Learning Feature Selection and Combination Strategies for Generic Salient Object Detection

For a diverse range of applications in machine vision from social media searches to robotic home care providers, it is important to replicate the mechanism by which the human brain selects the most important visual information, while suppressing the remaining non-usable information. Many computational methods attempt to model this process by following the traditional model of visual attention. The traditional model of attention involves feature extraction, conditioning and combination to capture this behaviour of human visual attention. Consequently, the model has inherent design choices at its various stages. These choices include selection of parameters related to the feature computation process, setting a conditioning approach, feature importance and setting a combination approach. Despite rapid research and substantial improvements in benchmark performance, the performance of many models depends upon tuning these design choices in an ad hoc fashion. Additionally, these design choices are heuristic in nature, thus resulting in good performance only in certain settings. Consequentially, many such models exhibit low robustness to difficult stimuli and the complexities of real-world imagery. Machine learning and optimisation technique have long been used to increase the generalisability of a system to unseen data. Surprisingly, artificial learning techniques have not been investigated to their full potential to improve generalisation of visual attention methods. The proposed thesis is that artificial learning can increase the generalisability of the traditional model of visual attention by effective selection and optimal combination of features. The following new techniques have been introduced at various stages of the traditional model of visual attention to improve its generalisation performance, specifically on challenging cases of saliency detection: 1. Joint optimisation of feature related parameters and feature importance weights is introduced for the first time to improve the generalisation of the traditional model of visual attention. To evaluate the joint learning hypothesis, a new method namely GAOVSM is introduced for the tasks of eye fixation prediction. By finding the relationships between feature related parameters and feature importance, the developed method improves the generalisation performance of baseline method (that employ human encoded parameters). 2. Spectral matting based figure-ground segregation is introduced to overcome the artifacts encountered by region-based salient object detection approaches. By suppressing the unwanted background information and assigning saliency to object parts in a uniform manner, the developed FGS approach overcomes the limitations of region based approaches. 3. Joint optimisation of feature computation parameters and feature importance weights is introduced for optimal combination of FGS with complementary features for the first time for salient object detection. By learning feature related parameters and their respective importance at multiple segmentation thresholds and by considering the performance gaps amongst features, the developed FGSopt method improves the object detection performance of the FGS technique also improving upon several state-of-the-art salient object detection models. 4. The introduction of multiple combination schemes/rules further extends the generalisability of the traditional attention model beyond that of joint optimisation based single rules. The introduction of feature composition based grouping of images, enables the developed IGA method to autonomously identify an appropriate combination strategy for an unseen image. The results of a pair-wise ranksum test confirm that the IGA method is significantly better than the deterministic and classification based benchmark methods on the 99% confidence interval level. Extending this line of research, a novel relative encoding approach enables the adapted XCSCA method to group images having similar saliency prediction ability. By keeping track of previous inputs, the introduced action part of the XCSCA approach enables learning of generalised feature importance rules. By more accurate grouping of images as compared with IGA, generalised learnt rules and appropriate application of feature importance rules, the XCSCA approach improves upon the generalisation performance of the IGA method. 5. The introduced uniform saliency assignment and segmentation quality cues enable label free evaluation of a feature/saliency map. By accurate ranking and effective clustering, the developed DFS method successfully solves the complex problem of finding appropriate features for combination (on an-image-by-image basis) for the first time in saliency detection. The DFS method enables ground truth free evaluation of saliency methods and advances the state-of-the-art in data driven saliency aggregation by detection and deselection of redundant information. The final contribution is that the developed methods are formed into a complete system where analysis shows the effects of their interactions on the system. Based on the saliency prediction accuracy versus computational time trade-off, specialised variants of the proposed methods are presented along with the recommendations for further use by other saliency detection systems. This research work has shown that artificial learning can increase the generalisation of the traditional model of attention by effective selection and optimal combination of features. Overall, this thesis has shown that it is the ability to autonomously segregate images based on their types and subsequent learning of appropriate combinations that aid generalisation on difficult unseen stimuli

Target detection in clutter for sonar imagery

This thesis is concerned with the analysis of side-looking sonar images, and specif- ically with the identification of the types of seabed that are present in such images, and with the detection of man-made objects in such images. Side-looking sonar images are, broadly speaking, the result of the physical interaction between acous- tic waves and the bottom of the sea. Because of this interaction, the types of seabed appear as textured areas in side-looking sonar images. The texture descrip- tors commonly used in the field of sonar imagery fail at accurately identifying the types of seabed because the types of seabed, hence the textures, are extremely variable. In this thesis, we did not use the traditional texture descriptors to identify the types of seabed. We rather used scattering operators which recently appeared in the field of signal and image processing. We assessed how well the types of seabed are identified through two inference algorithms, one based on affine spaces, and the other based on the concept of similarity by composition. This thesis is also concerned with the detection of man-made objects in side-looking sonar im- ages. An object detector may be described as a method which, when applied to a certain number of sonar images, produces a set of detections. Some of these are true positives, and correspond to real objects. Others are false positives, and do not correspond to real objects. The present object detectors suffer from a high false positive rate in complex environments, that is to say, complex types of seabed. The hypothesis we will follow is that it is possible to reduce the number of false positives through a characterisation of the similarity between the detections and the seabed, the false positives being by nature part of the seabed. We will use scattering operators to represent the detections and the same two inference algorithms to quantify how similar the detections are to the seabed

Securing Additive Manufacturing Systems from Cyber and Intellectual Property Attacks

Additive Manufacturing (AM), also known as 3D printing, refers to a collection of manufacturing processes where materials are joined together layer by layer to make objects directly from 3D models. Due to many advantages of AM, such as rapid prototyping, massive customization, material saving, and flexibility of designs, there is a trend for AM to replace traditional manufacturing processes. However, AM highly relies on computers to work. As AM systems are gaining popularity in many critical industry sectors, there is an increased risk of cyberattacks on AM systems. To protect AM systems from cyberattacks that aim to sabotage the AM systems, Intrusion Detection Systems (IDSs) can be used. In recent years, researchers proposed a series of IDSs that work by leveraging side-channel signals. A side-channel signal is typically a physical signal that is correlated with the state of the AM system, such as the acoustic sound or the electromagnetic wave emitted by a 3D printer in a printing process. Because of the correlation between a side-channel signal and the state of a 3D printer, it is possible to perform intrusion detection by analyzing the side-channel signal. In fact, most existing IDSs leveraging side-channel signals in AM systems function by comparing an observed side-channel signal against a reference side-channel signal. However, we found that these IDSs are not practical due to a lack of synchronization. Many IDSs in the literature do not contain details on how to align two (or more) side-channel signals at their starting moments and their stopping moments. In addition, we found that there is time noise in AM processes. When the same G-code file is executed on the same 3D printer multiple times, the printing processes will have slightly different timing. Because of time noise, a direct comparison between two signals point by point or window by window will not make sense. To overcome this problem, we propose to use dynamic synchronization to find corresponding points between two signals in real time. To demonstrate the necessity of dynamic synchronization, we performed a total of 302 benign printing processes and a total of 200 malicious printing processes with two printers. Our experiment results show that existing IDSs leveraging side-channel signals in AM systems can only achieve an accuracy from 0.50 to 0.88, whereas our IDS with dynamic synchronization can reach an accuracy of 0.99. Other than cyberattacks to sabotage AM systems, there are also cyberattacks to steal intellectual property in AM systems. For example, there are acoustic side-channel attacks on AM systems which can recover the printing path by analyzing the acoustic sound by a printer in a printing process. However, we found that the acoustic side-channel attack is hard to perform due to challenges such as integration drift and non-unique solution. In this thesis, we explore the optical side-channel attack, which is much easier to perform than the acoustic side-channel attack. The optical side-channel signal is basically the video of a printing process. We use a modified deep neural network, ResNet50, to recognize the coordinates of the printhead in each frame in the video. To defend against the optical side-channel attack, we propose the optical noise injection method. We use an optical projector to artificially inject crafted optical noise onto the printing area in an attempt to confuse the attacker and make it harder to recover the printing path. We found that existing noise generation algorithms, such as replaying, random blobs, white noise, and full power, can effortlessly defeat a naive attacker who is not aware of the existence of the injected noise. However, an advanced attacker who knows about the injected noise and incorporates images with injected noise in the training dataset can defeat all of the existing noise generation algorithms. To defend against such an advanced attacker, we propose three novel noise generation algorithms: channel uniformization, state uniformization, and state randomization. Our experiment results show that noise generated via state randomization can successfully defeat the advanced attacker.Ph.D

Recent Trends in Computational Intelligence

Traditional models struggle to cope with complexity, noise, and the existence of a changing environment, while Computational Intelligence (CI) offers solutions to complicated problems as well as reverse problems. The main feature of CI is adaptability, spanning the fields of machine learning and computational neuroscience. CI also comprises biologically-inspired technologies such as the intellect of swarm as part of evolutionary computation and encompassing wider areas such as image processing, data collection, and natural language processing. This book aims to discuss the usage of CI for optimal solving of various applications proving its wide reach and relevance. Bounding of optimization methods and data mining strategies make a strong and reliable prediction tool for handling real-life applications

Advances and Applications of DSmT for Information Fusion

This book is devoted to an emerging branch of Information Fusion based on new approach for modelling the fusion problematic when the information provided by the sources is both uncertain and (highly) conflicting. This approach, known in literature as DSmT (standing for Dezert-Smarandache Theory), proposes new useful rules of combinations

Advances and Applications of Dezert-Smarandache Theory (DSmT), Vol. 1

The Dezert-Smarandache Theory (DSmT) of plausible and paradoxical reasoning is a natural extension of the classical Dempster-Shafer Theory (DST) but includes fundamental differences with the DST. DSmT allows to formally combine any types of independent sources of information represented in term of belief functions, but is mainly focused on the fusion of uncertain, highly conflicting and imprecise quantitative or qualitative sources of evidence. DSmT is able to solve complex, static or dynamic fusion problems beyond the limits of the DST framework, especially when conflicts between sources become large and when the refinement of the frame of the problem under consideration becomes inaccessible because of vague, relative and imprecise nature of elements of it. DSmT is used in cybernetics, robotics, medicine, military, and other engineering applications where the fusion of sensors\u27 information is required

Deep Neural Networks and Data for Automated Driving

This open access book brings together the latest developments from industry and research on automated driving and artificial intelligence. Environment perception for highly automated driving heavily employs deep neural networks, facing many challenges. How much data do we need for training and testing? How to use synthetic data to save labeling costs for training? How do we increase robustness and decrease memory usage? For inevitably poor conditions: How do we know that the network is uncertain about its decisions? Can we understand a bit more about what actually happens inside neural networks? This leads to a very practical problem particularly for DNNs employed in automated driving: What are useful validation techniques and how about safety? This book unites the views from both academia and industry, where computer vision and machine learning meet environment perception for highly automated driving. Naturally, aspects of data, robustness, uncertainty quantification, and, last but not least, safety are at the core of it. This book is unique: In its first part, an extended survey of all the relevant aspects is provided. The second part contains the detailed technical elaboration of the various questions mentioned above