IMA

FUZZY IMAGE PROCESSING PRESENTED BY S.PUSHPA SWETHA S.VADIVAMBIGAI I MSC-IT SRI KRISHNA COLLEGE OF ARTS AND SCIENCE: 

FUZZY IMAGE PROCESSING PRESENTED BY S.PUSHPA SWETHA S.VADIVAMBIGAI I MSC-IT SRI KRISHNA COLLEGE OF ARTS AND SCIENCE

FUZZY IMAGE PROCESSING : 

FUZZY IMAGE PROCESSING Image processing is any form of information processing for which both the input and output are images, such as photographs or frames of video. The Fuzzy image processing is one of the important application areas of fuzzy logic.

Image Processing Includes : 

Image Processing Includes Image quality and statistical evaluation Radiometric correction Geometric correction Image enhancement and sharpening Image classification Pixel based Object-oriented based Accuracy assessment of classification Post-classification and GIS Change detection

Image Quality: 

Image Quality Many remote sensing datasets contain high-quality, accurate data. Unfortunately, sometimes error (or noise) is introduced into the remote sensor data by: the environment (e.g., atmospheric scattering, cloud), random or systematic malfunction of the remote sensing system (e.g., an uncalibrated detector creates striping), or improper pre-processing of the remote sensor data prior to actual data analysis (e.g., inaccurate analog-to-digital conversion).

Purposes of image classification: 

Purposes of image classification Land use and land cover (LULC) Vegetation types Geologic terrains Mineral exploration Alteration mapping

Image enhancement: 

Image enhancement Image reduction, Image magnification, Transect extraction, Contrast adjustments (linear and non-linear), Band rationing, Spatial filtering, Fourier transformations, Principle components analysis, Texture transformations, and Image sharpening

Hard vs. Fuzzy classification: 

Hard vs. Fuzzy classification Supervised and unsupervised classification algorithms typically use hard classification logic to produce a classification map that consists of hard, discrete categories (e.g., forest, agriculture). Conversely, it is also possible to use fuzzy set classification logic , which takes into account the heterogeneous and imprecise nature (mix pixels) of the real world. Proportion of the m classes within a pixel (e.g., 10% bare soil, 10% shrub, 80% forest). Fuzzy classification schemes are not currently standardized

SOME RELATED FIELDS: 

SOME RELATED FIELDS

IMAGE PROCESSING: 

IMAGE PROCESSING Binary Gray Level Color (RGB,HSV etc.)

Fuzziness Vs. Vagueness: 

Fuzziness Vs. Vagueness Fuzziness= U nsharp boundaries Vagueness= Insufficient specificity

Example: Finding an Image Threshold : 

Example: Finding an Image Threshold

GRAY SCALE OF AN ABOVE IMAGE: 

GRAY SCALE OF AN ABOVE IMAGE

Example: Finding Edges: 

Example: Finding Edges

THANK YOU: 

THANK YOU