Digitization Basics for Archives and Special Collections – Part 1: Select and...WiLS
Josh Hickman, Digital Resources Librarian, Beloit College
This is the first part of a two-part, full-day workshop introducing the core elements of creating digital collections of historic photographs, documents and other archival materials. Part 1 focuses on selecting materials to digitize and the basics of reformatting. We’ll start with some recommendations for planning a successful project and consider how your digital collections can fit into the statewide and national landscape of digital content. We’ll discuss copyright concerns in order to help you answer the question “CAN I put this online?” And we’ll explore the vocabulary of digital images, including pixels, resolution and bit depth as well as tools and best practices for scanning photographs and documents.
Panel Discussion. Alfred Borden
Principal, The Lighting Practice; Naomi Miller
Senior Lighting Engineer, Pacific Northwest National Laboratory; Willem Sillevis Smitt,- Xicato; Kevin Willmorth, Lumenique LLC
Do you want to create robust and composable abstractions? Here’s an iterative process to define the essence of a domain and build composability into the core and then demonstrates how to apply this process to the Processing graphics library to develop a composable vector graphics system.
We employ the SURF detector to define salient image patches of blob-like textures and use the MPEG-7 SCD, the MPEG-7 CLD, the MPEG-7 EHD and the CEDD descriptors to produce local features’ vectors named SIMPLE-SC, SIMPLE-CL, SIMPLE-EH and SIMPLE-CEDD or “LoCATe” (Local Color And Texture descriptor) respectively. In order to test the new descriptors in the most straightforward fashion, we use the Bag-Of-Visual-Words framework for indexing and retrieval. The experimental results conducted on two different benchmark databases, with varying codebook sizes revealed an astonishing boost in the retrieval performance of the proposed descriptors compared both to their own performance (in their original form) and to other state-of-the-art methods of local and global descriptors. From this page, one can download the open source implementation of the SIMPLE descriptors (C#, Java and MATLAB).
Digitization Basics for Archives and Special Collections – Part 1: Select and...WiLS
Josh Hickman, Digital Resources Librarian, Beloit College
This is the first part of a two-part, full-day workshop introducing the core elements of creating digital collections of historic photographs, documents and other archival materials. Part 1 focuses on selecting materials to digitize and the basics of reformatting. We’ll start with some recommendations for planning a successful project and consider how your digital collections can fit into the statewide and national landscape of digital content. We’ll discuss copyright concerns in order to help you answer the question “CAN I put this online?” And we’ll explore the vocabulary of digital images, including pixels, resolution and bit depth as well as tools and best practices for scanning photographs and documents.
Panel Discussion. Alfred Borden
Principal, The Lighting Practice; Naomi Miller
Senior Lighting Engineer, Pacific Northwest National Laboratory; Willem Sillevis Smitt,- Xicato; Kevin Willmorth, Lumenique LLC
Do you want to create robust and composable abstractions? Here’s an iterative process to define the essence of a domain and build composability into the core and then demonstrates how to apply this process to the Processing graphics library to develop a composable vector graphics system.
We employ the SURF detector to define salient image patches of blob-like textures and use the MPEG-7 SCD, the MPEG-7 CLD, the MPEG-7 EHD and the CEDD descriptors to produce local features’ vectors named SIMPLE-SC, SIMPLE-CL, SIMPLE-EH and SIMPLE-CEDD or “LoCATe” (Local Color And Texture descriptor) respectively. In order to test the new descriptors in the most straightforward fashion, we use the Bag-Of-Visual-Words framework for indexing and retrieval. The experimental results conducted on two different benchmark databases, with varying codebook sizes revealed an astonishing boost in the retrieval performance of the proposed descriptors compared both to their own performance (in their original form) and to other state-of-the-art methods of local and global descriptors. From this page, one can download the open source implementation of the SIMPLE descriptors (C#, Java and MATLAB).
1.Wireless Communication System_Wireless communication is a broad term that i...JeyaPerumal1
Wireless communication involves the transmission of information over a distance without the help of wires, cables or any other forms of electrical conductors.
Wireless communication is a broad term that incorporates all procedures and forms of connecting and communicating between two or more devices using a wireless signal through wireless communication technologies and devices.
Features of Wireless Communication
The evolution of wireless technology has brought many advancements with its effective features.
The transmitted distance can be anywhere between a few meters (for example, a television's remote control) and thousands of kilometers (for example, radio communication).
Wireless communication can be used for cellular telephony, wireless access to the internet, wireless home networking, and so on.
APNIC Foundation, presented by Ellisha Heppner at the PNG DNS Forum 2024APNIC
Ellisha Heppner, Grant Management Lead, presented an update on APNIC Foundation to the PNG DNS Forum held from 6 to 10 May, 2024 in Port Moresby, Papua New Guinea.
Bridging the Digital Gap Brad Spiegel Macon, GA Initiative.pptxBrad Spiegel Macon GA
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This 7-second Brain Wave Ritual Attracts Money To You.!nirahealhty
Discover the power of a simple 7-second brain wave ritual that can attract wealth and abundance into your life. By tapping into specific brain frequencies, this technique helps you manifest financial success effortlessly. Ready to transform your financial future? Try this powerful ritual and start attracting money today!
Italy Agriculture Equipment Market Outlook to 2027harveenkaur52
Agriculture and Animal Care
Ken Research has an expertise in Agriculture and Animal Care sector and offer vast collection of information related to all major aspects such as Agriculture equipment, Crop Protection, Seed, Agriculture Chemical, Fertilizers, Protected Cultivators, Palm Oil, Hybrid Seed, Animal Feed additives and many more.
Our continuous study and findings in agriculture sector provide better insights to companies dealing with related product and services, government and agriculture associations, researchers and students to well understand the present and expected scenario.
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3. What is Shutterstock?
• Shutterstock sells stock images, videos & music.
• Crowdsourced from artists around the world
• Shutterstock reviews and indexes them for search
• Customers buy a subscription and download them
13. Any operation you can do on a set of
numbers, you can do on an image
• getting histograms
• computing median values
• standard deviations / variance
• other statistics
17. # python example to get a histogram from an image
import PIL
from PIL import Image
from pprint import pprint
image = Image.open('./samplephoto.jpg')
width, height = image.size
colors = image.getcolors(width*height)
hist = {}
for i, c in enumerate(colors):
hex = '%02x%02x%02x' % (c[1][0],c[1][1],c[1][2])
hist[hex] = c[0]
pprint(hist)
19. Indexing color histograms
• index colors just like you would index text
• amount of color = frequency of the term
color_txt = "cfebc2
cfebc2 cfebc2 cfebc2
cfebc2 cfebc2 cfebc2
cfebc2 cfebc2 cfebc2
95bf40 95bf40 95bf40
95bf40 95bf40 95bf40
2e6b2e 2e6b2e 2e6b2e
ff0000 …"
20. Solr Schema & Queries
<field name="color" type="text_ws" …>
• Can use solr’s default ranking effectively
/solr/select?q=ff0000 e2c2d2&qf=color&defType=edismax…
• or use term frequencies directly for specific sort functions:
sort=product(tf(color,"ff0000"),tf(color,"e2c2d2")) desc
21. Indexing color statistics
Represent aggregate statistics of each image
lightness:
median: 2
standard dev: 1
largest bin: 0
largest bin size: 50
saturation
median: 0
standard dev: 0
largest bin: 0
largest bin size: 100
…
22. Solr Fields & Queries
<field name=”hue_median” type=”int” …>
• Sort by the distance between input param
and median value for each image
/solr/select?q=*&sort=abs(sub($query,hue_median)) asc
29. How much of the image contains the
selected color?
• Score each color by the number of pixels
sort=tf(color,"cfebc2") desc
30. Balance Precision and Recall
• Reduce your colorspace enough
to balance:
• color accuracy
• index size
• query complexity
• result counts
• only need 100-200 colors for a good UX
✓
31. Weighing Multiple Colors Together
• If you search for 2 or more colors, the top result should have
the most even distribution of those colors
✓
• simple option:
sort=product(tf(color,"ff9900"),tf(color,"2280e2")) desc
• more complex: compute the standard deviation or variance
of the term frequencies of matching color values for each
image, and sort the results with the lowest variance first.
32. Weighing Similar & Different Colors
• The score for one color should reflect all the colors in the image.
• At indexing time, increase the score based on similar colors;
decrease it based on differing colors.
34. Conclusion
• Steps for building color search in Solr:
• Extract colors using a tool like the Python Image Library
• Score colors based on the number of pixels
• Adjust scores based on similar / different colors
• Index colors into Solr as text document
• In your query, sort by the term frequency values for each
color