CH-4 – Edge Feature Extraction – Part-5

February 9, 2019April 25, 2024 Ali Imran Comment(0)

Important Note: This article is part of the series in which TechReport.us discuss theory of Video Stream Matching.

Basically Algo is one but here using its two possible approaches.

Edge Map
Edge Angles

4.4.2.1 Algorithm 3

Input:

BMP Image

Output:

Array Of Numerical values define the Edge Map.

Working:

It takes an Image and converts it into gray scale if it is not already in gray scale. Then apply Canny Algorithm 6 Steps define in chapter 2 and return the image edges.

Step 0 : Start

Step 1 : Takes input an Image (im).

Step 2 : If (im) in gray scale

If not

then

convert (im) into gray scale.

Step 3 : (im1) = Apply Gaussian to remove noise (im)

Step 4 : (im2) = Apply Sobel operator in Horizontal direction (im1)

Step 5 : (im3) = Apply Sobel operator in Vertical direction (im1)

Step 6 : AddIm = Im2 + Im3

Step 7 : Loop1: for I =rows of (AddIm) //calculate 4 angles according to define

Step 8 : Loop2: for J = columns of (AddIm)

Step 9 : Calculate angle for each AddIm(I,J)

Store in AngelMat(I,J)

Step 10 : end Loop1

Step 11 : end Loop2

Step 12 : t1 = Threshold(AddIm)

Step 13 : t1= t1 + t1* 0.5 // for non maxima suppression

Step 14 : Loop1 : for i=rows of AddIm

Step 15 : Loop2 : for j=columns of AddIm

If AddIm( i,j) >t1 and AngleMat(i,j) is according to rules

Set AddIm( i,j) =1

else

Set AddIm( i,j) =0

Step 16: end Loop2

Step 17: end Loop1

Step 18 : t1 = Threshold(AddIm)

Step 19 : t2 = t1 + t1* 0.5 // for Double Threshold

Step 20 : Loop1 : for i=rows of AddIm

Step 21 : Loop2 : for j=columns of AddIm

If AddIm( i,j) >t1

Set AddIm( i,j) =1

else

Set AddIm( i,j) =0

Step 22 : Cheak 8-N of ( i,j)

If AddIm( i,j) Neighbours > t2

then

Set to 1

Else

Set to 0

Step 23: end Loop2

Step 24: end Loop1

Step 25 : Return (AddIm)

Step 26 : Stop

4.4.2.2 Algorithm 4

Input:

BMP Image

Output:

Array Of Numerical values define the Edge Angles.

Working:

It takes an Image and converts it into gray scale if it is not already in gray scale. Then apply Canny Algorithm 6 Steps define in chapter 2 and return the image angles.

Step 0 : Start

Step 1 : Takes input an Image (im).

Step 2 : If (im) in gray scale

If not

then

convert (im) into gray scale.

Step 3 : (im1) = Apply Gaussian to remove noise (im)

Step 4 : (im2) = Apply Sobel operator in Horizontal direction (im1)

Step 5 : (im3) = Apply Sobel operator in Vertical direction (im1)

Step 6 : AddIm = Im2 + Im3

Step 7 : Loop1: for I =rows of (AddIm) //calculate 4 angles according to define

Step 8 : Loop2: for J = columns of (AddIm)

Step 9 : Calculate angle for each AddIm(I,J)

Store in AngelMat(I,J)

Step 10 : end Loop1

Step 11 : end Loop2

Step 12 : Row =1 , Col =1

Step 13 : Loop1 : for i=rows of AddIm // make tiles of image

Step 14 : Col = Col + 1 , Row = 1 ;

Step 15 : Loop2 : for j=columns of AddIm

Calculate number of times each angle occuers in each tile

And store it in ResAng(Row,Col)

Row = Row + 1

Step 16: end Loop2

Step 17: end Loop1

Step 18 : Return (ResAng)

Step 19 : Stop

Ali Imran

Over the past 20+ years, I have been working as a software engineer, architect, and programmer, creating, designing, and programming various applications. My main focus has always been to achieve business goals and transform business ideas into digital reality. I have successfully solved numerous business problems and increased productivity for small businesses as well as enterprise corporations through the solutions that I created. My strong technical background and ability to work effectively in team environments make me a valuable asset to any organization.

https://ITsAli.com

CH-1 – Video Stream Matching – VSM

February 9, 2019May 6, 2024 Ali Imran

The article begins by highlighting the dynamic nature of image processing in the context of advancing information technology. It predicts a future shift from image to video processing due to the increasing prevalence of videos over text-based media. The Video Stream Matcher (VSM) is introduced as a tool for analyzing video data using statistical models like KS Test, Variation, Mean, and Norm.

Key frames extraction is identified as a technique to minimize the vast collection of frames in a video. Four features are extracted from each frame: histogram, edge, slope, and wavelets. The literature review section references research papers and books that inform the implementation of VSM, including studies on similarity analysis of video sequences and key frame extraction.

The key elements of VSM are outlined, including the input of source and test data (videos or images), key frames extraction, feature extraction, and application of statistical models for decision making. The problem statement highlights the need for automated video evaluation systems, particularly in scenarios like security checks on public transportation and video database management in TV stations.

The proposed solution revolves around VSM’s ability to process videos, focusing on key frames extraction, feature extraction, and statistical model application for decision making. The scope of the project encompasses mean frame extraction, feature extraction algorithms, and dual-phase statistical decision making. The organization of the report is structured to cover research findings, implementation details, software functioning, and conclusions with future work discussions.

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