CS324e - Elements of Graphics and Visualization Color Histograms.

Slides:

Advertisements

Similar presentations

Computer Vision, Part 1. Topics for Vision Lectures 1.Content-Based Image Retrieval (CBIR) 2.Object recognition and scene understanding.

Advertisements

Point Processing Histograms. Histogram Equalization Histogram equalization is a powerful point processing enhancement technique that seeks to optimize.

Statistical Analysis WHY ?.

Relevance Feedback and User Interaction for CBIR Hai Le Supervisor: Dr. Sid Ray.

Histograms! Histograms group data that is close together into “classes” and shows how many or what percentage of the data fall into each “class”. It.

Evaluating Color Descriptors for Object and Scene Recognition Koen E.A. van de Sande, Student Member, IEEE, Theo Gevers, Member, IEEE, and Cees G.M. Snoek,

November 12, 2013Computer Vision Lecture 12: Texture 1Signature Another popular method of representing shape is called the signature. In order to compute.

From Images to Answers A Basic Understanding of Digital Imaging and Analysis.

1 Texture Texture is a description of the spatial arrangement of color or intensities in an image or a selected region of an image. Structural approach:

Image Retrieval Basics Uichin Lee KAIST KSE Slides based on “Relevance Models for Automatic Image and Video Annotation & Retrieval” by R. Manmatha (UMASS)

Chapter 8 Content-Based Image Retrieval. Query By Keyword: Some textual attributes (keywords) should be maintained for each image. The image can be indexed.

6. Gray level enhancement Some of the simplest, yet most useful, image processing operations involve the adjustment of brightness, contrast or colour in.

1Ellen L. Walker ImageJ Java image processing tool from NIH Reads / writes a large variety of images Many image processing operations.

Chapter 4: Image Enhancement

1 Texture Texture is a description of the spatial arrangement of color or intensities in an image or a selected region of an image. Structural approach:

Content-based Image Retrieval CE 264 Xiaoguang Feng March 14, 2002 Based on: J. Huang. Color-Spatial Image Indexing and Applications. Ph.D thesis, Cornell.

Color: Readings: Ch 6: color spaces color histograms color segmentation.

Image Enhancement.

Texture Readings: Ch 7: all of it plus Carson paper

IMAGE 1 An image is a two dimensional Function f(x,y) where x and y are spatial coordinates And f at any x,y is related to the brightness at that point.

Computing IV Chapter Three: imgproc module Image Processing Xinwen Fu.

September 25, 2014Computer Vision Lecture 6: Spatial Filtering 1 Computing Object Orientation We compute the orientation of an object as the orientation.

Color Monday, Feb 7 Prof. Kristen Grauman UT-Austin.

Color Based on Kristen Grauman's Slides for Computer Vision.

The Digital Image.

CS 376b Introduction to Computer Vision 02 / 26 / 2008 Instructor: Michael Eckmann.

Census A survey to collect data on the entire population. Data The facts and figures collected, analyzed, and summarized for presentation and.

Adobe Photoshop CS Design Professional COLORS ADJUSTING.

© 2011 Delmar, Cengage Learning Chapter 11 Adjusting Colors.

Chapter 11 Adjusting Colors. Chapter Lessons Correct and adjust color Enhance colors by altering saturation Modify color channels using levels Create.

Graphical Summary of Data Distribution Statistical View Point Histograms Skewness Kurtosis Other Descriptive Summary Measures Source:

Color. -Visual light -An integral part of the sculpture -Creates desired effect -Distinguish items -Strengthen interest.

The Diminishing Rhinoceros & the Crescive Cow Exploring, Organizing, and Describing, Qualitative Data.

Watch, Listen and Learn Sonal Gupta, Joohyun Kim, Kristen Grauman and Raymond Mooney -Pratiksha Shah.

Image Retrieval Part I (Introduction). 2 Image Understanding Functions Image indexing similarity matching image retrieval (content-based method)

Quantitative Skills 1: Graphing

Content-Based Image Retrieval

Are You Smarter Than a 5 th Grader?. 1,000,000 5th Grade Topic 15th Grade Topic 24th Grade Topic 34th Grade Topic 43rd Grade Topic 53rd Grade Topic 62nd.

COLOR HISTOGRAM AND DISCRETE COSINE TRANSFORM FOR COLOR IMAGE RETRIEVAL Presented by 2006/8.

Point Operations – Chapter 5. Definition Some of the things we do to an image involve performing the same operation on each and every pixel (point) –We.

Graphing Data: Introduction to Basic Graphs Grade 8 M.Cacciotti.

CS654: Digital Image Analysis Lecture 18: Image Enhancement in Spatial Domain (Histogram)

Advanced Multimedia Image Content Analysis Tamara Berg.

CS 376b Introduction to Computer Vision 03 / 18 / 2008 Instructor: Michael Eckmann.

Content-Based Image Retrieval QBIC Homepage The State Hermitage Museum db2www/qbicSearch.mac/qbic?selLang=English.

 By Bob “The Bird” Fiske & Anita “The Snail” Cost.

Content-Based Image Retrieval (CBIR) By: Victor Makarenkov Michael Marcovich Noam Shemesh.

Computer Science 121 Scientific Computing Winter 2014 Chapter 14 Images.

Applying Pixel Values to Digital Images

Digital Image Processing Part 2 Contrast processing.

Machine Vision. Image Acquisition > Resolution Ability of a scanning system to distinguish between 2 closely separated points. > Contrast Ability to detect.

Content-Based Image Retrieval Using Color Space Transformation and Wavelet Transform Presented by Tienwei Tsai Department of Information Management Chihlee.

The Normal Approximation for Data. History The normal curve was discovered by Abraham de Moivre around Around 1870, the Belgian mathematician Adolph.

Image Processing Intro2CS – week 6 1. Image Processing Many devices now have cameras on them Lots of image data recorded for computers to process. But.

The Diminishing Rhinoceros & the Crescive Cow

Thanks to David Jacobs for the use of some slides

1-Introduction (Computing the image histogram).

Color: Readings: Ch 6: color spaces color histograms

Content-based Image Retrieval

Feature description and matching

Histogram Histogram is a graph that shows frequency of anything. Histograms usually have bars that represent frequency of occuring of data. Histogram has.

Image Processing – Contrast Enhancement

Digital Image Processing

Relative Frequency Graphs

Digital Image Fundamentals

A Channel for 3D Environmental Shape in Anterior Inferotemporal Cortex

Digital Image Processing

Medial Axis Shape Coding in Macaque Inferotemporal Cortex

Stability of Cortical Responses and the Statistics of Natural Scenes

Histogram The histogram of an image is a plot of the gray _levels values versus the number of pixels at that value. A histogram appears as a graph with.

Presentation transcript:

CS324e - Elements of Graphics and Visualization Color Histograms

Color Histogram Plot number of pixels with given intensity horizontal axis: intensity ( ) Vertical axis: – number of pixels with given intensity – or normalize to a percentage 2

Sample Image 3

Histogram Of Grayscale 4

Histogram Equalization Note the cluster in the middle Not a lot of very bright or very dark pixels Apply a Histogram Equalization filter to the image 5

Histogram Equalization An algorithm to try and improve the local contrast of an image without altering overall contrast to a significant degree Spread out the clumps of intensities to improve the contrast 6

Histogram Equalization Example Consider a color model with only 10 shades of gray Consider a simple image with only 25 pixels 7

Histogram Equalization Example Step 1: count the number of pixels with each intensity intensitycount What must the sum of 8 1counts be? 9 4 8

Histogram Equalization Example Normalize the counts to fractions or percentages intensity countfraction 0 33/ / / / / / / / / /25 9 Why divide by 25?

Histogram Equalization Example Step 3: compute the cumulative distribution function CDF – probability a pixel's intensity is less than or equal to the given intensity – just a running total of the fractions / percentages from step 2 10

Histogram Equalization Example Step 3: intensity countfraction Cumulative Distribution 0 33/253/ /259/25 (3 + 6) 2 44/2513/25 ( ) 3 22/2515/ /2517/ /2518/ /2519/ /2520/ /2521/ /2525/25 11

Histogram Equalization Example Step 4: Scale Cumulative Distribution to intensity range intensity countfraction CDFScaled Intensity 0 33/25 3/250 (10 * 3 / 25 = = 0) 1 6 6/25 9/ /25 13/ /25 15/ /25 17/ /25 18/ /25 19/ /25 20/ /25 21/ /25 25/259 12

Histogram Equalization Example Step 5: The scaled intensities become a lookup table to apply to original image intensity in originalintensity in result

Histogram Equalization Example Step 6: apply lookup table 14 original result original result 0s stay 0 1s become 3 2s become 4 and so forth

Recall Actual Image 15

Resulting Histogram 16

Resulting Image 17

Comparison 18

Example 2 19

Original Histogram 20

Resulting Histogram 21

Resulting Image 22

Comparison 23

Histogram Equalization on Color Images apply to color images each channel (red, green, blue) treated as separate histogram equalize each independently can lead to radical color changes in result 24

Histograms 25

Example of Color Histogram Equalization 26

Color as a low-level cue for Color Based Image Retreival Blobworld system Carson et al, 1999 Swain and Ballard, Color Indexing, IJCV 1991Color Indexing Slides on CBIR from Kristen Grauman

R GB Color histograms: Use distribution of colors to describe image No spatial info – invariant to translation, rotation, scale Color intensity Pixel counts Color as a low-level cue for CBIR

Color-based image retrieval Given collection (database) of images: – Extract and store one color histogram per image Given new query image: – Extract its color histogram – For each database image: Compute intersection between query histogram and database histogram – Sort intersection values (highest score = most similar) – Rank database items relative to query based on this sorted order

Color-based image retrieval Example database

Example retrievals Color-based image retrieval

Example retrievals Color-based image retrieval