Download or read book Discovering Visual Saliency for Image Analysis written by Jongpil Kim and published by . This book was released on 2017 with total page 93 pages. Available in PDF, EPUB and Kindle. Book excerpt: Salient object detection is a key step in many image analysis tasks such as object detection and image segmentation, as it not only identifies relevant parts of a visual scene but may also reduce computational complexity by filtering out irrelevant segments of the scene. Traditional methods of salient object detection are based on binary classification to determine whether a given pixel or region belongs to a salient object. However, binary classification-based approaches are limited because they ignore the shape of the salient object by assigning a single output value to an input (pixel, patch, or superpixel). In this work, we introduce novel salient object detection methods that consider the shape of the object. We claim that encoding spatial image content to facilitate the information of the object shape can result in more-accurate prediction of the salient object than the traditional binary classification-based approaches. We propose two deep learning-based salient object detection methods to detect the object. The first proposed method combines a shape-preserving saliency prediction driven by a convolutional neural network (CNN) with pre-defined saliency shapes. Our model learns a saliency shape dictionary, which is subsequently used to train a CNN to predict the salient class of a target region and estimate the full, but coarse, saliency map of the target image. The map is then refined using image-specific, low- to mid-level information. In the second method, we explicitly predict the shape of the salient object using a specially designed CNN model. The proposed CNN model facilitates both global and local context of the image to produce better prediction than that obtained by considering only the local information. We train our models with pixel-wise annotated training data. Experimental results show that the proposed methods outperform previous state-of-the-art methods in salient object detection. Next, we propose novel methods to find characteristic landmarks and recognize ancient Roman imperial coins. The Roman coins play an important role in understanding the Roman Empire because they convey rich information about key historical events of the time. Moreover, as large amounts of coins are traded daily over the Internet, it becomes necessary to develop automatic coin recognition systems to prevent illegal trades. Because the coin images do not have the pixel-wise annotations, we use a weakly-supervised approach to discover the characteristic landmarks on the coin images instead of using the previously mentioned models. For this purpose, we first propose a spatial-appearance coin recognition system to visualize the contribution of the image regions on the Roman coins using Fisher vector representation. Next, we formulate an optimization task to discover class-specific salient coin regions using CNNs. Analysis of discovered salient regions confirms that they are largely consistent with human expert annotations. Experimental results show that the proposed methods can effectively recognize the ancient Roman coins as well as successfully identify landmarks in the coin images and in a general fine-grained classification problem. For this research, we have collected new Roman coin datasets in which all coin images are annotated.

Download or read book Visual Saliency From Pixel Level to Object Level Analysis written by Jianming Zhang and published by Springer. This book was released on 2019-01-21 with total page 138 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides an introduction to recent advances in theory, algorithms and application of Boolean map distance for image processing. Applications include modeling what humans find salient or prominent in an image, and then using this for guiding smart image cropping, selective image filtering, image segmentation, image matting, etc. In this book, the authors present methods for both traditional and emerging saliency computation tasks, ranging from classical low-level tasks like pixel-level saliency detection to object-level tasks such as subitizing and salient object detection. For low-level tasks, the authors focus on pixel-level image processing approaches based on efficient distance transform. For object-level tasks, the authors propose data-driven methods using deep convolutional neural networks. The book includes both empirical and theoretical studies, together with implementation details of the proposed methods. Below are the key features for different types of readers. For computer vision and image processing practitioners: Efficient algorithms based on image distance transforms for two pixel-level saliency tasks; Promising deep learning techniques for two novel object-level saliency tasks; Deep neural network model pre-training with synthetic data; Thorough deep model analysis including useful visualization techniques and generalization tests; Fully reproducible with code, models and datasets available. For researchers interested in the intersection between digital topological theories and computer vision problems: Summary of theoretic findings and analysis of Boolean map distance; Theoretic algorithmic analysis; Applications in salient object detection and eye fixation prediction. Students majoring in image processing, machine learning and computer vision: This book provides up-to-date supplementary reading material for course topics like connectivity based image processing, deep learning for image processing; Some easy-to-implement algorithms for course projects with data provided (as links in the book); Hands-on programming exercises in digital topology and deep learning.

Download or read book Visual Saliency Computation written by Jia Li and published by Springer. This book was released on 2014-04-12 with total page 245 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book covers fundamental principles and computational approaches relevant to visual saliency computation. As an interdisciplinary problem, visual saliency computation is introduced in this book from an innovative perspective that combines both neurobiology and machine learning. The book is also well-structured to address a wide range of readers, from specialists in the field to general readers interested in computer science and cognitive psychology. With this book, a reader can start from the very basic question of "what is visual saliency?" and progressively explore the problems in detecting salient locations, extracting salient objects, learning prior knowledge, evaluating performance, and using saliency in real-world applications. It is highly expected that this book will spark a great interest of research in the related communities in years to come.

Download or read book Visual Saliency Analysis on Fashion Images Using Image Processing and Deep Learning Approaches written by Aashish Neupane and published by . This book was released on 2020 with total page 96 pages. Available in PDF, EPUB and Kindle. Book excerpt: State-of-art computer vision technologies have been applied in fashion in multiple ways, and saliency modeling is one of those applications. In computer vision, a saliency map is a 2D topological map which indicates the probabilistic distribution of visual attention priorities. This study is focusing on analysis of the visual saliency on fashion images using multiple saliency models, evaluated by several evaluation metrics. A human subject study has been conducted to collect people's visual attention on 75 fashion images. Binary ground-truth fixation maps for these images have been created based on the experimentally collected visual attention data using Gaussian blurring function. Saliency maps for these 75 fashion images were generated using multiple conventional saliency models as well as deep feature-based state-of-art models. DeepFeat has been studied extensively, with 44 sets of saliency maps, exploiting the features extracted from GoogLeNet and ResNet50. Seven other saliency models have also been utilized to predict saliency maps on these images. The results were compared over 5 evaluation metrics - AUC, CC, KL Divergence, NSS and SIM. The performance of all 8 saliency models on prediction of visual attention on fashion images over all five metrics were comparable to the benchmarked scores. Furthermore, the models perform well consistently over multiple evaluation metrics, thus indicating that saliency models could in fact be applied to effectively predict salient regions in random fashion advertisement images.

Download or read book Computer Vision ECCV 2012 written by Andrew Fitzgibbon and published by Springer. This book was released on 2012-09-26 with total page 909 pages. Available in PDF, EPUB and Kindle. Book excerpt: The seven-volume set comprising LNCS volumes 7572-7578 constitutes the refereed proceedings of the 12th European Conference on Computer Vision, ECCV 2012, held in Florence, Italy, in October 2012. The 408 revised papers presented were carefully reviewed and selected from 1437 submissions. The papers are organized in topical sections on geometry, 2D and 3D shapes, 3D reconstruction, visual recognition and classification, visual features and image matching, visual monitoring: action and activities, models, optimisation, learning, visual tracking and image registration, photometry: lighting and colour, and image segmentation.

Download or read book Integrating Artificial Intelligence and Visualization for Visual Knowledge Discovery written by Boris Kovalerchuk and published by Springer Nature. This book was released on 2022-06-04 with total page 671 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is devoted to the emerging field of integrated visual knowledge discovery that combines advances in artificial intelligence/machine learning and visualization/visual analytic. A long-standing challenge of artificial intelligence (AI) and machine learning (ML) is explaining models to humans, especially for live-critical applications like health care. A model explanation is fundamentally human activity, not only an algorithmic one. As current deep learning studies demonstrate, it makes the paradigm based on the visual methods critically important to address this challenge. In general, visual approaches are critical for discovering explainable high-dimensional patterns in all types in high-dimensional data offering "n-D glasses," where preserving high-dimensional data properties and relations in visualizations is a major challenge. The current progress opens a fantastic opportunity in this domain. This book is a collection of 25 extended works of over 70 scholars presented at AI and visual analytics related symposia at the recent International Information Visualization Conferences with the goal of moving this integration to the next level. The sections of this book cover integrated systems, supervised learning, unsupervised learning, optimization, and evaluation of visualizations. The intended audience for this collection includes those developing and using emerging AI/machine learning and visualization methods. Scientists, practitioners, and students can find multiple examples of the current integration of AI/machine learning and visualization for visual knowledge discovery. The book provides a vision of future directions in this domain. New researchers will find here an inspiration to join the profession and to be involved for further development. Instructors in AI/ML and visualization classes can use it as a supplementary source in their undergraduate and graduate classes.

Download or read book Theory Applications of Image Analysis written by P. Johansen and published by World Scientific. This book was released on 1992 with total page 368 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book contains 31 papers carefully selected from among those presented at the 7th Scandinavian Conference on Image Analysis. The authors have extended their papers to give a more in-depth discussion of the theory, or of the experimental validation of the method they have proposed. The topics covered are current and wide-ranging and include both 2D- and 3D-vision, and low to high level vision.

Download or read book Visual Object Category Discovery in Images and Videos written by Yong Jae Lee and published by . This book was released on 2012 with total page 32 pages. Available in PDF, EPUB and Kindle. Book excerpt: The current trend in visual recognition research is to place a strict division between the supervised and unsupervised learning paradigms, which is problematic for two main reasons. On the one hand, supervised methods require training data for each and every category that the system learns; training data may not always be available and is expensive to obtain. On the other hand, unsupervised methods must determine the optimal visual cues and distance metrics that distinguish one category from another to group images into semantically meaningful categories; however, for unlabeled data, these are unknown a priori. I propose a visual category discovery framework that transcends the two paradigms and learns accurate models with few labeled exemplars. The main insight is to automatically focus on the prevalent objects in images and videos, and learn models from them for category grouping, segmentation, and summarization. To implement this idea, I first present a context-aware category discovery framework that discovers novel categories by leveraging context from previously learned categories. I devise a novel object-graph descriptor to model the interaction between a set of known categories and the unknown to-be-discovered categories, and group regions that have similar appearance and similar object-graphs. I then present a collective segmentation framework that simultaneously discovers the segmentations and groupings of objects by leveraging the shared patterns in the unlabeled image collection. It discovers an ensemble of representative instances for each unknown category, and builds top-down models from them to refine the segmentation of the remaining instances. Finally, building on these techniques, I show how to produce compact visual summaries for first-person egocentric videos that focus on the important people and objects. The system leverages novel egocentric and high-level saliency features to predict important regions in the video, and produces a concise visual summary that is driven by those regions. I compare against existing state-of-the-art methods for category discovery and segmentation on several challenging benchmark datasets. I demonstrate that we can discover visual concepts more accurately by focusing on the prevalent objects in images and videos, and show clear advantages of departing from the status quo division between the supervised and unsupervised learning paradigms. The main impact of my thesis is that it lays the groundwork for building large-scale visual discovery systems that can automatically discover visual concepts with minimal human supervision.

Download or read book Image Analysis and Processing ICIAP 2022 written by Stan Sclaroff and published by Springer Nature. This book was released on 2022-05-14 with total page 507 pages. Available in PDF, EPUB and Kindle. Book excerpt: The proceedings set LNCS 13231, 13232, and 13233 constitutes the refereed proceedings of the 21st International Conference on Image Analysis and Processing, ICIAP 2022, which was held during May 23-27, 2022, in Lecce, Italy, The 168 papers included in the proceedings were carefully reviewed and selected from 307 submissions. They deal with video analysis and understanding; pattern recognition and machine learning; deep learning; multi-view geometry and 3D computer vision; image analysis, detection and recognition; multimedia; biomedical and assistive technology; digital forensics and biometrics; image processing for cultural heritage; robot vision; etc.

Download or read book Fuzzy Systems and Knowledge Discovery written by Lipo Wang and published by Springer Science & Business Media. This book was released on 2005-08-17 with total page 1354 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book and its sister volume, LNAI 3613 and 3614, constitute the proce- ings of the Second International Conference on Fuzzy Systems and Knowledge Discovery (FSKD 2005), jointly held with the First International Conference on Natural Computation (ICNC 2005, LNCS 3610, 3611, and 3612) from - gust 27–29, 2005 in Changsha, Hunan, China. FSKD 2005 successfully attracted 1249 submissions from 32 countries/regions (the joint ICNC-FSKD 2005 received 3136 submissions). After rigorous reviews, 333 high-quality papers, i. e. , 206 long papers and 127 short papers, were included in the FSKD 2005 proceedings, r- resenting an acceptance rate of 26. 7%. The ICNC-FSKD 2005 conference featured the most up-to-date research - sults in computational algorithms inspired from nature, including biological, e- logical, and physical systems. It is an exciting and emerging interdisciplinary area in which a wide range of techniques and methods are being studied for dealing with large, complex, and dynamic problems. The joint conferences also promoted cross-fertilization over these exciting and yet closely-related areas, which had a signi?cant impact on the advancement of these important technologies. Speci?c areas included computation with words, fuzzy computation, granular com- tation, neural computation, quantum computation, evolutionary computation, DNA computation, chemical computation, information processing in cells and tissues, molecular computation, arti?cial life, swarm intelligence, ants colony, arti?cial immune systems, etc. , with innovative applications to knowledge d- covery, ?nance, operations research, and more.

Download or read book Visual Saliency in Image Quality Assessment written by Wei Zhang and published by . This book was released on 2017 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Understanding Visual Saliency and Perception of Digital Media written by Shafin Rahman and published by . This book was released on 2015 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Eye movements are directed by both high-level considerations such as contextual guidance or experience and also by observed visual patterns. A bright light or high contrast image structure will tend to draw an observer's attention, independent of task. The extent to which a visual pattern attracts attention in this manner is often described according to a quantitative measure of visual saliency. Although many computational models of visual saliency have been published in last two decades, current literature is mostly limited to the maximization of performance in predicting saliency. Apart from this common challenge, we address some new aspects of image understanding relating to saliency. For example, we explore the relationship between natural image statistics and human visual attention, measure the extent of center bias produced by saliency algorithms and develop ways to remove such bias, relate different perceptual task like free viewing, object search, saliency search and explicit judgment, and predict different image level subjective ratings. In this thesis, we present solutions to aforementioned problems for a better basic understanding of visual saliency while also producing some novel avenues of analysis that inform on perception of digital media.

Download or read book Computer Vision Graphics and Image Processing written by Snehasis Mukherjee and published by Springer. This book was released on 2017-10-19 with total page 412 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed conference proceedings of the ICVGIP 2016 Satellite Workshops, WCVA, DAR, and MedImage, held in Guwahati, India, in December 2016. The papers presented are extended versions of the papers of three of the four workshops: Computer Vision Applications, Document Analysis and Recognition and Medical Image Processing. The Computer Vision Application track received 52 submissions and after a rigorous review process, 18 papers were presented. The focus is mainly on industrial applications of computer vision and related technologies. The Document Analysis and Recognition track received 10 submissions from which 7 papers were selected. The MedImage workshops focuses on problems in medical image computing and received 14 papers from which 9 were accepted for presentation in this book.

Download or read book Proceedings of 3rd International Conference on Computer Vision and Image Processing written by Bidyut B. Chaudhuri and published by Springer Nature. This book was released on 2019-10-31 with total page 472 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is a collection of carefully selected works presented at the Third International Conference on Computer Vision & Image Processing (CVIP 2018). The conference was organized by the Department of Computer Science and Engineering of PDPM Indian Institute of Information Technology, Design & Manufacturing, Jabalpur, India during September 29–October 01, 2018. All the papers have been rigorously reviewed by the experts from the domain. This 2 volume proceedings include technical contributions in the areas of Image/Video Processing and Analysis; Image/Video Formation and Display; Image/Video Filtering, Restoration, Enhancement and Super-resolution; Image/Video Coding and Transmission; Image/Video Storage, Retrieval and Authentication; Image/Video Quality; Transform-based and Multi-resolution Image/Video Analysis; Biological and Perceptual Models for Image/Video Processing; Machine Learning in Image/Video Analysis; Probability and uncertainty handling for Image/Video Processing; and Motion and Tracking.

Download or read book Pattern Recognition and Image Analysis written by Luís A. Alexandre and published by Springer. This book was released on 2017-06-08 with total page 550 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 8th Iberian Conference on Pattern Recognition and Image Analysis, IbPRIA 2017, held in Faro, Portugal, in June 2017. The 60 regular papers presented in this volume were carefully reviewed and selected from 86 submissions. They are organized in topical sections named: Pattern Recognition and Machine Learning; Computer Vision; Image and Signal Processing; Medical Image; and Applications.

Download or read book Computer Vision ACCV 2020 written by Hiroshi Ishikawa and published by Springer Nature. This book was released on 2021-02-24 with total page 757 pages. Available in PDF, EPUB and Kindle. Book excerpt: The six volume set of LNCS 12622-12627 constitutes the proceedings of the 15th Asian Conference on Computer Vision, ACCV 2020, held in Kyoto, Japan, in November/ December 2020.* The total of 254 contributions was carefully reviewed and selected from 768 submissions during two rounds of reviewing and improvement. The papers focus on the following topics: Part I: 3D computer vision; segmentation and grouping Part II: low-level vision, image processing; motion and tracking Part III: recognition and detection; optimization, statistical methods, and learning; robot vision Part IV: deep learning for computer vision, generative models for computer vision Part V: face, pose, action, and gesture; video analysis and event recognition; biomedical image analysis Part VI: applications of computer vision; vision for X; datasets and performance analysis *The conference was held virtually.

Download or read book Exploring visual saliency mechanisms for motion detection and object tracking with application to surveillance written by Shijie Zhang and published by . This book was released on 2009 with total page 380 pages. Available in PDF, EPUB and Kindle. Book excerpt: