Processing multimedia data using Hadoop bigdata

Friends , Image processing is not just limited to adjust the spatial resolution of the everyday images captured by the camera. It is not just limited to increase the brightness of the photo, e.t.c. Rather it is far more than that.

Applications of Digital Image Processing

Some of the major fields in which digital image processing is widely used are mentioned below

  • Image sharpening and restoration
  • Medical field
    • Gamma ray imaging
    • PET scan
    • X Ray Imaging
    • Medical CT
    • UV imaging
    • Remote sensing
  • Transmission and encoding
  • Machine/Robot vision
  • Color processing
  • Pattern recognition
  • Video processing
  • Microscopic Imaging
  • E-commerce product image manipulation based on selection.
  • Security
Many image processing and computer vision algorithms are applicable to large-scale data tasks.
It is often desirable to run these algorithms on large data sets (e.g. larger than 1 TB) that are currently limited by the computational power of one computer
These tasks are typically performed on a distributed system by dividing the task across one or more of the following features: algorithm parameters, images, or pixels
Performing tasks across a particular parameter is incredibly parallel and can often be perfectly parallel. Face detection and landmark classification are examples of such algorithms
The ability to parallelize such tasks allows for scalable, efficient execution of resource-intensive applications.
The MapReduce framework provides a platform for such applications.

The HIPI Framework:

HIPI was created to empower researchers and present them with a capable tool that would enable research involving image processing and vision to be performed extremely easily. With the knowledge that HIPI would be used for researchers and as an educational tool, designed with following goal in mind.
1.Provide an open, extendible library for image processing and computer vision applications in a MapReduce framework
2. Store images efficiently for use in MapReduce applications
3. Allow for simple filtering of a set of images
4. Present users with an intuitive interface for image-based operations and hide the details of the MapReduce framework
5. HIPI will set up applications so that they are highly parallelized and balanced so that users do not have to worry about
such details.
In next tutorial i will show you how we can process images with HIPI Framework.
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