Skip to content
Projects
Groups
Snippets
Help
Loading...
Sign in
Toggle navigation
D
dlib
Project
Project
Details
Activity
Cycle Analytics
Repository
Repository
Files
Commits
Branches
Tags
Contributors
Graph
Compare
Charts
Issues
0
Issues
0
List
Board
Labels
Milestones
Merge Requests
0
Merge Requests
0
CI / CD
CI / CD
Pipelines
Jobs
Schedules
Charts
Wiki
Wiki
Snippets
Snippets
Members
Members
Collapse sidebar
Close sidebar
Activity
Graph
Charts
Create a new issue
Jobs
Commits
Issue Boards
Open sidebar
钟尚武
dlib
Commits
2bbecc51
Commit
2bbecc51
authored
Apr 02, 2013
by
Davis King
Browse files
Options
Browse Files
Download
Email Patches
Plain Diff
updated docs
parent
5b3859a1
Show whitespace changes
Inline
Side-by-side
Showing
2 changed files
with
40 additions
and
1 deletion
+40
-1
imaging.xml
docs/docs/imaging.xml
+39
-1
term_index.xml
docs/docs/term_index.xml
+1
-0
No files found.
docs/docs/imaging.xml
View file @
2bbecc51
...
...
@@ -126,6 +126,7 @@
<item>
fine_hog_image
</item>
<item>
poly_image
</item>
<item>
hashed_feature_image
</item>
<item>
binned_vector_feature_image
</item>
<item>
nearest_neighbor_feature_image
</item>
<item>
randomly_sample_image_features
</item>
</section>
...
...
@@ -430,6 +431,40 @@
</component>
<!-- ************************************************************************* -->
<component>
<name>
binned_vector_feature_image
</name>
<file>
dlib/image_keypoint.h
</file>
<spec_file
link=
"true"
>
dlib/image_keypoint/binned_vector_feature_image_abstract.h
</spec_file>
<description>
This object is a tool for performing image feature extraction. In
particular, it wraps another image feature extractor and converts the
wrapped image feature vectors into a high dimensional sparse vector. For
example, if the lower level feature extractor outputs the vector [3,4,5]
and this vector is hashed into the second bin of four bins then the output
sparse vector is:
<blockquote>
[0,0,0,0, 3,4,5,1, 0,0,0,0, 0,0,0,0].
</blockquote>
That is, the output vector has a dimensionality that is equal to the number
of hash bins times the dimensionality of the lower level vector plus one.
The value in the extra dimension concatenated onto the end of the vector is
always a constant value of of 1 and serves as a bias value. This means
that, if there are N hash bins, these vectors are capable of representing N
different linear functions, each operating on the vectors that fall into
their corresponding hash bin.
<br/><br/>
The following feature extractors can be wrapped by the binned_vector_feature_image:
<ul
style=
"margin-top:0em"
>
<li><a
href=
"#hog_image"
>
hog_image
</a></li>
<li><a
href=
"#fine_hog_image"
>
fine_hog_image
</a></li>
<li><a
href=
"#poly_image"
>
poly_image
</a></li>
</ul>
</description>
</component>
<!-- ************************************************************************* -->
<component>
...
...
@@ -1623,6 +1658,7 @@
The following feature extractors can be used with the scan_image_pyramid object:
<ul
style=
"margin-top:0em"
>
<li><a
href=
"#hashed_feature_image"
>
hashed_feature_image
</a></li>
<li><a
href=
"#binned_vector_feature_image"
>
binned_vector_feature_image
</a></li>
<li><a
href=
"#nearest_neighbor_feature_image"
>
nearest_neighbor_feature_image
</a></li>
</ul>
</description>
...
...
@@ -1666,6 +1702,7 @@
The following feature extractors can be used with the scan_image_boxes object:
<ul
style=
"margin-top:0em"
>
<li><a
href=
"#hashed_feature_image"
>
hashed_feature_image
</a></li>
<li><a
href=
"#binned_vector_feature_image"
>
binned_vector_feature_image
</a></li>
<li><a
href=
"#nearest_neighbor_feature_image"
>
nearest_neighbor_feature_image
</a></li>
</ul>
</description>
...
...
@@ -1844,7 +1881,8 @@
<file>
dlib/image_processing.h
</file>
<spec_file
link=
"true"
>
dlib/image_processing/setup_hashed_features_abstract.h
</spec_file>
<description>
This is a tool for configuring the
<a
href=
"#hashed_feature_image"
>
hashed_feature_image
</a>
object
This is a tool for configuring the
<a
href=
"#hashed_feature_image"
>
hashed_feature_image
</a>
or
<a
href=
"#binned_vector_feature_image"
>
binned_vector_feature_image
</a>
object
with a random
<a
href=
"algorithms.html#projection_hash"
>
projection hash
</a>
.
</description>
<examples>
...
...
docs/docs/term_index.xml
View file @
2bbecc51
...
...
@@ -1204,6 +1204,7 @@
<term
file=
"imaging.html"
name=
"fine_hog_image"
/>
<term
file=
"imaging.html"
name=
"poly_image"
/>
<term
file=
"imaging.html"
name=
"hashed_feature_image"
/>
<term
file=
"imaging.html"
name=
"binned_vector_feature_image"
/>
<term
file=
"imaging.html"
name=
"nearest_neighbor_feature_image"
/>
<term
file=
"imaging.html"
name=
"haar_x"
/>
<term
file=
"imaging.html"
name=
"haar_y"
/>
...
...
Write
Preview
Markdown
is supported
0%
Try again
or
attach a new file
Attach a file
Cancel
You are about to add
0
people
to the discussion. Proceed with caution.
Finish editing this message first!
Cancel
Please
register
or
sign in
to comment