MRT - Revision history

AndreaBonarini: /* Navigation */

2010-06-30T12:59:54Z

‎Navigation

AndreaBonarini at 12:54, 30 June 2010

2010-06-30T12:54:17Z

AndreaBonarini: /* MrBrian: Multilevel Ruling Brian Reacts by Inferential ActioNs */

2009-07-27T10:29:42Z

‎MrBrian: Multilevel Ruling Brian Reacts by Inferential ActioNs

AndreaBonarini: /* MrBrian: Multilevel Ruling Brian Reacts by Inferential ActioNs */

2009-07-27T10:29:26Z

‎MrBrian: Multilevel Ruling Brian Reacts by Inferential ActioNs

AndreaBonarini: /* MrBrian: Multilevel Ruling Brian Reacts by Inferential ActioNs */

2009-07-27T10:29:14Z

‎MrBrian: Multilevel Ruling Brian Reacts by Inferential ActioNs

AndreaBonarini: /* MRT Architecture */

2009-07-27T10:27:57Z

‎MRT Architecture

AndreaBonarini: /* MRT Architecture */

2009-07-27T10:27:41Z

‎MRT Architecture

ErmesViviani: /* Areas */

2009-07-04T17:34:20Z

‎Areas

ErmesViviani: /* Color Classifier */

2009-07-04T17:34:04Z

‎Color Classifier

ErmesViviani: /* MRT Vision Software */

2009-07-04T16:34:43Z

‎MRT Vision Software

@@ Line 372: / Line 372: @@
 ==== Navigation ====
-A module developed by [user:SimoneCeriani|Simone Ceriani] gives the possibility to know the position of the robot in a map. This is possible thanks to a module originally developed for [Lurch], which analyzes images to find  given markers, and computes the position of the robot w.r.t. the markers. A Kalman filter module integrating odometry is also available. All is implemented as a MRT expert and can provide to MAP the robot position.
+A module developed by [user:SimoneCeriani|Simone Ceriani] gives the possibility to know the position of the robot in a map. This is possible thanks to a module originally developed for [[Lurch]], which analyzes images to find  given markers, and computes the position of the robot w.r.t. the markers. A Kalman filter module integrating odometry is also available. All is implemented as a MRT expert and can provide to MAP the robot position.
 ====  Fixed duration tasks ====
 It is possible to define tasks active for a given time through SCARE, which gives the possibility to dfine roles active for some time

← Older revision		Revision as of 12:54, 30 June 2010
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	== SCARE Coordinates Agents in Robotic Environments ==		== SCARE Coordinates Agents in Robotic Environments ==
	== SPIKE Plans In Known Environments ==		== SPIKE Plans In Known Environments ==
		+	== Tips, Tricks and HowTo ==
		+	In this section we report some ways of obtaining desired effects in MRT.
		+
		+	==== Percept persistence ====
		+	Disappearing percepts are maintained in map for some time (settable parameter) by lowering their reliability. For instance, the presence of the ball for the Robocup application is maintained also if the ball is no longer perceived. After a given time, the percept is still maintained for another given time in the model, but no longer provided to the other modules. This is used for modeling purposes.
		+
		+	==== Navigation ====
		+	A module developed by [user:SimoneCeriani\|Simone Ceriani] gives the possibility to know the position of the robot in a map. This is possible thanks to a module originally developed for [Lurch], which analyzes images to find given markers, and computes the position of the robot w.r.t. the markers. A Kalman filter module integrating odometry is also available. All is implemented as a MRT expert and can provide to MAP the robot position.
		+
		+	==== Fixed duration tasks ====
		+	It is possible to define tasks active for a given time through SCARE, which gives the possibility to dfine roles active for some time

@@ Line 32: / Line 32: @@
 == MrBrian: Multilevel Ruling Brian Reacts by Inferential ActioNs ==
-The content of this is for the moment on the pdf manual mentioned above. Anyone wishing to improve it should take the conent and put it here below, in the proper section.
+The content of this is for the moment on the pdf manual mentioned above. Anyone wishing to improve it should take the content and put it here below, in the proper section.
 ===The Behavior-based Paradigm ===

@@ Line 32: / Line 32: @@
 == MrBrian: Multilevel Ruling Brian Reacts by Inferential ActioNs ==
-The content of this is for the moment on the pdf manual mentioned above. Anyone wishing to improve it should take the conent and put it here below, in the propoer section.
+The content of this is for the moment on the pdf manual mentioned above. Anyone wishing to improve it should take the conent and put it here below, in the proper section.
 ===The Behavior-based Paradigm ===

@@ Line 31: / Line 31: @@
 == MrBrian: Multilevel Ruling Brian Reacts by Inferential ActioNs ==
 ===The Behavior-based Paradigm ===
 === The Overall Architecture ===

@@ Line 25: / Line 25: @@
 The MRT architecture is described in the [[Media:STARbook.pdf‎ | paper]] published on the Springer STAR Series.
-The SW is available from the SVN site of teh department of Electronics. To register there follow this link
+The SW is available from the SVN site of the department of Electronics. To register there follow this link
 [https://acme.ws.dei.polimi.it/request_account.plp]

← Older revision		Revision as of 17:34, 4 July 2009
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	* classified.ppm, that is a classified version of the image;		* classified.ppm, that is a classified version of the image;
−
	* output.ppm (or <output file> if provided), that is the non-classified version of the image.		* output.ppm (or <output file> if provided), that is the non-classified version of the image.

@@ Line 274: / Line 274: @@
 The classification works in an immediate way. Depending on three color values (RGB or YUV triplets) it searches for the corresponding cell in an appropriate lookup matrix and returns a label which represents the sample color to which the real color is most similar. This way, after a whole image has been classified, it can be properly processed without having to worry about hundreds of possible colors but only about some useful ones. Therefore we can simplify the image processing removing color shades we know of no relevance for our application.
-The creation process of the lookup matrix is the most important part of the color classifier, since the whole image analyzation process depends on the quality of the color classification. The matrix is built starting from XXX sample colors, to which the corresponding cells are labelled accordingly; then following one of two possible clustering algorithms (KNN and DBC), the others cells are labelled. The DBC version has not been debugged enough so KNN is the only supported one at the present stage.
+The creation process of the lookup matrix is the most important part of the color classifier, since the whole image analyzation process depends on the quality of the color classification. The matrix is built starting from k sample colors, to which the corresponding cells are labelled accordingly; then following one of two possible clustering algorithms (KNN and DBC), the others cells are labelled. The DBC version has not been debugged enough so KNN is the only supported one at the present stage.
 KNN (K-Nearest Neighbours) that is based on the presence of a number (K) of other labels within a fixed distance form the target cell, that is therefore labelled according to the most frequent label found.
@@ Line 282: / Line 282: @@
 Since the module works both in RGB and YUV color spaces the color classifier is able to build an YUV matrix directly from the corresponding RGB one, and can load already existing matrix from file. The building of the classifier from a YUV file is not implemented yet, qt the present stage the classifier is saved in RGB format and if you want to use it as YUV classifier you have to run the "build YUV" method.
 === Receptor-based Vision ===

@@ Line 272: / Line 272: @@
 It's the part of the software responsible for the image classification process and for the creation of the necessary data structures.
-The classification works in an immediate way. Depending on three color values (RGB or YUV triplets) it searches for the corresponding cell in an appropriate lookup matrix and returns a label which represents the sample color to which the real color is most similar. This way, after a whole image has been classified, it can be properly processed without having to worry about hundreds of possible colors but only about XXX useful ones. Therefore we can simplify the image processing removing color shades we know of no relevance for our application.
+The classification works in an immediate way. Depending on three color values (RGB or YUV triplets) it searches for the corresponding cell in an appropriate lookup matrix and returns a label which represents the sample color to which the real color is most similar. This way, after a whole image has been classified, it can be properly processed without having to worry about hundreds of possible colors but only about some useful ones. Therefore we can simplify the image processing removing color shades we know of no relevance for our application.
 The creation process of the lookup matrix is the most important part of the color classifier, since the whole image analyzation process depends on the quality of the color classification. The matrix is built starting from XXX sample colors, to which the corresponding cells are labelled accordingly; then following one of two possible clustering algorithms (KNN and DBC), the others cells are labelled. The DBC version has not been debugged enough so KNN is the only supported one at the present stage.
@@ Line 311: / Line 311: @@
 ==== Areas ====
-This software is used as a testing utility whose task is to display, given an analyzed image, its color classification and the information gathered by the blob growing and feature extraction processes.
+This software is used as a testing utility whose task is to display, given an analyzed image, its color classification and the information gathered by the blob growing and feature extraction processes. The image is acquired through the FileAcquisition class and the color classification is done by means of an already created color classifier.
-The usage is: ./areas yuv|rgb 640x480|320x240 <image file> <area file> <color classifier> [<output file>]
+The usage is: ./areas yuv|rgb 640x480|320x240 <image file> <area file> <color classifier> [<output file>].
-That is, it accepts images in both YUV422 and RGB24 color spaces and supports 640x480 and 320x240 resolutions. <image file> should be a valid YUV422 raw image or RGB24 ppm file. <area file> is a text file containing all the information the program should represent on the image, such as blob's bounding boxes and lines.
+That is, it accepts images in both YUV422 and RGB24 color spaces and supports 640x480 and 320x240 resolutions. <image file> should be a valid YUV422 raw image or RGB24 ppm file. <area file> is a text file containing all the information the program should represent on the image, such as blob's bounding boxes and lines, saved in the following format:
 ==== Recvision Setup ====