Download or read book Robot Semantic Place Recognition Based on Deep Belief Networks and a Direct Use of Tiny Images written by Ahmad Hasasneh and published by . This book was released on 2012 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Usually, human beings are able to quickly distinguish between different places, solely from their visual appearance. This is due to the fact that they can organize their space as composed of discrete units. These units, called ``semantic places'', are characterized by their spatial extend and their functional unity. Such a semantic category can thus be used as contextual information which fosters object detection and recognition. Recent works in semantic place recognition seek to endow the robot with similar capabilities. Contrary to classical localization and mapping works, this problem is usually addressed as a supervised learning problem. The question of semantic places recognition in robotics - the ability to recognize the semantic category of a place to which scene belongs to - is therefore a major requirement for the future of autonomous robotics. It is indeed required for an autonomous service robot to be able to recognize the environment in which it lives and to easily learn the organization of this environment in order to operate and interact successfully. To achieve that goal, different methods have been already proposed, some based on the identification of objects as a prerequisite to the recognition of the scenes, and some based on a direct description of the scene characteristics. If we make the hypothesis that objects are more easily recognized when the scene in which they appear is identified, the second approach seems more suitable. It is however strongly dependent on the nature of the image descriptors used, usually empirically derived from general considerations on image coding.Compared to these many proposals, another approach of image coding, based on a more theoretical point of view, has emerged the last few years. Energy-based models of feature extraction based on the principle of minimizing the energy of some function according to the quality of the reconstruction of the image has lead to the Restricted Boltzmann Machines (RBMs) able to code an image as the superposition of a limited number of features taken from a larger alphabet. It has also been shown that this process can be repeated in a deep architecture, leading to a sparse and efficient representation of the initial data in the feature space. A complex problem of classification in the input space is thus transformed into an easier one in the feature space. This approach has been successfully applied to the identification of tiny images from the 80 millions image database of the MIT. In the present work, we demonstrate that semantic place recognition can be achieved on the basis of tiny images instead of conventional Bag-of-Word (BoW) methods and on the use of Deep Belief Networks (DBNs) for image coding. We show that after appropriate coding a softmax regression in the projection space is sufficient to achieve promising classification results. To our knowledge, this approach has not yet been investigated for scene recognition in autonomous robotics. We compare our methods with the state-of-the-art algorithms using a standard database of robot localization. We study the influence of system parameters and compare different conditions on the same dataset. These experiments show that our proposed model, while being very simple, leads to state-of-the-art results on a semantic place recognition task.

Download or read book Machine Learning based Natural Scene Recognition for Mobile Robot Localization in An Unknown Environment written by Xiaochun Wang and published by Springer. This book was released on 2019-08-12 with total page 328 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book advances research on mobile robot localization in unknown environments by focusing on machine-learning-based natural scene recognition. The respective chapters highlight the latest developments in vision-based machine perception and machine learning research for localization applications, and cover such topics as: image-segmentation-based visual perceptual grouping for the efficient identification of objects composing unknown environments; classification-based rapid object recognition for the semantic analysis of natural scenes in unknown environments; the present understanding of the Prefrontal Cortex working memory mechanism and its biological processes for human-like localization; and the application of this present understanding to improve mobile robot localization. The book also features a perspective on bridging the gap between feature representations and decision-making using reinforcement learning, laying the groundwork for future advances in mobile robot navigation research.

Download or read book Semantic Labeling of Places with Mobile Robots written by Óscar Martinez Mozos and published by Springer Science & Business Media. This book was released on 2010-02-04 with total page 145 pages. Available in PDF, EPUB and Kindle. Book excerpt: During the last years there has been an increasing interest in the area of service robots. Under this category we find robots working in tasks such as elderly care, guiding, office and domestic assistance, inspection, and many more. Service robots usually work in indoor environments designed for humans, with offices and houses being some of the most typical examples. These environments are typically divided into places with different functionalities like corridors, rooms or doorways. The ability to learn such semantic categories from sensor data enables a mobile robot to extend its representation of the environment, and to improve its capabilities. As an example, natural language terms like corridor or room can be used to indicate the position of the robot in a more intuitive way when communicating with humans. This book presents several approaches to enable a mobile robot to categorize places in indoor environments. The categories are indicated by terms which represent the different regions in these environments. The objective of this work is to enable mobile robots to perceive the spatial divisions in indoor environments in a similar way as people do. This is an interesting step forward to the problem of moving the perception of robots closer to the perception of humans. Many approaches introduced in this book come from the area of pattern recognition and classification. The applied methods have been adapted to solve the specific problem of place recognition. In this regard, this work is a useful reference to students and researchers who want to introduce classification techniques to help solve similar problems in mobile robotics.

Download or read book Online Appearance Based Place Recognition and Mapping written by Konstantinos A. Tsintotas and published by Springer Nature. This book was released on 2022-09-01 with total page 125 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book introduces several appearance-based place recognition pipelines based on different mapping techniques for addressing loop-closure detection in mobile platforms with limited computational resources. The motivation behind this book has been the prospect that in many contemporary applications efficient methods are needed that can provide high performance under run-time and memory constraints. Thus, three different mapping techniques for addressing the task of place recognition for simultaneous localization and mapping (SLAM) are presented. The book at hand follows a tutorial-based structure describing each of the main parts needed for a loop-closure detection pipeline to facilitate the newcomers. It mainly goes through a historical review of the problem, focusing on how it was addressed during the years reaching the current age. This way, the reader is initially familiarized with each part while the place recognition paradigms follow.

Download or read book Using Support Vector Machines Convolutional Neural Networks and Deep Belief Networks for Partially Occluded Object Recognition written by Joseph Lin Chu and published by . This book was released on 2014 with total page 107 pages. Available in PDF, EPUB and Kindle. Book excerpt: Artificial neural networks have been widely used for machine learning tasks such as object recognition. Recent developments have made use of biologically inspired architectures, such as the Convolutional Neural Network, and the Deep Belief Network. A theoretical method for estimating the optimal number of feature maps for a Convolutional Neural Network maps using the dimensions of the receptive field or convolutional kernel is proposed. Empirical experiments are performed that show that the method works to an extent for extremely small receptive fields, but doesn't generalize as clearly to all receptive field sizes. We then test the hypothesis that generative models such as the Deep Belief Network should perform better on occluded object recognition tasks than purely discriminative models such as Convolutional Neural Networks. We find that the data does not support this hypothesis when the generative models are run in a partially discriminative manner. We also find that the use of Gaussian visible units in a Deep Belief Network trained on occluded image data allows it to also learn to classify non-occluded images.

Download or read book 2016 2nd International Conference on Advanced Technologies for Signal and Image Processing ATSIP written by IEEE Staff and published by . This book was released on 2016-03-21 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: ATSIP 2016 will host multiple chance for research across the world Tracks cover several areas both standard and innovative in research and technology This second international conference ATSIP 2016 aims to provide a high level international forum for researchers, engineers and scientists from around the world to present and discuss recent advances, technologies and applications in the fields of Signal and Image Processing The conference will feature world class speakers, plenary sessions, business and industrial exhibits, and poster sessions

Download or read book Multimodal Scene Understanding written by Michael Yang and published by Academic Press. This book was released on 2019-07-16 with total page 422 pages. Available in PDF, EPUB and Kindle. Book excerpt: Multimodal Scene Understanding: Algorithms, Applications and Deep Learning presents recent advances in multi-modal computing, with a focus on computer vision and photogrammetry. It provides the latest algorithms and applications that involve combining multiple sources of information and describes the role and approaches of multi-sensory data and multi-modal deep learning. The book is ideal for researchers from the fields of computer vision, remote sensing, robotics, and photogrammetry, thus helping foster interdisciplinary interaction and collaboration between these realms. Researchers collecting and analyzing multi-sensory data collections – for example, KITTI benchmark (stereo+laser) - from different platforms, such as autonomous vehicles, surveillance cameras, UAVs, planes and satellites will find this book to be very useful. Contains state-of-the-art developments on multi-modal computing Shines a focus on algorithms and applications Presents novel deep learning topics on multi-sensor fusion and multi-modal deep learning

Download or read book Semantic Transfer with Deep Neural Networks written by Mandar Dixit and published by . This book was released on 2017 with total page 132 pages. Available in PDF, EPUB and Kindle. Book excerpt: Visual recognition is a problem of significant interest in computer vision. The current solution to this problem involves training a very deep neural network using a dataset with millions of images. Despite the recent success of this approach on classical problems like object recognition, it seems impractical to train a large scale neural network for every new vision task. Collecting and correctly labeling a large amount of images is a big project in itself. The process of training a deep network is also fraught with excessive trial and error and may require many weeks with relatively modest hardware infrastructure. Alternatively one could leverage the information already stored in a trained network for several other visual tasks using transfer learning. In this work we consider two novel scenarios of visual learning where knowledge transfer is affected from off-the-shelf convolutional neural networks (CNNs). In the first case we propose a holistic scene representation derived with the help of pre-trained object recognition neural nets. The object CNNs are used to generate a bag of semantics (BoS) description of a scene, which accurately identifies object occurrences~(semantics) in image regions. The BoS of an image is, then, summarized into a fixed length vector with the help of the sophisticated Fisher vector embedding from the classical vision literature. The high selectivity of object CNNs and the natural invariance of their semantic scores facilitate the transfer of knowledge for holitistic scene level reasoning. Embedding the CNN semantics, however, is shown to be a difficult problem. Semantics are probability multinomials that reside in a highly non-Euclidean simplex. The difficulty of modeling in this space is shown to be a bottle-neck to implementing a discriminative Fisher vector embedding. This problem is overcome by reversing the probability mapping of CNNs with a natural parameter transformation. In the natural parameter space, the object CNN semantics are efficiently combined with a Fisher vector embedding and used for scene level inference. The resulting semantic Fisher vector achieves state-of-the-art scene classification indicating the benefits of BoS based object-to-scene transfer. To improve the efficacy of object-to-scene transfer, we propose an extension of the Fisher vector embedding. Traditionally, this is implemented as a natural gradient of Gaussian mixture models (GMMs) with diagonal covariance. A significant amount of information is lost due to the inability of these models to capture covariance information. A mixture of Factor analyzers (MFAs) are used instead to allow efficient modeling of a potentially non-linear data distribution in the semantic manifold. The Fisher vectors derived using MFAs are shown to improve substantially over the GMM based embedding of object CNN semantics. The improved transfer-based semantic Fisher vectors are shown to outperform even the CNNs trained on large scale scene datasets. Next we consider a special case of transfer learning, known as few-shot learning, where the training images available for the new task are very few in number (typically less than 10). Extreme scarcity of data points prevents learning a generalize-able model even in the rich feature space of pre-trained CNNs. We present a novel approach of attribute guided data augmentation to solve this problem. Using an auxiliary dataset of object images labeled with 3D depth and pose, we learn trajectories of variations along these attributes. To the training examples in a few-shot dataset, we transfer these learned attribute trajectories and generate synthetic data points. Along with the original few-shot examples, the additional synthesized data can also be used for the target task. The proposed guided data augmentation strategy is shown to improve both few-shot object recognition and scene recognition performance.

Download or read book Deep Learning for Computer Vision written by Jason Brownlee and published by Machine Learning Mastery. This book was released on 2019-04-04 with total page 564 pages. Available in PDF, EPUB and Kindle. Book excerpt: Step-by-step tutorials on deep learning neural networks for computer vision in python with Keras.

Download or read book Synthetic Data Based Semantic Mapping Application to Object Recognition in Industry 5 0 written by Ms Sarah Ouarab and published by . This book was released on 2023-09-14 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: As Industry 5.0 becomes an increasingly tangible reality, the imperative for humans and robots to collaborate fully within the workplace has become more crucial than ever before. To address this challenge, robots need to recognize their surroundings. This involves a need for a semantic mapping of the robot's environment. Semantic mapping entails the process of creating a digital representation of a physical environment that captures not only its geometric properties but also its semantic features. In the context of industrial environments; this involves identifying and labeling objects, surfaces, and other features, and associating them with semantic information, such as their function, category, or behavior. This manuscript outlines the techniques used for creating semantic mapping, utilizing Simultaneous Localization and Mapping (SLAM) techniques, including the integration of artificial intelligence techniques. Additionally, this manuscript also explores the previous work conducted in training deep learning models using synthetically generated data.

Download or read book Big data analytics for smart healthcare applications written by Celestine Iwendi and published by Frontiers Media SA. This book was released on 2023-04-17 with total page 1365 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Deep Learning Based Place Recognition for Challenging Environments written by Devinder Kumar and published by . This book was released on 2016 with total page 45 pages. Available in PDF, EPUB and Kindle. Book excerpt: Visual based place recognition involves recognising familiar locations despite changes in environment or view-point of the camera(s) at the locations. There are existing methods that deal with these seasonal changes or view-point changes separately, but few methods exist that deal with these kind of changes simultaneously. Such robust place recognition systems are essential to long term localization and autonomy. Such systems should be able to deal both with conditional and viewpoint changes simultaneously. In recent times Convolutional Neural Networks (CNNs) have shown to outperform other state-of-the art method in task related to classification and recognition including place recognition. In this thesis, we present a deep learning based planar omni-directional place recognition approach that can deal with conditional and viewpoint variations together. The proposed method is able to deal with large viewpoint changes, where current methods fail. We evaluate the proposed method on two real world datasets dealing with four different seasons through out the year along with illumination changes and changes occurred in the environment across a period of 1 year respectively. We provide both quantitative (recall at 100% precision) and qualitative (confusion matrices) comparison of the basic pipeline for place recognition for the omni-directional approach with single-view and side-view camera approaches. The proposed approach is also shown to work very well across difierent seasons. The results prove the efficacy of the proposed method over the single-view and side-view cameras in dealing with conditional and large viewpoint changes in different conditions including illumination, weather, structural changes etc.

Download or read book Place Recognition Based Visual Localization in Changing Environments written by Yongliang Qiao and published by . This book was released on 2017 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: In many applications, it is crucial that a robot or vehicle localizes itself within the world especially for autonomous navigation and driving. The goal of this thesis is to improve place recognition performance for visual localization in changing environment. The approach is as follows: in off-line phase, geo-referenced images of each location are acquired, features are extracted and saved. While in the on-line phase, the vehicle localizes itself by identifying a previously-visited location through image or sequence retrieving. However, visual localization is challenging due to drastic appearance and illumination changes caused by weather conditions or seasonal changing. This thesis addresses the challenge of improving place recognition techniques through strengthen the ability of place describing and recognizing. Several approaches are proposed in this thesis:1) Multi-feature combination of CSLBP (extracted from gray-scale image and disparity map) and HOG features is used for visual localization. By taking the advantages of depth, texture and shape information, visual recognition performance can be improved. In addition, local sensitive hashing method (LSH) is used to speed up the process of place recognition;2) Visual localization across seasons is proposed based on sequence matching and feature combination of GIST and CSLBP. Matching places by considering sequences and feature combination denotes high robustness to extreme perceptual changes;3) All-environment visual localization is proposed based on automatic learned Convolutional Network (ConvNet) features and localized sequence matching. To speed up the computational efficiency, LSH is taken to achieve real-time visual localization with minimal accuracy degradation.

Download or read book Investigating Semantic Properties of Images Generated from Natural Language Using Neural Networks written by Samuel Ward Schrader and published by . This book was released on 2019 with total page 60 pages. Available in PDF, EPUB and Kindle. Book excerpt: "This work explores the attributes, properties, and potential uses of generative neural networks within the realm of encoding semantics. It works toward answering the questions of: If one uses generative neural networks to create a picture based on natural language, does the resultant picture encode the text's semantics in a way a computer system can process? Could such a system be more precise than current solutions at detecting, measuring, or comparing semantic properties of generated images, and thus their source text, or their source semantics? This work is undertaken in the hope that detecting previously unknown properties, or better understanding them, could lead to new or improved methods of encoding and processing semantics in a computer system. Improvements in this space could affect many systems that make semantically based decisions. Being able to detect general or specific semantic properties, semantic similarity, or other semantic properties more effectively could improve tasks such as information retrieval, question answering, duplication (clone) detection, sentiment analysis, and others. Additionally, it could provide insight into how to better represent semantics in computer systems and thus bring us closer to general artificial intelligence. To explore this space, this work starts with an experiment consisting of transforming pairs of texts into pairs of images via a generative neural network and exploring properties of those image pairs. The text pairs were known to either be textually and semantically identical, semantically similar, or semantically dissimilar. The resultant image pairs are then tested for similarity via a second neural network based process to investigate if the semantic similarity is preserved during the transformation process and thus, exists in the resultant image pairs in a quantifiable way. Preliminary results showed strong evidence of resultant images encoding semantics in a measurable way. However, when the experiment was conducted on a larger dataset, and with the generative network more thoroughly trained, the results are weaker. An alternative experiment conducted on different datasets and configurations produced results that are still weaker than the preliminary experiments. These findings lead us to believe the promise of the preliminary results was possibly due to semantics being encoded by the vectorization of the words, and not by the generative neural network. This explanation seeks to clarify why, as the generative neural network took a larger role in the process, the results were worse, and as it took a smaller role, the results were better. Further tests were conducted to establish this belief and proved supportive."--Boise State University ScholarWorks.

Download or read book 2019 European Conference on Mobile Robots ECMR written by IEEE Staff and published by . This book was released on 2019-09-04 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: ECMR is an internationally open biennial European forum, allowing researchers to learn about and discuss the latest accomplishments and innovations in mobile robotics and mobile human robot systems ECMR 2019 is the 9th edition of the conference, and will be held in Prague, Czech Republic ECMR welcomes articles describing fundamental developments in the field of mobile robotics, with special emphasis on autonomous systems An important goal of this conference is to extend the state of the art in the context of autonomous systems featuring interdisciplinary approaches covering computer science, control systems, electrical engineering, mathematics, mechanical engineering, and other fields The conference topics may incorporate mobile robots, intelligent machines and systems for critical use, industrial production, in particular within Industry 4 0 activities, service robotics and other related fields

Download or read book A Deep Learning Approach To Coarse Robot Localization written by Luc Alexandre Bettaieb and published by . This book was released on 2017 with total page 120 pages. Available in PDF, EPUB and Kindle. Book excerpt: This thesis explores the use of deep learning for robot localization with applications in re-localizing a mislocalized robot. Seed values for a localization algorithm are assigned based on the interpretation of images. A deep neural network was trained on images acquired in and associated with named regions. In application, the neural net was used to recognize a region based on camera input. By recognizing regions from the camera, the robot can be localized grossly, and subsequently refined with existing techniques. Explorations into different deep neural network topologies and solver types are discussed. A process for gathering training data, training the classifier, and deployment through a robot operating system (ROS) package is provided.

Download or read book Deep Learning in Object Recognition Detection and Segmentation written by Xiaogang Wang and published by . This book was released on 2016 with total page 165 pages. Available in PDF, EPUB and Kindle. Book excerpt: As a major breakthrough in artificial intelligence, deep learning has achieved very impressive success in solving grand challenges in many fields including speech recognition, natural language processing, computer vision, image and video processing, and multimedia. This article provides a historical overview of deep learning and focus on its applications in object recognition, detection, and segmentation, which are key challenges of computer vision and have numerous applications to images and videos. The discussed research topics on object recognition include image classification on ImageNet, face recognition, and video classification. The detection part covers general object detection on ImageNet, pedestrian detection, face landmark detection (face alignment), and human landmark detection (pose estimation). On the segmentation side, the article discusses the most recent progress on scene labeling, semantic segmentation, face parsing, human parsing and saliency detection. Object recognition is considered as whole-image classification, while detection and segmentation are pixelwise classification tasks. Their fundamental differences will be discussed in this article. Fully convolutional neural networks and highly efficient forward and backward propagation algorithms specially designed for pixelwise classification task will be introduced. The covered application domains are also much diversified. Human and face images have regular structures, while general object and scene images have much more complex variations in geometric structures and layout. Videos include the temporal dimension. Therefore, they need to be processed with different deep models. All the selected domain applications have received tremendous attentions in the computer vision and multimedia communities. Through concrete examples of these applications, we explain the key points which make deep learning outperform conventional computer vision systems. (1) Different than traditional pattern recognition systems, which heavily rely on manually designed features, deep learning automatically learns hierarchical feature representations from massive training data and disentangles hidden factors of input data through multi-level nonlinear mappings. (2) Different than existing pattern recognition systems which sequentially design or train their key components, deep learning is able to jointly optimize all the components and crate synergy through close interactions among them. (3) While most machine learning models can be approximated with neural networks with shallow structures, for some tasks, the expressive power of deep models increases exponentially as their architectures go deep. Deep models are especially good at learning global contextual feature representation with their deep structures. (4) Benefitting from the large learning capacity of deep models, some classical computer vision challenges can be recast as high-dimensional data transform problems and can be solved from new perspectives. Finally, some open questions and future works regarding to deep learning in object recognition, detection, and segmentation will be discussed.