Meta-optimization of Bio-inspired Techniques for Object Recognition

Il riconoscimento di oggetti consiste nel trovare automaticamente un oggetto all'interno di un'immagine o in una sequenza video. Questo compito è molto importante in molti campi quali diagnosi mediche, assistenza di guida avanzata, visione artificiale, sorveglianza, realtà aumentata. Tuttavia, questo compito può essere molto impegnativo a causa di artefatti (dovuti al sistema di acquisizione, all'ambiente o ad altri effetti ottici quali prospettiva, variazioni di illuminazione, etc.) che possono influenzare l'aspetto anche di oggetti facili da identificare e ben definiti . Una possibile tecnica per il riconoscimento di oggetti consiste nell'utilizzare approcci basati su modello: in questo scenario viene creato un modello che rappresenta le proprietà dell'oggetto da individuare; poi, vengono generate possibili ipotesi sul posizionamento dell'oggetto, e il modello viene trasformato di conseguenza, fino a trovare la migliore corrispondenza con l'aspetto reale dell'oggetto. Per generare queste ipotesi in maniera intelligente, è necessario un buon algoritmo di ottimizzazione. Gli algoritmi di tipo bio-ispirati sono metodi di ottimizzazione che si basano su proprietà osservate in natura (quali cooperazione, evoluzione, socialità). La loro efficacia è stata dimostrata in molte attività di ottimizzazione, soprattutto in problemi di difficile soluzione, multi-modali e multi-dimensionali quali, per l'appunto, il riconoscimento di oggetti. Anche se queste euristiche sono generalmente efficaci, esse dipendono da molti parametri che influenzano profondamente le loro prestazioni; pertanto, è spesso richiesto uno sforzo significativo per capire come farle esprimere al massimo delle loro potenzialità. Questa tesi descrive un metodo per (i) individuare automaticamente buoni parametri per tecniche bio-ispirate, sia per un problema specifico che più di uno alla volta, e (ii) acquisire maggior conoscenza sul ruolo di un parametro in questi algoritmi. Inoltre, viene mostrato come le tecniche bio-ispirate possono essere applicate con successo in diversi ambiti nel riconoscimento di oggetti, e come è possibile migliorare ulteriormente le loro prestazioni mediante il tuning automatico dei loro parametri.Object recognition is the task of automatically finding a given object in an image or in a video sequence. This task is very important in many fields such as medical diagnosis, advanced driving assistance, image understanding, surveillance, virtual reality. Nevertheless, this task can be very challenging because of artefacts (related with the acquisition system, the environment or other optical effects like perspective, illumination changes, etc.) which may affect the aspect even of easy-to-identify and well-defined objects. A possible way to achieve object recognition is using model-based approaches: in this scenario a model (also called template) representing the properties of the target object is created; then, hypotheses on the position of the object are generated, and the model is transformed accordingly, until the best match with the actual appearance of the object is found. To generate these hypotheses intelligently, a good optimization algorithm is required. Bio-inspired techniques are optimization methods whose foundations rely on properties observed in nature (such as cooperation, evolution, emergence). Their effectiveness has been proved in many optimization tasks, especially in multi-modal, multi-dimensional hard problems like object recognition. Although these heuristics are generally effective, they depend on many parameters that strongly affect their performances; therefore, a significant effort must be spent to understand how to let them express their full potentialities. This thesis describes a method to (i) automatically find good parameters for bio-inspired techniques, both for a specific problem and for more than one at the same time, and (ii) acquire more knowledge of a parameter's role in such algorithms. Then, it shows how bio-inspired techniques can be successfully applied to different object recognition tasks, and how it is possible to further improve their performances by means of automatic parameter tuning

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

Parallel bio-inspired methods for model optimization and pattern recognition

Nature based computational models are usually inherently parallel. The collaborative intelligence in those models emerges from the simultaneous instruction processing by simple independent units (neurons, ants, swarm members, etc...). This dissertation investigates the benefits of such parallel models in terms of efficiency and accuracy. First, the viability of a parallel implementation of bio-inspired metaheuristics for function optimization on consumer-level graphic cards is studied in detail. Then, in an effort to expose those parallel methods to the research community, the metaheuristic implementations were abstracted and grouped in an open source parameter/function optimization library libCudaOptimize. The library was verified against a well known benchmark for mathematical function minimization, and showed significant gains in both execution time and minimization accuracy. Crossing more into the application side, a parallel model of the human neocortex was developed. This model is able to detect, classify, and predict patterns in time-series data in an unsupervised way. Finally, libCudaOptimize was used to find the best parameters for this neocortex model, adapting it to gesture recognition within publicly available datasets

Hybrid group anomaly detection for sequence data: application to trajectory data analytics

Many research areas depend on group anomaly detection. The use of group anomaly detection can maintain and provide security and privacy to the data involved. This research attempts to solve the deficiency of the existing literature in outlier detection thus a novel hybrid framework to identify group anomaly detection from sequence data is proposed in this paper. It proposes two approaches for efficiently solving this problem: i) Hybrid Data Mining-based algorithm, consists of three main phases: first, the clustering algorithm is applied to derive the micro-clusters. Second, the kNN algorithm is applied to each micro-cluster to calculate the candidates of the group's outliers. Third, a pattern mining framework gets applied to the candidates of the group's outliers as a pruning strategy, to generate the groups of outliers, and ii) a GPU-based approach is presented, which benefits from the massively GPU computing to boost the runtime of the hybrid data mining-based algorithm. Extensive experiments were conducted to show the advantages of different sequence databases of our proposed model. Results clearly show the efficiency of a GPU direction when directly compared to a sequential approach by reaching a speedup of 451. In addition, both approaches outperform the baseline methods for group detection.acceptedVersio

Computational intelligence approaches to robotics, automation, and control [Volume guest editors]

No abstract available

Perception architecture exploration for automotive cyber-physical systems

2022 Spring.Includes bibliographical references.In emerging autonomous and semi-autonomous vehicles, accurate environmental perception by automotive cyber physical platforms are critical for achieving safety and driving performance goals. An efficient perception solution capable of high fidelity environment modeling can improve Advanced Driver Assistance System (ADAS) performance and reduce the number of lives lost to traffic accidents as a result of human driving errors. Enabling robust perception for vehicles with ADAS requires solving multiple complex problems related to the selection and placement of sensors, object detection, and sensor fusion. Current methods address these problems in isolation, which leads to inefficient solutions. For instance, there is an inherent accuracy versus latency trade-off between one stage and two stage object detectors which makes selecting an enhanced object detector from a diverse range of choices difficult. Further, even if a perception architecture was equipped with an ideal object detector performing high accuracy and low latency inference, the relative position and orientation of selected sensors (e.g., cameras, radars, lidars) determine whether static or dynamic targets are inside the field of view of each sensor or in the combined field of view of the sensor configuration. If the combined field of view is too small or contains redundant overlap between individual sensors, important events and obstacles can go undetected. Conversely, if the combined field of view is too large, the number of false positive detections will be high in real time and appropriate sensor fusion algorithms are required for filtering. Sensor fusion algorithms also enable tracking of non-ego vehicles in situations where traffic is highly dynamic or there are many obstacles on the road. Position and velocity estimation using sensor fusion algorithms have a lower margin for error when trajectories of other vehicles in traffic are in the vicinity of the ego vehicle, as incorrect measurement can cause accidents. Due to the various complex inter-dependencies between design decisions, constraints and optimization goals a framework capable of synthesizing perception solutions for automotive cyber physical platforms is not trivial. We present a novel perception architecture exploration framework for automotive cyber- physical platforms capable of global co-optimization of deep learning and sensing infrastructure. The framework is capable of exploring the synthesis of heterogeneous sensor configurations towards achieving vehicle autonomy goals. As our first contribution, we propose a novel optimization framework called VESPA that explores the design space of sensor placement locations and orientations to find the optimal sensor configuration for a vehicle. We demonstrate how our framework can obtain optimal sensor configurations for heterogeneous sensors deployed across two contemporary real vehicles. We then utilize VESPA to create a comprehensive perception architecture synthesis framework called PASTA. This framework enables robust perception for vehicles with ADAS requiring solutions to multiple complex problems related not only to the selection and placement of sensors but also object detection, and sensor fusion as well. Experimental results with the Audi-TT and BMW Minicooper vehicles show how PASTA can intelligently traverse the perception design space to find robust, vehicle-specific solutions

Ant Colony Optimization

Ant Colony Optimization (ACO) is the best example of how studies aimed at understanding and modeling the behavior of ants and other social insects can provide inspiration for the development of computational algorithms for the solution of difficult mathematical problems. Introduced by Marco Dorigo in his PhD thesis (1992) and initially applied to the travelling salesman problem, the ACO field has experienced a tremendous growth, standing today as an important nature-inspired stochastic metaheuristic for hard optimization problems. This book presents state-of-the-art ACO methods and is divided into two parts: (I) Techniques, which includes parallel implementations, and (II) Applications, where recent contributions of ACO to diverse fields, such as traffic congestion and control, structural optimization, manufacturing, and genomics are presented