This blog is both a personal journey of research and exploration and also a means to an end. The objectives set out in the Introduction have not changed. The scope of the project is essentially contained within the first figure in that introduction. The ultimate goal is a standalone guide and set of tools to aid in the identification of birds from photographs.
As 2015 winds to a close it is time to halt the research and consolidate the learning. By the end of the year I hope to have published a revision of the Quick Reference Guide. Between now and then I will be pulling together and summarising the key findings of the blog.
There is almost no end to the realms of research that this blog could potentially expand upon. Identification is itself a hugely broad area, undergoing constant development. But the blog is not really about cutting edge bird identification. It is about designing a set of tools and standards to aid bird identification from photographs. And yes, strange as it might seem, some of these tools haven't existed up to this point. For instance, there hasn't even been a standard method or nomenclature for properly sampling and describing colours from digital bird images. And colour is just one of the areas I have been looking at closely. I have cast out a number of nets in order to gather the information needed to develop the tools I need. I think finally it may be time to start pulling back in some of these nets to examine the catch.
All of this essentially boils down to light. I have spent a great deal of time deconstructing light, almost to a point where it has become a bit of an obsession. A clear understanding of light is key to bird identification from photographs and that is why I feel all this has been fundamental and therefore clearly worth the effort. Because the camera is such a masterful tool for playing with and dissecting light I have been able to use my camera to obtain clearer answers to many of the questions I have. I have particularly enjoyed constructing experiments that help understand this subject. This year the posting which I think has borne the most fruit was the set of experiments looking once again at lighting and perspective (part 2).
The major reveal from the particular experiment above was that from the perspective of the camera lens the diffuse shadows on an overcast day all fall towards the centre of the digital image. In outdoor photography we are used to positioning ourselves relative to the sun in order to obtain optimal lighting on our subject. But on an overcast day sunlight is scattered very efficiently by the clouds, to the extent that the entire sky dome becomes a fairly uniform light source. All angles should offer fairly decent lighting and the sun's position shouldn't matter a great deal. This is why an overcast day is far superior to a bright sunny day for photography and observation generally. While this might seem fairly logical its not something we often consider or analyse in our images. These types of experiments have made me think a lot more about how light and shadow falls on a subject in a photograph and, more importantly, why light and shade works as it does.
Spotlight - On Colour
Having expended considerable effort during 2014 on the subject of digital colour reproduction, including even a sojourn into the ultraviolet realm to try and see a bit more as bird's do, I thought perhaps I had figured colour out. Not so it seems. Not only did the pride of my earlier exploits the Birders Colour Pallet need a bit more thought and explanation in Rev. 2, but I had failed to spot a fundamental point about digital colour capture and reproduction. I hadn't given enough thought to one of the three parameters that make up colour, namely saturation. Cameras capture and measure the first of these parameters very well - the brightness or 'luminance' of colour. This is possible because each photosite on a digital camera sensor measures quite effectively the actual intensity of the light falling on it. Then, thanks to the Bayer colour filter array which sits over the sensor itself, cameras can identify quite accurately the 'hue' of each of the colours reaching the sensor, albeit within the constraints of the digital colour space we operate to. However, what about saturation? the sensor does not have the facility to measure the third colour axis, namely colour 'saturation'. Image saturation is actually one if the camera pre-sets by the camera manufacturer and takes effect during image processing. I am very mindful of it's importance of accurate colour saturation reproduction in terms of an accurate colour standard. After all the saturation of colours form an integral part of colour nomenclature, both traditionally (eg. Robert Ridgway's Color Standards and Color Nomenclature) and indeed in my own Birder's Colour Pallet. I have finally tackled this question HERE. For more see HERE.
Spotlight - On Field Marks
Field Marks form the core of many a bird identification, and certainly most ID's based on photographs. I started looking into this whole area early in 2015 and by mid-year I had a significant body of work done. Whether rightly or wrongly, the approach that I took was to look at plumage patterns starting from the centre of the feather and working outwards. So I started by looking at simple plumage patterns arising from shaft-streaks which collectively form tramlines. Then I looked at solid, diffuse and more complicated patterns associated with the broad feature centre. Lastly I focused on the feather edge and tip. I was kind of surprised to find that this simple approach tended to encapsulate most of the vast array of plumage field marks that exist in birds. Lastly I tidied up the set of postings with a look at bareparts patterns and also colour and field marks, in a broad sense.
As I worked through the problem it became apparent fairly quickly that there are broadly two sets of field marks in birds, the bold and the bland. While testing the effects of different image quality parameters on bold versus bland features I began to observe consistent patterns. Bold field marks are more robust, able to withstand a far greater level of image quality deterioration than bland field marks. So, in effect, when we analyse bird images for field marks we need to know how 'volatile' those field marks are and we need to consider that point within the overall image quality context.
Another area that particularly interested me was the concept of false field marks. These are false markings produced by the interplay between light, shadow and avian anatomical structures. By its very nature anomalies due to lighting or posture tend to be harder to detect in a still image then they would be while observing a bird in life. Because birds are normally moving about we judge and compensate for lighting and the momentary movement of feathers often without even having to think about it. But faced with a single still frame, all of a sudden that shadow or bright spot, or slightly odd posture or misaligned feather becomes a major source of confusion. In the end I thought it appropriate to develop a topographical nomenclature to describe some of the consistent anomalies we find in bird images and I call this Shadow Topography. This is not simply me making up a lexicon for the hell of it. Before we can understand and deal with an issue we need a way to describe it. Or as one of my daughter's kindergarten teacher's cleverly puts it..."name it to tame it".
Spotlight - On Forensic Image Analysis
I haven't added too many postings to this part of the blog so far this year. Having made reasonable strides towards a forensics manual last year the postings this year tended to be more about delving that bit more deeply into one technical subject or another. It's probably fair to say none of the postings make for exciting reading and I don't suspect that the really in-depth analysis of digital images will float many a birder's boat.
If I had to select one posting worthy of particular mention here it would be fringe artefacts while working in RAW. I had imagined the RAW work flow as this pure, unadulterated form of image analysis. So when I started to see strange artefacts appear in files that had undergone hard restoration with Camera RAW I started to wonder was I imagining things. Sure enough I found an explanation for these artefacts. It turns out that some RAW work flow tools leave behind artefacts when they are a little over-used and this is something to be really mindful of, especially when the goal of working in RAW is to bring out hidden field marks.
Spotlight - On Gestalt
The gestalt page of the blog is another aspect of the journey that I have only really started to develop in 2015. I know that there is going to be a real limit to the extent to which a bird's gestalt or jizz can be revealed by digital stills images. Most of the time, when we are talking about identification of birds from images we are referring to as few as a single digital image. So lets not kid ourselves. That said, in defining the distinction between field marks and gestalt for the purposes of this blog I have been clear to point out that I consider a bird's size and shape, structure or morphology as all falling within the broad definition of gestalt. Some might include these in the definition of field marks. The obvious question when faced with a single image - can we take size or proportional measurements from an image which would help us identify the species in the photograph. Most of my postings on gestalt to date have been about tackling this question. The conclusions so far would tend to be a resounding NO to that question. The problem very simply is that the real world is three-dimensional while a digital image is two-dimensional. Whether we are trying to measure primary projection, bill to eye ratio, tibia to tarsus ratio or some other measurement or proportion we constantly run into problems of foreshortening and/or features which are offset from one another by small angles which we cannot hope to measure. In other words all attempted measurements from digital images tend to be estimates at best.
The solutions to these problems lie in 3D modelling (eg. HERE). Modern technology is starting to provide us with practical 3D modelling solutions. Before too long we may well be able to judge size and proportion extremely accurately in the digital images of the future thanks to 3D photography. But for now at least we need to be mindful of the limitations that exist with our 2D images.
This incomplete 3D model created using some clever, freely available software was made by simply feeding a number of 2D images into the software and letting it crunch the numbers. More sophisticated forms of this type of technology may offer better solutions in the future to allow the accurate measurement of features on birds based on images captured in the field.
Spotlight - On Human Bias
This is the last of the specialist fields of enquiry that I have so far opened up on the blog. Starting in late December 2014 into early January 2015 I opened up the blogging year with a lot of cognitive science jargon and concluded with 10 tips for avoiding cognitive bias during the process of identifying a bird from digital images. It's probably fair to say that cognitive bias can play just as big a role in the identification and assessment process as ones technical knowledge of an ID subject. On a bad day even the most expert birder can fall foul to their own biases and be misled by a misguided trail of clues. I guess if someone were to say to me that they have a difficult identification to pour over from a set of bird images and were wondering where to go first on my blog for some useful advice, this posting is where I would direct them. Its about having the right mindset before engaging any identification puzzle and trying to approach it as objectively and open-minded as one possibly can. Unfortunately, despite our best efforts we can never fully turn off our biases - they are a fundamental part of how we work.
Many of the biases that I have gone on to discuss are associated more with the mechanics and wiring of the human visual system than human cognition. As observers and identifiers our ability to visualise and analyse the images we see are subject to the limitations of our eyes and brain. The dress viral phenomenon created quite a storm of attention on social media for a short period in March 2015.
Those who observed the poorly exposed photograph of a dress were divided between observers who believed it was blue and black and those who were equally convinced it was white and gold. The bias in this case seems to be from a subset of optical illusions referred to as Brightness Illusions. In a roundabout way this leads us full circle back to Birds and Light. While I hope the blog will continue to grow and develop I am getting the sense that it may be time to start pulling together the threads to weave the first couple of chapters of the manual. At the end of the day, like a PHD student who just can't quite finish a thesis, I could go on and on with all of these disparate fields of study. But the average birder is likely to only want a few simple and effective tools to approach a bird identification with a degree of knowledge and confidence to deal with the variables and challenges that might be thrown up. Time to consolidate.
Spotlight - On Colour
Having expended considerable effort during 2014 on the subject of digital colour reproduction, including even a sojourn into the ultraviolet realm to try and see a bit more as bird's do, I thought perhaps I had figured colour out. Not so it seems. Not only did the pride of my earlier exploits the Birders Colour Pallet need a bit more thought and explanation in Rev. 2, but I had failed to spot a fundamental point about digital colour capture and reproduction. I hadn't given enough thought to one of the three parameters that make up colour, namely saturation. Cameras capture and measure the first of these parameters very well - the brightness or 'luminance' of colour. This is possible because each photosite on a digital camera sensor measures quite effectively the actual intensity of the light falling on it. Then, thanks to the Bayer colour filter array which sits over the sensor itself, cameras can identify quite accurately the 'hue' of each of the colours reaching the sensor, albeit within the constraints of the digital colour space we operate to. However, what about saturation? the sensor does not have the facility to measure the third colour axis, namely colour 'saturation'. Image saturation is actually one if the camera pre-sets by the camera manufacturer and takes effect during image processing. I am very mindful of it's importance of accurate colour saturation reproduction in terms of an accurate colour standard. After all the saturation of colours form an integral part of colour nomenclature, both traditionally (eg. Robert Ridgway's Color Standards and Color Nomenclature) and indeed in my own Birder's Colour Pallet. I have finally tackled this question HERE. For more see HERE.
Spotlight - On Field Marks
Field Marks form the core of many a bird identification, and certainly most ID's based on photographs. I started looking into this whole area early in 2015 and by mid-year I had a significant body of work done. Whether rightly or wrongly, the approach that I took was to look at plumage patterns starting from the centre of the feather and working outwards. So I started by looking at simple plumage patterns arising from shaft-streaks which collectively form tramlines. Then I looked at solid, diffuse and more complicated patterns associated with the broad feature centre. Lastly I focused on the feather edge and tip. I was kind of surprised to find that this simple approach tended to encapsulate most of the vast array of plumage field marks that exist in birds. Lastly I tidied up the set of postings with a look at bareparts patterns and also colour and field marks, in a broad sense.
As I worked through the problem it became apparent fairly quickly that there are broadly two sets of field marks in birds, the bold and the bland. While testing the effects of different image quality parameters on bold versus bland features I began to observe consistent patterns. Bold field marks are more robust, able to withstand a far greater level of image quality deterioration than bland field marks. So, in effect, when we analyse bird images for field marks we need to know how 'volatile' those field marks are and we need to consider that point within the overall image quality context.
Spotlight - On Forensic Image Analysis
I haven't added too many postings to this part of the blog so far this year. Having made reasonable strides towards a forensics manual last year the postings this year tended to be more about delving that bit more deeply into one technical subject or another. It's probably fair to say none of the postings make for exciting reading and I don't suspect that the really in-depth analysis of digital images will float many a birder's boat.
If I had to select one posting worthy of particular mention here it would be fringe artefacts while working in RAW. I had imagined the RAW work flow as this pure, unadulterated form of image analysis. So when I started to see strange artefacts appear in files that had undergone hard restoration with Camera RAW I started to wonder was I imagining things. Sure enough I found an explanation for these artefacts. It turns out that some RAW work flow tools leave behind artefacts when they are a little over-used and this is something to be really mindful of, especially when the goal of working in RAW is to bring out hidden field marks.
Spotlight - On Gestalt
The gestalt page of the blog is another aspect of the journey that I have only really started to develop in 2015. I know that there is going to be a real limit to the extent to which a bird's gestalt or jizz can be revealed by digital stills images. Most of the time, when we are talking about identification of birds from images we are referring to as few as a single digital image. So lets not kid ourselves. That said, in defining the distinction between field marks and gestalt for the purposes of this blog I have been clear to point out that I consider a bird's size and shape, structure or morphology as all falling within the broad definition of gestalt. Some might include these in the definition of field marks. The obvious question when faced with a single image - can we take size or proportional measurements from an image which would help us identify the species in the photograph. Most of my postings on gestalt to date have been about tackling this question. The conclusions so far would tend to be a resounding NO to that question. The problem very simply is that the real world is three-dimensional while a digital image is two-dimensional. Whether we are trying to measure primary projection, bill to eye ratio, tibia to tarsus ratio or some other measurement or proportion we constantly run into problems of foreshortening and/or features which are offset from one another by small angles which we cannot hope to measure. In other words all attempted measurements from digital images tend to be estimates at best.
The solutions to these problems lie in 3D modelling (eg. HERE). Modern technology is starting to provide us with practical 3D modelling solutions. Before too long we may well be able to judge size and proportion extremely accurately in the digital images of the future thanks to 3D photography. But for now at least we need to be mindful of the limitations that exist with our 2D images.
Spotlight - On Human Bias
This is the last of the specialist fields of enquiry that I have so far opened up on the blog. Starting in late December 2014 into early January 2015 I opened up the blogging year with a lot of cognitive science jargon and concluded with 10 tips for avoiding cognitive bias during the process of identifying a bird from digital images. It's probably fair to say that cognitive bias can play just as big a role in the identification and assessment process as ones technical knowledge of an ID subject. On a bad day even the most expert birder can fall foul to their own biases and be misled by a misguided trail of clues. I guess if someone were to say to me that they have a difficult identification to pour over from a set of bird images and were wondering where to go first on my blog for some useful advice, this posting is where I would direct them. Its about having the right mindset before engaging any identification puzzle and trying to approach it as objectively and open-minded as one possibly can. Unfortunately, despite our best efforts we can never fully turn off our biases - they are a fundamental part of how we work.
Many of the biases that I have gone on to discuss are associated more with the mechanics and wiring of the human visual system than human cognition. As observers and identifiers our ability to visualise and analyse the images we see are subject to the limitations of our eyes and brain. The dress viral phenomenon created quite a storm of attention on social media for a short period in March 2015.
Those who observed the poorly exposed photograph of a dress were divided between observers who believed it was blue and black and those who were equally convinced it was white and gold. The bias in this case seems to be from a subset of optical illusions referred to as Brightness Illusions. In a roundabout way this leads us full circle back to Birds and Light. While I hope the blog will continue to grow and develop I am getting the sense that it may be time to start pulling together the threads to weave the first couple of chapters of the manual. At the end of the day, like a PHD student who just can't quite finish a thesis, I could go on and on with all of these disparate fields of study. But the average birder is likely to only want a few simple and effective tools to approach a bird identification with a degree of knowledge and confidence to deal with the variables and challenges that might be thrown up. Time to consolidate.
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