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Search Results: 1 - 10 of 518 matches for " Shmuel Peleg "
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An Egocentric Look at Video Photographer Identity
Yedid Hoshen,Shmuel Peleg
Computer Science , 2014,
Abstract: Egocentric cameras are being worn by an increasing number of users, among them many security forces worldwide. GoPro cameras already penetrated the mass market, reporting substantial increase in sales every year. As head-worn cameras do not capture the photographer, it may seem that the anonymity of the photographer is preserved even when the video is publicly distributed. We show that camera motion, as can be computed from the egocentric video, provides unique identity information. The photographer can be reliably recognized from a few seconds of video captured when walking. The proposed method achieves more than 90% recognition accuracy in cases where the random success rate is only 3%. Applications can include theft prevention by locking the camera when not worn by its rightful owner. Searching video sharing services (e.g. YouTube) for egocentric videos shot by a specific photographer may also become possible. An important message in this paper is that photographers should be aware that sharing egocentric video will compromise their anonymity, even when their face is not visible.
Live Video Synopsis for Multiple Cameras
Yedid Hoshen,Shmuel Peleg
Computer Science , 2015,
Abstract: Video surveillance cameras generate most of recorded video, and there is far more recorded video than operators can watch. Much progress has recently been made using summarization of recorded video, but such techniques do not have much impact on live video surveillance. We assume a camera hierarchy where a Master camera observes the decision-critical region, and one or more Slave cameras observe regions where past activity is important for making the current decision. We propose that when people appear in the live Master camera, the Slave cameras will display their past activities, and the operator could use past information for real-time decision making. The basic units of our method are action tubes, representing objects and their trajectories over time. Our object-based method has advantages over frame based methods, as it can handle multiple people, multiple activities for each person, and can address re-identification uncertainty.
Visual Learning of Arithmetic Operations
Yedid Hoshen,Shmuel Peleg
Computer Science , 2015,
Abstract: A simple Neural Network model is presented for end-to-end visual learning of arithmetic operations from pictures of numbers. The input consists of two pictures, each showing a 7-digit number. The output, also a picture, displays the number showing the result of an arithmetic operation (e.g., addition or subtraction) on the two input numbers. The concepts of a number, or of an operator, are not explicitly introduced. This indicates that addition is a simple cognitive task, which can be learned visually using a very small number of neurons. Other operations, e.g., multiplication, were not learnable using this architecture. Some tasks were not learnable end-to-end (e.g., addition with Roman numerals), but were easily learnable once broken into two separate sub-tasks: a perceptual \textit{Character Recognition} and cognitive \textit{Arithmetic} sub-tasks. This indicates that while some tasks may be easily learnable end-to-end, other may need to be broken into sub-tasks.
EgoSampling: Fast-Forward and Stereo for Egocentric Videos
Yair Poleg,Tavi Halperin,Chetan Arora,Shmuel Peleg
Computer Science , 2014,
Abstract: While egocentric cameras like GoPro are gaining popularity, the videos they capture are long, boring, and difficult to watch from start to end. Fast forwarding (i.e. frame sampling) is a natural choice for faster video browsing. However, this accentuates the shake caused by natural head motion, making the fast forwarded video useless. We propose EgoSampling, an adaptive frame sampling that gives more stable fast forwarded videos. Adaptive frame sampling is formulated as energy minimization, whose optimal solution can be found in polynomial time. In addition, egocentric video taken while walking suffers from the left-right movement of the head as the body weight shifts from one leg to another. We turn this drawback into a feature: Stereo video can be created by sampling the frames from the left most and right most head positions of each step, forming approximate stereo-pairs.
Event Retrieval Using Motion Barcodes
Gil Ben-Artzi,Michael Werman,Shmuel Peleg
Computer Science , 2014,
Abstract: We introduce a simple and effective method for retrieval of videos showing a specific event, even when the videos of that event were captured from significantly different viewpoints. Appearance-based methods fail in such cases, as appearances change with large changes of viewpoints. Our method is based on a pixel-based feature, "motion barcode", which records the existence/non-existence of motion as a function of time. While appearance, motion magnitude, and motion direction can vary greatly between disparate viewpoints, the existence of motion is viewpoint invariant. Based on the motion barcode, a similarity measure is developed for videos of the same event taken from very different viewpoints. This measure is robust to occlusions common under different viewpoints, and can be computed efficiently. Event retrieval is demonstrated using challenging videos from stationary and hand held cameras.
Compact CNN for Indexing Egocentric Videos
Yair Poleg,Ariel Ephrat,Shmuel Peleg,Chetan Arora
Computer Science , 2015,
Abstract: While egocentric video is becoming increasingly popular, browsing it is very difficult. In this paper we present a compact 3D Convolutional Neural Network (CNN) architecture for long-term activity recognition in egocentric videos. Recognizing long-term activities enables us to temporally segment (index) long and unstructured egocentric videos. Existing methods for this task are based on hand tuned features derived from visible objects, location of hands, as well as optical flow. Given a sparse optical flow volume as input, our CNN classifies the camera wearer's activity. We obtain classification accuracy of 89%, which outperforms the current state-of-the-art by 19%. Additional evaluation is performed on an extended egocentric video dataset, classifying twice the amount of categories than current state-of-the-art. Furthermore, our CNN is able to recognize whether a video is egocentric or not with 99.2% accuracy, up by 24% from current state-of-the-art. To better understand what the network actually learns, we propose a novel visualization of CNN kernels as flow fields.
Camera Calibration from Dynamic Silhouettes Using Motion Barcodes
Gil Ben-Artzi,Yoni Kasten,Shmuel Peleg,Michael Werman
Computer Science , 2015,
Abstract: Computing the epipolar geometry between cameras with very different viewpoints is often problematic as matching points are hard to find. In these cases, it has been proposed to use information from dynamic objects in the scene for suggesting point and line correspondences. We propose a speed up of about two orders of magnitude, as well as an increase in robustness and accuracy, to methods computing epipolar geometry from dynamic silhouettes. This improvement is based on a new temporal signature: motion barcode for lines. Motion barcode is a binary temporal sequence for lines, indicating for each frame the existence of at least one foreground pixel on that line. The motion barcodes of two corresponding epipolar lines are very similar, so the search for corresponding epipolar lines can be limited only to lines having similar barcodes. The use of motion barcodes leads to increased speed, accuracy, and robustness in computing the epipolar geometry.
Refractory Ulcerative Proctitis—A Brief Review  [PDF]
Shmuel Odes
Open Journal of Gastroenterology (OJGas) , 2016, DOI: 10.4236/ojgas.2016.610029
Abstract: Ulcerative proctitis (UP) is a common condition in adult patients and can be very dif-ficult to treat. This review considers critically the definition of this entity, its epidemi-ology and course, and the modes of therapy currently available. Therapies currently in development are considered as well.
Singularity Free Quasi-Classical Schwarzschild Space-Times
Yoav Peleg
Physics , 1994,
Abstract: Using canonical (Schrodinger) quantization of spherically symetric gravitational dust systems, we find the quasi-classical (coherent) state, |\alpha^{(s)}>, that corresponds to the classical Schwarzschild solution. We calculate the ``quasi-classical Schwarzschild mertic", which is the expectation value of the quantized metric in thhis quasi-classical state. Depending on the quantization scheme that we use, we study three different quasi- classical geometries, all of which turn out to be singularity free. Their maximal extensions are complete manifolds with no singularities, describing a tower of asymptotically flat universes connected through Planck size wormholes.
The Wave Function of a Collapsing Star and Quantization Conditions
Yoav Peleg
Physics , 1993,
Abstract: A very simple minisuperspace describing the Oppenheimer-Snyder collapsing star is found. The semiclasical wave function of that model turn out to describe a bound state. For fixed initial radius of the collapsing star, the corrssponding Bohr-Sommerfeld quantization condition implies mass quantization. An extension of this model, and some consequences, are considered.
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