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A reality-enhanced autostereoscopic display system is presented. In this system, the viewers who do not wear any special glasses can perceive 3-D images within their hands' reach with little sense of incongruity. The feature of th...
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A reality-enhanced autostereoscopic display system is presented. In this system, the viewers who do not wear any special glasses can perceive 3-D images within their hands' reach with little sense of incongruity. The feature of this system is combination of real image generation and parallax presentation. Real image of the display in the back is generated in the air by using Fresnel lenses, which has made it possible to narrow artificial parallax to display 3-D objects in the workspace near the viewer without interfering the viewers' motion. Smaller artificial parallax leads to 3-D perception with more reality and less eyestrain than the conventional 3-D displays. For parallax presentation an mobile filter which plays the role of stereoscpopic goggles is set between the display in the back and the Fresnel lenses and is controlled so that it follows the motion of the viewer to keep on presenting different images to each eye. To present undistorted 3-D space the optical path including refraction by Fresnel lenses is calculated and the image on the screen is updated based on it. Real-time undistorted image presentation to unrestricted eye positions is realized by suing texture mapping technique.
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With the latest advances in image sensor technology, cameras are able to generate video with tens of megapixels per frame. These high resolution videos streams offer great potential to be used in the surveillance domain. For groun...
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With the latest advances in image sensor technology, cameras are able to generate video with tens of megapixels per frame. These high resolution videos streams offer great potential to be used in the surveillance domain. For ground based systems, gigapixel streams are already used with great effect as illustrated by the ICME 2019 crowd counting challenge. However, for Unmanned Aerial Vehicles (UAVs), this vast stream of data exceeds the limit of transmission bandwidth to send this data back to the ground. On board data analysis and selection is thus required to use and benefit from high resolution cameras. This paper presents a result of the CAVIAR project, where a combination of hardware and algorithms was designed to answer the question: "how to exploit a high resolution high frame rate camera on board a UAV?'. With the associated size, weight and power limitations, we implement data reduction by deploying deep learning on hardware to find the relevant information and transmit it to an operator station. The proposed solution aims at employing the high resolution potential of the sensor only onto objects of interest. We encode and transmit the identified regions containing those objects of interest (ROI) at the original resolution and framerate, while also transmitting the downscaled background to provide context for an operator. We demonstrate using a 35 fps, 65 Megapixel camera that this set-up indeed saves considerable bandwidth while retaining all important video data at high quality at the same time.
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Manual inspections of glass facade of high rising buildings are expensive, time-consuming and potentially life-threatening for both inspectors and pedestrians on the street. Advances in machine learning for image/video analysis an...
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Manual inspections of glass facade of high rising buildings are expensive, time-consuming and potentially life-threatening for both inspectors and pedestrians on the street. Advances in machine learning for image/video analysis and availability of affordable unmanned aerial vehicles (UAVs) with onboard video recording and processing sensors provide opportunities for smart, safe and automatic glass facade inspections. This paper is concerned with developing an effective solution for recognizing cracked glass panels, which can be installed on board a UAV. From static 2D photographic images, the proposed solution analyzes textural patterns of smooth glass surface and crack segments, linearity of detected crack segments, geometrical characteristics of crack curvatures and the crack pixel patterns, captures these discriminative features for glass cracks using Uniform Local Binary Pattern (ULBP), histograms of linearity, geometrical curvature descriptors with fixed length connected pixel configurations, and accordingly classifies images of cracked and non-cracked glass panels using a kNN classifier. Experimental results with images of different resolutions acquired by a UAV drone in a real office building setting and images collected through Google search demonstrate that the proposed solution achieves promising results with accuracy rates in excess of 80% and even as high as 91% despite the presence of reflections.
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Region growing is defined as a procedure of finding regions containing user defined objects of interest.
Growing region is a vital phase for various image processing applications. Growing region
in images has been very challenging...
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Region growing is defined as a procedure of finding regions containing user defined objects of interest.
Growing region is a vital phase for various image processing applications. Growing region
in images has been very challenging as it is the base for further image analysis,interpretation
and classification.Region growing varies for different purpose of aim.However,the identified region
are widely used for various domain-skin detection, detect object in image,hand gesture detection
etc.In this paper,the main concentration is to defining region of interest from an image
based on skin detection.A clustering method was used.Skin detection can be used as a preprocessing
step for several applications included but not limited to various Human Computer Interaction
(HCI) tasks.However,skin detection is a challenging problem due to sparse variations of skin
tone of human. Skin tone can be confused with background color,attire color,ethnicity,individual
characteristics-age,sex,body parts,makeup,hair color,presence of non-human objects and camera
calibration.Besides that,lightning conditions also plays a vital role. Researchers have been working
tirelessly for an e cient skin detection method but those are not beyond limitations.Various approach
including pixel wise threshold for various color spaces,segmentation, face and hand detection
based approaches are proposed.But it still lacks from a method which can be applied for all types
of skin detection. In this paper,a novel skin detection method is proposed which is free from any
manual threshold values and automatically define number of clusters.
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In the field of IR technology, careful regulation of temperature elements such as blackbodies or temperature targets is important, particularly for calibration. Feedback based controllers, independent of state space, such as Propo...
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In the field of IR technology, careful regulation of temperature elements such as blackbodies or temperature targets is important, particularly for calibration. Feedback based controllers, independent of state space, such as Proportional Integral Derivative (PID) controllers, are a popular and effective way to control these temperature systems. In this paper we explore different types of control for a prototype heated reference target. We show that we can use a combination of PID and Least Means Square (LMS) closed loop adaptive control to determine both the optimal weight proportion and the magnitude of the weights for optimal power draw. This enables us to develop a faster, more optimal controller than by manually tuning the weights by hand.
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Recent breakthroughs in deep learning and artificial intelligence technologies have enabled numerous mobile
applications. While traditional computation paradigms rely on mobile sensing and cloud computing, deep learning
implemente...
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Recent breakthroughs in deep learning and artificial intelligence technologies have enabled numerous mobile
applications. 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.
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The FELIX 3D Display belongs to the class of volumetric displays using the swept volume technique. It is designed to display images created by standard CAD applications, which can be easily imported and interactively transformed i...
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The FELIX 3D Display belongs to the class of volumetric displays using the swept volume technique. It is designed to display images created by standard CAD applications, which can be easily imported and interactively transformed in real-time by the FELIX control software. The images are drawn on a spinning screen by acousto-optic, galvanometric or polygon mirror deflection units with integrated lasers and a color mixer. The modular design of the display enables the user to operate with several equal or different projection units in parallel and to use appropriate screens for the specific purpose. The FELIX 3D Display is a compact, light, extensible and easy to transport system. It mainly consists of inexpensive standard, off-the-shelf components for an easy implementation. This setup makes it a powerful and flexible tool to keep track with the rapid technological progress of today. Potential applications include imaging in the fields of entertainment, air traffic control, medical imaging, computer aided design as well as scientific data visualization. The FELIX 3D project team has evolved from a scientific working group of students and teachers at a normal High School in Germany. Despite minor funding resources within this non-commercial group considerable results have been achieved.
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Recent breakthroughs in EO/IR sensing, real-time signal processing, and deep machine learning technologies have enabled standoff heart rate estimation from facial and body video. This technology is also known as remote photoplethy...
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Recent breakthroughs in EO/IR sensing, real-time signal processing, and deep machine learning technologies have enabled standoff heart rate estimation from facial and body video. This technology is also known as remote photoplethys-mography (rPPG). Research and development of rPPG has attracted much attention recently. This paper gives a timely review of this fast-paced field to give the researcher, engineer, and graduate student a quick grasp of the recent advancement of rPPG. We first review two rPPG design approaches: color variation based and motion-based detections. To enable rPPG for less constrained use cases, various signal processing and machine learning algorithms are developed to handle signal variabilities introduced by lighting source, view angle, and subject motion. To help newcomers quickly start work in this field, we then describe some existing rPPG research datasets, open-source rPPG research tools, and some demonstration systems. Six commonly used rPPG algorithm evaluation metrics are described to evaluate and visualize the research advance in this field. As the rPPG technology matures, more application domains become possible. We cover six applications of rPPG in commercial, security, and defense domains, including emerging applications in bio-metric liveness and video media authenticity. Finally, we outline some challenges yet to overcome, especially in the domain of security and defense. These challenges include unconstrained outdoor environment, rPPG form air-platform, night time operation, moving and non-cooperative subjects. These challenges require special algorithmic considerations.
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This paper offers a new multiple signal restoration tool to solve the inverse problem, when signals are convoluted with a multiple impulse response and then degraded by an additive noise signal with multiple components. Inverse pr...
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This paper offers a new multiple signal restoration tool to solve the inverse problem, when signals are convoluted with a multiple impulse response and then degraded by an additive noise signal with multiple components. Inverse problems arise practically in all areas of science and engineering and refers to as methods of estimating data/parameters, in our case of multiple signals that cannot directly be observed. The presented tool is based on the mapping multiple signals into the quaternion domain, and then solving the inverse problem. Due to the non-commutativity of quaternion arithmetic, it is difficult to find the optimal filter in the frequency domain for degraded quaternion signals. As an alternative, we introduce an optimal filter by using special 4x4 matrices on the discrete Fourier transforms of signal components, at each frequency-point. The optimality of the solution is with respect to the mean-square-root error, as in the classical theory of the signal restoration by the Wiener filter. The Illustrative example of optimal filtration of multiple degraded signals in the quaternion domain is given. The computer simulations validate the effectiveness of the proposed method.
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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 know...
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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.
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