Download or read book Biologically based Interactive Neural Network Models for Visual Attention and Object Recognition written by Mohammad Saifullah and published by . This book was released on 2012 with total page 200 pages. Available in PDF, EPUB and Kindle. Book excerpt: The main focus of this thesis is to develop biologically-based computational models for object recognition. A series of models for attention and object recognition were developed in the order of increasing functionality and complexity. These models are based on information processing in the primate brain, and specially inspired from the theory of visual information processing along the two parallel processing pathways of the primate visual cortex. To capture the true essence of incremental, constraint satisfaction style processing in the visual system, interactive neural networks were used for implementing our models. Results from eye-tracking studies on the relevant visual tasks, as well as our hypothesis regarding the information processing in the primate visual system, were implemented in the models and tested with simulations. As a first step, a model based on the ventral pathway was developed to recognize single objects. Through systematic testing, structural and algorithmic parameters of these models were fine tuned for performing their task optimally. In the second step, the model was extended by considering the dorsal pathway, which enables simulation of visual attention as an emergent phenomenon. The extended model was then investigated for visual search tasks. In the last step, we focussed on occluded and overlapped object recognition. A couple of eye-tracking studies were conducted in this regard and on the basis of the results we made some hypotheses regarding information processing in the primate visual system. The models were further advanced on the lines of the presented hypothesis, and simulated on the tasks of occluded and overlapped object recognition. On the basis of the results and analysis of our simulations we have further found that the generalization performance of interactive hierarchical networks improves with the addition of a small amount of Hebbian learning to an otherwise pure error-driven learning. We also concluded that the size of the receptive fields in our networks is an important parameter for the generalization task and depends on the object of interest in the image. Our results show that networks using hard coded feature extraction perform better than the networks that use Hebbian learning for developing feature detectors. We have successfully demonstrated the emergence of visual attention within an interactive network and also the role of context in the search task. Simulation results with occluded and overlapped objects support our extended interactive processing approach, which is a combination of the interactive and top-down approach, to the segmentation-recognition issue. Furthermore, the simulation behavior of our models is in line with known human behavior for similar tasks. In general, the work in this thesis will improve the understanding and performance of biologically-based interactive networks for object recognition and provide a biologically-plausible solution to recognition of occluded and overlapped objects. Moreover, our models provide some suggestions for the underlying neural mechanism and strategies behind biological object recognition.
Download or read book Learning to Attend with Neural Networks written by Jimmy Ba and published by . This book was released on 2020 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: As more computational resources become widely available, artificial intelligence and machine learning researchers design ever larger and more complicated neural networks to learn from millions of data points. Although the traditional convolutional neural networks (CNNs) can achieve superhuman accuracy in object recognition tasks, they brute-force the problem by scanning over every location in the input images with the same fidelity. This thesis introduces a new class of neural networks inspired by the human visual system. Unlike CNNs that process the entire image at once into the current hidden layer, attention allows for salient features to dynamically come to the forefront as needed. The ability to attend is especially important when there is a lot of clutter in a scene. However, learning attention-based neural networks poses some challenges to the current machine learning techniques: What information should the neural network ``pay attention''? Where does the network store its sequences of ``glimpses''? Can our learning algorithms do better than simply ``trial-and-error''? To address these computational questions, we first describe a novel recurrent visual attention model in the context of variational inference. Because the standard REINFORCE or the trial-and-error algorithm can be slow due to its high variance gradient estimates, we show a re-weighted wake-sleep objective can improve the training performance. We also demonstrate the visual attention models outperform the previous state-of-the-art methods based on CNNs in the images and captions generation tasks. Furthermore, we discuss how the visual attention mechanism can improve the working memory of recurrent neural networks (RNNs) through a novel form of self-attention. The second half of the thesis focuses on gradient-based learning algorithms. We developed a new first-order optimization algorithm to overcome the slow convergence of the stochastic gradient descent algorithms in RNNs and attention-based models. In the end, we explored the benefit of applying second-order optimization methods in training neural networks.
Download or read book Deep Learning to See written by Alessandro Betti and published by Springer Nature. This book was released on 2022-04-26 with total page 116 pages. Available in PDF, EPUB and Kindle. Book excerpt: The remarkable progress in computer vision over the last few years is, by and large, attributed to deep learning, fueled by the availability of huge sets of labeled data, and paired with the explosive growth of the GPU paradigm. While subscribing to this view, this work criticizes the supposed scientific progress in the field, and proposes the investigation of vision within the framework of information-based laws of nature. This work poses fundamental questions about vision that remain far from understood, leading the reader on a journey populated by novel challenges resonating with the foundations of machine learning. The central thesis proposed is that for a deeper understanding of visual computational processes, it is necessary to look beyond the applications of general purpose machine learning algorithms, and focus instead on appropriate learning theories that take into account the spatiotemporal nature of the visual signal. Serving to inspire and stimulate critical reflection and discussion, yet requiring no prior advanced technical knowledge, the text can naturally be paired with classic textbooks on computer vision to better frame the current state of the art, open problems, and novel potential solutions. As such, it will be of great benefit to graduate and advanced undergraduate students in computer science, computational neuroscience, physics, and other related disciplines.
Download or read book Object Recognition Attention and Action written by Naoyuki Osaka and published by Springer Science & Business Media. This book was released on 2009-03-12 with total page 252 pages. Available in PDF, EPUB and Kindle. Book excerpt: Human object recognition is a classical topic both for philosophy and for the natural sciences. Ultimately, understanding of object recognition will be promoted by the cooperation of behavioral research, neurophysiology, and computation. This original book provides an excellent introduction to the issues that are involved. It contains chapters that address the ways in which humans and machines attend to, recognize, and act toward objects in the visual environment.
Download or read book Selective Visual Attention written by Liming Zhang and published by John Wiley & Sons. This book was released on 2013-03-05 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Visual attention is a relatively new area of study combining a number of disciplines: artificial neural networks, artificial intelligence, vision science and psychology. The aim is to build computational models similar to human vision in order to solve tough problems for many potential applications including object recognition, unmanned vehicle navigation, and image and video coding and processing. In this book, the authors provide an up to date and highly applied introduction to the topic of visual attention, aiding researchers in creating powerful computer vision systems. Areas covered include the significance of vision research, psychology and computer vision, existing computational visual attention models, and the authors' contributions on visual attention models, and applications in various image and video processing tasks. This book is geared for graduates students and researchers in neural networks, image processing, machine learning, computer vision, and other areas of biologically inspired model building and applications. The book can also be used by practicing engineers looking for techniques involving the application of image coding, video processing, machine vision and brain-like robots to real-world systems. Other students and researchers with interdisciplinary interests will also find this book appealing. Provides a key knowledge boost to developers of image processing applications Is unique in emphasizing the practical utility of attention mechanisms Includes a number of real-world examples that readers can implement in their own work: robot navigation and object selection image and video quality assessment image and video coding Provides codes for users to apply in practical attentional models and mechanisms
Download or read book Bio Inspired Models of Network Information and Computing Systems written by Emma Hart and published by Springer. This book was released on 2012-08-10 with total page 270 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the thoroughly refereed post-conference proceedings of the 6th International Conference on Bio-Inspired Models of Network, Information, and Computing Systems (Bionetics). The event took place in the city of York, UK, in December 2011. Bionetics main objective is to bring bio-inspired paradigms into computer engineering and networking, and to enhance the fruitful interactions between these fields and biology. The papers of the conference were accepted in 2 categories: full papers and work-in progress. Full papers describe significant advances in the Bionetics field, while work-in-progress papers present an opportunity to discuss breaking research which is currently being evaluated. The topics are ranging from robotic coordination to attack detection in peer-to-peer networks, biological mechanisms including evolution, flocking and artificial immune systems, and nano-scale communication and networking.
Download or read book A Neural Network Model of Visual Attention and Object Classification written by Hayden Walles and published by . This book was released on 2013 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Studying Simulations with Distributed Cognition written by Jonas Rybing and published by Linköping University Electronic Press. This book was released on 2018-03-20 with total page 115 pages. Available in PDF, EPUB and Kindle. Book excerpt: Simulations are frequently used techniques for training, performance assessment, and prediction of future outcomes. In this thesis, the term “human-centered simulation” is used to refer to any simulation in which humans and human cognition are integral to the simulation’s function and purpose (e.g., simulation-based training). A general problem for human-centered simulations is to capture the cognitive processes and activities of the target situation (i.e., the real world task) and recreate them accurately in the simulation. The prevalent view within the simulation research community is that cognition is internal, decontextualized computational processes of individuals. However, contemporary theories of cognition emphasize the importance of the external environment, use of tools, as well as social and cultural factors in cognitive practice. Consequently, there is a need for research on how such contemporary perspectives can be used to describe human-centered simulations, re-interpret theoretical constructs of such simulations, and direct how simulations should be modeled, designed, and evaluated. This thesis adopts distributed cognition as a framework for studying human-centered simulations. Training and assessment of emergency medical management in a Swedish context using the Emergo Train System (ETS) simulator was adopted as a case study. ETS simulations were studied and analyzed using the distributed cognition for teamwork (DiCoT) methodology with the goal of understanding, evaluating, and testing the validity of the ETS simulator. Moreover, to explore distributed cognition as a basis for simulator design, a digital re-design of ETS (DIGEMERGO) was developed based on the DiCoT analysis. The aim of the DIGEMERGO system was to retain core distributed cognitive features of ETS, to increase validity, outcome reliability, and to provide a digital platform for emergency medical studies. DIGEMERGO was evaluated in three separate studies; first, a usefulness, usability, and facevalidation study that involved subject-matter-experts; second, a comparative validation study using an expert-novice group comparison; and finally, a transfer of training study based on self-efficacy and management performance. Overall, the results showed that DIGEMERGO was perceived as a useful, immersive, and promising simulator – with mixed evidence for validity – that demonstrated increased general self-efficacy and management performance following simulation exercises. This thesis demonstrates that distributed cognition, using DiCoT, is a useful framework for understanding, designing and evaluating simulated environments. In addition, the thesis conceptualizes and re-interprets central constructs of human-centered simulation in terms of distributed cognition. In doing so, the thesis shows how distributed cognitive processes relate to validity, fidelity, functionality, and usefulness of human-centered simulations. This thesis thus provides a new understanding of human-centered simulations that is grounded in distributed cognition theory.
Download or read book VOCUS A Visual Attention System for Object Detection and Goal Directed Search written by Simone Frintrop and published by Springer. This book was released on 2006-03-28 with total page 219 pages. Available in PDF, EPUB and Kindle. Book excerpt: This monograph presents a complete computational system for visual attention and object detection. VOCUS (Visual Object detection with a Computational attention System) represents a major step forward on integrating data-driven and model-driven information into a single framework. Additionally, the volume contains an extensive review of the literature on visual attention, detailed evaluations of VOCUS in different settings, and applications of the system.
Download or read book Brain Vision and Artificial Intelligence written by Massimo De Gregorio and published by Springer Science & Business Media. This book was released on 2005-10-11 with total page 570 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the First International Symposium on Brain, Vision and Artificial Intelligence, BVAI 2005, held in Naples, Italy in October 2005. The 48 revised papers presented together with 6 invited lectures were carefully reviewed and selected from more than 80 submissions for inclusion in the book. The papers are addressed to the following main topics and sub-topics: brain basics - neuroanatomy and physiology, development, plasticity and learning, synaptic, neuronic and neural network modelling; natural vision - visual neurosciences, mechanisms and model systems, visual perception, visual cognition; artificial vision - shape perception, shape analysis and recognition, shape understanding; artificial inteligence - hybrid intelligent systems, agents, and cognitive models.
Download or read book Beyond Recognition written by Le Minh-Ha and published by Linköping University Electronic Press. This book was released on 2024-05-06 with total page 103 pages. Available in PDF, EPUB and Kindle. Book excerpt: This thesis addresses the need to balance the use of facial recognition systems with the need to protect personal privacy in machine learning and biometric identification. As advances in deep learning accelerate their evolution, facial recognition systems enhance security capabilities, but also risk invading personal privacy. Our research identifies and addresses critical vulnerabilities inherent in facial recognition systems, and proposes innovative privacy-enhancing technologies that anonymize facial data while maintaining its utility for legitimate applications. Our investigation centers on the development of methodologies and frameworks that achieve k-anonymity in facial datasets; leverage identity disentanglement to facilitate anonymization; exploit the vulnerabilities of facial recognition systems to underscore their limitations; and implement practical defenses against unauthorized recognition systems. We introduce novel contributions such as AnonFACES, StyleID, IdDecoder, StyleAdv, and DiffPrivate, each designed to protect facial privacy through advanced adversarial machine learning techniques and generative models. These solutions not only demonstrate the feasibility of protecting facial privacy in an increasingly surveilled world, but also highlight the ongoing need for robust countermeasures against the ever-evolving capabilities of facial recognition technology. Continuous innovation in privacy-enhancing technologies is required to safeguard individuals from the pervasive reach of digital surveillance and protect their fundamental right to privacy. By providing open-source, publicly available tools, and frameworks, this thesis contributes to the collective effort to ensure that advancements in facial recognition serve the public good without compromising individual rights. Our multi-disciplinary approach bridges the gap between biometric systems, adversarial machine learning, and generative modeling to pave the way for future research in the domain and support AI innovation where technological advancement and privacy are balanced.
Download or read book Enabling Scalable and Efficient Visual Attention Object based Attention and Object Recognition for Humanoid Robots a Biologically inspired Approach written by Andreas Holzbach and published by . This book was released on 2016 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book A Neural Model Combining Attentional Orienting to Object Recognition Preliminary Explorations on the Interplay Between Where and What written by and published by . This book was released on 2001 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: We propose a model of primate vision that integrates both an attentional orienting ("where") pathway and an object recognition ("what") pathway. The fast visual attention front-end rapidly selects the few most conspicuous image locations, and the slower object recognition back-end identifies objects at the selected locations. The model is applied to classical visual search tasks, consisting of finding a specific target among an array of distracting visual patterns (e.g., a circle among many squares). The encouraging results obtained, in which substantial speedup is achieved by the combined attention- recognition model while maintaining good recognition performance compared to an exhaustive search, suggest that the biologically-inspired architecture proposed represents an efficient solution to the difficult problem of rapid scene analysis.
Download or read book Building Design Capability in the Public Sector written by Lisa Malmberg and published by Linköping University Electronic Press. This book was released on 2017-02-14 with total page 250 pages. Available in PDF, EPUB and Kindle. Book excerpt: Public sector organizations are in need of new approaches to development and innovation. There is a need to develop a capability to better understand priorities, needs and wishes of public sector service users and become more proactive, in order to meet the demands on keeping costs down and quality high. Design is increasingly put forward as a potential answer to this need and there are many initiatives taken across the world to encourage the use of a design approach to development and innovation within public sector. In relation to this trend there is a need to improve the understanding of how public sector organizations develop ability to exploit design; how they develop design capability. This is the focus of this thesis, which through an exploratory study has observed the two initiatives aiming to introduce design and develop design capability within healthcare and social service organizations. One main contribution of this work is an understanding of the design capability concept based on a structured review of the use of the design capability concept in the literature. The concept has previously been used in relation to different aspects of designs in organizations. Another important contribution is the development of an understanding for how design capability is developed based on interpretations founded in the organizational learning perspective of absorptive capacity. The study has identified how different antecedents to development of design capability have influenced this development in the two cases. The findings have identified aspects that both support and impede the development of design capability which are important to acknowledge and address when aiming to develop design capability within a public sector organization. In both cases, the set up of the knowledge transferring efforts focus mainly on developing awareness of design. Similar patterns are seen in other prior and parallel initiatives. The findings however suggest that it is also important to ensure that the organization have access to design competence and that structures like routines, processes and culture support and enable the use of design practice, in order to make design a natural part of the continuous development work.
Download or read book The Role of Object Recognition in Active Vision written by Alexander John Cope and published by . This book was released on 2011 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Eye movements are essential to the way that primates and humans investigate the visual world. These eye movements depend upon the task being performed, and thus cannot be accounted for by bottom-up features, as in the model of Itti et al. (1998). Here we seek to investigate the role of task information in the redirection of gaze, by starting with an existing biologically based model of the primate oculomotor system (Chambers. 2(07). We integrate a revised version of the model with a new model of object recognition, produced using inspiration from the HMAX model of Riesenhuber and Poggio (1999), combined with a computational advantageous and biologically accurate method of visual attention. This approach of utilising and combining existing models where possible we describe as 'systems integration'. The full model reproduces a wealth of experimental evidence, in- cluding the effect of set size on reaction time for different difficulty visual search tasks (Treisman and Gelade, 1980), and additionally on saccadic latency and fixation duration for difficult visual search tasks (Motter and Belky, 1998a), as well as the effect of onset on search behavior found by Yantis and Jonides (1996). Novel explanations for these behaviours are suggested, under an overarching framework, which can only be provided because of 'the biological realism present in this model. We then extend the model with additional competencies, using an enhanced 'systems integration' approach. This involves including engineered phenomenological components that replicate neural competencies. This extended model is embodied in robotic hardware - thereby improving the veracity of the world-model interaction. The extended competencies include reward based associative learning, and habituation to repeated task irrelevant distraction. This model is exercised with an ethological experiment, and provides predictions regarding the nature of the mechanisms behind reward based as- sociative learning - notably, the model predicts that reversal learning is important when the reward associated with objects changes.
Download or read book VOCUS A Visual Attention System for Object Detection and Goal Directed Search written by Simone Frintrop and published by Springer Science & Business Media. This book was released on 2006-04-06 with total page 219 pages. Available in PDF, EPUB and Kindle. Book excerpt: This monograph presents a complete computational system for visual attention and object detection. VOCUS (Visual Object detection with a Computational attention System) represents a major step forward on integrating data-driven and model-driven information into a single framework. Additionally, the volume contains an extensive review of the literature on visual attention, detailed evaluations of VOCUS in different settings, and applications of the system.
Download or read book Thermal Issues in Testing of Advanced Systems on Chip written by Nima Aghaee Ghaleshahi and published by Linköping University Electronic Press. This book was released on 2015-09-23 with total page 219 pages. Available in PDF, EPUB and Kindle. Book excerpt: Many cutting-edge computer and electronic products are powered by advanced Systems-on-Chip (SoC). Advanced SoCs encompass superb performance together with large number of functions. This is achieved by efficient integration of huge number of transistors. Such very large scale integration is enabled by a core-based design paradigm as well as deep-submicron and 3D-stacked-IC technologies. These technologies are susceptible to reliability and testing complications caused by thermal issues. Three crucial thermal issues related to temperature variations, temperature gradients, and temperature cycling are addressed in this thesis. Existing test scheduling techniques rely on temperature simulations to generate schedules that meet thermal constraints such as overheating prevention. The difference between the simulated temperatures and the actual temperatures is called temperature error. This error, for past technologies, is negligible. However, advanced SoCs experience large errors due to large process variations. Such large errors have costly consequences, such as overheating, and must be taken care of. This thesis presents an adaptive approach to generate test schedules that handle such temperature errors. Advanced SoCs manufactured as 3D stacked ICs experience large temperature gradients. Temperature gradients accelerate certain early-life defect mechanisms. These mechanisms can be artificially accelerated using gradient-based, burn-in like, operations so that the defects are detected before shipping. Moreover, temperature gradients exacerbate some delay-related defects. In order to detect such defects, testing must be performed when appropriate temperature-gradients are enforced. A schedule-based technique that enforces the temperature-gradients for burn-in like operations is proposed in this thesis. This technique is further developed to support testing for delay-related defects while appropriate gradients are enforced. The last thermal issue addressed by this thesis is related to temperature cycling. Temperature cycling test procedures are usually applied to safety-critical applications to detect cycling-related early-life failures. Such failures affect advanced SoCs, particularly through-silicon-via structures in 3D-stacked-ICs. An efficient schedule-based cycling-test technique that combines cycling acceleration with testing is proposed in this thesis. The proposed technique fits into existing 3D testing procedures and does not require temperature chambers. Therefore, the overall cycling acceleration and testing cost can be drastically reduced. All the proposed techniques have been implemented and evaluated with extensive experiments based on ITC’02 benchmarks as well as a number of 3D stacked ICs. Experiments show that the proposed techniques work effectively and reduce the costs, in particular the costs related to addressing thermal issues and early-life failures. We have also developed a fast temperature simulation technique based on a closed-form solution for the temperature equations. Experiments demonstrate that the proposed simulation technique reduces the schedule generation time by more than half.
