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 Deep Learning for Medical Image Analysis written by S. Kevin Zhou and published by Academic Press. This book was released on 2023-11-23 with total page 544 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deep Learning for Medical Image Analysis, Second Edition is a great learning resource for academic and industry researchers and graduate students taking courses on machine learning and deep learning for computer vision and medical image computing and analysis. Deep learning provides exciting solutions for medical image analysis problems and is a key method for future applications. This book gives a clear understanding of the principles and methods of neural network and deep learning concepts, showing how the algorithms that integrate deep learning as a core component are applied to medical image detection, segmentation, registration, and computer-aided analysis.· Covers common research problems in medical image analysis and their challenges · Describes the latest deep learning methods and the theories behind approaches for medical image analysis · Teaches how algorithms are applied to a broad range of application areas including cardiac, neural and functional, colonoscopy, OCTA applications and model assessment · Includes a Foreword written by Nicholas Ayache
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 Analysis Prediction and Visualization written by Ali Majeed Mahdi and published by . This book was released on 2019 with total page 240 pages. Available in PDF, EPUB and Kindle. Book excerpt: In the recent years, a huge success has been accomplished in prediction of human eye fixations. Several studies employed deep learning to achieve high accuracy of prediction of human eye fixations. These studies rely on pre-trained deep learning for object classification. They exploit deep learning either as a transfer-learning problem, or the weights of the pre-trained network as the initialization to learn a saliency model. The utilization of such pre-trained neural networks is due to the relatively small datasets of human fixations available to train a deep learning model. Another relatively less prioritized problem is amount of computation of such deep learning models requires expensive hardware. In this dissertation, two approaches are proposed to tackle abovementioned problems. The first approach, codenamed DeepFeat, incorporates the deep features of convolutional neural networks pre-trained for object and scene classifications. This approach is the first approach that uses deep features without further learning. Performance of the DeepFeat model is extensively evaluated over a variety of datasets using a variety of implementations. The second approach is a deep learning saliency model, codenamed ClassNet. Two main differences separate the ClassNet from other deep learning saliency models. The ClassNet model is the only deep learning saliency model that learns its weights from scratch. In addition, the ClassNet saliency model treats prediction of human fixation as a classification problem, while other deep learning saliency models treat the human fixation prediction as a regression problem or as a classification of a regression problem.
Download or read book Human Centric Visual Analysis with Deep Learning written by Liang Lin and published by Springer. This book was released on 2019-11-27 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book introduces the applications of deep learning in various human centric visual analysis tasks, including classical ones like face detection and alignment and some newly rising tasks like fashion clothing parsing. Starting from an overview of current research in human centric visual analysis, the book then presents a tutorial of basic concepts and techniques of deep learning. In addition, the book systematically investigates the main human centric analysis tasks of different levels, ranging from detection and segmentation to parsing and higher-level understanding. At last, it presents the state-of-the-art solutions based on deep learning for every task, as well as providing sufficient references and extensive discussions. Specifically, this book addresses four important research topics, including 1) localizing persons in images, such as face and pedestrian detection; 2) parsing persons in details, such as human pose and clothing parsing, 3) identifying and verifying persons, such as face and human identification, and 4) high-level human centric tasks, such as person attributes and human activity understanding. This book can serve as reading material and reference text for academic professors / students or industrial engineers working in the field of vision surveillance, biometrics, and human-computer interaction, where human centric visual analysis are indispensable in analysing human identity, pose, attributes, and behaviours for further understanding.
Download or read book Visual Saliency Prediction Based on Deep Learning written by Bashir Ghariba and published by . This book was released on 2020 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: The Human Visual System (HVS) has the ability to focus on specific parts of a scene, rather than the whole image. Human eye movement is also one of the primary functions used in our daily lives that helps us understand our surroundings. This phenomenon is one of the most active research topics in the computer vision and neuroscience fields. The outcomes that have been achieved by neural network methods in a variety of tasks have highlighted their ability to predict visual saliency. In particular, deep learning models have been used for visual saliency prediction. In this thesis, a deep learning method based on a transfer learning strategy is proposed (Chapter 2), wherein visual features in the convolutional layers are extracted from raw images to predict visual saliency (e.g., saliency map). Specifically, the proposed model uses the VGG-16 network (i.e., Pre-trained CNN model) for semantic segmentation. The proposed model is applied to several datasets, including TORONTO, MIT300, MIT1003, and DUT-OMRON, to illustrate its efficiency. The results of the proposed model are then quantitatively and qualitatively compared to classic and state-of-the-art deep learning models. In Chapter 3, I specifically investigate the performance of five state-of-the-art deep neural networks (VGG-16, ResNet-50, Xception, InceptionResNet-v2, and MobileNet-v2) for the task of visual saliency prediction. Five deep learning models were trained over the SALICON dataset and used to predict visual saliency maps using four standard datasets, namely TORONTO, MIT300, MIT1003, and DUT-OMRON. The results indicate that the ResNet-50 model outperforms the other four and provides a visual saliency map that is very close to human performance. In Chapter 4, a novel deep learning model based on a Fully Convolutional Network (FCN) architecture is proposed. The proposed model is trained in an end-to-end style and designed to predict visual saliency. The model is based on the encoder-decoder structure and includes two types of modules. The first has three stages of inception modules to improve multi-scale derivation and enhance contextual information. The second module includes one stage of the residual module to provide a more accurate recovery of information and to simplify optimization. The entire proposed model is fully trained from scratch to extract distinguishing features and to use a data augmentation technique to create variations in the images. The proposed model is evaluated using several benchmark datasets, including MIT300, MIT1003, TORONTO, and DUT-OMRON. The quantitative and qualitative experiment analyses demonstrate that the proposed model achieves superior performance for predicting visual saliency. In Chapter 5, I study the possibility of using deep learning techniques for Salient Object Detection (SOD) because this work is slightly related to the problem of Visual saliency prediction. Therefore, in this work, the capability of ten well-known pre-trained models for semantic segmentation, including FCNs, VGGs, ResNets, MobileNet-v2, Xception, and InceptionResNet-v2, are investigated. These models have been trained over an ImageNet dataset, fine-tuned on a MSRA-10K dataset, and evaluated using other public datasets, such as ECSSD, MSRA-B, DUTS, and THUR15k. The results illustrate the superiority of ResNet50 and ResNet18, which have Mean Absolute Errors (MAE) of approximately 0.93 and 0.92, respectively, compared to other well-known FCN models. Finally, conclusions are drawn, and possible future works are discussed in chapter 6.
Download or read book DEEP SALIENCY DETECTION COLO written by Guanbin Li and published by Open Dissertation Press. This book was released on 2017-01-26 with total page 120 pages. Available in PDF, EPUB and Kindle. Book excerpt: This dissertation, "Deep Saliency Detection and Color Sketch Generation" by Guanbin, Li, 李冠彬, was obtained from The University of Hong Kong (Pokfulam, Hong Kong) and is being sold pursuant to Creative Commons: Attribution 3.0 Hong Kong License. The content of this dissertation has not been altered in any way. We have altered the formatting in order to facilitate the ease of printing and reading of the dissertation. All rights not granted by the above license are retained by the author. Abstract: In recent years, with a wide spread of mobile devices with cameras, image has become an important medium for people to record and share their life, and has thus been witnessed a massive increase. Intelligent technique of image analysis and understanding, which focuses on extracting meaningful information from images, is becoming increasingly important. To keep up with its rapid development, the research and industry community has endeavored to develop advanced image analysis algorithms and their accompanying applications. This thesis demonstrates both novel algorithms in image analysis and a practical application system. It consists of two novel deep learning based salient object detection algorithms and a color sketch generation system. For salient object detection, we present two different approaches. The first one formulates saliency detection as a segment-wise regression problem and introduces a neural network architecture to map each segment to a saliency score. The proposed neural network architecture consists of fully connected layers on top of CNNs responsible for feature extraction at three different scales. The second approach is a deep network which consists of two complementary components, a pixel-level fully convolutional stream and a segment-wise spatial pooling stream. The first stream directly produces a saliency map with pixel-level accuracy from an input image while the second stream extracts segment-wise features very efficiently, and better models saliency discontinuities along object boundaries. Finally, a fully connected CRF model can be optionally incorporated to improve spatial coherence and contour localization in saliency maps generated from both of the two proposed methods. Experimental results demonstrate that our two deep learning based saliency detection models significantly improve the state of the art. For color sketch generation, we introduce an interactive drawing system, called ColorSketch, for helping novice users generate color sketches from photos. Our system is motivated by the fact that novice users are often capable of tracing object boundaries using pencil strokes, but have difficulties to choose proper colors and brush over an image region in a visually pleasing way. To preserve artistic freedom and expressiveness, our system lets users have full control over pencil strokes for depicting object shapes and geometric details at an appropriate level of abstraction, and automatically augment pencil sketches using color brushes, such as color mapping, brush stroke rendering as well as blank area creation. Experimental and user study results demonstrate that users, especially novice ones, can generate much better color sketches more efficiently with our system than using traditional manual tools. Subjects: Computer drawing Computer vision
Download or read book Computational Intelligence And Image Processing In Medical Applications written by Chi Hau Chen and published by World Scientific. This book was released on 2022-05-30 with total page 336 pages. Available in PDF, EPUB and Kindle. Book excerpt: In recent years, there have been significant progress in computational intelligence and image processing with machine learning and deep learning as important components of modern artificial intelligence. All these progresses face challenges in dealing with Covid-19 pandemic for detection and treatment.This comprehensive compendium provides not only updated advances of computational intelligence and image processing in the detection and treatment of Covid-19, but also other medical applications such as in cancer detection and cardiovascular diseases, etc. More traditional approaches such as 2D segmentation and 3D reconstruction are included.The useful reference text is an updated version of the edited title, Computer Vision in Medical Imaging (World Scientific, 2014) and its companion volume, Frontiers of Medical Imaging (World Scientific, 2015). The book is written for engineers, scientists and the medical community to meet the increased challenges in medical applications.
Download or read book Hyperspectral Image Analysis written by Saurabh Prasad and published by Springer Nature. This book was released on 2020-04-27 with total page 464 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book reviews the state of the art in algorithmic approaches addressing the practical challenges that arise with hyperspectral image analysis tasks, with a focus on emerging trends in machine learning and image processing/understanding. It presents advances in deep learning, multiple instance learning, sparse representation based learning, low-dimensional manifold models, anomalous change detection, target recognition, sensor fusion and super-resolution for robust multispectral and hyperspectral image understanding. It presents research from leading international experts who have made foundational contributions in these areas. The book covers a diverse array of applications of multispectral/hyperspectral imagery in the context of these algorithms, including remote sensing, face recognition and biomedicine. This book would be particularly beneficial to graduate students and researchers who are taking advanced courses in (or are working in) the areas of image analysis, machine learning and remote sensing with multi-channel optical imagery. Researchers and professionals in academia and industry working in areas such as electrical engineering, civil and environmental engineering, geosciences and biomedical image processing, who work with multi-channel optical data will find this book useful.
Download or read book Structuring of Image Databases for the Suggestion of Products for Online Advertising written by Lixuan Yang and published by . This book was released on 2017 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: The topic of the thesis is the extraction and segmentation of clothing items from still images using techniques from computer vision, machine learning and image description, in view of suggesting non intrusively to the users similar items from a database of retail products. We firstly propose a dedicated object extractor for dress segmentation by combining local information with a prior learning. A person detector is applied to localize sites in the image that are likely to contain the object. Then, an intra-image two-stage learning process is developed to roughly separate foreground pixels from the background. Finally, the object is finely segmented by employing an active contour algorithm that takes into account the previous segmentation and injects specific knowledge about local curvature in the energy function.We then propose a new framework for extracting general deformable clothing items by using a three stage global-local fitting procedure. A set of template initiates an object extraction process by a global alignment of the model, followed by a local search minimizing a measure of the misfit with respect to the potential boundaries in the neighborhood. The results provided by each template are aggregated, with a global fitting criterion, to obtain the final segmentation.In our latest work, we extend the output of a Fully Convolution Neural Network to infer context from local units(superpixels). To achieve this we optimize an energy function,that combines the large scale structure of the image with the locallow-level visual descriptions of superpixels, over the space of all possiblepixel labellings. In addition, we introduce a novel dataset called RichPicture, consisting of 1000 images for clothing extraction from fashion images.The methods are validated on the public database and compares favorably to the other methods according to all the performance measures considered.
Download or read book Visual Based Fashion Clothes Recommendation with Convolutional Neural Networks written by Satya Keerthi Gorripati and published by . This book was released on 2019 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: We proposed a two-step deep learning framework that recommends fashion clothes based on visual similarity style of other images in the dataset. Mainly it uses input as images, and tries to understand feature information from the given input image. For that purpose, a neural network classifier is used as an image-based feature extractor and a datadriven method. To rank items' for recommendation this feature extractor assists as an input for similarity algorithm. Our proposed method is tested on the online DeepFashion dataset. When compare with traditional textbased recommendation systems, our proposed framework helpful to increase robustness and performance, for instance, by better visual-based recommender matching a specific item.
Download or read book Augmented Cognition Neurocognition and Machine Learning written by Dylan D. Schmorrow and published by Springer. This book was released on 2017-06-28 with total page 600 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume constitutes the proceedings of the 11th International Conference on Augmented Cognition, AC 2017, held as part of the International Conference on Human-Computer Interaction, HCII 2017, which took place in Vancouver, BC, Canada, in July 2017. HCII 2017 received a total of 4340 submissions, of which 1228 papers were accepted for publication after a careful reviewing process. The papers thoroughly cover the entire field of Human-Computer Interaction, addressing major advances in knowledge and effective use of computers in a variety of application areas. The two volumes set of AC 2017 presents 81 papers which are organized in the following topical sections: electroencephalography and brain activity measurement, eye tracking in augmented cognition, physiological measuring and bio-sensing, machine learning in augmented cognition, cognitive load and performance, adaptive learning systems, brain-computer interfaces, human cognition and behavior in complex tasks and environments.
Download or read book Emotion recognition using brain computer interfaces and advanced artificial intelligence written by Yizhang Jiang and published by Frontiers Media SA. This book was released on 2023-02-17 with total page 413 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Efficient Processing of Deep Neural Networks written by Vivienne Sze and published by Springer Nature. This book was released on 2022-05-31 with total page 254 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a structured treatment of the key principles and techniques for enabling efficient processing of deep neural networks (DNNs). DNNs are currently widely used for many artificial intelligence (AI) applications, including computer vision, speech recognition, and robotics. While DNNs deliver state-of-the-art accuracy on many AI tasks, it comes at the cost of high computational complexity. Therefore, techniques that enable efficient processing of deep neural networks to improve key metrics—such as energy-efficiency, throughput, and latency—without sacrificing accuracy or increasing hardware costs are critical to enabling the wide deployment of DNNs in AI systems. The book includes background on DNN processing; a description and taxonomy of hardware architectural approaches for designing DNN accelerators; key metrics for evaluating and comparing different designs; features of DNN processing that are amenable to hardware/algorithm co-design to improve energy efficiency and throughput; and opportunities for applying new technologies. Readers will find a structured introduction to the field as well as formalization and organization of key concepts from contemporary work that provide insights that may spark new ideas.
Download or read book ECAI 2020 written by G. De Giacomo and published by IOS Press. This book was released on 2020-09-11 with total page 3122 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents the proceedings of the 24th European Conference on Artificial Intelligence (ECAI 2020), held in Santiago de Compostela, Spain, from 29 August to 8 September 2020. The conference was postponed from June, and much of it conducted online due to the COVID-19 restrictions. The conference is one of the principal occasions for researchers and practitioners of AI to meet and discuss the latest trends and challenges in all fields of AI and to demonstrate innovative applications and uses of advanced AI technology. The book also includes the proceedings of the 10th Conference on Prestigious Applications of Artificial Intelligence (PAIS 2020) held at the same time. A record number of more than 1,700 submissions was received for ECAI 2020, of which 1,443 were reviewed. Of these, 361 full-papers and 36 highlight papers were accepted (an acceptance rate of 25% for full-papers and 45% for highlight papers). The book is divided into three sections: ECAI full papers; ECAI highlight papers; and PAIS papers. The topics of these papers cover all aspects of AI, including Agent-based and Multi-agent Systems; Computational Intelligence; Constraints and Satisfiability; Games and Virtual Environments; Heuristic Search; Human Aspects in AI; Information Retrieval and Filtering; Knowledge Representation and Reasoning; Machine Learning; Multidisciplinary Topics and Applications; Natural Language Processing; Planning and Scheduling; Robotics; Safe, Explainable, and Trustworthy AI; Semantic Technologies; Uncertainty in AI; and Vision. The book will be of interest to all those whose work involves the use of AI technology.
Download or read book A Computational Perspective on Visual Attention written by John K. Tsotsos and published by MIT Press. This book was released on 2021-06-22 with total page 333 pages. Available in PDF, EPUB and Kindle. Book excerpt: The derivation, exposition, and justification of the Selective Tuning model of vision and attention. Although William James declared in 1890, "Everyone knows what attention is," today there are many different and sometimes opposing views on the subject. This fragmented theoretical landscape may be because most of the theories and models of attention offer explanations in natural language or in a pictorial manner rather than providing a quantitative and unambiguous statement of the theory. They focus on the manifestations of attention instead of its rationale. In this book, John Tsotsos develops a formal model of visual attention with the goal of providing a theoretical explanation for why humans (and animals) must have the capacity to attend. He takes a unique approach to the theory, using the full breadth of the language of computation—rather than simply the language of mathematics—as the formal means of description. The result, the Selective Tuning model of vision and attention, explains attentive behavior in humans and provides a foundation for building computer systems that see with human-like characteristics. The overarching conclusion is that human vision is based on a general purpose processor that can be dynamically tuned to the task and the scene viewed on a moment-by-moment basis. Tsotsos offers a comprehensive, up-to-date overview of attention theories and models and a full description of the Selective Tuning model, confining the formal elements to two chapters and two appendixes. The text is accompanied by more than 100 illustrations in black and white and color; additional color illustrations and movies are available on the book's Web site.
Download or read book Image Fusion written by Gang Xiao and published by Springer Nature. This book was released on 2020-08-31 with total page 415 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book systematically discusses the basic concepts, theories, research and latest trends in image fusion. It focuses on three image fusion categories – pixel, feature and decision – presenting various applications, such as medical imaging, remote sensing, night vision, robotics and autonomous vehicles. Further, it introduces readers to a new category: edge-preserving-based image fusion, and provides an overview of image fusion based on machine learning and deep learning. As such, it is a valuable resource for graduate students and scientists in the field of digital image processing and information fusion.
