Computer implemented system and method for image texture determination

• US 10,255,685 B2
• Filed: 02/09/2016
• Issued: 04/09/2019
• Est. Priority Date: 03/25/2015
• Status: Active Grant

• Current Assignee: TATA Consultancy Services Limited (Tata Sons Pvt Ltd.)
• Original Assignee: TATA Consultancy Services Limited (Tata Sons Pvt Ltd.)
• Inventors: Sharma, Monika, Ghosh, Hiranmay
• Primary Examiner(s): Saini, Amandeep
• Application Number:
  US15/019,794
• Publication Number:
  US 20160284100A1
• Time in Patent Office: 1,155 Days
• Field of Search: 382108
• CPC Class Codes: G06T 2207/10032 Satellite or aerial image; Remote sensing
• CPC Class Codes: G06T 2207/20021 Dividing image into blocks, subimages or windows
• CPC Class Codes: G06T 2207/30188 Vegetation; Agriculture
• CPC Class Codes: G06T 5/40 using histogram techniques
• CPC Class Codes: G06T 5/70 Denoising; Smoothing
• CPC Class Codes: G06T 5/73 Deblurring; Sharpening
• CPC Class Codes: G06T 7/11 Region-based segmentation
• CPC Class Codes: G06T 7/40 Analysis of texture depth or shape recovery from texture G06T7/529
• CPC Class Codes: G06T 7/44 using image operators, e.g. filters, edge density metrics or local histograms
• CPC Class Codes: G06V 10/50 by performing operations within image blocks; by using histograms, e.g. histogram of oriented gradients [HoG]; by summing image-intensity values; Projection analysis
• CPC Class Codes: G06V 10/54 relating to texture
• CPC Class Codes: G06V 10/758 Involving statistics of pixels or of feature values, e.g. histogram matching

Abstract

This disclosure relates generally to image processing, and more particularly to image texture determination. In one embodiment a processor a memory coupled to the processor, wherein the processor coupled with a plurality of modules stored in the memory: At least one image having a plurality of pixels is accepted. Any noise is removed from the image to obtain at least one noise free image. The at least one noise free image is converted to at least one gray scale image. Horizontal and Vertical Gradient for plurality of pixels of the at least one gray scale image are computed. Gradient magnitude is calculated for the generated gradient. Histogram of the gradient magnitude is generated based on the gradient magnitude, and the plurality of generated histograms are compared with a plurality of predetermined histograms.

Claims

1. A computer implemented system for determining image texture, said system comprising:

a processor;

a memory coupled to the processor, wherein the processor is coupled with a plurality of modules stored in the memory, and wherein the plurality of modules comprises;

an input module that accepts at least one image having a plurality of pixels;

a noise removal module that receives said at least one image and is further configured to remove noise from the at least one image to obtain at least one noise free image;

a gray scale converter module that receives the at least one noise free image and is further configured to convert the at least one noise free image into at least one gray scale image;

a gradient module that receives at least one gray scale image and is further configured to compute a gradient in horizontal and vertical direction for a plurality of pixels of the at least one gray scale image;

a magnitude determiner that receives said gradient in horizontal and vertical direction for said plurality of pixels of the at least one gray scale image and is further configured to compute a gradient magnitude for the plurality of pixels of the at least one gray scale image;

a histogram generator module receives said gradient magnitude for the plurality of pixels of said at least one gray scale image and is further configured to generate a histogram of gradient magnitude for said at least one gray scale image based on said received gradient magnitude; and

a texture determiner module having a comparator that receives said plurality of predetermined histogram and receives said generated histogram of gradient magnitude for the said gray scale image and is further configured to compare the generated histogram of gradient magnitude with a plurality of predetermined histograms to determine the image texture.

2. The system as claimed in claim 1, wherein the noise removal module is configured to use Gaussian blur technique to remove noise from the at least one image.

3. The system as claimed in claim 1, wherein the generated histogram of gradient magnitude is independent of rotation of the at least one image.

4. A computer implemented method for determining image texture, said method comprising:

accepting, by a processor, at least one image having a plurality of pixels;

removing, by the processor, noise from the at least one image to obtain at least one noise free image;

converting, by the processor, the at least one noise free image into at least one gray scale image;

computing, by the processor, a gradient in horizontal and vertical direction for a plurality of pixels of the at least one gray scale image;

computing, by the processor, a gradient magnitude for the at least one gray scale image;

generating, by the processor, a histogram of gradient magnitude for the at least one gray scale image; and

comparing, by the processor, the histogram of gradient magnitude with a plurality of predetermined histograms to determine the image texture.

5. The method as claimed in claim 4, wherein the generated histogram of gradient magnitude is rotational invariant.

6. The method as claimed in claim 4, further includes storing the plurality of predetermined histograms.

7. A non-transitory computer-readable medium having embodied thereon a computer program for executing a method for determining image texture, the method comprising:

accepting a plurality of images having a plurality of pixels;

removing noise from at least one image to obtain at least one noise free image;

converting the at least one noise free image into at least one gray scale image;

computing a gradient in horizontal and vertical direction for a plurality of pixels of the at least one gray scale image;

computing a gradient magnitude for the at least one gray scale image;

generating a histogram of gradient magnitude for the at least one gray scale image; and

comparing the generated histogram of gradient magnitude with a plurality of predetermined histograms to determine the image texture.