Democratisation of Usable Machine Learning in Computer Vision

Many industries are now investing heavily in data science and automation to replace manual tasks and/or to help with decision making, especially in the realm of leveraging computer vision to automate many monitoring, inspection, and surveillance tasks. This has resulted in the emergence of the 'data scientist' who is conversant in statistical thinking, machine learning (ML), computer vision, and computer programming. However, as ML becomes more accessible to the general public and more aspects of ML become automated, applications leveraging computer vision are increasingly being created by non-experts with less opportunity for regulatory oversight. This points to the overall need for more educated responsibility for these lay-users of usable ML tools in order to mitigate potentially unethical ramifications. In this paper, we undertake a SWOT analysis to study the strengths, weaknesses, opportunities, and threats of building usable ML tools for mass adoption for important areas leveraging ML such as computer vision. The paper proposes a set of data science literacy criteria for educating and supporting lay-users in the responsible development and deployment of ML applications.Comment: 4 page

Artificial Intelligence & Machine Learning in Computer Vision Applications

Deep learning and machine learning innovations are at the core of the ongoing revolution in Artificial Intelligence for the interpretation and analysis of multimedia data. The convergence of large-scale datasets and more affordable Graphics Processing Unit (GPU) hardware has enabled the development of neural networks for data analysis problems that were previously handled by traditional handcrafted features. Several deep learning architectures such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Long Short Term Memory (LSTM)/Gated Recurrent Unit (GRU), Deep Believe Networks (DBN), and Deep Stacking Networks (DSNs) have been used with new open source software and libraries options to shape an entirely new scenario in computer vision processing

Robust Machine Learning In Computer Vision

Deep neural networks have been shown to be successful in various computer vision tasks such as image classification and object detection. Although deep neural networks have exceeded human performance in many tasks, robustness and reliability are always the concerns of using deep learning models. On the one hand, degraded images and videos aggravate the performance of computer vision tasks. On the other hand, if the deep neural networks are under adversarial attacks, the networks can be broken completely. Motivated by the vulnerability of deep neural networks, I analyze and develop image restoration and adversarial defense algorithms towards a vision of robust machine learning in computer vision. In this dissertation, I study two types of degradation making deep neural networks vulnerable. The first part of the dissertation focuses on face recognition at long range, whose performance is severely degraded by atmospheric turbulence. The theme is on improving the performance and robustness of various tasks in face recognition systems such as facial keypoints localization, feature extraction, and image restoration. The second part focuses on defending adversarial attacks in the images classification task. The theme is on exploring adversarial defense methods that can achieve good performance in standard accuracy, robustness to adversarial attacks with known threat models, and good generalization to other unseen attacks

Machine learning from coronas using parametrization of images

We were interested to develop an algorithm for detection of coronas of people in altered states of consciousness (two-classes problem). Such coronas are known to have rings (double coronas), special branch-like structure of streamers and/or curious spots. We used several approaches to parametrization of images and various machine learning algorithms. We compared results of computer algorithms with the human expert’s accuracy. Results show that computer algorithms can achieve the same or even better accuracy than that of human experts

Using camera motion to identify different types of American football plays

This paper presents a method that uses camera motion parameters to recognise 7 types of American football plays. The approach is based on the motion information extracted from the video and it can identify short and long pass plays, short and long running plays, quarterback sacks, punt plays and kickoff plays. This method has the advantage that it is fast and it does not require player or ball tracking. The system was trained and tested using 782 plays and the results show that the system has an overall classification accuracy of 68%.<br /

On the incremental learning and recognition of the pattern of movement of multiple labelled objects in dynamic scenes

In this paper we discuss combining incremental learning and incremental recognition to classify patterns consisting of multiple objects, each represented by multiple spatio-temporal features. Importantly the technique allows for ambiguity in terms of the positions of the start and finish of the pattern. This involves a progressive classification which considers the data at each time instance in the query and thus provides a probable answer before all the query information becomes available. We present two methods that combine incremental learning and incremental recognition: a time instance method and an overall best match method.<br /