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Author: Caroline Rebecca Pantofaru Publisher: ISBN: Category : Computer vision Languages : en Pages : 161
Book Description
Abstract: "Recognizing object classes is a central problem in computer vision, and recently there has been renewed interest in also precisely localizing objects with pixel-accurate masks. Since classes of deformable objects can take a very large number of shapes in any given image, a requirement for recognizing and generating masks for such objects is a method for reducing the number of pixel sets which need to be examined. One method for proposing accurate spatial support for objects and features is data-driven pixel grouping through unsupervised image segmentation. The goals of this thesis are to define and address the issues associated with incorporating image segmentation into an object recognition framework. The first part of this thesis examines the nature of image segmentation and the implications for an object recognition system. We develop a scheme for comparing and evaluating image segmentation algorithms which includes the definition of criteria that an algorithm must satisfy to be a useful black box, experiments for evaluating these criteria, and a measure of automatic segmentation correctness versus human image labeling. This evaluation scheme is used to perform experiments with popular segmentation algorithms, the results of which motivate our work in the remainder of this thesis. The second part of this thesis explores approaches to incorporating the regions generated by unsupervised image segmentation into an object recognition framework. Influenced by our experiments with segmentation, we propose principled methods for describing such regions. Given the instability inherent in image segmentation, we experiment with increasing robustness by integrating the information from multiple segmentations. Finally, we examine the possibility of learning explicit spatial relationships between regions. The efficacy of these techniques is demonstrated on a number of challenging data sets."
Author: Caroline Rebecca Pantofaru Publisher: ISBN: Category : Computer vision Languages : en Pages : 161
Book Description
Abstract: "Recognizing object classes is a central problem in computer vision, and recently there has been renewed interest in also precisely localizing objects with pixel-accurate masks. Since classes of deformable objects can take a very large number of shapes in any given image, a requirement for recognizing and generating masks for such objects is a method for reducing the number of pixel sets which need to be examined. One method for proposing accurate spatial support for objects and features is data-driven pixel grouping through unsupervised image segmentation. The goals of this thesis are to define and address the issues associated with incorporating image segmentation into an object recognition framework. The first part of this thesis examines the nature of image segmentation and the implications for an object recognition system. We develop a scheme for comparing and evaluating image segmentation algorithms which includes the definition of criteria that an algorithm must satisfy to be a useful black box, experiments for evaluating these criteria, and a measure of automatic segmentation correctness versus human image labeling. This evaluation scheme is used to perform experiments with popular segmentation algorithms, the results of which motivate our work in the remainder of this thesis. The second part of this thesis explores approaches to incorporating the regions generated by unsupervised image segmentation into an object recognition framework. Influenced by our experiments with segmentation, we propose principled methods for describing such regions. Given the instability inherent in image segmentation, we experiment with increasing robustness by integrating the information from multiple segmentations. Finally, we examine the possibility of learning explicit spatial relationships between regions. The efficacy of these techniques is demonstrated on a number of challenging data sets."
Author: Valliappa Lakshmanan Publisher: "O'Reilly Media, Inc." ISBN: 1098102339 Category : Computers Languages : en Pages : 481
Book Description
This practical book shows you how to employ machine learning models to extract information from images. ML engineers and data scientists will learn how to solve a variety of image problems including classification, object detection, autoencoders, image generation, counting, and captioning with proven ML techniques. This book provides a great introduction to end-to-end deep learning: dataset creation, data preprocessing, model design, model training, evaluation, deployment, and interpretability. Google engineers Valliappa Lakshmanan, Martin Görner, and Ryan Gillard show you how to develop accurate and explainable computer vision ML models and put them into large-scale production using robust ML architecture in a flexible and maintainable way. You'll learn how to design, train, evaluate, and predict with models written in TensorFlow or Keras. You'll learn how to: Design ML architecture for computer vision tasks Select a model (such as ResNet, SqueezeNet, or EfficientNet) appropriate to your task Create an end-to-end ML pipeline to train, evaluate, deploy, and explain your model Preprocess images for data augmentation and to support learnability Incorporate explainability and responsible AI best practices Deploy image models as web services or on edge devices Monitor and manage ML models
Author: Tao Lei Publisher: John Wiley & Sons ISBN: 111985900X Category : Technology & Engineering Languages : en Pages : 340
Book Description
Image Segmentation Summarizes and improves new theory, methods, and applications of current image segmentation approaches, written by leaders in the field The process of image segmentation divides an image into different regions based on the characteristics of pixels, resulting in a simplified image that can be more efficiently analyzed. Image segmentation has wide applications in numerous fields ranging from industry detection and bio-medicine to intelligent transportation and architecture. Image Segmentation: Principles, Techniques, and Applications is an up-to-date collection of recent techniques and methods devoted to the field of computer vision. Covering fundamental concepts, new theories and approaches, and a variety of practical applications including medical imaging, remote sensing, fuzzy clustering, and watershed transform. In-depth chapters present innovative methods developed by the authors—such as convolutional neural networks, graph convolutional networks, deformable convolution, and model compression—to assist graduate students and researchers apply and improve image segmentation in their work. Describes basic principles of image segmentation and related mathematical methods such as clustering, neural networks, and mathematical morphology. Introduces new methods for achieving rapid and accurate image segmentation based on classic image processing and machine learning theory. Presents techniques for improved convolutional neural networks for scene segmentation, object recognition, and change detection, etc. Highlights the effect of image segmentation in various application scenarios such as traffic image analysis, medical image analysis, remote sensing applications, and material analysis, etc. Image Segmentation: Principles, Techniques, and Applications is an essential resource for undergraduate and graduate courses such as image and video processing, computer vision, and digital signal processing, as well as researchers working in computer vision and image analysis looking to improve their techniques and methods.
Author: Andrew Rabinovich Publisher: ISBN: Category : Image analysis Languages : en Pages : 9
Book Description
Image segmentation and object recognition are among the most fundamental problems in computer vision, and the potential interaction between these tasks has been discussed for many years. The usefulness of recognition for segmentation has been demonstrated with various top-down segmentation algorithms, however, the impact of bottom-up image segmentation as pre-processing for object recognition is not well understood. One factor impeding the utility of segmentation for recognition is the unsatisfactory quality of image segmentation algorithms. In this work we take advantage of a recently proposed method for computing multiple stable segmentations and illustrate the application of bottom-up image segmentation as a preprocessing step for object recognition and categorization. We extend a popular bag-of-features recognition model to provide multiple class categorization and localization of objects in images. We compare our categorization results to that of a conventional bag-of-features recognition model on the Caltech and PASCAL image databases.
Author: Bir Bhanu Publisher: Springer Science & Business Media ISBN: 1461527740 Category : Computers Languages : en Pages : 283
Book Description
Image segmentation is generally the first task in any automated image understanding application, such as autonomous vehicle navigation, object recognition, photointerpretation, etc. All subsequent tasks, such as feature extraction, object detection, and object recognition, rely heavily on the quality of segmentation. One of the fundamental weaknesses of current image segmentation algorithms is their inability to adapt the segmentation process as real-world changes are reflected in the image. Only after numerous modifications to an algorithm's control parameters can any current image segmentation technique be used to handle the diversity of images encountered in real-world applications. Genetic Learning for Adaptive Image Segmentation presents the first closed-loop image segmentation system that incorporates genetic and other algorithms to adapt the segmentation process to changes in image characteristics caused by variable environmental conditions, such as time of day, time of year, weather, etc. Image segmentation performance is evaluated using multiple measures of segmentation quality. These quality measures include global characteristics of the entire image as well as local features of individual object regions in the image. This adaptive image segmentation system provides continuous adaptation to normal environmental variations, exhibits learning capabilities, and provides robust performance when interacting with a dynamic environment. This research is directed towards adapting the performance of a well known existing segmentation algorithm (Phoenix) across a wide variety of environmental conditions which cause changes in the image characteristics. The book presents a large number of experimental results and compares performance with standard techniques used in computer vision for both consistency and quality of segmentation results. These results demonstrate, (a) the ability to adapt the segmentation performance in both indoor and outdoor color imagery, and (b) that learning from experience can be used to improve the segmentation performance over time.
Author: Jean Ponce Publisher: Springer ISBN: 3540687955 Category : Computers Languages : en Pages : 622
Book Description
This volume is a post-event proceedings volume and contains selected papers based on presentations given, and vivid discussions held, during two workshops held in Taormina in 2003 and 2004. The 30 thoroughly revised papers presented are organized in the following topical sections: recognition of specific objects, recognition of object categories, recognition of object categories with geometric relations, and joint recognition and segmentation.
Author: R. Arokia Priya Publisher: John Wiley & Sons ISBN: 1119842190 Category : Computers Languages : en Pages : 293
Book Description
OBJECT DETECTION BY STEREO VISION IMAGES Since both theoretical and practical aspects of the developments in this field of research are explored, including recent state-of-the-art technologies and research opportunities in the area of object detection, this book will act as a good reference for practitioners, students, and researchers. Current state-of-the-art technologies have opened up new opportunities in research in the areas of object detection and recognition of digital images and videos, robotics, neural networks, machine learning, stereo vision matching algorithms, soft computing, customer prediction, social media analysis, recommendation systems, and stereo vision. This book has been designed to provide directions for those interested in researching and developing intelligent applications to detect an object and estimate depth. In addition to focusing on the performance of the system using high-performance computing techniques, a technical overview of certain tools, languages, libraries, frameworks, and APIs for developing applications is also given. More specifically, detection using stereo vision images/video from its developmental stage up till today, its possible applications, and general research problems relating to it are covered. Also presented are techniques and algorithms that satisfy the peculiar needs of stereo vision images along with emerging research opportunities through analysis of modern techniques being applied to intelligent systems. Audience Researchers in information technology looking at robotics, deep learning, machine learning, big data analytics, neural networks, pattern & data mining, and image and object recognition. Industrial sectors include automotive electronics, security and surveillance systems, and online retailers.
Author: Zhuo Deng Publisher: ISBN: Category : Languages : en Pages : 113
Book Description
With the advent of Microsoft Kinect, the landscape of various vision-related tasks has been changed. Firstly, using an active infrared structured light sensor, the Kinect can provide directly the depth information that is hard to infer from traditional RGB images. Secondly, RGB and depth information are generated synchronously and can be easily aligned, which makes their direct integration possible. In this thesis, I propose several algorithms or systems that focus on how to integrate depth information with traditional visual appearances for addressing different computer vision applications. Those applications cover both low level (image segmentation, class agnostic object proposals) and high level (object detection, semantic segmentation) computer vision tasks. To firstly understand whether and how depth information is helpful for improving computer vision performances, I start research on the image segmentation field, which is a fundamental problem and has been studied extensively in natural color images. We propose an unsupervised segmentation algorithm that is carefully crafted to balance the contribution of color and depth features in RGB-D images. The segmentation problem is then formulated as solving the Maximum Weight Independence Set (MWIS) problem. Given superpixels obtained from different layers of a hierarchical segmentation, the saliency of each superpixel is estimated based on balanced combination of features originating from depth, gray level intensity, and texture information. We evaluate the segmentation quality based on five standard measures on the commonly used NYU-v2 RGB-Depth dataset. A surprising message indicated from experiments is that unsupervised image segmentation of RGB-D images yields comparable results to supervised segmentation. In image segmentation, an image is partitioned into several groups of pixels (or super-pixels). We take one step further to investigate on the problem of assigning class labels to every pixel, i.e., semantic scene segmentation. We propose a novel image region labeling method which augments CRF formulation with hard mutual exclusion (mutex) constraints. This way our approach can make use of rich and accurate 3D geometric structure coming from Kinect in a principled manner. The final labeling result must satisfy all mutex constraints, which allows us to eliminate configurations that violate common sense physics laws like placing a floor above a night stand. Three classes of mutex constraints are proposed: global object co-occurrence constraint, relative height relationship constraint, and local support relationship constraint. Segments obtained from image segmentation can be either too fine or too coarse. A full object region not only conveys global features but also arguably enriches contextual features as confusing background is separated. We propose a novel unsupervised framework for automatically generating bottom up class independent object candidates for detection and recognition in cluttered indoor environments. Utilizing raw depth map, we propose a novel plane segmentation algorithm for dividing an indoor scene into predominant planar regions and non-planar regions. Based on this partition, we are able to effectively predict object locations and their spatial extensions. Our approach automatically generates object proposals considering five different aspects: Non-planar Regions (NPR), Planar Regions (PR), Detected Planes (DP), Merged Detected Planes (MDP) and Hierarchical Clustering (HC) of 3D point clouds. Object region proposals include both bounding boxes and instance segments. Although 2D computer vision tasks can roughly identify where objects are placed on image planes, their true locations and poses in the physical 3D world are difficult to determine due to multiple factors such as occlusions and the uncertainty arising from perspective projections. However, it is very natural for human beings to understand how far objects are from viewers, object poses and their full extents from still images. These kind of features are extremely desirable for many applications such as robotics navigation, grasp estimation, and Augmented Reality (AR) etc. In order to fill the gap, we addresses the problem of amodal perception of 3D object detection. The task is to not only find object localizations in the 3D world, but also estimate their physical sizes and poses, even if only parts of them are visible in the RGB-D image. Recent approaches have attempted to harness point cloud from depth channel to exploit 3D features directly in the 3D space and demonstrated the superiority over traditional 2D representation approaches. We revisit the amodal 3D detection problem by sticking to the 2D representation framework, and directly relate 2D visual appearance to 3D objects. We propose a novel 3D object detection system that simultaneously predicts objects' 3D locations, physical sizes, and orientations in indoor scenes.
Author: Arcangelo Distante Publisher: Springer Nature ISBN: 3030423743 Category : Computers Languages : en Pages : 448
Book Description
Across three volumes, the Handbook of Image Processing and Computer Vision presents a comprehensive review of the full range of topics that comprise the field of computer vision, from the acquisition of signals and formation of images, to learning techniques for scene understanding. The authoritative insights presented within cover all aspects of the sensory subsystem required by an intelligent system to perceive the environment and act autonomously. Volume 2 (From Image to Pattern) examines image transforms, image restoration, and image segmentation. Topics and features: • Describes the fundamental processes in the field of artificial vision that enable the formation of digital images from light energy • Covers light propagation, color perception, optical systems, and the analog-to-digital conversion of the signal • Discusses the information recorded in a digital image, and the image processing algorithms that can improve the visual qualities of the image • Reviews boundary extraction algorithms, key linear and geometric transformations, and techniques for image restoration • Presents a selection of different image segmentation algorithms, and of widely-used algorithms for the automatic detection of points of interest • Examines important algorithms for object recognition, texture analysis, 3D reconstruction, motion analysis, and camera calibration • Provides an introduction to four significant types of neural network, namely RBF, SOM, Hopfield, and deep neural networks This all-encompassing survey offers a complete reference for all students, researchers, and practitioners involved in developing intelligent machine vision systems. The work is also an invaluable resource for professionals within the IT/software and electronics industries involved in machine vision, imaging, and artificial intelligence. Dr. Cosimo Distante is a Research Scientist in Computer Vision and Pattern Recognition in the Institute of Applied Sciences and Intelligent Systems (ISAI) at the Italian National Research Council (CNR). Dr. Arcangelo Distante is a researcher and the former Director of the Institute of Intelligent Systems for Automation (ISSIA) at the CNR. His research interests are in the fields of Computer Vision, Pattern Recognition, Machine Learning, and Neural Computation.
Author: Caroline Rebecca Pantofaru
Author: Caroline Rebecca Pantofaru
Author: Valliappa Lakshmanan
Author: Tao Lei
Author: Andrew Rabinovich
Author: Bir Bhanu
Author: Jean Ponce
Author: Lilly Spirkovska
Author: R. Arokia Priya
Author: Zhuo Deng
Author: Arcangelo Distante