Download or read book Detection of Salient Objects in Images Using Frequency Domain and Deep Convolutional Features written by Masoumeh Rezaei Abkenar and published by . This book was released on 2019 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: In image processing and computer vision tasks such as object of interest image segmentation, adaptive image compression, object based image retrieval, seam carving, and medical imaging, the cost of information storage and computational complexity is generally a great concern. Therefore, for these and other applications, identifying and focusing only on the parts of the image that are visually most informative is much desirable. These most informative parts or regions that also have more contrast with the rest of the image are called the salient regions of the image, and the process of identifying them is referred to as salient object detection. The main challenges in devising a salient object detection scheme are in extracting the image features that correctly differentiate the salient objects from the non-salient ones, and then utilizing them to detect the salient objects accurately. Several salient object detection methods have been developed in the literature using spatial domain image features. However, these methods generally cannot detect the salient objects uniformly or with clear boundaries between the salient and non-salient regions. This is due to the fact that in these methods, unnecessary frequency content of the image get retained or the useful ones from the original image get suppressed. Frequency domain features can address these limitations by providing a better representation of the image. Some salient object detection schemes have been developed based on the features extracted using the Fourier or Fourier like transforms. While these methods are more successful in detecting the entire salient object in images with small salient regions, in images with large salient regions these methods have a tendency to highlight the boundaries of the salient region rather than doing so for the entire salient region. This is due to the fact that in the Fourier transform of an image, the global contrast is more dominant than the local ones. Moreover, it is known that the Fourier transform cannot provide simultaneous spatial and frequency localization. It is known that multi-resolution feature extraction techniques can provide more accurate features for different image processing tasks, since features that might not get extracted at one resolution may be detected at another resolution. However, not much work has been done to employ multi-resolution feature extraction techniques for salient object detection. In view of this, the objective of this thesis is to develop schemes for image salient object detection using multi-resolution feature extraction techniques both in the frequency domain and the spatial domain. The first part of this thesis is concerned with developing salient object detection methods using multi-resolution frequency domain features. The wavelet transform has the ability of performing multi-resolution simultaneous spatial and frequency localized analysis, which makes it a better feature extraction tool compared to the Fourier or other Fourier like transforms. In this part of the thesis, first a salient object detection scheme is developed by extracting features from the high-pass coefficients of the wavelet decompositions of the three color channels of images, and devising a scheme for the weighted linear combination of the color channel features. Despite the advantages of the wavelet transform in image feature extraction, it is not very effective in capturing line discontinuities, which correspond to directional information in the image. In order to circumvent the lack of directional flexibility of the wavelet-based features, in this part of the thesis, another salient object detection scheme is also presented by extracting local and global features from the non-subsampled contourlet coefficients of the image color channels. The local features are extracted from the local variations of the low-pass coefficients, whereas the global features are obtained based on the distribution of the subband coefficients afforded by the directional flexibility provided by the non-subsampled contourlet transform. In the past few years, there has been a surge of interest in employing deep convolutional neural networks to extract image features for different applications. These networks provide a platform for automatically extracting low-level appearance features and high-level semantic features at different resolutions from the raw images. The second part of this thesis is, therefore, concerned with the investigation of salient object detection using multiresolution deep convolutional features. The existing deep salient object detection schemes are based on the standard convolution. However, performing the standard convolution is computationally expensive specially when the number of channels increases through the layers of a deep network. In this part of the thesis, using a lightweight depthwise separable convolution, a deep salient object detection network that exploits the fusion of multi-level and multi-resolution image features through judicious skip connections between the layers is developed. The proposed deep salient object detection network is aimed at providing good performance with a much reduced complexity compared to the existing deep salient object detection methods. Extensive experiments are conducted in order to evaluate the performance of the proposed salient object detection methods by applying them to the natural images from several datasets. It is shown that the performance of the proposed methods are superior to that of the existing methods of salient object detection.

Download or read book Object Detection with Deep Learning Models written by S Poonkuntran and published by CRC Press. This book was released on 2022-11-01 with total page 276 pages. Available in PDF, EPUB and Kindle. Book excerpt: Object Detection with Deep Learning Models discusses recent advances in object detection and recognition using deep learning methods, which have achieved great success in the field of computer vision and image processing. It provides a systematic and methodical overview of the latest developments in deep learning theory and its applications to computer vision, illustrating them using key topics, including object detection, face analysis, 3D object recognition, and image retrieval. The book offers a rich blend of theory and practice. It is suitable for students, researchers and practitioners interested in deep learning, computer vision and beyond and can also be used as a reference book. The comprehensive comparison of various deep-learning applications helps readers with a basic understanding of machine learning and calculus grasp the theories and inspires applications in other computer vision tasks. Features: A structured overview of deep learning in object detection A diversified collection of applications of object detection using deep neural networks Emphasize agriculture and remote sensing domains Exclusive discussion on moving object detection

Download or read book Intelligent Information Processing X written by Zhongzhi Shi and published by Springer Nature. This book was released on 2020-06-26 with total page 326 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 11th IFIP TC 12 International Conference on Intelligent Information Processing, IIP 2020, held in Hangzhou, China, in July 2020. The 24 full papers and 5 short papers presented were carefully reviewed and selected from 36 submissions. They are organized in topical sections on machine learning; multi-agent system; recommendation system; social computing; brain computer integration; pattern recognition; and computer vision and image understanding.

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.

Download or read book Image and Graphics Technologies and Applications written by Yongtian Wang and published by Springer. This book was released on 2018-08-11 with total page 674 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 13th Chinese Conference on Image and Graphics Technologies and Applications, IGTA 2018, held in Beijing, China in April, 2018. The 64 papers presented were carefully reviewed and selected from 138 submissions. They provide a forum for sharing progresses in the areas of image processing technology; image analysis and understanding; computer vision and pattern recognition; big data mining, computer graphics and VR; as well as image technology applications.

Download or read book Advances in Artificial Intelligence and Security written by Xingming Sun and published by Springer Nature. This book was released on 2021-06-29 with total page 760 pages. Available in PDF, EPUB and Kindle. Book excerpt: The 3-volume set CCIS 1422, CCIS 1423 and CCIS 1424 constitutes the refereed proceedings of the 7th International Conference on Artificial Intelligence and Security, ICAIS 2021, which was held in Dublin, Ireland, in July 2021. The total of 131 full papers and 52 short papers presented in this 3-volume proceedings was carefully reviewed and selected from 1013 submissions. The papers were organized in topical sections as follows: Part I: artificial intelligence; Part II: artificial intelligence; big data; cloud computing and security internet; Part III: cloud computing and security; encryption and cybersecurity; information hiding; IoT security.

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 Object Detection by Stereo Vision Images written by R. Arokia Priya and published by John Wiley & Sons. This book was released on 2022-09-14 with total page 293 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Computer Communication and Signal Processing AI Knowledge Engineering and IoT for Smart Systems written by Eunika Mercier-Laurent and published by Springer Nature. This book was released on 2023-08-27 with total page 345 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 7th International Conference on Computer, Communication, and Signal Processing, ICCCSP 2023, held in Chennai, India, during January 4–6, 2023, in hybrid mode. The 17 full and 9 short papers presented in this volume were carefully reviewed and selected from 123 submissions. The papers are categorized into topical sections: artificial intelligence in health care; machine learning and deep learning; signal processing; and Internet of Things for smart systems.

Download or read book Evolving Technologies for Computing Communication and Smart World written by Pradeep Kumar Singh and published by Springer Nature. This book was released on 2020-11-25 with total page 536 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents best selected papers presented at the International Conference on Evolving Technologies for Computing, Communication and Smart World (ETCCS 2020) held on 31 January–1 February 2020 at C-DAC, Noida, India. It is co-organized by Southern Federal University, Russia; University of Jan Wyżykowski (UJW), Polkowice, Poland; and CSI, India. C-DAC, Noida received funding from MietY during the event. The technical services are supported through EasyChair, Turnitin, MailChimp and IAC Education. The book includes current research works in the areas of network and computing technologies, wireless networks and Internet of things (IoT), futuristic computing technologies, communication technologies, security and privacy.

Download or read book Statistical Machine Learning for Human Behaviour Analysis written by Thomas Moeslund and published by MDPI. This book was released on 2020-06-17 with total page 300 pages. Available in PDF, EPUB and Kindle. Book excerpt: This Special Issue focused on novel vision-based approaches, mainly related to computer vision and machine learning, for the automatic analysis of human behaviour. We solicited submissions on the following topics: information theory-based pattern classification, biometric recognition, multimodal human analysis, low resolution human activity analysis, face analysis, abnormal behaviour analysis, unsupervised human analysis scenarios, 3D/4D human pose and shape estimation, human analysis in virtual/augmented reality, affective computing, social signal processing, personality computing, activity recognition, human tracking in the wild, and application of information-theoretic concepts for human behaviour analysis. In the end, 15 papers were accepted for this special issue. These papers, that are reviewed in this editorial, analyse human behaviour from the aforementioned perspectives, defining in most of the cases the state of the art in their corresponding field.

Download or read book Progress in Pattern Recognition Image Analysis Computer Vision and Applications written by Ruben Vera-Rodriguez and published by Springer. This book was released on 2019-03-02 with total page 1001 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed post-conference proceedings of the 23rd Iberoamerican Congress on Pattern Recognition, CIARP 2018, held in Madrid, Spain, in November 2018 The 112 papers presented were carefully reviewed and selected from 187 submissions The program was comprised of 6 oral sessions on the following topics: machine learning, computer vision, classification, biometrics and medical applications, and brain signals, and also on: text and character analysis, human interaction, and sentiment analysis

Download or read book Computer Vision ACCV 2016 Workshops written by Chu-Song Chen and published by Springer. This book was released on 2017-03-14 with total page 666 pages. Available in PDF, EPUB and Kindle. Book excerpt: The three-volume set, consisting of LNCS 10116, 10117, and 10118, contains carefully reviewed and selected papers presented at 17 workshops held in conjunction with the 13th Asian Conference on Computer Vision, ACCV 2016, in Taipei, Taiwan in November 2016. The 134 full papers presented were selected from 223 submissions. LNCS 10116 contains the papers selected

Download or read book Mobile Ad hoc and Sensor Networks written by Liehuang Zhu and published by Springer. This book was released on 2018-03-27 with total page 536 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 13th International Conference on Mobile Ad-hoc and Sensor Networks, MSN 2017, held in Beijing, China, in December 2017. The 39 revised full papers presented were carefully reviewed and selected from 145 submissions. The papers address issues such as multi-hop wireless networks and wireless mesh networks; sensor and actuator networks; vehicle ad hoc networks; mobile social network; delay tolerant networks and opportunistic networking; cyber-physical systems; internet of things; system modeling and performance analysis; routing and network protocols; data transport and management in mobile networks; resource management and wireless QoS provisioning; security and privacy; cross layer design and optimization; novel applications and architectures.

Download or read book Intelligence Science and Big Data Engineering Visual Data Engineering written by Zhen Cui and published by Springer Nature. This book was released on 2019-11-28 with total page 594 pages. Available in PDF, EPUB and Kindle. Book excerpt: The two volumes LNCS 11935 and 11936 constitute the proceedings of the 9th International Conference on Intelligence Science and Big Data Engineering, IScIDE 2019, held in Nanjing, China, in October 2019. The 84 full papers presented were carefully reviewed and selected from 252 submissions.The papers are organized in two parts: visual data engineering; and big data and machine learning. They cover a large range of topics including information theoretic and Bayesian approaches, probabilistic graphical models, big data analysis, neural networks and neuro-informatics, bioinformatics, computational biology and brain-computer interfaces, as well as advances in fundamental pattern recognition techniques relevant to image processing, computer vision and machine learning.

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 Proceedings of 2021 International Conference on Autonomous Unmanned Systems ICAUS 2021 written by Meiping Wu and published by Springer Nature. This book was released on 2022-03-18 with total page 3575 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book includes original, peer-reviewed research papers from the ICAUS 2021, which offers a unique and interesting platform for scientists, engineers and practitioners throughout the world to present and share their most recent research and innovative ideas. The aim of the ICAUS 2021 is to stimulate researchers active in the areas pertinent to intelligent unmanned systems. The topics covered include but are not limited to Unmanned Aerial/Ground/Surface/Underwater Systems, Robotic, Autonomous Control/Navigation and Positioning/ Architecture, Energy and Task Planning and Effectiveness Evaluation Technologies, Artificial Intelligence Algorithm/Bionic Technology and Its Application in Unmanned Systems. The papers showcased here share the latest findings on Unmanned Systems, Robotics, Automation, Intelligent Systems, Control Systems, Integrated Networks, Modeling and Simulation. It makes the book a valuable asset for researchers, engineers, and university students alike.