Exploiting Spatio-Temporal Coherence for Video Object Detection in Robotics

This paper proposes a method to enhance video object detection for indoor environments in robotics. Concretely, it exploits knowledge about the camera motion between frames to propagate previously detected objects to successive frames. The proposal is rooted in the concepts of planar homography to propose regions of interest where to find objects, and recursive Bayesian filtering to integrate observations over time. The proposal is evaluated on six virtual, indoor environments, accounting for the detection of nine object classes over a total of ∼ 7k frames. Results show that our proposal improves the recall and the F1-score by a factor of 1.41 and 1.27, respectively, as well as it achieves a significant reduction of the object categorization entropy (58.8%) when compared to a two-stage video object detection method used as baseline, at the cost of small time overheads (120 ms) and precision loss (0.92).</p

Edge intelligence-enabled cyber-physical systems

With the advent of the Internet of everything era, people's demand for intelligent Internet of Things (IoT) devices is steadily increasing. A more intelligent cyber-physical system (CPS) is needed to meet the diverse business requirements of users, such as ultra-reliable low-latency communication, high quality of services (QoS), and quality of experience (QoE). Edge intelligence (EI) is recognized by academia and industry as one of the key emerging technologies for the CPS, which provides the ability to analyze data at the edge rather than sending it to the cloud for analysis, and will be a key enabler to realize a world of a trillion hyperconnected smart sensing devices.As a distributed intelligent computing paradigm in which computation is largely or completely performed at distributed nodes, EI provides for the rapid development of artificial intelligence (AI) and edge computing resources to support real-time insight and analysis for applications in CPS, which brings memory, computing power and processing ability closer to the location where it is needed, reduces the volumes of data that must be moved, the consequent traffic, and the distance the data must travel. As an emerging intelligent computing paradigm, EI can accelerate content delivery and improve the QoS of applications, which is attracting more and more research attentions from academia and industry because of its advantages in throughput, delay, network scalability and intelligence in CPS.The guest editors would like to thank all the authors and the reviewers for their hard work and contributions in helping to organize this special issue. They also would like to express their heartfelt gratitude to the Editor-in-Chief, Prof. David W. Walker, for giving us this great opportunity, and the members of the Editorial Staff for their support during the process.Scopu

Artificial Intelligence in the Creative Industries: A Review

This paper reviews the current state of the art in Artificial Intelligence (AI) technologies and applications in the context of the creative industries. A brief background of AI, and specifically Machine Learning (ML) algorithms, is provided including Convolutional Neural Network (CNNs), Generative Adversarial Networks (GANs), Recurrent Neural Networks (RNNs) and Deep Reinforcement Learning (DRL). We categorise creative applications into five groups related to how AI technologies are used: i) content creation, ii) information analysis, iii) content enhancement and post production workflows, iv) information extraction and enhancement, and v) data compression. We critically examine the successes and limitations of this rapidly advancing technology in each of these areas. We further differentiate between the use of AI as a creative tool and its potential as a creator in its own right. We foresee that, in the near future, machine learning-based AI will be adopted widely as a tool or collaborative assistant for creativity. In contrast, we observe that the successes of machine learning in domains with fewer constraints, where AI is the `creator', remain modest. The potential of AI (or its developers) to win awards for its original creations in competition with human creatives is also limited, based on contemporary technologies. We therefore conclude that, in the context of creative industries, maximum benefit from AI will be derived where its focus is human centric -- where it is designed to augment, rather than replace, human creativity

Single Satellite Imagery Simultaneous Super-resolution and Colorization using Multi-task Deep Neural Networks

Satellite imagery is a kind of typical remote sensing data, which holds preponderance in large area imaging and strong macro integrity. However, for most commercial space usages, such as virtual display of urban traffic flow, virtual interaction of environmental resources, one drawback of satellite imagery is its low spatial resolution, failing to provide the clear image details. Moreover, in recent years, synthesizing the color for grayscale satellite imagery or recovering the original color of camouflage sensitive regions becomes an urgent requirement for large spatial objects virtual reality interaction. In this work, unlike existing works which solve these two problems separately, we focus on achieving image super-resolution (SR) and image colorization synchronously. Based on multi-task learning, we provide a novel deep neural network model to fulfill single satellite imagery SR and colorization simultaneously. By feeding back the color feature representations into the SR network and jointly optimizing such two tasks, our deep model successfully achieves the mutual cooperation between imagery reconstruction and image colorization. To avoid color bias, we not only adopt the non-satellite imagery to enrich the color diversity of satellite image, but also recalculate the prior color distribution and the valid color range based on the mixed data. We evaluate the proposed model on satellite images from different data sets, such as RSSCN7 and AID. Both the evaluations and comparisons reveal that the proposed multi-task deep learning approach is superior to the state-of-the-art methods, where image SR and colorization can be accomplished simultaneously and efficiently

Iris Identification using Keypoint Descriptors and Geometric Hashing

Iris is one of the most reliable biometric trait due to its stability and randomness. Conventional recognition systems transform the iris to polar coordinates and perform well for co-operative databases. However, the problem aggravates to manifold for recognizing non-cooperative irises. In addition, the transformation of iris to polar domain introduces aliasing effect. In this thesis, the aforementioned issues are addressed by considering Noise Independent Annular Iris for feature extraction. Global feature extraction approaches are rendered as unsuitable for annular iris due to change in scale as they could not achieve invariance to ransformation and illumination. On the contrary, local features are invariant to image scaling, rotation and partially invariant to change in illumination and viewpoint. To extract local features, Harris Corner Points are detected from iris and matched using novel Dual stage approach. Harris corner improves accuracy but fails to achieve scale invariance. Further, Scale Invariant Feature Transform (SIFT) has been applied to annular iris and results are found to be very promising. However, SIFT is computationally expensive for recognition due to higher dimensional descriptor. Thus, a recently evolved keypoint descriptor called Speeded Up Robust Features (SURF) is applied to mark performance improvement in terms of time as well as accuracy. For identification, retrieval time plays a significant role in addition to accuracy. Traditional indexing approaches cannot be applied to biometrics as data are unstructured. In this thesis, two novel approaches has been developed for indexing iris database. In the first approach, Energy Histogram of DCT coefficients is used to form a B-tree. This approach performs well for cooperative databases. In the second approach, indexing is done using Geometric Hashing of SIFT keypoints. The latter indexing approach achieves invariance to similarity transformations, illumination and occlusion and performs with an accuracy of more than 98% for cooperative as well as non-cooperative databases

KFREAIN: Design of A Kernel-Level Forensic Layer for Improving Real-Time Evidence Analysis Performance in IoT Networks

An exponential increase in number of attacks in IoT Networks makes it essential to formulate attack-level mitigation strategies. This paper proposes design of a scalable Kernel-level Forensic layer that assists in improving real-time evidence analysis performance to assist in efficient pattern analysis of the collected data samples. It has an inbuilt Temporal Blockchain Cache (TBC), which is refreshed after analysis of every set of evidences. The model uses a multidomain feature extraction engine that combines lightweight Fourier, Wavelet, Convolutional, Gabor, and Cosine feature sets that are selected by a stochastic Bacterial Foraging Optimizer (BFO) for identification of high variance features. The selected features are processed by an ensemble learning (EL) classifier that use low complexity classifiers reducing the energy consumption during analysis by 8.3% when compared with application-level forensic models. The model also showcased 3.5% higher accuracy, 4.9% higher precision, and 4.3% higher recall of attack-event identification when compared with standard forensic techniques. Due to kernel-level integration, the model is also able to reduce the delay needed for forensic analysis on different network types by 9.5%, thus making it useful for real-time &amp; heterogenous network scenarios

A Survey on Evolutionary Computation for Computer Vision and Image Analysis: Past, Present, and Future Trends

Computer vision (CV) is a big and important field in artificial intelligence covering a wide range of applications. Image analysis is a major task in CV aiming to extract, analyse and understand the visual content of images. However, imagerelated tasks are very challenging due to many factors, e.g., high variations across images, high dimensionality, domain expertise requirement, and image distortions. Evolutionary computation (EC) approaches have been widely used for image analysis with significant achievement. However, there is no comprehensive survey of existing EC approaches to image analysis. To fill this gap, this paper provides a comprehensive survey covering all essential EC approaches to important image analysis tasks including edge detection, image segmentation, image feature analysis, image classification, object detection, and others. This survey aims to provide a better understanding of evolutionary computer vision (ECV) by discussing the contributions of different approaches and exploring how and why EC is used for CV and image analysis. The applications, challenges, issues, and trends associated to this research field are also discussed and summarised to provide further guidelines and opportunities for future research