Sunday 10 April 2016

Gestalt - Gestalt Keys (An Introduction)

So far all my analysis has concluded that gestalt is not accurately measurable from digital images using the classic techniques birders have been using up until now.  If only there was a key that could unlock the tricky subject of gestalt.  

The Problem
Gestalt is complex.  Take this Bulwer's Petrel (Bulweria bulwerii) for example.  While it's field marks don't readily separate it from other dark-rumped petrels, it's unique structure and flight style combine to give it a distinctive character or 'jizz'.  But it's not at all easy to convey that concept in words, let alone to capture it in a still image.  Certain video grabs from this video will contain just the right combination of field marks and structural cues to allow an accurate identification.  Others will convey very little or may even throw the identification completely off target.  What is it that we need from a digital image to be able to get everything just right?

Digital images freeze gestalt as it were.  But, for various reasons we often struggle to make sense of what we are actually seeing.  The original video footage of this Bulwer's was captured using a handheld camcorder in full optical zoom on board a fast-moving sailboat.  I devised a technique to deal with the horrendous camera shake in the footage (HERE) and this animated gif is the result of that effort.  The process involved locking each image around a single locus.  You will notice the bird's eye doesn't change position on screen - that was the key to presenting this bird's features and it's gestalt to full effect.  Could there be a similar key to allow us 'lock down' not just a birds position on screen but it's structural traits - i.e. the essence of it's gestalt, in a single digital still image?  The key it transpires is to find a locus or loci that help define a bird's distinctive morphology and lock them down using a special tool which I call a Gestalt Key.

When we delete all field marks and strip back a bird to its basic shape we start to get a feel for an identification based upon gestalt.  But we also give too much freedom to the brain to latch on to anything that it thinks we can use to make a judgement call - and as a result we frequently get our analysis wrong.  Have you guessed what these four bird's in this image might be?  Answers can be found HERE.

We may use comparative morphology techniques and even 3D modelling (explored HERE) to conjure up and test ID theories to some extent.  But does any of that ever actually get us any bit closer to a firm identification?  Or, do these tools simply reveal how little our digital images convey about a bird's true shape and gestalt.  How do we actually nail down an identification more firmly?

Throughout this blog I have explored many of the classic tools birders employ to try and measure morphological features from digital images.  Without exception I have found all the current techniques to be limited in their accuracy.  There are a number of reasons for this.  In the posting on primary projection (PP) for instance (HERE) I observed that the challenge to measure PP correctly stems from among other things, the anatomy of the avian wing and subtle movements of the 'arm' and 'hand' relative to the body.

Similarly, while reviewing leg proportion analysis (HERE) I found a lot of the difficulty with that technique once again stems from the bird's hidden morphology.  Much of a bird's leg anatomy actually resides inside the body cavity.
But perhaps the most important challenge, more important than any anatomical movement is the problem of perspective and foreshortening.  All digital images are merely two-dimensional projections of a complex three-dimensional world.  We can only hope to take accurate measurements of lengths and angles from the X and Y axes which constitute our 2D image.  We have no way of measuring anything in the Z axis that projects from the camera's sensor out into the world.  Time and again foreshortening limits the accuracy of our conventional measurement tools.  It is very important to realise that it is not simply a matter of obtaining an image of a bird in profile.  The real problem is that none of the features we try to measure are ever truly in profile at all.  Afterall, these are all three-dimensional structures.  Take for instance PP.  When the primaries are in side profile with the camera, the wing is normally bowed slightly, rarely flat.  More importantly the tertials and tail which may be used as part of the PP calculation are both skewed at different angles relative to the primaries.  So nothing is measurable on a perfectly flat plane.

Similarly when we try to measure tibia/tarsus ratios chances are these are both splayed at subtly different angles from the body, rarely perfectly in profile with the camera or in the same plane as one another.  Or if for example we are trying to compare the ratio of a bird's bill length or width relative to it's eye proportions, once again these structures are never both in profile together because a bird's head is conical in shape, not square, so it's eye is always offset at an angle from the bill.

The challenge is the same, whether we are trying to measure the lengths of structures or angles created by them.  Perspective alters everything.

So, to summarise, we face the problem of trying to lock down highly dynamic, three-dimensional loci in the very restrictive two-dimensional plane that we call our image.  After the realisation that it is simply impossible to control all these loci, comes the reality that what we need is a different approach to looking at all these problems.

Gestalt Keys
The solution I have come up with is the Gestalt Key (GK).  The concept is simple enough. Firstly we must design a partially transparent mask or stencil which we incorporate as a layer over our image.  We can do this using any of the image editing software programmes ( is freely downloadable).  The mask contains key points which I refer to as loci.  These are key to identifying a bird using gestalt.  This mask or stencil is our Gestalt Key.  For the purpose of this posting I have designed two gestalt keys following on from my most recent posting on structural angles.
So, lets give it a go.  In the video below I present one of these two prototype keys - the Dowitcher Key for measuring Loral Angle.  Over the course of the next few postings I hope to bring a number of keys up to a reasonable standard and make them freely available for free download and testing.

Here I am using gestalt keys together with structural angles.  I intend creating keys that can accurately and consistently measure primary projection, bill to eye ratio, leg proportions etc.

This posting is the culmination of much research, trial and error.  Techniques for assessing the morphology of birds based on digital images are not a new concept, but what has been lacking invariably is a rigorous tool to help narrow and possibly even eliminate margins for error in measurement.  I hope Gestalt Keys will help bridge a gap and make this form of analysis just as valuable as the analysis of fine feather detail from digital images.  I hope this is the key to finally cracking gestalt.

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