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A method for aligning video images according to sequence. The problem of image alignment has been extensively studied, and successful approaches have been developed for solving this problem. However, these approaches turn out as problematic when there is insufficient overlap between the two images to...

A method for aligning video images according to sequence. The problem of image alignment has been extensively studied, and successful approaches have been developed for solving this problem. However, these approaches turn out as problematic when there is insufficient overlap between the two images to allow extraction of common image properties, i.e., when there is no sufficient similarity (e.g., gray-level, frequencies, statistical) between the two images. Whereas two individual images cannot be aligned when there is no spatial overlap between them, this is not the case when dealing with image sequences. The outlined technology consists of fusion and alignment of discrete, non-overlapping moving images from different sources, by aligning spatio-temporal changes in each sequence rather than in each image.

Applications


  • Multi-sensor image alignment for multi-sensor fusion
  • Alignment of images (sequences) obtained at significantly different zooms (can be useful in surveillance applications)
  • Generation of wide-screen movies from multiple non-overlapping narrow field-of-view movies (such as in IMAX movies) 
  • Alignment and integration of information across video sequences to exceed the physical visual limitations of any individual sensor (e.g., dynamic range, spectral range, spatial resolution, temporal resolution, etc). ~

Advantages


  • Useful for spatially non-overlapping sequences
  • Useful in cases which are inherently difficult for standard image alignment techniques, such as when there is insufficient common spatial information across the two sequences

Technology's Essence


An image sequence contains much more information than any individual image frame does. In particular, temporal changes in a video sequence (e.g., due to camera motion) do not appear in any individual image frame, but are encoded between video frames. When these temporal changes are common to the two sequences, then these sequences can be aligned both in time and in space, even if there is no common spatial information whatsoever. The need for coherent visual appearance, which is a fundamental assumption in image alignment methods, is replaced in this invention with the requirement of coherent temporal behavior. This can be achieved by attaching the two video cameras closely to each other (so that their centers of projections are very close), and moving them jointly in space (e.g., such as when the two cameras are mounted on a moving platform or rig).

 

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  • Prof. Michal Irani
1381

Applications


The new method for detecting irregularities has many applications which include:

  1. Detecting suspicious and/or salient behaviors in video
  2. Attention and saliency in images
  3. Detecting irregular tissue in medical images
  4. Automatic visual inspection for quality assurance (e.g., detecting defects in goods)
  5. Generating a video summary/synopsis
  6. Intelligent fast forward
  7. Non-visual data

    Technology's Essence


    Researchers at the Weizmann Institute have developed a new method for detecting irregularities based only on few regular examples, without any assumed models. In the new method the validity of data is determined as a process of constructing a puzzle: one tries to compose a new observed image region or a new video segment (''the query'') using chunks of data (''pieces of puzzle'') extracted from previous visual examples (''the database''). Regions in the observed data which can be composed using large contiguous chunks of data from the database are considered very likely, whereas regions in the observed data which cannot be composed from the database (or can be composed, but only using small fragmented pieces) are regarded as unlikely/suspicious. The problem is posed as an inference process in a probabilistic graphical model. The invention also includes an efficient algorithm for detecting irregularities. Moreover, the same method can also be used for detecting irregularities/anomalies within data without any prior examples, by learning the notion of regularity/irregularity directly from the query data itself.

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  • Prof. Michal Irani
1461
Bidirectional Similarity offers a new approach to summarization of visual data (images and video) based on optimization of well defined similarity measure. Common visual summarization methods (mainly scaling and cropping) suffer from significant deficiencies related to image quality and loss of...

Bidirectional Similarity offers a new approach to summarization of visual data (images and video) based on optimization of well defined similarity measure.

Common visual summarization methods (mainly scaling and cropping) suffer from significant deficiencies related to image quality and loss of important data. Many attempts have been made to overcome these problems, however, success was very limited and neither has become commercially applicable.

Using an optimization problem approach and state-of-the-art algorithms, our method provides superior summarization of visual data as well as a measure to determine similarity, which together provides a basis for a wide range of applications in image and video processing.

Applications


The technology can be utilized in any application where an image size is changed or were similarity of images is important. Sample applications include:

  • Image processing software (as an added-on feature)

  • Resizing software

  • Creation of Thumbnails

  • Adjustment of images to different screen sizes (TV-cellular etc.)

  • Optimization of space-time patches in video processing

  • Image montages

  • Automatic image & video cropping

  • Images synthesis, photo reshuffling and many more


Advantages


While Bidirectional Similarity summarization will not replace existing technologies in all applications, it enjoys significant advantages that will offer better results in many of them. Among its advantages, the Bidirectional Similarity summarization:

  • Provides better resolution and in many cases reduces distortion compared to scaling
  • Reduces (or avoids) loss of important data compared to cropping
  • Allows importance-based summarization even when important information is widespread and hard to define
  • Uses quantitative objective similarity measure
  • Offers a generic tool for different image processing applications (synthesis, montage, reshuffling etc.)

Technology's Essence


Bidirectional Similarity Summarization is a patent-pending image and video processing method, which maximizes “completeness” and “coherence” between images and videos, using a measure for quantifying how “good” a visual summary is.

The algorithm uses and iterative process, gradually reducing the image size, while keeping all source patches in the target image, without introducing visual artifacts that are not in the input data. Using a Similarity Index, the algorithm identifies redundant information and compromise the “less important” data while generating the required target image or video.

The Similarity Index, which stands in the heart of the Bidirectional Similarity summarization algorithm, can be utilized by its own, as an objective function within other optimization processes, as well as in comparing the quality of visual summaries generated by different methods

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  • Prof. Michal Irani

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