The one of the programming language is matlab which utilize the specialized data types in an interactive environment. Matlab projects for students are convenient to work and study ,which include the all generate programming concepts and application projects.The method of bisecting an image into an non intersecting regions so that each region as homogeneous and the combination of the adjacent regions is homogeneous called image segmentation.



Features matlab projects for students:

Auto detection.

No training,font independent,executable only.



Variety of application for Matlab projects for students using segmentation algorithm:

  • Motion picture colorization.
  • Optical character recognition(OCR).
  • Measurement & detection of tissue,bone in medical images.
  • Automatic target acquisition.

Steps for extracting the features:

  • Convolution of input fingerprint image with eight labour filters,obtaining eight filtered images.
  • Filtered image testellation into equal sized square disjoint cells.
  • Finger code extraction.

Pattern recognition is the method of taking input as raw data & performing an action based on the pattern category.definition of feature is described as one or more measurement function ,each one declare some object with quantifiable property,it computed which quantifies the some significant object characteristics

Aid of filtering:

  • For given road map,car position are tracked.
  • Aircraft positions from radar are tracked.
  • Economic data prediction.
  • Using radio frequency measurement,position of car tarck determined.
  • In noisy environment communications signals are estimated.
  • In surveilliance videos,particular peoples or cars are find out.

The techniques of fingerprint matching is the direct comparison of images with gray by an correlation based methods,the features are extracted from the gray scale image process are performed by most of the algorithm with fingerprint matching.

Filtering reduction stages:

  • Fisher linear discriminate.
  • The curse of dimensionality.
  • Locally linear embedding.
  • Principal component analysis.

Remote sensed data classification are applied to declare levels based on homogeneous characteristics group,with the goal of understanding multiple objects among each other within range.

Classification stages:

  • Classification class defining.
  • Feature selection.
  • Training data sampling.
  • Universal statistics estimation.
  • Classification.
  • Result verification.

The one of the technology is optical character recognition which recognize and capture alpha numeric characters with a computer high speed.It gives the processing form complete and solution of document capture.It also refered as intelligent character reader(ICR) or optical character reader.