Template matching - Revision history

Raffaele Coray: /* Basic concepts */

2025-07-30T13:18:47Z

Basic concepts

Raffaele Coray: /* Basic concepts */

2025-07-30T13:17:23Z

Basic concepts

Raffaele Coray at 13:14, 30 July 2025

2025-07-30T13:14:27Z

Raffaele Coray at 13:14, 30 July 2025

2025-07-30T13:14:05Z

Raffaele Coray: Created page with "Template consist in using an object of interest by systematically comparing it against a tomogram to identify the positions and orientations of copies of that object in the to..."

2025-07-30T13:13:38Z

Created page with "Template consist in using an object of interest by systematically comparing it against a tomogram to identify the positions and orientations of copies of that object in the to..."

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Template consist in using an object of interest by systematically comparing it against a tomogram to identify the positions and orientations of copies of that object in the tomogram. This procedure can be seen either as an approach for particle picking or even segmentation. In ''Dynamo'' this is done either with the <tt>dynamo_match<tt> function or directly through the <tt>dtmslice<tt> GUI.

== Basic concepts==

In this technique, a template representing a molecule of interest is systematically cross-correlated against a tomogram, producing a ''cross-correlation map'' of the tomogram. Each pixel in this map represents a score assigned the corresponding pixel in the tomogram map. This score measures the similarity of the neighbourhood of the tomogram pixel to the used template.
This similarity is measured exclusively inside a mask.

Once this cross-correlation map has been computed, local maxima can be identified to select the positions and orientations of the found particles in the tomogram.

In summary, ''Dynamo'' allows users to compute these cross-correlation map in multiple ways:
* Basic template matching: this is the simplest way to use template matching in ''Dynamo''
* Template matching by GUI: ''Dynamo'' provides a GUI to use template matching in a interactive way.
* Model-aware template matching: this mode allows users to improve the speed and quality of the procedure by including additional prior informations on the geometry (i.e. model) of the sample to guide template matching.
* On-the-fly template matching: in this mode, a tilt series is given as input instead of a tomogram and sections are reconstructed on-the-fly.
* Single chunk modus template matching: this model allow for the parallel inspection of single specific region of the tomogram (the so-called single chunk). Ideal for fine and exhaustive angular searches in restricted regions of the sample.

== Basic template matching==

Basic (non-model-aware) template matching is the simplest way to use template match in ''Dynamo''. In this mode, the template will be cross-correlated against a tomogram with the same set of rotation everywhere. The tomogram may be split into chunks, but this is done only so as not to crowd the memory during computation.

A full walkthrough of [[Walkthrough for template matching | template matching in this mode]] is available.

== GUI-based template matching==

Template matching can be done in ''Dynamo'' directly from the <tt>dtmslice</tt> GUI with a tomogram as input. It has a tab called Template Matching in the top bar of the GUI. It is through this tab that general attributes, parameters and region of interest for template matching processes can be manipulated. More information on how to use the GUI can be found in [[Walkthrough for GUI-based template matching | the GUI walkthrough]]. It is also possible to add custom functionalities to the GUI as described in the [[Programmatic GUI customization for template matching | corresponding tutorial]].

== Model-aware template matching==

Model-aware template matching is meant as an extension of the normal template matching algorithm in ''Dynamo''. As such, all basic commands described in the previous sections are valid in this mode as well.

The main change introduced in model-aware template matching is that the partition of the tomogram in regions of interest (chunks) is meant to be meaningful from the point of view of the tomogram models. This allows for the reduction of needed computation by restricting the angular combinations of the template to be scanned. The description of which chunks are created and their corresponding restricted angular combinations will be called a tessellation policy.

A walkthrough on [[Walkthrough for model-aware template matching | model-aware template matching]] is available. Additionally, here template mathcing using on-the-fly reconstructions and single-chunk modus are also described.

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 * '''On-the-fly modus''': in this modus, a tilt series is given as input instead of a tomogram and sections are reconstructed on-the-fly.
 * '''Single chunk modus''': this modus allow for the parallel inspection of single specific region of the tomogram (the so-called single chunk). Ideal for fine and exhaustive angular searches in a restricted region of the sample.
 == Basic template matching==

← Older revision		Revision as of 13:14, 30 July 2025
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−	Template consist in using an object of interest by systematically comparing it against a tomogram to identify the positions and orientations of copies of that object in the tomogram. This procedure can be seen either as an approach for particle picking or even segmentation. In ''Dynamo'' this is done either with the <tt>dynamo_match<tt> function or directly through the <tt>dtmslice<tt> GUI.	+	Template consist in using an object of interest by systematically comparing it against a tomogram to identify the positions and orientations of copies of that object in the tomogram. This procedure can be seen either as an approach for particle picking or even segmentation. In ''Dynamo'' this is done either with the <tt>dynamo_match</tt> function or directly through the <tt>dtmslice</tt> GUI.

	== Basic concepts==		== Basic concepts==

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 In summary, ''Dynamo'' allows users to compute these cross-correlation map in multiple ways:
-* Basic template matching: this is the simplest way to use template matching in ''Dynamo''
+* '''Basic template matching''': this is the simplest way to use template matching in ''Dynamo''
-* Template matching by GUI: ''Dynamo'' provides a GUI to use template matching in a interactive way.
+* '''Template matching by GUI''': ''Dynamo'' provides a GUI to use template matching in a interactive way.
-* Model-aware template matching: this mode allows users to improve the speed and quality of the procedure by including additional prior informations on the geometry (i.e. model) of the sample to guide template matching.
+* '''Model-aware template matching''': this mode allows users to improve the speed and quality of the procedure by including additional prior informations on the geometry (i.e. model) of the sample to guide template matching.
-* On-the-fly template matching: in this mode, a tilt series is given as input instead of a tomogram and sections are reconstructed on-the-fly.
+* '''On-the-fly modus''': in this modus, a tilt series is given as input instead of a tomogram and sections are reconstructed on-the-fly.
-* Single chunk modus template matching: this model allow for the parallel inspection of single specific region of the tomogram (the so-called single chunk). Ideal for fine and exhaustive angular searches in restricted regions of the sample.
+* '''Single chunk modus''': this modus allow for the parallel inspection of single specific region of the tomogram (the so-called single chunk). Ideal for fine and exhaustive angular searches in a restricted region of the sample.
 == Basic template matching==

@@ Line 3: / Line 3: @@
 == Basic concepts==
-In this technique, a template representing a molecule of interest is systematically cross-correlated against a tomogram, producing a ''cross-correlation map'' of the tomogram. Each pixel in this map represents a score assigned the corresponding pixel in the tomogram map. This score measures the similarity of the neighbourhood of the tomogram pixel to the used template.
+In this technique, a template representing an object of interest is systematically cross-correlated against a tomogram, producing a ''cross-correlation map'' of the tomogram. Each pixel in this map represents a score assigned the corresponding pixel in the tomogram map. This score measures the similarity of the neighbourhood of the tomogram pixel to the used template.
 This similarity is measured exclusively inside a mask.