Fair comparison of skin detection approaches on publicly available datasets

Skin detection is the process of discriminating skin and non-skin regions in a digital image and it is widely used in several applications ranging from hand gesture analysis to track body parts and face detection. Skin detection is a challenging problem which has drawn extensive attention from the research community, nevertheless a fair comparison among approaches is very difficult due to the lack of a common benchmark and a unified testing protocol. In this work, we investigate the most recent researches in this field and we propose a fair comparison among approaches using several different datasets. The major contributions of this work are an exhaustive literature review of skin color detection approaches, a framework to evaluate and combine different skin detector approaches, whose source code is made freely available for future research, and an extensive experimental comparison among several recent methods which have also been used to define an ensemble that works well in many different problems. Experiments are carried out in 10 different datasets including more than 10000 labelled images: experimental results confirm that the best method here proposed obtains a very good performance with respect to other stand-alone approaches, without requiring ad hoc parameter tuning. A MATLAB version of the framework for testing and of the methods proposed in this paper will be freely available from https://github.com/LorisNann

Postprocessing for skin detection

Skin detectors play a crucial role in many applications: face localization, person tracking, objectionable content screening, etc. Skin detection is a complicated process that involves not only the development of apposite classifiers but also many ancillary methods, including techniques for data preprocessing and postprocessing. In this paper, a new postprocessing method is described that learns to select whether an image needs the application of various morphological sequences or a homogeneity function. The type of postprocessing method selected is learned based on categorizing the image into one of eleven predetermined classes. The novel postprocessing method presented here is evaluated on ten datasets recommended for fair comparisons that represent many skin detection applications. The results show that the new approach enhances the performance of the base classifiers and previous works based only on learning the most appropriate morphological sequences

Deep Ensembles Based on Stochastic Activations for Semantic Segmentation

Semantic segmentation is a very popular topic in modern computer vision, and it has applications in many fields. Researchers have proposed a variety of architectures for semantic image segmentation. The most common ones exploit an encoder–decoder structure that aims to capture the semantics of the image and its low-level features. The encoder uses convolutional layers, in general with a stride larger than one, to extract the features, while the decoder recreates the image by upsampling and using skip connections with the first layers. The objective of this study is to propose a method for creating an ensemble of CNNs by enhancing diversity among networks with different activation functions. In this work, we use DeepLabV3+ as an architecture to test the effectiveness of creating an ensemble of networks by randomly changing the activation functions inside the network multiple times. We also use different backbone networks in our DeepLabV3+ to validate our findings. A comprehensive evaluation of the proposed approach is conducted across two different image segmentation problems: the first is from the medical field, i.e., polyp segmentation for early detection of colorectal cancer, and the second is skin detection for several different applications, including face detection, hand gesture recognition, and many others. As to the first problem, we manage to reach a Dice coefficient of 0.888, and a mean intersection over union (mIoU) of 0.825, in the competitive Kvasir-SEG dataset. The high performance of the proposed ensemble is confirmed in skin detection, where the proposed approach is ranked first concerning other state-of-the-art approaches (including HarDNet) in a large set of testing datasets

Stochastic selection of activation layers for convolutional neural networks

open4noIn recent years, the field of deep learning has achieved considerable success in pattern recognition, image segmentation, and many other classification fields. There are many studies and practical applications of deep learning on images, video, or text classification. Activation functions play a crucial role in discriminative capabilities of the deep neural networks and the design of new “static” or “dynamic” activation functions is an active area of research. The main difference between “static” and “dynamic” functions is that the first class of activations considers all the neurons and layers as identical, while the second class learns parameters of the activation function independently for each layer or even each neuron. Although the “dynamic” activation functions perform better in some applications, the increased number of trainable parameters requires more computational time and can lead to overfitting. In this work, we propose a mixture of “static” and “dynamic” activation functions, which are stochastically selected at each layer. Our idea for model design is based on a method for changing some layers along the lines of different functional blocks of the best performing CNN models, with the aim of designing new models to be used as stand-alone networks or as a component of an ensemble. We propose to replace each activation layer of a CNN (usually a ReLU layer) by a different activation function stochastically drawn from a set of activation functions: in this way, the resulting CNN has a different set of activation function layers. The code developed for this work will be available at https://github.com/LorisNanni.openNanni L.; Lumini A.; Ghidoni S.; Maguolo G.Nanni, L.; Lumini, A.; Ghidoni, S.; Maguolo, G

Stochastic selection of activation layers for convolutional neural networks

In recent years, the field of deep learning has achieved considerable success in pattern recognition, image segmentation, and many other classification fields. There are many studies and practical applications of deep learning on images, video, or text classification. Activation functions play a crucial role in discriminative capabilities of the deep neural networks and the design of new \u201cstatic\u201d or \u201cdynamic\u201d activation functions is an active area of research. The main difference between \u201cstatic\u201d and \u201cdynamic\u201d functions is that the first class of activations considers all the neurons and layers as identical, while the second class learns parameters of the activation function independently for each layer or even each neuron. Although the \u201cdynamic\u201d activation functions perform better in some applications, the increased number of trainable parameters requires more computational time and can lead to overfitting. In this work, we propose a mixture of \u201cstatic\u201d and \u201cdynamic\u201d activation functions, which are stochastically selected at each layer. Our idea for model design is based on a method for changing some layers along the lines of different functional blocks of the best performing CNN models, with the aim of designing new models to be used as stand-alone networks or as a component of an ensemble. We propose to replace each activation layer of a CNN (usually a ReLU layer) by a different activation function stochastically drawn from a set of activation functions: in this way, the resulting CNN has a different set of activation function layers. The code developed for this work will be available at https://github.com/LorisNanni

PROGRAM and PROCEEDINGS THE NEBRASKA ACADEMY OF SCIENCES: 139th Anniversary Year, One Hundred-Twenty-Ninth Annual Meeting, April 12, 2019, NEBRASKA WESLEYAN UNIVERSITY, LINCOLN, NEBRASKA

PROGRAM AT-A-GLANCE FRIDAY, APRIL 12, 2019 7:30 a.m. REGISTRATION OPENS - Lobby of Lecture Wing, Olin Hall 8:00 Aeronautics and Space Science, Session A – Acklie 109 Aeronautics and Space Science, Session B – Acklie 111 Collegiate Academy; Biology, Session B - Olin B Biological and Medical Sciences, Session A - Olin 112 Biological and Medical Sciences, Session B - Smith Callen Conference Center Chemistry and Physics; Chemistry - Olin A 8:00 “Teaching and Learning the Dynamics of Cellular Respiration Using Interactive Computer Simulations” Workshop – Olin 110 9:30 “Life After College: Building Your Resume for the Future” Workshop – Acklie 218 8:25 Collegiate Academy; Chemistry and Physics, Session A – Acklie 007 8:36 Collegiate Academy; Biology, Session A - Olin 111 9:00 Chemistry and Physics; Physics – Acklie 320 9:10 Aeronautics and Space Science, Poster Session – Acklie 109 & 111 10:30 Aeronautics and Space Science, Poster Session – Acklie 109 & 111 11:00 MAIBEN MEMORIAL LECTURE: Dr David Swanson - OLIN B Scholarship and Friend of Science Award announcements 12:00 p.m. LUNCH – WESLEYAN CAFETERIA Round-Table Discussion – “Assessing the Academy: Current Issues and Avenues for Growth” led by Todd Young – Sunflower Room 12:50 Anthropology – Acklie 109 1:00 Applied Science and Technology - Olin 111 Biological and Medical Sciences, Session C - Olin 112 Biological and Medical Sciences, Session D - Smith Callen Conference Center Chemistry and Physics; Chemistry - Olin A Collegiate Academy; Biology, Session B - Olin B Earth Science – Acklie 007 Environmental Sciences – Acklie 111 Teaching of Science and Math – Acklie 218 1:20 Chemistry and Physics; Physics – Acklie 320 4:30 BUSINESS MEETING - OLIN B NEBRASKA ASSOCIATION OF TEACHERS OF SCIENCE (NATS) The 2019 Fall Conference of the Nebraska Association of Teachers of Science (NATS) will be held at the Younes Conference Center, Kearney, NE, September 19-21, 2019. President: Betsy Barent, Norris Public Schools, Firth, NE President-Elect: Anya Covarrubias, Grand Island Public Schools, Grand Island, NE AFFILIATED SOCIETIES OF THE NEBRASKA ACADEMY OF SCIENCES, INC. 1. American Association of Physics Teachers, Nebraska Section Web site: http://www.aapt.org/sections/officers.cfm?section=Nebraska 2. Friends of Loren Eiseley Web site: http://www.eiseley.org/ 3. Lincoln Gem & Mineral Club Web site: http://www.lincolngemmineralclub.org/ 4. Nebraska Chapter, National Council for Geographic Education 5. Nebraska Geological Society Web site: http://www.nebraskageologicalsociety.org Sponsors of a $50 award to the outstanding student paper presented at the Nebraska Academy of Sciences Annual Meeting, Earth Science /Nebraska Chapter, Nat\u27l Council Sections 6. Nebraska Graduate Women in Science 7. Nebraska Junior Academy of Sciences Web site: http://www.nebraskajunioracademyofsciences.org/ 8. Nebraska Ornithologists’ Union Web site: http://www.noubirds.org/ 9. Nebraska Psychological Association http://www.nebpsych.org/ 10. Nebraska-Southeast South Dakota Section Mathematical Association of America Web site: http://sections.maa.org/nesesd/ 11. Nebraska Space Grant Consortium Web site: http://www.ne.spacegrant.org

Video Vortex reader : responses to Youtube

The Video Vortex Reader is the first collection of critical texts to deal with the rapidly emerging world of online video – from its explosive rise in 2005 with YouTube, to its future as a significant form of personal media. After years of talk about digital convergence and crossmedia platforms we now witness the merger of the Internet and television at a pace no-one predicted. These contributions from scholars, artists and curators evolved from the first two Video Vortex conferences in Brussels and Amsterdam in 2007 which focused on responses to YouTube, and address key issues around independent production and distribution of online video content. What does this new distribution platform mean for artists and activists? What are the alternatives

Your Post Has Been Removed:Tech Giants and Free Speech

This open access monograph argues established democratic norms for freedom of expression should be implemented on the internet. Moderating policies of tech companies as Facebook, Twitter and Google have resulted in posts being removed on an industrial scale. While this moderation is often encouraged by governments - on the pretext that terrorism, bullying, pornography, “hate speech” and “fake news” will slowly disappear from the internet - it enables tech companies to censure our society. It is the social media companies who define what is blacklisted in their community standards. And given the dominance of social media in our information society, we run the risk of outsourcing the definition of our principles for discussion in the public domain to private companies. Instead of leaving it to social media companies only to take action, the authors argue democratic institutions should take an active role in moderating criminal content on the internet. To make this possible, tech companies should be analyzed whether they are approaching a monopoly. Antitrust legislation should be applied to bring those monopolies within democratic governmental oversight. Despite being in different stages in their lives, Anne Mette is in the startup phase of her research career, while Frederik is one of the most prolific philosophers in Denmark, the authors found each other in their concern about Free Speech on the internet. The book was originally published in Danish as Dit opslag er blevet fjernet - techgiganter & ytringsfrihed. Praise for 'Your Post has been Removed' "From my perspective both as a politician and as private book collector, this is the most important non-fiction book of the 21st Century. It should be disseminated to all European citizens. The learnings of this book and the use we make of them today are crucial for every man, woman and child on earth. Now and in the future.” Jens Rohde, member of the European Parliament for the Alliance of Liberals and Democrats for Europe “This timely book compellingly presents an impressive array of information and analysis about the urgent threats the tech giants pose to the robust freedom of speech and access to information that are essential for individual liberty and democratic self-government. It constructively explores potential strategies for restoring individual control over information flows to and about us. Policymakers worldwide should take heed!” Nadine Strossen, Professor, New York Law School. Author, HATE: Why We Should Resist It with Free Speech, Not Censorshi

Your Post has been Removed

This open access monograph argues established democratic norms for freedom of expression should be implemented on the internet. Moderating policies of tech companies as Facebook, Twitter and Google have resulted in posts being removed on an industrial scale. While this moderation is often encouraged by governments - on the pretext that terrorism, bullying, pornography, “hate speech” and “fake news” will slowly disappear from the internet - it enables tech companies to censure our society. It is the social media companies who define what is blacklisted in their community standards. And given the dominance of social media in our information society, we run the risk of outsourcing the definition of our principles for discussion in the public domain to private companies. Instead of leaving it to social media companies only to take action, the authors argue democratic institutions should take an active role in moderating criminal content on the internet. To make this possible, tech companies should be analyzed whether they are approaching a monopoly. Antitrust legislation should be applied to bring those monopolies within democratic governmental oversight. Despite being in different stages in their lives, Anne Mette is in the startup phase of her research career, while Frederik is one of the most prolific philosophers in Denmark, the authors found each other in their concern about Free Speech on the internet. The book was originally published in Danish as Dit opslag er blevet fjernet - techgiganter & ytringsfrihed. Praise for 'Your Post has been Removed' "From my perspective both as a politician and as private book collector, this is the most important non-fiction book of the 21st Century. It should be disseminated to all European citizens. The learnings of this book and the use we make of them today are crucial for every man, woman and child on earth. Now and in the future.” Jens Rohde, member of the European Parliament for the Alliance of Liberals and Democrats for Europe “This timely book compellingly presents an impressive array of information and analysis about the urgent threats the tech giants pose to the robust freedom of speech and access to information that are essential for individual liberty and democratic self-government. It constructively explores potential strategies for restoring individual control over information flows to and about us. Policymakers worldwide should take heed!” Nadine Strossen, Professor, New York Law School. Author, HATE: Why We Should Resist It with Free Speech, Not Censorshi