الصفحة 2
الصفحة 2
Image and video encryption : From digital rights management to secured personal communication
Image and Video Encryption provides a unified overview of techniques for encryption of images and video data. This ranges from commercial applications like DVD or DVB to more research oriented topics and recently published material. This volume introduces different techniques from unified viewpoint, then evaluates these techniques with respect to their respective properties (e.g., security, speed.....). Encryption of visual data is an important topic in the area of mutimedia security, applications range from digital rights management (DVD, DVB) ) to secure personal communications. Within this topic area, we face several aims which contradict each other. What we thrive on is good compression, fast compression, high security, fast encryption, bitstream compliance, little power consumption and little memory requirements. The authors experimentally compare different approaches proposed in the literature and include an extensive bibliography of corresponding published material. Image and Video Encryption is designed for a professional audience composed of researchers and practitioners in industry. The book is also suitable for graduate-level students in computer science and electrical engineering.
Image Analysis and Recognition ; 17th International Conference, ICIAR 2020, Póvoa de Varzim, Portugal, June 24–26, 2020, Proceedings, Part II
This two-volume set LNCS 12131 and LNCS 12132 constitutes the refereed proceedings of the 17th International Conference on Image Analysis and Recognition, ICIAR 2020, held in Póvoa de Varzim, Portugal, in June 2020. The 54 full papers presented together with 15 short papers were carefully reviewed and selected from 123 submissions. The papers are organized in the following topical sections: image processing and analysis; video analysis; computer vision; 3D computer vision; machine learning; medical image and analysis; analysis of histopathology images; diagnosis and screening of ophthalmic diseases; and grand challenge on automatic lung cancer patient management.
Image Analysis and Recognition ; 17th International Conference, ICIAR 2020, Póvoa de Varzim, Portugal, June 24–26, 2020, Proceedings, Part I
This two-volume set LNCS 12131 and LNCS 12132 constitutes the refereed proceedings of the 17th International Conference on Image Analysis and Recognition, ICIAR 2020, held in Póvoa de Varzim, Portugal, in June 2020. The 54 full papers presented together with 15 short papers were carefully reviewed and selected from 123 submissions. The papers are organized in the following topical sections: image processing and analysis; video analysis; computer vision; 3D computer vision; machine learning; medical image and analysis; analysis of histopathology images; diagnosis and screening of ophthalmic diseases; and grand challenge on automatic lung cancer patient management.
Image Analysis ; 15th Scandinavian Conference, SCIA 2007, Aalborg, Denmark, June 10-24, 2007, Proceedings
The present volume contains the proceedings of the Scandinavian Conference on Image Analysis, SCIA 2007, held at Hotel Hvide Hus, Aalborg, Denmark, June 10–14, 2007. Initiated in 1979 by Torleiv Orhaug in Sweden, SCIA 2007 represented the 15th in the biennial series of conferences.
Hydra = هايدرا
Forgery involves the use of advanced algorithms to replicate and distribute deceptive products across various categories, casting shadows of doubt on the authenticity of goods. Although counterfeit detection can be useful in identifying and mitigating fraudulent activities, the widespread presence of counterfeit goods poses significant dangers, undermining consumer confidence and brand reputation. To underscore the severity of this issue, consider instances such as fake luxury items flooding the market, counterfeit electronics compromising safety, or bogus pharmaceuticals endangering health. Addressing this issue is critical in maintaining the integrity of brands, safeguarding consumer well-being, and preserving trust in the marketplace. The ability to distinguish between authentic and counterfeit products is paramount in ensuring accurate decision-making and preventing the harmful consequences of fraudulent goods. This technological context underscores the urgency of developing and deploying cutting-edge solutions to combat the evolving landscape of product forgery. Hydra emerges as a robust solution, utilizing a comprehensive approach that includes extracting posts and images from search engine tools, and is integrated with AI models to detect forgery. The Hydra platform not only provides users with a powerful tool for detecting counterfeit products but also offers tangible benefits such as enhanced brand security, increased awareness about the prevalence of forgeries, and the opportunity to actively participate in a real-time community.
High accuracy detection of mobile malware using machine learning
As increasingly sophisticated and evasive malware attacks continue to emerge, more effective detection solutions to tackle the problem are being sought through the application of advanced machine learning techniques. This reprint presents several advances in the field including: a new method of generating adversarial samples through byte sequence feature extraction using deep learning; a state-of-the-art comparative evaluation of deep learning approaches for mobile botnet detection; a novel visualization-based approach that utilizes images for Android botnet detection; a study on the detection of drive-by exploits in images using deep learning; etc. Furthermore, this reprint presents state-of-the-art reviews about machine learning-based detection techniques that will increase researchers' knowledge in the field and enable them to identify future research and development directions.
Hexagonal image processing : A practical approach
Hexagonal Image Processing provides an introduction to the processing of hexagonally sampled images, includes a survey of the work done in the field, and presents a novel framework for hexagonal image processing (HIP) based on hierarchical aggregates. Digital image processing is currently dominated by the use of square sampling lattices, however, hexagonal sampling lattices can also be used to define digital images. The strengths offered by hexagonal lattices over square lattices are considerable: • higher packing density, • uniform connectivity of points (pixels) in the lattice, • better angular resolution by virtue of having more nearest neighbours, and • superlative representation of curves. The utility of the HIP framework is demonstrated by implementing several basic image processing techniques (for the spatial and frequency domain) and some applications. The HIP framework serves as a tool for comparing processing of images defined on a square vs hexagonal grid, to determine their relative merits and demerits. The theory and algorithms covered are supplemented by attention to practical details such as accommodating hardware that support only images sampled on a square lattice. Including a Foreword written by Professor Narendra Ahuja, an eminent researcher in the field of Image Processing and Computer Vision, the book’s fresh approach to the subject offers insight and workable know-how to both researchers and postgraduates.
Handbook of Mathematical Models in Computer Vision
In this edited volume we present the most prominent mathematical models that are considered in computational vision. To this end, tasks of increasing complexity are considered and we present the state-of-the-art methods to cope with such tasks. The volume consists of six thematic areas that provide answers to the most dominant questions of computational vision: Image reconstruction, Segmentation and object extraction, Shape modeling and registration, Motion analysis and tracking, 3D from images, geometry and reconstruction Applications in medical image analysis
Graph-based representations in pattern recognition ; 6th IAPR-TC-15 International Workshop, GbRPR 2007, Alicante, Spain, June 11-13, 2007, Proceedings
Constitutes the refereed proceedings of the 6th IAPR-TC-15 International Workshop on Graph-Based Representations in Pattern Recognition, GbRPR 2007, held in Alicante, Spain in June 2007.
Generative adversarial text to image synthesis
Synthesizing high-quality images from text descriptions is a challenging problem in computer vision and has many practical applications. Samples generated by existing text-to-image approaches can roughly reflect the meaning of the given descriptions, but they fail to contain necessary details and vivid object parts. In order to make the project more specialized, it was approved that the project be dedicated to fashion image generation, we present an effective approach for generating new clothing through generative adversarial learning. Generative Adversarial Networks (GANs) successfully show the capability of synthesizing sharper images compared to other generative models.
Fuzzy Models and Algorithms for Pattern Recognition and Image Processing
Fuzzy Models and Algorithms for Pattern Recognition and Image Processing presents a comprehensive introduction of the use of fuzzy models in pattern recognition and selected topics in image processing and computer vision. Unique to this volume in the Kluwer Handbooks of Fuzzy Sets Series is the fact that this book was written in its entirety by its four authors. A single notation, presentation style, and purpose are used throughout. The result is an extensive unified treatment of many fuzzy models for pattern recognition. The main topics are clustering and classifier design, with extensive material on feature analysis relational clustering, image processing and computer vision. Also included are numerous figures, images and numerical examples that illustrate the use of various models involving applications in medicine, character and word recognition, remote sensing, military image analysis, and industrial engineering.
Fashionity
This project is an AI fashion design system to generate fashion images based on user textual description. The proposed system incorporates advanced technology for dissemination and machine translation with the aim of facilitating a seamless user experience for input in both Arabic and English languages. Moreover, the project encompasses the incorporation of a recommendation system that proposes appropriate visual content based on user style. The primary objective of this project is to develop a robust framework capable of generating high-quality images based on textual descriptions, providing recommendations for similar clothing items, and facilitating the retrieval of photographic and apparel articles through image search.
Face Biometrics for Personal Identification : Multi-Sensory Multi-Modal Systems
This book provides an ample coverage of theoretical and experimental state-of-the-art work as well as new trends and directions in the biometrics field. It offers students and software engineers a thorough understanding of how some core low-level building blocks of a multi-biometric system are implemented. While this book covers a range of biometric traits including facial geometry, 3D ear form, fingerprints, vein structure, voice, and gait, its main emphasis is placed on multi-sensory and multi-modal face biometrics algorithms and systems. "Multi-sensory" refers to combining data from two or more biometric sensors, such as synchronized reflectance-based and temperature-based face images. "Multi-modal" biometrics means fusing two or more biometric modalities, like face images and voice timber. The first part addresses new and emerging face biometrics. Emphasis is placed on biometric systems where single sensor and single modality are employed in challenging imaging conditions. The second part on multi-sensory face biometrics deals with the personal identification task in challenging variable illuminations and outdoor operating scenarios by employing visible and thermal sensors. The third part of the book focuses on multi-modal face biometrics by integrating voice, ear, and gait modalities with facial data. The last part presents generic chapters on multi-biometrics fusion methodologies and performance prediction techniques.
Essential Renderman
RenderMan has long been associated with top-end film production and is an essential tool for creating many of the effects and images in recent animated films (such as Monsters, Inc., Finding Nemo and The Incredibles). RenderMan is widely available and, with the demand for higher quality images, is now used by computer-based artists at all levels of the graphics industry. Intended to provide a straightforward and easy introduction to the basic techniques involved, this book provides an excellent grounding, enabling readers to confidently move to more advanced texts.
Dream catcher
Dream Catcher is a video generation application that helps in many fields as science fiction, imagine event’s scenarios, education, animation and montage. By applying artificial algorithms implemented and trained on a dataset containing video samples and there descriptions to generate videos from any given text. The idea of generating videos from text is a new idea that was first presented at 2017, even that international companies like Google and OpenAi In the last year, was working on developing models to generate images from text. To make it easier to use the application, there are many ways to enter the text either by an image, voice or Typing from the keyboard.
Discrete Geometry for Computer Imagery Vol. 4245 ; 13th International Conference, DGCI 2006, Szeged, Hungary, October 25-27, 2006, Proceedings
This book constitutes the refereed proceedings of the 13th International Conference on Discrete Geometry for Computer Imagery, DGCI 2006, held in Szeged, Hungary in October 2006. The 28 revised full papers and 27 revised poster papers presented together with two invited papers were carefully reviewed and selected from 99 submissions.
Discrete geometry for computer imagery ; Vol. 3429 ; 12th International Conference, DGCI 2005, Poitiers, France, April 11-13, 2005, Proceedings
This book constitutes the refereed proceedings of the 12th International Conference on Discrete Geometry for Computer Imagery, DGCI 2005, held in Poitiers, France in April 2005. The 36 revised full papers presented together with an invited paper were carefully reviewed and selected from 53 submissions. The papers are organized in topical sections on applications, discrete hierarchical geometry, discrete tomography, discrete topology, object properties, reconstruction and recognition, uncertain geometry, and visualization.
Digital watermarking ; 6th International Workshop, IWDW 2007 Guangzhou, China, December 3-5, 2007 Proceedings
This book constitutes the refereed proceedings of the 6th International Workshop, IWDW 2007, held in Guangzhou, China, in December 2007.
Digital image processing
Completely self-contained and heavily illustrated, this introduction to basic concepts and methodologies for digital image processing is written at a level that is suitable for seniors and first-year graduate students in almost any technical discipline
Deepfake detection = اكتشاف التزييف العميق
In the rapidly evolving era of artificial intelligence, addressing the escalating threats of deepfake technology becomes a necessity because of the increasing sophistication of AI algorithms in generating deceptive content, and since it threatens the integrity of information across diverse data. The main objective is to build a sophisticated AI-driven system to detect different types of deepfake in text, audio, and images. In English text deepfake detection, multiple pre-trained tokenizers have been used, but XLNET and BERT stand out with identifying objects outside the dataset with an accuracy of 0.9809 and both have been generalized & trained using LSTM. In Arabic text deepfake detection, Arabert has been trained using LSTM which led with an accuracy of 99.53% by generalizing the model. Both English and Arabic datasets have been generated to enhance the accuracy and effectiveness of the models. Audio deepfake detection has been generalized too, using Random Forest with an accuracy of 98.259%.
