Free distorted perceptions provided software for linux

Visual difference metrics can predict whether differences between two images are visible to the human observer or not. Such metrics are used for testing either visibility of information (whether we can see important visual information) or visibility of noise (to make sure we do not see any distortions in images, e.g. due to lossy compression).

The image below shows how two input images, a reference image (upper left) and a distorted image (lower left), are processed with the VDP to produce a probability of detection map (right). Such probability of detection map tells how likely we will notice a difference between two images for each part of an image.

Red color denotes high probability, green - low probability. Red color is mostly present in the areas where there is a snow covered path. Because of smooth texture of the snow, there is not much visual masking and distortions are easily visible.

Although there are dozens of visible difference metrics that serve a similar purpose, our Visual Difference Predictor for HDR images (HDR-VDP) has two unique advantages: firstly, our metric works with a full range of luminance values that can be meet in a real word (HDR images), and secondly, we offer a complete source code for free.

High Dynamic Range Visible Difference Predictor (HDR-VDP) can work within the complete range of luminance the human eye can see. An input to our metric is a high dynamic range (HDR) image, or an ordinary 8-bits-per-color image, converted to the actual luminance values. The proposed metric takes into account the aspects of high contrast vision, like scattering of the light in the optics (OTF), nonlinear response to light for the full range of luminance, and local adaptation.

Set::Integer::Gapfillers is a Perl module that can fill in the gaps between integer ranges.

SYNOPSIS

use Set::Integer::Gapfillers;
$gf = Set::Integer::Gapfillers->new(
lower => -12,
upper => 62,
sets => [
[ 1, 17 ], # Note: Use comma, not
[ 25, 42 ], # range operator (..)
[ 44, 50 ],
],
);

$segments_needed_ref = $gf->segments_needed();

$gapfillers_ref = $gf->gapfillers();

$all_segments_ref = $gf->all_segments();
Any of the three preceding output methods can also be called with an expand option:
$segments_needed_ref = $gf->segments_needed( expand => 1 );

This Perl extension provides methods which may be useful in manipulating sets whose elements are consecutive integers. Suppose that you are provided with the following non-intersecting, non-overlapping sets of consecutive integers:

{ 1 .. 17 }
{ 25 .. 42 }
{ 44 .. 50 }

Suppose further that you are provided with the following lower and upper bounds to a range of consecutive integers:

lower: 12
upper: 62

Provide a set of sets which:

when joined together, would form a set of consecutive integers from the lower to the upper bound, inclusive; and
are derived from:

the sets provided;
proper subsets thereof; or
newly generated sets which fill in the gaps below, in between or above the provided sets.

Once a Set::Integer::Gapfillers object has been constructed, its segments_needed() method can be used to provide these results:

{ 12 .. 17 } # subset of 1st set provided
{ 18 .. 24 } # gap-filler set
{ 25 .. 42 } # 2nd set provided
{ 43 .. 43 } # gap-filler set
# (which happens to consist of a single element)
{ 44 .. 50 } # 3rd set provided
{ 51 .. 62 } # gap-filler set for range above highest provided set

Alternatively, you may only wish to examine the gap-filler sets. The gapfillers() method provides this set of sets.

{ 18 .. 24 } # gap-filler set
{ 43 .. 43 } # gap-filler set
{ 51 .. 62 } # gap-filler set

And, as an additional alternative, you may wish to have your set of sets begin or end with all the values of a given provided set, rather than a proper subset thereof containing only those values needed to populate the desired range. In that case, use the all_segments() method.

{ 1 .. 17 } # 1st set provided
{ 18 .. 24 } # gap-filler set
{ 25 .. 42 } # 2nd set provided
{ 43 .. 43 } # gap-filler set
# (which happens to consist of a single element)
{ 44 .. 50 } # 3rd set provided
{ 51 .. 62 } # gap-filler set for range above highest provided set

The results returned by the all_segments() method differ from those returned by the segments_needed() method only at the lower or upper ends. If, as in the above example, the lower bound of the target range of integers falls inside a provided segment, the first set returned by all_segments() will be the entire first set provided; the first set returned by segments_needed() will be a proper subset of the first set provided, starting with the requested lower bound.

guile spread is a guile module that conveniently wraps the Spread C libraries provided with the Spread 3.15.2 distribution. Spread is available at http://www.spread.org/.

Spread is a local/wide area group communication toolkit that runs on most modern operating systems.

guile spread allows convenient mechanisms for reliable multicasting information between applications as well as providing many more complicate assurances.

libFoundation is an Objective-C library that provides an almost complete implementation of the OpenStep specification. libFoundation also has many other extensions that can be found in the Apples Mac OS X Foundation library.
Enhancements:
- This release works with the libobjc provided by GCC (3.0 and up), and has been ported to 64-bit platforms.
- A few minor bugs have been fixed, and support for OpenBSD has been improved.

TVSpectre reads the TV programme information provided by tvmovie.de. Its GUI resembles that of the free, closed source Windows client TVGhost.

Installation:

- Create the directory "data" unter the installation directory.
- Modify TVSpectre.pm to find these directories.
- If the included Perl skripts should be callable from any directory, add use lib "installation directory"; to the top of them. Include the quotation marks.

Sporkie is a challenge/response spam email filter. Sporkie project uses CAPTCHAs (randomly generated, distorted images of numbers) in order to verify that a person sending email is human.

It works in the same manner as the ASK project, except that Sporkie uses CAPTCHAs. The main advantage to using CAPTCHAs is that it catches automated spam bots that dont spoof their email address. With ASK, such bots would get through the system.

This software works by sending a verifcation e-mail with a link back to anyone who sends you an e-mail. The user must then follow the link and enter the numbers they see in an image. If they input the correct numbers their e-mail gets put on a white list. All past (e-mail sent within the past 90 days) and future e-mails from that user will go directly to the intended account. It will completely eliminate all spam since it verifies that the e-mail address the mail was sent from was the real address, and that a human person uses that e-mail address. Other systems simply verify the address is a real e-mail address.

Sporkie is written completly in Java. It requires Java 5.0 or higher, however very minimal portions of code use 1.5 specific idioms. It would not be hard to write a Java 1.4 port.

Sporkie is provided under the OSCL Type-C license.

Sporkie is currently in an alpha stage so there is no stable release as of yet.

ObjectiveLib is a framework for Objective-C that provides a set of object containers and generic algorithms. It is designed to provide the same services to Objective-C programmers that the Standard Template Library provides to C++ programmers.
All of the standard containers (vector, deque, list, map, set, and others) and standard algorithms from the STL are included. Additionally, a set of iterator classes is provided to connect containers to generic algorithms.
A full set of binary streams is provided, which include file, memory buffer and socket streams, as well as layering streams that can buffer and/or compress data.
Enhancements:
- A complete set of binary streams has been added.
- These include streams that can read and write to/from files, memory buffers, and sockets. Additionally, layering streams are included that can buffer data, compress data (3 compression streams are provided), and manage object graphs written to the stream.
- Some method names that did not conform to the Cocoa naming conventions have been changed.

PFScalibration project provides an implementation of the Robertson et al. 2003 method for the photometric calibration of cameras and for the recovery of high dynamic range (HDR) images from the set of low dynamic range (LDR) exposures.

Tools provided with this software can be used for photometric calibration of both off-the-shelf digital cameras and HDR cameras as described in the MPI Research Report. A short tutorial on calibration of the LDR cameras and the recovery of the HDR images from multiple exposures is provided below. For details on the calibration of the HDR cameras please refer to the research report.