Query: Black and white image automatic coloring machine

Candidate papers:

abstract: A wafer bin map (WBM) which is the result of an electrical die sorting test, provides information on which bins failed what tests, and plays an important role in finding defective wafer patterns in semiconductor manufacturing. Current wafer inspection based on WBM has two problems: good/bad WBM classification is performed by engineers and the bin code coloring scheme does not reflect the relationship between bin codes. To solve these problems, we propose a neural network based bin coloring method called Bin2Vec to make similar bin codes are represented by similar colors. We also build a convolutional neural network based WBM classification model to reduce the variations in the decisions made by engineers with different expertise by learning the company wide historical WBM classification results. Based on a real dataset with a total of 27,701 WBMs, our WBM classification model significantly outperformed benchmarked machine learning models. In addition, the visualization results of the proposed Bin2Vec method makes it easier to discover meaningful WBM patterns compared with the random RGB coloring scheme. We expect the proposed framework to improve both efficiencies by automating the bad wafer classification process and effectiveness by assigning similar bin codes and their corresponding colors on the WBM.
author_names: Junhong Kim

Hyungseok Kim

Jaesun Park

Kyounghyun Mo

Pilsung Kang
title: Bin2Vec: A Better Wafer Bin Map Coloring Scheme for Comprehensible Visualization and Effective Bad Wafer Classification
venue: Applied Sciences
year: 2019

abstract: Generative Adversarial Networks (GANs) has shown its dramatical success, especially in computer vision applications. In this paper, inspired by traditional GANs, we propose Multi GANs which is an architecture of multiple generative adversarial networks that works together. Whilst, the GANs are successful to generate images which looks realistic but the real world problems are much more complicated than a GANs can perform a desirable outcome to the whole of the problem space. Therefore, our approach divides each problem space into the several smaller and of course much more homogeneous subspaces. We propose then a GANs for each sub space that can learn to mimic any distribution of data with lower lost. The results of each GANs for all sub spaces then merge together to perform the original preliminary space. We evaluated our approach on Pseudo Coloring which is a very difficult and ill posed problem among the computer vision community. The experimental results show much more realistic characteristics for the generated images also its superiority in comparison to the traditional approaches.
author_names: Mohammad Zare

Kimia Bazargan Lari

Mahdi Jampour

Pirooz Shamsinejad
title: Multi GANs and its application for Pseudo Coloring
venue: 2019 4th International Conference on Pattern Recognition and Image Analysis (IPRIA)
year: 2019

abstract: The recovery of musical heritage currently necessarily involves its digitalization, not only by scanning images, but also by the encoding in computer readable formats of the musical content described in the original manuscripts. In general, this encoding can be done using automated tools based with what is named Optical Music Recognition (OMR) or manually writing directly the corresponding computer code. The OMR technology is not mature enough yet to extract the musical content of sheet music images with enough quality, and even less from handwritten sources, so in many cases it is more efficient to encode the works manually. However, being currently MEI (Music Encoding Initiative) the most appropriate format to store the encoding, it is a totally tedious code to be manually written. Therefore, we propose a new format named *mens allowing a quick manual encoding, from which both the MEI format itself and other common representations such as Lilypond or the transcription in MusicXML can be generated. By using this approach, the antiphony Salve Regina for eight voice choir written by Jeronimo de la Torre (1607 1673) has been successfully encoded and transcribed.
author_names: David Rizo Valero

Nieves Pascual Leon

Craig Stuart Sapp
title: White Mensural Manual Encoding: from Humdrum to MEI
year: 2019

abstract: We introduce the new Birds to Words dataset of 41k sentences describing fine grained differences between photographs of birds. The language collected is highly detailed, while remaining understandable to the everyday observer (e.g. "heart shaped face," "squat body" Paragraph length descriptions naturally adapt to varying levels of taxonomic and visual distance drawn from a novel stratified sampling approach with the appropriate level of detail. We propose a new model called Neural Naturalist that uses a joint image encoding and comparative module to generate comparative language, and evaluate the results with humans who must use the descriptions to distinguish real images. Our results indicate promising potential for neural models to explain differences in visual embedding space using natural language, as well as a concrete path for machine learning to aid citizen scientists in their effort to preserve biodiversity.
author_names: Maxwell Forbes

Christine Kaeser-Chen

Piyush Sharma

Serge J Belongie
title: Neural Naturalist: Generating Fine Grained Image Comparisons
venue: EMNLP/IJCNLP
year: 2019

abstract: This paper describes a non invasive, automatic, and robust method for calibrating a scalable RGB D sensor network based on retroreflective ArUco markers and the iterative closest point (ICP) scheme. We demonstrate the system by calibrating a sensor network comprised of six sensor nodes positioned in a relatively large industrial robot cell with an approximate size of 10 mx10 mx4 m. Here, the automatic calibration achieved an average Euclidean error of 3 cm at distances up to 9.45 m. To achieve robustness, we apply several innovative techniques: Firstly, we mitigate the ambiguity problem that occurs when detecting a marker at long range or low resolution by comparing the camera projection with depth data. Secondly, we use retroreflective fiducial markers in the RGB D calibration for improved accuracy and detectability. Finally, the repeating ICP refinement uses an exact region of interest such that we employ the precise depth measurements of the retroreflective surfaces only. The complete calibration software and a recorded dataset are publically available and open source.
author_names: Atle Aalerud

Joacim Dybedal

Geir Hovland
title: Automatic Calibration of an Industrial RGB D Camera Network Using Retroreflective Fiducial Markers
venue: Sensors
year: 2019

abstract: Artificial Intelligent Systems are increasingly used to support early diagnosis of multiple relevant diseases. The spread of these systems is boosted by the application of machine learning techniques on datasets (also in the form of videos and images) obtained from different information sources. A key role is played by artificial vision systems that are in charge of reasoning on data acquired from different devices, including smartphones. The facility to disseminate and share information let to the globalization of medical protocols previously used just in some world's areas. This is the case of tongue inspection, widely used in Traditional Chinese Medicine (TCM) to perform a diagnosis, which allows physicians to obtain useful indications on the state of internal organs by observing the color and the consistency of patient's tongue. The current interest in tongue's image analysis is also motivated by the possibility of performing a first self analysis on a possible disease suggesting further medical investigation. The paper is a non exhaustive overview of the features most frequently used in artificial vision systems contextualized to tongue analysis. It highlights shortcomings in some of the existing studies and provides insights for future research. Our work aims to provide a unifying view that can support the researchers working on Tongue Colored Image Analysis.
author_names: Eugenio Vocaturo

Ester Zumpano

Pierangelo Veltri
title: On discovering relevant features for tongue colored image analysis
venue: IDEAS
year: 2019

abstract: Verification and robustness testing of machine learning algorithms for autonomous driving is crucial. Due to the increasing complexity and quantity of those systems in a single vehicle, just driving the required distance with a newly developed vehicle is not feasible anymore: billions of hours on the street without failure are necessary to qualify for industry standards like ISO 26262. That is where simulation comes into play: machine learning algorithms are trained and evaluated on well known image data sets like KITTI or Cityscapes. But today's data sets mostly contain images taken under perfect weather conditions and therefore do not harden optical object detection algorithms against various weather conditions. This paper focuses on reusing these established and labeled data sets by augmenting them with adverse weather effects like snow and fog. Those effects are rendered physically correct and life like while being added to existing real world images. Thanks to easy parametrization the weather influences may be varied as necessary and allow for finely tuned learning and optimization processes. The weather effects are evaluated with regard to realism and impact on an established object detection algorithm. These newly created weather influenced images may be used to validate or train new object detection algorithms.
author_names: Alexander von Bernuth

Georg Volk

Oliver Bringmann
title: Simulating Photo realistic Snow and Fog on Existing Images for Enhanced CNN Training and Evaluation
venue: 2019 IEEE Intelligent Transportation Systems Conference (ITSC)
year: 2019

abstract: The article discusses one of the latest ways to colorize a black and white image using deep learning methods. For colorization, a convolutional neural network with a large number of layers (Deep convolutional) is used, the architecture of which includes a ResNet model. This model was pre trained on images of the ImageNet dataset. A neural network receives a black and white image and returns a colorized color. Since, due to the characteristics of ResNet, an input multiple of 255 is received, a program was written that, using frames, enlarges the image for the required size. During the operation of the neural network, the CIE Lab color model is used, which allows to separate the black and white component of the image from the color. For training the neural network, the Place 365 dataset was used, containing 365 different classes, such as animals, landscape elements, people, and so on. The training was carried out on the Nvidia GTX 1080 video card. The result was a trained neural network capable of colorizing images of any size and format. As example we had a speed of 0.08 seconds and an image of 256 by 256 pixels in size. In connection with the concept of the dataset used for training, the resulting model is focused on the recognition of natural landscapes and urban areas.
author_names: Dar'ia Mikhalina

Daria Mikhalina

Aleksandr Kuz'menko

Konstantin Dergachev

Vitalii Shkaberin

Vitaliy Shkaberin
title: Image Colorization
venue: GraphiCon'2019 Proceedings. Volume 2
year: 2019

abstract: In this study, deep learning method coupled with near infrared (NIR) hyperspectral imaging (HSI) technique was used for nondestructively determining total viable count (TVC) of peeled Pacific white shrimp. Firstly, stacked auto encoders (SAE) was conducted as a big data analytical method to extract 20 deep hyperspectral features from NIR hyperspectral image (900 1700 nm) of peeled shrimp stored at 4 degC, and the extracted features were used to predict TVC by fully connected neural network (FNN) The SAE FNN method obtained high prediction accuracy for determining TVC, with RP2 0.927. Additionally, TVC spatial distribution of peeled shrimp during storage could be visualized via applying the established SAE FNN model. The results demonstrate that SAE FNN combined with HSI technique has a potential for non destructive prediction of TVC in peeled shrimp, which supply a novel method for the hygienic quality and safety inspections of shrimp product.
author_names: Shiting Wen

Jinqiu Yang
title: Using deep learning and hyperspectral imaging to predict total viable count (TVC) in peeled Pacific white shrimp
venue: Journal of Food Measurement and Characterization
year: 2019

abstract: Blood cell segmentation is an important research topic in Hematology and other related fields. In this article, a technique for microscopic images segmentation is proposed in order to extract the white blood cells (WBC) and its components (nucleus, cytoplasm) from the red blood cells and plasma. The image is represented in different color spaces, Haralick features extracted from the chromatic co occurrence matrices (CCM) are used to characterize the textures present in these color images. A pre treatment is carried out to extract the background (plasma) in order to reduce the execution time and noise. Segmentation has been done by supervised pixel based classification using support vector machines (SVM) The proposed method was tested on twenty seven real microscopic color images with promising results and nucleus recognition accuracy reaching 95%
author_names: Mohammed Lamine Benomar

Mourtada Benazzouz

Mostafa El Habib Daho
title: Colour Texture Features Based Approach for White Blood Cells Segmentation
venue: 2019 International Conference on Networking and Advanced Systems (ICNAS)
year: 2019