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Scientist
1250
A robust method of identifying moving or changing objects in a video sequence groups each pixel with other adjacent pixels according to either motion or intensity values. Pixels are then repeatedly regrouped into clusters in a hierarchical manner. As these clusters are regrouped, the motion pattern is...

A robust method of identifying moving or changing objects in a video sequence groups each pixel with other adjacent pixels according to either motion or intensity values. Pixels are then repeatedly regrouped into clusters in a hierarchical manner. As these clusters are regrouped, the motion pattern is refined, until the full pattern is reached.

Applications


These methods for motion-based segmentation may be used in a multitude of applications that need to correctly identify meaningful regions in image sequences and compute their motion. Such applications include:

  1. Surveillance and homeland security - detecting changes, activities, objects.
  2. Medical Imaging - imaging of dynamic tissues.
  3. Quality control in manufacturing, and more.

Technology's Essence


Researchers at the Weizmann Institute of Science have developed a multiscale, motion-based segmentation method which, unlike previous methods, uses the inherent multiple scales of information in images. The method begins by measuring local optical flow at every picture elements (pixels). Then, using algebraic multigrid (AMG) techniques, it assembles together adjacent pixels which are similar in either their motion or intensity values into small aggregates - each pixel being allowed to belong to different aggregates with different weights. These aggregates in turn are assembled into larger aggregates, then still larger, etc., yielding eventually full segments.

As the aggregation process proceeds, the estimation of the motion of each aggregate is refined and ambiguities are resolved. In addition, an adaptive motion model is used to describe the motion of an aggregate, depending on the amount of flow information that is available within each aggregate. In particular, a translation model is used to describe the motion of pixels and small aggregates, switch to an affine model to describe the motion of intermediate sized aggregates, and finally turn to a perspective model to describe aggregates at the coarsest levels of scale. In addition to this, methods for identifying correspondences between aggregates in different images are also being developed. These methods are suitable for image sequences separated by fairly large motion.

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  • Prof. Ronen Ezra Basri
1447
A cheap and effective solution for protecting RFID tags from power attacks. RFID tags are secure tags present in many applications (e.g. secure passports). They are poised to become the most far-reaching wireless technology since the cell phone, with worldwide revenues expected to reach $2.8 billion in...

A cheap and effective solution for protecting RFID tags from power attacks.

RFID tags are secure tags present in many applications (e.g. secure passports). They are poised to become the most far-reaching wireless technology since the cell phone, with worldwide revenues expected to reach $2.8 billion in 2009. RFID tags were believed to be immune to power analysis attacks since they have no direct connection to an external power supply. However, recent research has shown that they are vulnerable to such attacks, since it is possible to measure their power consumption without actually needing either tag or reader to be physically touched by the attacker. Furthermore, this attack may be carried out even if no data is being transmitted between the tag and the attacker, making the attack very hard to detect. The current invention overcomes these problems by a slight modification of the tag's electronic system, so that it will not be vulnerable to power analysis.

Applications


  • Improved security of RFID tags.

Advantages


  • Simple and cost-effective
  • The design involves changes only to the RF front-end of the tag, making it the quickest to roll-out


Technology's Essence


An RFID system consists of a high-powered reader communicating with a tag using a wireless medium. The reader generates a powerful electromagnetic field around itself and the tag responds to this field. In passive systems, placing a tag inside the reader's field also provides it with the power it needs to operate. According to the inventive concept, the power consumption of the computational element is detached from the power supply of the tag. Thus, the present invention can almost eliminate the power consumption information.

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  • Prof. Adi Shamir
1522
A method for enhancing the spatial and or temporal resolution (if applicable) of an input signal such as images and videos.   Many imaging devices produce signals of unsatisfactory resolution (e.g. a photo from a cell-phone camera may have low spatial resolution or a video from a web camera may have...

A method for enhancing the spatial and or temporal resolution (if applicable) of an input signal such as images and videos.

 

Many imaging devices produce signals of unsatisfactory resolution (e.g. a photo from a cell-phone camera may have low spatial resolution or a video from a web camera may have both spatial and temporal low resolution). This method applies digital processing to reconstruct more satisfactory high resolution signals.

 

Previous methods for Super-Resolution (SR) require multiple images of the same scene, or else an external database of examples. This method provides the ability to perform SR from a single image (or a single visual source). The algorithm exploits the inherent local data redundancy within visual signals (redundancy both within the same scale, and across different scales).

 

Examples of the methods' capabilities can be found here: http://www.wisdom.weizmann.ac.il/~vision/SingleImageSR.html

 

Applications


  • Enhancing the spatial resolution of images

  • Enhancing the spatial and or temporal resolution of video sequences

  • Enhancing the spatial and or temporal resolution (if applicable) of other signals (e.g., MRI, fMRI, ultrasound, possibly also audio, etc.)

 


Advantages


  • No need for multiple low resolution sources or the use of an external database of examples.

  • Superior results are produced due to exploitation of inherent information in the source signal.


Technology's Essence


The framework combines the power of classical multi image super resolution and example based super resolution. This combined framework can be applied to obtain super resolution from as little as a single low-resolution signal, without any additional external information. The approach is based on an observation that patches in a single natural signal tend to redundantly recur many times inside the signal, both within the same scale, as well as across different scales.

Recurrence of patches within the same scale (at subpixel misalignments) forms the basis for applying the 'classical super resolution' constraints to information from a single signal. Recurrence of patches across different (coarser) scales implicitly provides examples of low-resolution / high-resolution pairs of patches, thus giving rise to 'example-based super-resolution' from a single signal (but without any external database or any prior examples).

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  • Prof. Michal Irani

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