Relying on the cloud for storing data and performing computations has become a popular solution in today's society, which demands large data collections and/or analysis over them to be readily available, for examp...
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ISBN:
(纸本)9781510626522
Relying on the cloud for storing data and performing computations has become a popular solution in today's society, which demands large data collections and/or analysis over them to be readily available, for example, to make knowledge-based decisions. While bringing undeniable benefits to both data owners and end users accessing the outsourced data, moving to the cloud raises a number of issues, ranging from choosing the most suitable cloud provider for outsourcing to effectively protecting data and computation results. In this paper, we discuss the main issues related to data protection arising when data and/or computations over them are moved to the cloud. We also illustrate possible solutions and approaches for addressing such issues.
Recent breakthroughs in deep learning and artificial intelligence technologies have enabled numerous mobileapplications. While traditional computation paradigms rely on mobile sensing and cloud computing, deep learni...
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ISBN:
(纸本)9781510626522
Recent breakthroughs in deep learning and artificial intelligence technologies have enabled numerous mobileapplications. While traditional computation paradigms rely on mobile sensing and cloud computing, deep learning implemented on mobile devices provides several advantages. These advantages include low communication bandwidth, small cloud computing resource cost, quick response time, and improved data privacy. Research and development of deep learning on mobile and embedded devices has recently attracted much attention. This paper provides a timely review of this fast-paced field to give the researcher, engineer, practitioner, and graduate student a quick grasp on the recent advancements of deep learning on mobile devices. In this paper, we discuss hardware architectures for mobile deep learning, including Field Programmable Gate Arrays (FPGA), Application Specific Integrated Circuit (ASIC), and recent mobile Graphic processing Units (GPUs). We present Size, Weight, Area and Power (SWAP) considerations and their relation to algorithm optimizations, such as quantization, pruning, compression, and approximations that simplify computation while retaining performance accuracy. We cover existing systems and give a state-of-the-industry review of TensorFlow, MXNet, mobile AI Compute Engine (MACE), and Paddle-mobile deep learning platform. We discuss resources for mobile deep learning practitioners, including tools, libraries, models, and performance benchmarks. We present applications of various mobile sensing modalities to industries, ranging from robotics, healthcare and multimedia, biometrics to autonomous drive and defense. We address the key deep learning challenges to overcome, including low quality data, and small training/adaptation data sets. In addition, the review provides numerous citations and links to existing code bases implementing various technologies. These resources lower the user's barrier to entry into the field of mobile deep learning.
Many images like medical images, satellite images, and real-life photographs may suffer poor contrast degradation. image enhancement is the imageprocessing of improving the quality that the results are more suitable ...
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ISBN:
(纸本)9781510626522
Many images like medical images, satellite images, and real-life photographs may suffer poor contrast degradation. image enhancement is the imageprocessing of improving the quality that the results are more suitable for display or further image analysis. In this paper, we present the detailed model for color image enhancement using the Hamiltonian quaternion framework. We introduce a novel quaternionic frequency enhancement algorithm which can combine the color channels and the local and global imageprocessing. The basic idea is to apply a-rooting image enhancement approach for different image blocks. For this purpose, we split image in moving windows on disjoint blocks. The parameter alfa for every block driven through optimization of measure of enhancement (EME). The resulting image is a weighted mean of all processing blocks. The weights for every local and global enhanced image driven through optimization of measure of enhancement (EME). Some presented experimental results illustrate the performance of the proposed approach on color images in comparison with the state-of-the-art methods.
Translating environmental knowledge from bird's eye view perspective, such as a map, to first person egocentric perspective is notoriously challenging, but critical for effective navigation and environment learnin...
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ISBN:
(纸本)9781510626522
Translating environmental knowledge from bird's eye view perspective, such as a map, to first person egocentric perspective is notoriously challenging, but critical for effective navigation and environment learning. Pointing error, or the angular difference between the perceived location and the actual location, is an important measure for estimating how well the environment is learned. Traditionally, errors in pointing estimates were computed by manually noting the angular difference. With the advent of commercial low-cost mobile eye trackers, it becomes possible to couple the advantages of automated imageprocessing based techniques with these spatial learning studies. This paper presents a vision based analytic approach for calculating pointing error measures in real-world navigation studies relying only on data from mobile eye tracking devices. The proposed method involves three steps: panorama generation, probe image localization using feature matching, and navigation pointing error estimation. This first-of-its-kind application has game changing potential in the field of cognitive research using eye-tracking technology for understanding human navigation and environment learning and has been successfully adopted by cognitive psychologists.
Detection of object and recognition of objects in real world computing environment is one of the challenging tasks in computer vision. To solve this task there are many challenges in designing algorithm, we have to in...
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ISBN:
(纸本)9781538681909
Detection of object and recognition of objects in real world computing environment is one of the challenging tasks in computer vision. To solve this task there are many challenges in designing algorithm, we have to introduce different and innovative techniques to detect objects in natural environments. imagesecurity is of high importance in various applications including military, medical and many others. security is one of the most challenging aspects in the internet and network applications. Encrypting entire image is very slow. Selective encryption is a scheme which intends to save computational power, network resources, and execution time. To encrypt object selectively object detection techniques are used. This technique is one of the most promising solutions to increase the speed of encryption as compared to the traditional encryption techniques. Selective encryption is helpful for the multimedia contents like image, video and audio. The compression ratio achieved is very high, since we store only the residues of extracted pixel of an image. So this paper discussed about Selective encryption with object detection technique.
作者：
Priya DeshmukhSharad MohodResearch scholar
Prof. Ram Meghe Institute of Technology & Research Badnera-Amravati India Professor
Prof. Ram Meghe Institute of Technology & Research Badnera-Amravati India
Biometric is a powerful tool use for the security in various domains such as mobile device security, law enforcement fields, civilian applications and military application. As the biometric has advantages properties a...
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ISBN:
(数字)9781728141428
ISBN:
(纸本)9781728141435
Biometric is a powerful tool use for the security in various domains such as mobile device security, law enforcement fields, civilian applications and military application. As the biometric has advantages properties and its wide application in various domains make it more popular also help to capture the commercial market. Even though the biometric system has to face the most common problem like is a spoofing attack and acquisition of biometric data. To detect and solve such attack problems involves use of a fake and liveness detection techniques in the system. Also to prevent the acquisition of image as it is the biometric valuable asset. The novel approach is put forward which suggested the use of fingerprint and palmprint as biometric data in the image form. The biometric data is pre-process and the further processing involves use of random forest classifier for the security enhancement and jam the spoofing attack on biometric system and asset.
This paper introduces the real image Super-Resolution (SR) challenge that was part of the Advances in image Manipulation (AIM) workshop, held in conjunction with ECCV 2020. This challenge involves three tracks to supe...
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imageprocessing and Computer Vision solutions have become commodities for software developers, thanks to the growing availability of Application Programming Interfaces (APIs) that encapsulate rich functionality, powe...
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ISBN:
(数字)9781510618480
ISBN:
(纸本)9781510618480
imageprocessing and Computer Vision solutions have become commodities for software developers, thanks to the growing availability of Application Programming Interfaces (APIs) that encapsulate rich functionality, powered by advanced algorithms. Tech giants like Apple, Google, IBM, and Microsoft have made APIs and micro-services available in the cloud for the agile integration of machine learning and intelligent features onto everyday applications. As privacy and cyber welfare become prime concerns, special efforts have been devoted in the field of face processing and recognition. In this context, this paper provides a friendly, intuitive and fun to use mobile app that leverages the state-of-the-art APIs for face, age, gender and emotion recognition. The Face-It-Up app was implemented for the iOS platform and uses the Microsoft Cognitive Services APIs as a tool for human vision and face processing research. Experimental work on image compression, upside-down orientation, the Thatcher effect, negative inversion, high frequency, facial artifacts, caricatures and image degradation were performed to test the application. For this purpose, we used the Radboud and 10k US Adult Faces Databases. The app benefits from accessing high-resolution imagery and touch input from the smart-devices, allowing for a wide range of new experiments from the user perspective. Furthermore, our approach serves as a potential framework for new initiatives in image-based biometrics, the Internet of Things, and citizen science.
With the rapid development of multimediaapplications which involves images, video and audio, security has become an important aspect of modern day digital communication. Various fields like multimedia systems, teleme...
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ISBN:
(纸本)9781538611418
With the rapid development of multimediaapplications which involves images, video and audio, security has become an important aspect of modern day digital communication. Various fields like multimedia systems, telemedicine, military communications, medical imaging internet communications etc, are widely based on imagesecurity. Our focus in this paper is to propose an algorithm which enhances imagesecurity. The algorithm proposed provides two-level security for the images that are transmitted over networks. We are using an existing and novel Data Mining technique, called Closed Frequent Itemset Mining to encode the image. Upon this, we are applying an imagesecurity technique called Steganography to hide the very existence of the image whilst being sent. With this higher security levels, an image can be sent securely over any network with the image's existence completely hidden. Two prominent quality metrics are tested: They are Mean Square Error (MSE) and Peak Signal to Noise Ratio (PSNR). Mean Square Error has been reduced efficiently and Peak Signal to Noise Ratio showed much improvement, which proves that the algorithm not only provides multi-layer security but also preserves the quality of images. The effect of Minimum Support Count and message image size on PSNR has also been observed. Also, their effect on time taken by the algorithm has been observed and plotted.
Eye tracking technology allows researchers to monitor position of the eye and infer one's gaze direction, which is used to understand the nature of human attention within psychology, cognitive science, marketing a...
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ISBN:
(数字)9781510618480
ISBN:
(纸本)9781510618480
Eye tracking technology allows researchers to monitor position of the eye and infer one's gaze direction, which is used to understand the nature of human attention within psychology, cognitive science, marketing and artificial intelligence. Commercially available head-mounted eye trackers allow researchers to track pupil movements (saccades and fixations) using infrared camera and capture the field of vision by a front-facing scene camera. The wearable eye tracker opened a new way to research in unconstrained environment settings;however, the recorded scene video typically has non-uniform illumination, low quality image frames, and moving scene objects. One of the most important tasks for analyzing the recorded scene video data is finding the boundary between different objects in a single frame. This paper presents a multi-level fixation-oriented object segmentation method (MFoOS) to solve the above challenges in segmenting the scene objects in video data collected by the eye tracker in order to support cognition research. MFoOS shows its advancement in position-invariance, illumination, noise tolerance and is task-driven. The proposed method is tested using real-world case studies designed by our team of psychologists focused on understanding visual attention in human problem solving. The extensive computer simulation demonstrates the method's accuracy and robustness for fixation-oriented object segmentation. Moreover, a deep-learning image semantic segmentation combining MFoOS results as label data was explored to demonstrate the possibility of on-line deployment of eye tracker fixation-oriented object segmentation.
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