SIP stands for Scilab Image Processing toolbox. SIP
intends to do imaging tasks such as filtering, blurring, edge detection,
thresholding, histogram manipulation, segmentation,
mathematical morphology, color image
processing, etc.
These operations are useful for problem solving in
real-world applications ranging from
car motion planning to automatic diagnosis of medical images.
SIP is meant to be a
complete, useful, and FREE digital image
processing toolbox for
Scilab.
What SIP can do:
Although SIP is far from complete, it has the following
useful features:
- I/O of image files in many
formats, including
BMP, JPEG, GIF, PNG, TIFF, XPM, PCX, and more.
(Thanks to ImageMagick)
- Numerous functions with flexible interface and error
treatment (see a listing)
- Documentation with examples for all the functions
See the ChangeLog and the
Development pages for more information.
SIP vs. SIVP vs. IPD - the 'other' Scilab Image Processing toolboxes
Basically, SIP aims towards comprehensive functionality. The
only price is a longer installation process to get all the features
due to many third-party dependencies.
- SIP prioritizes GNU/Linux.
SIVP and IPD are currently easier for Windows users.
- SIP provides unified bindings to several image processing libraries: ImageMagick,
OpenCV, animal, and Leptonica.
- SIP has the most number of functions. As of april 2012, SIP has 74 help
pages, compared to 55 from SIVP and 53 from IPD.
- SIP appeared first, SIVP as a friendly fork of SIP. IPD appeared more
recently. Most
of the SIVP improvements have recently been merged back into SIP.
- SIP is targeted to more advanced users: aims at more functionality than SIVP
and IPD, but can be harder to install. This is a design decision.
-
SIP is designed for very rapid prototyping of imaging solutions.
- SIP has a friendly and responsive developer and user community.
- SIP provides ample illustrated documentation with examples.
- SIP provides state-of-the-art Euclidean morphology-related algorithms, such
as dilations, erosions, distance transforms, skeletons, watershed segmentation,
with reference implementations that are mostly superior to Matlab and other
software.
- SIVP has explicit handling of integer pixel depths, while SIP is purposedly built for
double representation, for simplicity.
- SIVP currently has support for video processing beyond SIP.
-
SIP focuses on ease of use, functionality, and
on the internal speed of the provided functions, not on
any low-level user-visible details that could complicate quick usage, such as
specialized support for huge images or integer pixel depths. Most image processsing
production code or complex time-consuming algorithms that need to deal with
very large images should switch from Scilab
to C/C++ (for example) as soon as the idea is working for medium-sized images in
Scilab+SIP.
- SIP was awarded Google Summer of Code 2012!
- Conclusion: All 3+ Scilab IP toolboxes are great and complement each
other. They even share code.
License: GPL.
Bugs:
Please report bugs at the
Bug
Tracker.
Current main authors:
Ricardo Fabbri (rfabbri at (not this part) gmail d0t
com), and
Zhang Cheng (zhangcheng.johnchain at gmail,com).
SIP is fostered by
prof. Luciano da Fontoura Costa (luciano at
if,sc,usp,br),
and
prof. Odemir Martinez Bruno (bruno at icmc,usp,br)
|
C++ Computer vision
libraries
