, Image Colorization, Autoencoders.

Image-Based Insider Threat Detection via Geometric Transformation

Insider threat detection has been a challenging task over decades; existing approaches generally employ the traditional generative unsupervised learning methods to produce normal user behavior model and detect significant deviations as anomalies. However, such approaches are insufficient in precision and computational complexity. In this paper, we propose a novel insider threat detection method, Image-based Insider Threat Detector via Geometric Transformation (IGT), which converts the unsupervised anomaly detection into supervised image classification task, and therefore the performance can be boosted via computer vision techniques. To illustrate, our IGT uses a novel image-based feature representation of user behavior by transforming audit logs into grayscale images. By applying multiple geometric transformations on these behavior grayscale images, IGT constructs a self-labelled dataset and then trains a behavior classifier to detect anomaly in a self-supervised manner. The motivation behind our proposed method is that images converted from normal behavior data may contain unique latent features which remain unchanged after geometric transformation, while malicious ones cannot. Experimental results on CERT dataset show that IGT outperforms the classical autoencoder-based unsupervised insider threat detection approaches, and improves the instance and user based Area under the Receiver Operating Characteristic Curve (AUROC) by 4% and 2%, respectively.

Colorectal Polyp Image Detection and Classification through Grayscale Images and Deep Learning

Colonoscopy screening and colonoscopic polypectomy can decrease the incidence and mortality rate of colorectal cancer (CRC). The adenoma detection rate and accuracy of diagnosis of colorectal polyp which vary in different experienced endoscopists have impact on the colonoscopy protection effect of CRC. The work proposed a colorectal polyp image detection and classification system through grayscale images and deep learning. The system collected the data of CVC-Clinic and 1000 colorectal polyp images of Linkou Chang Gung Medical Hospital. The red-green-blue (RGB) images were transformed to 0 to 255 grayscale images. Polyp detection and classification were performed by convolutional neural network (CNN) model. Data for polyp detection was divided into five groups and tested by 5-fold validation. The accuracy of polyp detection was 95.1% for grayscale images which is higher than 94.1% for RGB and narrow-band images. The diagnostic accuracy, precision and recall rates were 82.8%, 82.5% and 95.2% for narrow-band images, respectively. The experimental results show that grayscale images achieve an equivalent or even higher accuracy of polyp detection than RGB images for lightweight computation. It is also found that the accuracy of polyp detection and classification is dramatically decrease when the size of polyp images small than 1600 pixels. It is recommended that clinicians could adjust the distance between the lens and polyps appropriately to enhance the system performance when conducting computer-assisted colorectal polyp analysis.

AUTOMATIC COLORIZATION USING CONVOLUTIONAL NEURAL NETWORKS

Automatic Colorization helps to hallucinate what an input gray scale image would look like when colorized. Automatic coloring makes it look and feel better than Grayscale. One of the most important technologies used in Machine learning is Deep Learning. Deep learning is nothing but to train the computer with certain algorithms which imitates the working of the human brain. Some of the areas in which it is used are medical, Industrial Automation, Electronics etc. The main objective of this project is coloring Grayscale images. We have umbrellaed the concepts of convolutional neural networks along with the use of the Opencv library in Python to construct our desired model. A user interface has also been fabricated to get personalized inputs using PIL. The user had to give details about boundaries, what colors to put, etc. Colorization requires considerable user intervention and remains a tedious, time consuming, and expensive task. So, in this paper we try to build a model to colorize the grayscale images automatically by using some modern deep learning techniques. In colorization task, the model needs to find characteristics to map grayscale images with colored ones.

Data Augmentation Methods Applying Grayscale Images for Convolutional Neural Networks in Machine Vision

In increasing manufacturing productivity with automated surface inspection in smart factories, the demand for machine vision is rising. Recently, convolutional neural networks (CNNs) have demonstrated outstanding performance and solved many problems in the field of computer vision. With that, many machine vision systems adopt CNNs to surface defect inspection. In this study, we developed an effective data augmentation method for grayscale images in CNN-based machine vision with mono cameras. Our method can apply to grayscale industrial images, and we demonstrated outstanding performance in the image classification and the object detection tasks. The main contributions of this study are as follows: (1) We propose a data augmentation method that can be performed when training CNNs with industrial images taken with mono cameras. (2) We demonstrate that image classification or object detection performance is better when training with the industrial image data augmented by the proposed method. Through the proposed method, many machine-vision-related problems using mono cameras can be effectively solved by using CNNs.

Using human pose information for handgun detection

Fast automatic handgun detection can be very useful to avoid or mitigate risks in public spaces. Detectors based on deep learning methods have been proposed in the literature to trigger an alarm if a handgun is detected in the image. However, those detectors are solely based on the weapon appearance on the image. In this work, we propose to combine the detector with the individual’s pose information in order to improve overall performance. To this end, a model that integrates grayscale images from the output of the handgun detector and heatmap-like images that represent pose is proposed. The results show an improvement over the original handgun detector. The proposed network provides a maximum improvement of a 17.5% in AP of the proposed combinational model over the baseline handgun detector.

Análise Comparativa de Métodos para Esteganografia Digital em Imagens

Sensitive information being shared on the internet is growing. Becauseof this, it is increasingly necessary to take security measureswhilst this information travels in the network. Digital steganographyallows one to send sensitive information in a hidden manner.Although there is a plethora of techniques for such a goal, findingan appropriate one is not always simple. This paper implementsand compares spatial-domain digital steganography techniques inboth RGB and grayscale images. A frequency-domain heuristic forreducing the visual impact of digital steganography in grayscaleimages is presented. As another result of this work, a dataset is alsoavailable in the Kaggle platform with 18 GB of images, containingsecret messages using the techniques under study. In addition, aPython language library was also made available in the PyPI repository,allowing for both concealment and revelation of messagesusing the presented digital steganography methods.