Tuesday, 28 October 2014

Forensics - Maximising Image Content

Scope and Objective

In this posting I am exploring the most effective way to manipulate an image to maximise image content for identification purposes.

Tonal Range and Histograms

The tonal range of an image is simply the range of shades in the image from dark to bright.

While 256 tones (or levels) may be the maximum tonal range of an 8-bit digital image, there is no guarantee that we will be able to see all of these tones, if indeed they are even needed.  For a start, the gamut of the display device, coupled with the brightness and contrast settings may curtail the available tonal range.  Then we have to consider the ambient lighting where the image is being observed.  People may be reading this posting on a tablet or on a smart phone.  Strong ambient lighting may overwhelm the tonal range of the screen.  For best results I recommend reading this post and viewing the images at a desk, away from distracting lighting, such as daylight coming through a window.

More importantly, the content of an image will determine it's tonal range.  In nature, pure black and pure white are rarely encountered, yet they form part of the standard tonal range.  On a dull, foggy day images will tend to be flat-looking, with perhaps fewer than 100 discrete tones required to create the full image.  Most if not all of these tones will fall in the midtone range.  By contrast, on a sunny, summer's day the dynamic range (the gap between the darkest and brightest tones) of the ambient light will far surpass the recording capabilities of a standard camera sensor, and therefore it will be impossible to capture the full range of tones within a single image.  This manifests as high contrast images with detail clipped, perhaps often at both ends of the histogram.

An image histogram, for those unfamiliar with it, is simply a graphical representation of an image based on it's tonal content, looking specifically at the quantity of pixels occurring at each tonal level.  If an image has been well exposed the image data will fall roughly in the middle of the histogram without touching the left or right hand edges.  The shape of the histogram is dictated by image content - there is no ideal-looking histogram.

Poorer quality images very often have issues which can be diagnosed simply by studying the histogram.  For instance in the image below this Soft-plumaged Petrel Pterodroma mollis appears over exposed in the original JPEG image from the camera.  The histogram confirms the worst - the highlights fall off the edge of the chart and are 'clipped' from the image.  During JPEG compression clipped information tends to be discarded to help shrink the image file size.  This data is irretrievable.

While use of the levels tool can darken the image and retrieve some detail (lower left image), the histogram confirms that highlights remain clipped.  Not only has encoded data been lost at the edge of the tonal range, data which existed throughout the tonal range in the original RAW file, but which wasn't expressed in the original JPEG, is likely to be discarded.  Data loss in the midtone range tends to become obvious when image contrast is increased.  Data gaps appear in the histogram as spikes, referred to as a 'combing effect' (see lower left hand image histogram below).

Luckily I was shooting in RAW as well as JPEG when I photographed this bird.  By creating a new JPEG manually using Camera RAW software I have heen able to retrieve highlight data together with a lot more tonal information not presented in the original JPEG.  For more on working in RAW and a video demonstration of Camera Raw workflow see HERE.

In summary, the solution to maximising image detail for ID purposes is simple - shoot and process the image in RAW.

Soft-plumaged Petrel Pterodroma mollis, off Argentina.

Wednesday, 22 October 2014

Forensics - Analysis of Shadows

This is the first of a series of posts, looking at specific forms of analysis for specific challenges.  I am going to establish a consistent approach and, in time I will compile my analysis into a series of specification sheets so that they can be put into a form of Forensics Manual.  As stated in the introductory posting HERE, this is all trial and error, so don't be surprised to see various changes to postings, recipes and specs over time.  As the spec sheets are put together I will keep the current version available for download on a dedicated page in the blog.

Scope and Objective

This posting is an overview of lighting or, more specifically shade.  The objective is to identify a standard approach for analysing shadows in digital bird images.  This is not a simple task and no doubt it will need plenty of revisiting.

Understanding Shadows

The first step in any analysis is to understand what it is we are seeking to analyse.  Shadows are formed where light meets an opaque obstacle.  Shadows cast by objects have a complex 'anatomy' as discussed HERE but most of the time we are only aware of the darkest portion of the shadow, called the umbra.  From the perspective of bird identification we are far less interested in the shadow cast by the subject onto the surrounding landscape, and more interested in the pattern of shadows falling on the subject itself.  These shadows may be cast by the subject onto itself or may be cast by surrounding objects onto the subject.

Shadows only form part of the whole lighting story.  We need to consider all direct and indirect light sources and the properties of materials, including their reflectance and if they are opaque, translucent or transparent to light.

Why Analyse Shadows?

Shadows frequently obscure identification details.  Fine shadows can also be confused with genuine identification field marks (eg. it may be difficult to tell the difference between a grey underwing and and a white underwing in shadow - an important feature for example for telling European Golden Plover from it's American and Pacific cousins.  Or for example judging the axillaries on a putative American Wigeon in a flock of European Wigeon, or visa versa).  A recent posting on ID Frontiers (LISTSERV@LISTSERVE.KSU.EDU) actually prompted me to review this subject.  In the mystery bird in question there is a putative field mark on the underside of the secondaries.  The challenge is to determine if it is a real field mark or merely a shadow.   By studying the pattern and even the colour of shadows we may be able to distill some clues, or even firm evidence to support or refute an identification.

The Plan

Here is a recipe I have been devising for studying shadows in bird images.

Step 1 Careful review of the images
All the available photographs should be studied carefully to establish as much information as possible about the ambient lighting and environment around the subject.  Ideally the images should be unedited, originals from the camera (preferable RAW images if possible).
A record of the following information should be documented for reference.
- Date, time and location images were obtained.
- Likely position of the sun relative to the subject.
- Environmental factors influencing image lighting and shade (eg. cloud, foliage, water etc.)
- The location of obvious shadows in each image
- Give attention to translucent parts (flight feathers, displaced contour feathers, even bareparts in       
   some species).  Note in certain circumstances these can be darker not lighter than surrounding 
   features.  Don't assume because they are translucent that they should always be lighter 
   (for example the image above).   
- Any obvious inconsistencies or discrepancies between the images.

It may be necessary to adjust the exposure levels in an image to bring out shadow detail (eg. if the image is under or overexposed).  In a lot of cases the shadows we are interested in inspecting are preserved within the midtones and not in the shadows, where one might expect.  Simply darkening or brightening an image using a unsophisticated editing tool may do little to improve the visibility of shadows.  A more sophisticated image exposure tool, like the Levels tool (eg. in photoshop) is therefore possibly the best tool to use, as it is possible to adjust midtones separately from highlights and shadows.  For more on Levels see HERE and for more on this and other useful image lighting tools for forensic image analysis (in particular, the Adobe Elements Shadows tool and Midtone Contrast tools) see HERE.  

- If it is necessary to modify image exposure I would suggest making a record of that too (eg. the 
  values for black, white and midtone level and other lighting tool sliders used in the final image and the editing package used - so that exposure conditions can be replicated).

Please see also HERE.

Requirements: Any image viewing package.  Exif software may be useful/necessary for establishing image capture data (I recommend the freeware exif software, Opanda Exif).  Image editing software (Adobe Elements recommended), if required.

Step 2 Greyscale Contours
Switching to greyscale removes distracting colour and, crucially for this test, simplifies the colour pallet down to as few as 256 colours (or rather shades of grey).  Contours are a useful way to follow the pattern of light and shade, to establish where shadows do fall and/or should fall on a subject.  This is a useful way for instance of identifying subtle shifts in the alignment of feathers, or the subtle bow of the wing - something that is not always clear when studying a two-dimensional image.  Bare in mind of course that a greyscale image is not merely a map showing the position of shadows on a subject.  A shadow will appear prominent against a white (highly reflective) surface but will be invisible on a black (poorly reflective) surface.  So the reflectance of the subject is obviously an important consideration, and one of the things that confounds our ability to judge shadows easily.

I have come across a really useful tool for tracing contours.  Color Quantizer is freeware (free software) and is essentially a postarising or Colour Quantization tool with a very nice user-interface and a possible aid for a number of image interrogation techniques.
Requirements: Freeware Color Quantizer tool, highly recommended.

Step 3 Pixel Colouration
While the Color Quantizer software is open I would next drop in the original colour image and begin looking at the distribution of colours throughout the image.  Why is this of value one would ask?  Well, put simply, we would expect shadows to be of a subtly different colour to illuminated areas, at least on sunny days.  The reason for this is that shadows tend to be illuminated by the blue sky while those areas illuminated by the sun are illuminated by white or yellowish light.  For more on this see HERE .  One of the really useful advantages of the Color Quantizer tool is the ability to toggle between 'TrueColor' (the full range of image colours) and a range of reduced colour pallets at various increments from 4096 colours down to a mere 16 colours.  There is also an option to replace colours, so having selected a specific colour pixel, one can replace it with a tracer (say bright yellow) and then study the distribution of this tracer in the image relative to other colours.  I think it is important to stress that this technique on it's own may yield fairly mixed results, which is why it is included as part of a suite of analysis.  At the moment I would tend to regard this technique as a 'clue gatherer' rather than an 'evidence gatherer'.

Where the tool tends to lose it's impact is with images taken on overcast days.  Cloud scatters white light evenly, thus shadows are grey not blue in colour.  There may still be an advantage in that plumage markings are rarely neutral grey in colour and therefore it may still be possible to tell the difference between a shadow and a plumage marking, just about.  But I have found it becomes a lot more difficult.   

I am not sure if this technique has been considered before as part of digital image analysis and I am still only playing around with the idea.  But, I think the theory behind this concept makes a lot of sense.  Where I have found this useful, and where I consider this approach might work best is when working with seabird images.  As outlined HERE, lighting at sea is in many ways at it's simplest and it's purest.  Essentially, we have a bird flying about in a bluish or a greyish light, where the seascape mostly merely reflects the sky canopy. Shadows do tend to mirror the colour of the sky canopy quite a bit.  We also have the added factor of surface water reflectance to consider at sea.  I have noticed with images where birds are flying close to the water that the underside of their plumage often takes on the same colour pattern as the surrounding sea.  Trying to tell real plumage markings from shadows and water reflections can be a real challenge, particularly on a bright, sunny day.   I hope, and have reasonable confidence that this tool and technique may be a valuable analytical tool to help unlock some of the challenges involved in seabird identification from images. 
Requirements: Freeware Color Quantizer tool, highly recommended.

Step 4 Document
Hopefully having stuck to this suite of analysis there will be enough clear evidence and supporting clues gathered to establish the answer we were looking for.  I think it is important, in order for others to be able to check and confirm the evidence to document as much as possible.  Screen grabs can be a useful way to capture a piece of evidence observed on a screen during analysis and details can be annotated as a slide presentation, word processor document or some other format.

This is the first stab at the very first of these postings.  I fully anticipate changes to it before I compile it into a specification for the manual.  Comments and advice as always welcome.  

Tuesday, 21 October 2014

Forensics - An Introduction

We are used to analysing images based on patterns that are familiar to us.  We recognise that the subject in a photo is a bird, that it is flying and that it has a subtle pattern of light and shade on it's wings.  We can observe that one wing appears to be a little more in shadow than the other.  At some point we may be tempted to over-stretch our analysis and perhaps draw conclusions that are based more on guesswork than on sound evidence.

Digital image processing tools allow us to dig deeper and perhaps reveal properties about the image and the subject that are very often simply not presented in the original JPEG image produced by the camera.  But there are two sides to that coin.  All forms of image manipulation have the potential to lead us astray.  So, we need some structure or methodology to guide us.  We also need to understand the benefits and limitation of each of the tools we apply.

Poultry 101

There are different ways to cook chicken.  At the local chipper, the chicken may have a distinctive and comforting flavor.  It may not be the most exciting, nor the healthiest option.  It is JPEG chicken!  At home, the same piece of chicken can be cooked from raw to create a masterpiece, or perhaps something fit for the trash bin.  Unlike raw chicken however, the same RAW image file can be reworked ad infinitum until the desired results are achieved.  We know that RAW image processing is a powerful tool, not just for creating the best images, but for interrogating image files for identification purposes.  For more see HERE and HERE.

The original JPEG (left) is overexposed.  The JPEG on the right has been created from RAW, and demonstrates the power of RAW editing software, both for improving images and as a forensic tool.

In the absence of a RAW data file we can still, to a very limited extent, use image editing tools to improve the quality of JPEG and other file formats.  We can also, to an extent, interrogate JPEG and other files using image editing software.  Often, editing for the sake of image quality and editing for the sake of image interrogation are mutually exclusive.  In other words, improving the quality of an image may not always improve the resolution of fine detail or subtle colours, and visa versa, as illustrated HERE.  Also, image manipulation may often come at a cost.  It is easy for example to lose direction and end up further away from an objective than ever before.  We may be reluctant to take a chance with a form of analysis that we may not readily understand.  Sometimes, it is safer to put ones trust in the local chipper than to take a chance on the newly-opened Thai restaurant next door, or on the Thai cookbook gathering dust on the shelf for that matter!

The JPEG on the left is typical of a very bright day.  The harsh light challenges the camera's dynamic range.  Though, far less effective than RAW editing software, a standard image editing package like Adobe Elements makes a reasonable stab at retrieving details from this poor image.   Note how image quality dis-improves yet the ability to pick out plumage detail is much improved.

Learning a new skill takes much more effort than the willingness to simply 'have a go'.  Whether it is, 'how to use spices to cook the perfect Thai curry', or 'how to use software to analyse digital images', it all takes patience, time and practise.  At least, for the former we have a cook book to guide us. We do not have an equivalent text for what we are looking for here.  In many ways, what I am writing about here is based on pure trial and error on my part.  Some techniques will be handy in some cases but not in others.  And, I am starting close to the bottom of the learning curve, perhaps like most of you.  So, please bare with me.  One of the goals here is to work on a Forensics Manual and I will have a blog page devoted to the subject in time.

Firstly, the Ingredients

Each digital image is made up of millions of pixels, often consisting of tens if not hundreds of thousands of distinct colours.  Each pixel has a Hue value, a Saturation value and a Luminance value (for more see HERE).  But none of these parameters are fixed.  Image processing and editing tools allow these values to be changed.  These tools are simply complex equations or algorithms that modify these parameters across some or all of the pixels in an image.

As with cooking, where different processes and different seasoning and spices help enhance or mask flavors, image editing tools draw out different patterns in digital images.  Whether our goal is to be a master chef or a master forensicist we need to try and gain a good understanding of all the tools and processes as well as their impacts.  However as with all my postings I will try and avoid getting too technical, and will use illustrations to convey concepts and demonstrate results.


One would never throw all of the ingredients into a bowl, mix them together and then decide at that point whether the meal is going to be bread or a pie!  We need to know what our objectives are from the outset.  No doubt we will have some idea what the species might be and which field marks or plumage colours need expressing in the images we scrutinise.  We also hopefully will have some idea of the image quality issues that are facing us, be it underexposure, defocus, a white balance error, etc.  The objective determines what tools we will choose to use and in what particular order we might use them.  Once again, that cooking analogy.  For consistent results it is best to work to a recipe.


Image editing and processing tools can be very powerful.  They can reveal hidden detail and colours, or they can just as easily ruin a good photo or send analysis off on a tangent.  As with cooking, less is more!  Generally alterations should only be made if they contribute to the objective, and only to the extent that they continue to reward clear results.  Unnecessary modification is always a bad thing.

The left hand image is the JPEG produced by the camera.  Greyscale is one of the simplest image processing tools we have.  By reducing Saturation values to zero we also eliminate Hue from the equation, leaving only Luminance values.  Though the image quality hasn't suffered we have a much simpler pallet of colours, or rather shades to analyse.  There are in fact just 256 luminance values (called levels) in sRGB colour space (213 expressed in this image).  Working in greyscale can give us a clearer appreciation of subtle lighting and shade.  Saturation is another simple tool.  Here I use it to demonstrate how effective a 400% increase in Saturation can be at highlighting edge distortions like JPEG compression artefacts and sharpening halos around the bill of this tern.  It is also interesting to note that, for this image an increase in saturation has brought about a near four fold increase in uniquely-coloured pixels.  This might imply that that there is a lot of hidden colour detail in the original JPEG image.  Of course, the flip side of that is that many of these extra colours are false and are merely signal noise (a digital artefact).  All forms of image modification carry a sting in their tail!


Having created the perfect meal, presentation is key.  A chef might also want to take notes, make alterations to the recipe and file away for future reference.  I think it is really important to carefully record the tools and methods of analysis used so that the analysis can be replicated.  Ideally modified images should be stamped in such a way that they can't be mistaken for an original camera output file.  As part of my work in this area I will be providing some log sheets to accompany analytical recipes  and forensic tool specification sheets.  That way, hopefully everything I analyse and put up on the blog can be subject to peer review and scrutiny.

Saturday, 11 October 2014

Birds and Light - At Sea

Light and Shade At Sea

Because the ocean is very uniform, there are few obstructions, reflecting or transmitting light on a subject. All we have are the sun, the sky and the sea.  There are basically just a handful of lighting conditions to deal with at sea.

The time of day, the angle of the bird and camera relative to the sun and whether the day is overcast or sunny all determine how plumage colours and patterns are presented in digital images.

There is a predominance of blue light at sea, together with white light.  After having spent a few days at sea I have found myself craving greens and reds, as they have been lacking at sea!  The image below explains the predominance of blue light in the atmosphere and at sea.

Colours At Sea

As the image above discusses, the colour of the sea can vary depending on its depth and the constituents dissolved and floating in it.  But there tends to be a reasonable uniformity over a relatively small area of sea.

I photographed this Great Shearwater Ardenna gravis from the bow of a tall ship as it passed directly beneath.  This inky dark water is the natural colour of the deep ocean.

The other great influences on the sea are the sky and the prevailing weather conditions.  Overall, the colour pallet of the backdrop (sea and sky-scape) tends to be fairly limited and very blue in colour as explained above.  On a sunny day this can be an advantage because the colour pallet of most seabirds is at variance with this, typically blue pallet.  

The image above represents the colour temperature scale, which, put simply, is the typical variation in the colour of sunlight on earth between dawn and dusk (for more see HERE).  At sea, this scale closely matches the colour pallet of light, of the sky, and in many cases, the colour of the sea itself.  After all the sea is a big reflector as explained above.  In contrast to this, the plumage of most seabirds consists of earthy browns and greys, with the odd dash of brighter colour (normally in the bareparts).  Seabirds also tend to have a lot of pure white in their plumage and this can be handy as white also tends to mirror the colour of light reflecting off of it,

In the posting Colour By Numbers I outlined how colour in digital images is expressed.  The colour of each pixel is defined based in three parameters.  Luminosity is the brightness of a pixel and is directly related to the light intensity of that point in the scene being photographed, but is also influenced by camera exposure and dynamic range (see HERE).  Hue is the actual colour - it corresponds closely with the colour of a specific wavelength of light, but in reality is the net result of multiple wavelengths coming together at that one point.  Lastly, Saturation is the purity of the colour and is expressed as a percentage of grey mixed with hue.  The parameter that most interests us here is hue.  If we can take a look at the hue of the pixels making up the bird we should be able to determine what are likely to be real plumage features, as opposed to false markings produced by shadow and reflection on white plumage.  Note that shadows can either have a blue or grey cast to them at sea.  On a sunny day, shadows are blue because of the predominance of blue in the light coming from the sky.  Sky light is the light that illuminates the shadows.  It is also the light that reflects off the sea and illuminates the underside of the bird.  When the sky is overcast the scattered light peering into the shade is actually diffuse white light, therefore white areas appear grey in the shade, on an overcast day.

Tuesday, 7 October 2014

Forensics - Shadow Anatomy

The multidimensional shadow

When we look at a bird in the field and we see the shadow which is cast by it, most of us would tend to consider the shadow as a simple, two-dimensional form.  In reality a shadow has a very complex, three-dimensional structure to it and what we see is merely a cross-section of this structure.  Normally in the cross-section there are three, overlapping shadow forms visible - a dark central one and two much paler outer shadows.  In overlap, these three shadows create a complex arrangement consisting of an umbra, two penumbrae and an antumbra, as illustrated below (I used a child's toy as a convenient prop).

The umbra (the dark central shadow) is effectively the sun fully eclipsed by the subject, so if a pinhole camera was in this position the sun would appear fully obscured.  Meanwhile, the two penumbrae are the shadows created by partial eclipses at either side of the subject.  A pinhole camera positioned within either of these shadows would see a sun in partial eclipse - sunlight would be visible from the outermost rim of the sun only (crescent-shaped).  Finally, within the antumbra, all of the sun's rim is visible (ring-shaped).  

Once again, remember we only see a 2D cross-section here.  The penumbrea actually occurs as a cylindrical shadow form around the darker umbra.  Like a slice through a cake, we only see the cylinder in cross-section on the ground.  Of course, as we know, birds perch and fly over complex terrain where the surface is rarely flat.  The cross-section through the shadow matches the complexity of the terrain upon which it falls.     

As the sun drops and the angle of the sunlight relative to the ground becomes narrower, the shadows lengthen and the penumbrae appear to diverge.  The opposite is true as the sun climbs in the sky.  Overhead, at it's zenith, the penumbrea appear to converge with the umbra, leaving one, short, tight, and fairly sharp, dark shadow.  Obviously, this principal only applies to flat, level ground.  The angle of terrain relative to the sun and observer can range from 0 to 360 degrees with all the complex shadow anatomy which that implies.

So where is the value in all of this from a bird identification perspective one might ask?  Shadows play a big role in identification.  I explored this a little HERE.  While HERE I made the important distinction between lighting and image artefacts.  Now that we know that shadows have a very complex anatomy we can anticipate the occasional surprise.  For, instance, the contrasting tones and prominent edges between the umbra and penumbra could be mistaken for a field mark on a bird's plumage.  For this reason it is always worth establishing where the sun is positioned in an image and how shadows are falling.  There might be nearby objects casting shadows on the subject or even a dappled lighting effect resulting in layers of overlapping shadows.  Normally shadow patterns stand out, but occasionally, where shadows align with plumage markings and patterns the situation can be very confusing.  I hope to explore some of these instances in future postings. 

For more see HERE.

Friday, 3 October 2014

Gestalt - Gestalt and Video - A Useful Tip

If I have some video of a bird requiring identification but the footage is shaky there are a couple of things I do to improve quality.  

Firstly I find the most convenient and readily available package for viewing footage to be Quicktime.  Simply by using the arrow keys video can be moved forward and backwards frame by frame.

But if the video is very shaky and I want to get an appreciation of gestalt (HERE) I will sometimes take a number of consecutive frames and create an Animated Gif from them.  To restore the appearance of a steady image I overlay the frames with the bird's eye in the same position on the screen in each.  Run the video and, like magic, the video appears shake-free.  The final image will have to be cropped to accommodate the different positions of the frame edge.  There is a loss of colour and definition but that isn't the point of animated gif.  We are only interested in gestalt.

Below I have added a number of animated gifs made from video footage I took between Madeira and the Salvagen Islands in 2006.  As one might imagine, given that I was on a small sail boat in the Atlantic the original footage was far from steady.

Fea's Petrel Pterodroma feae off Madeira and Salvagen Islands.  Upper image is of a bird gaining momentum having just left the surface of the water.  Steadying the footage allowed an appreciation of the subtle movement of the bird's wing as it enters and sustains it's classic arching flight.

I love animated gif but unfortunately the package which I use to create these files (Adobe ImageReady) is not supported by Windows 7.0 so I have to use an older computer with XP to create these files.  There are some free animated gif generators online but I havn't tried any and I don't know if they are as accommodating and dynamic as ImageReady,

Bulwer's Petrels Bulweria bulweria off Salvagen Islands.  Steady footage allows us to appreciate the distinctive paddle-shaped tail as Bulwer's takes off from the water, a feature not seen in steady flight.

White-faced Storm-petrel Pelagodroma marina off the Salvagen Islands, where there is a large breeding colony.  Steadying the image below helped capture and display this bird's crazy feeding antics.  Unfortunately the camcorder used for this footage (Sony DCRTRV-110E) had a habit of focus tracking a little too much so focus drops every few frames.  Still I think you can see the huge potential of this medium.

Wednesday, 1 October 2014

Gestalt - A Gestalt Field Guide

'Gestalt' (or 'G.I.S.S.' - 'general impressions of size and shape' - also spelt 'JIZZ') is the name we give to the recognisable 'feel' of an individual species in the wild.  It is a combination of it's structure, how it moves and it's behaviour.  As birders gain experience in the field we quickly become aware of the gestalt of common species we encounter regularly.  When a new species appears in a familiar setting, very often it's presence is first signalled by it's gestalt - something unusual about it's size and shape, or the way it feeds or moves about.  Of course a common species with an uncharacteristic behaviour or shape can fool an observer into believing they are watching a different species.  It is also very difficult to describe a bird's gestalt in any objective or measurable way.  Thus this subtle field craft has it's pros and cons, it's strong advocates and those who are more into field marks.  Most experienced birders would tend to use gestalt a lot in forming an initial impression but combine that with topographic field marks to form a solid identification.

It would be wrong to say that gestalt can be properly captured in an individual photograph but video can go a long way to capturing it.  Then again, if a video is merely a collection of photos, surely a flavor of a bird's gestalt is captured in every single photograph or frame of video.  The question is, how do we reliably and consistently draw out from our photographs this 'essence' (for want of a better description).

Movement and Behaviour
It nearly goes without saying that any effort to try and establish how a bird moves or behaves purely from still images is futile.  Video offers some clues but may be misleading if the video is shaky or the frame rate is off-putting.  For a useful tip in dealing with shaky video see HERE.

Structure and Measurement

Birds come in all shapes and sizes as we all know.  Measurements (avian biometrics) play an important  role in bird identification.  It is this aspect of gestalt which we may find useful here.

Comparative measurements can be taken from photographs but this must be done with great care and should be checked and verified using a number of photographs and from different angles or distances.  There are various ways in which the distance between two points can be distorted in a photograph so nothing should be taken for granted here.

The image below shows how perspective can alter the relative proportions of a subject.  Luckily this is rarely a problem as we don't tend to get close enough to birds for perspective to enter the equation.

We face similar problems when the subject or an object we are trying to measure is not in perfect side profile.  Our measurements are influenced by perspective foreshortening.  This is possibly the biggest challenge we face with measurements from bird images as explored HERE, HERE and HERE.  The problem ultimately comes down to the fact that a photograph is a two-dimensional projection of a three-dimensional space.  For more see HERE.

The image below illustrates lens distortion, a common problem with lenses where magnification differs from the middle to the edge of the lens.  It is not very easy to detect and focus is unaffected.

As if size analysis wasn't difficult enough we have to contend with strange quirks associated with the human visual system.  Size illusion is something we should always be mindful of in bird identification.

As the above examples illustrate, making size or structural pronouncements based on digital images requires a lot of  careful consideration and cross-referencing.  Not only do we have to contend with real image distortions, but we also need to factor in illusory distortions!  For more see HERE.

But lets for a moment assume that we are confident that a set of photos of a bird accurately depicts the bird's structure and proportions in life, without too much distortion.  Where is the means for making a valid comparison between a photograph of a bird and a gestalt standard or guide?  

Gestalt Studies in Field Guides

Well, the fact is, we don't currently have too many such standards.  Conventional field guides only present birds in ideal portrait profile, plus occasionally one or two other compositions.  There are not too many field guides that accurately depict gestalt.  There has been the odd book devoted to the subject, most notably Birds by Character by Rob Hume, and similar works involving artists like Dan Powell, DIM Wallace and others.  

The Crossley ID Guide by Richard Crossley certainly took us a step further in terms of photographic guides, but perhaps the best example of what I am referring to can be found in the pages at the back of Hawks At A Distance by Jerry Liguori.  The shape montage format (eg. Northern Goshawk and Northern Harrier below) is a fantastic tool for field observers and those trying to learn the subtle gestalt of closely related birds.  I also think this type of format is of great value when trying to identify a bird from photographs.

DIY Tools

Unfortunately, this is a pretty new approach, not often replicated in print.  While on the IRBC I occasionally resorted to generating similar montages to assist in making my own ID determinations from tricky photos.  The internet is a perfect reservoir of images for this purpose, with thanks to all the birders who put their images up online for all to see.  So, in the absence of a perfect Gestalt Field Guide, why not make your own!

What I have tended to do is take each image in turn and look for photos of various candidate species captured in similar lighting and composition.  I put these together into a montage.  Needless to say, the more images and contemporaneous notes that are available the better the chances of reaching a satisfactory conclusion via this process.  It may not work for all species but I have found it handy for raptors, swifts and seabirds to give some examples.