Getting the most from the Olympus C740 Ultrazoom

Introduction

Sometime back in 2003 I was fortunate enough to be able to purchase an Olympus C740 Ultrazoom camera. At the time I was attracted by the long 10x optical zoom lens and the overall impression of the camera. I also owned an Olympus mju2 film camera which had delivered very pleasing results so this gave me some extra confidence in outlaying a substantial sum. Being fairly ignorant of how cameras work I spent a number of years using the camera with its automatic settings. It wasn't until more recently that I discovered just what the camera is capable of, results that in my aesthetic (but not technical) opinion outshine many a modern DSLR, hence this blog post.

Optimising the C740

My journey of discovery started with the realisation that aperture settings really make a difference to the true sharpness of the final image. Using the optimal aperture for the lens, in this case f/5.6 across the entire zoom range, provides a natural sharpness that no software sharpening can come close to achieving. It's a bit like using a better microphone for an audio recording, no amount of post processing is going make up the difference.

The next stage was to understand that high ISO shots have more grain and generally less good colour, even on the best modern cameras. No noise reduction I've ever used or seen is able to counterbalance this despite some bold claims from software manufacturers. Using an ISO setting of 100 or 200 (100 being preferable for this camera) and switching off any active noise reduction is essential to getting a really smooth, and rich colour.

The final stage was a long one... it tooks years of fiddling around with some pretty sophisticated software to realise that the quality of the colour in a image is universally wrecked by software sharpening processes, either within the camera or on a computer, and that turning the in camera sharpening off (or to its lowest setting), and using optimal lens sharpness instead, yields far better results.

Summary

Thankfully the Olympus C740 allows fully control over all these parameters, with ISO and noise reduction adjustment, aperture priority mode, and sharpening level settings. I now have our C740 setup with a custom 'My' setting which automatically sets the camera to use ISO 100 at f/5.6 with the lowest sharpening level. Most, probably all, prosumer and SLR cameras will allow you to do this easily. My Olympus E420 responds to the approach very well, albeit with the 17.5-45 kit lens having an optimal aperture setting of f/11 rather than f/5.6 which can be a little limiting in low light if you can't use a tripod or flash. Usefully, with this more modern camera, I can use higher ISO levels without incurring unacceptable levels of grain which helps to offset the problem of a small aperture.

As the proof of the pudding is in the eating, here are some sample shots which I took with the C740. In my view the macro shots in particular put many DLR's to shame with a richness of colour that almost bursts out of the screen whilst at the same time appearing, to my eye at least, to be very natural.

clicking here will take you to a web album containing full size versions of all the photos shown

Have fun!

Synergies between Social Tagging and Traditional Search

Some of my recent work has taken me into the realm of web search technology. In particular I've been thinking about the relationship between regular 'Google' style search and social tagging on tools such as Delicious and Scuttle. This post outlines some of my thinking about how these two search services can start to merge into a single complimentary service, comparing the two technologies and taking a deeper dive in to some of the less obvious strengths of tag based searching.

Let's start by examining some key facts about traditional and social search

Traditional Search

Positives	Negatives
Searches entire content of a page Searches linked content not just a single page or object	Index entries have no cognitive input at time of creation Content is indexed regardless of its degree of relevance

Social Search

Positives	Negatives
Only indexes content which is cognitively perceived to be valuable to one more persons Tags used to describe information provide a cognitive bias to each index entry	Creation of each uniquely identified resource in the search index requires an investment of time and energy from one or more persons The body of content indexed as a whole reflects a bias toward the socio-political status quo over time (this may also be perceived as a positive)

Given the differences between these two types of search, it's not difficult to perceive why they co-exist happily side by side (at least for the present anyway) owing to the fact that they each offer capabilities to users and to site/resource owners which the other does not. What is not so immediately obvious is how to bring these together in a one-stop shop search service. Current offerings on the web typically tend to focus on one or other search methods or perhaps build a site based on a mixture of the two but without any real integration (i.e. just a basic dump of top search results for each type of search). Fortunately there is light on the horizon due to a great deal of potential for greater integration going forward. This is due to the close relationship between the process of performing a search and choosing a result, and of tagging a resource for inclusion in a social search database. Let's take a closer look at these two user oriented methods of defining relationships...

1. Social search - During a delicious style tagging process, a user enters a series of keywords which describe a resource. This creates a relationship between the words entered which lives within the context of the link/resource subject itself. In other words if I create a new bookmark for eBay with the tag words 'auction', 'online', 'ecommerce', 'bargain', 'international', 'sell', and 'search', I am in effect creating a relationship between these words in the context of the subject 'eBay'. Additionally, as more people bookmark eBay, the closest and most universally relevant of these relationships are reinforced which means there is a constant process of refinement of relationships. Furthermore, and most importantly for our current train of thought, as different resources are tagged en masse it becomes possible to start creating relationships between tags across different resources based on common tag words, a kind of user generated thesaurus.
2. Traditional search - the generation of relationships here is similar to that for social search and also applies to social search itself. During the search process, a user enters words relating to the site/resource they wish to find. The user then typically will click on or more links returned by the search query, information which can be, and in the case of traditional search usually is, captured by the search engine. Here we have the reverse of tagging, the user puts the tags in first and then links them to the resource afterwards. Although this may be a little hit and miss initially, over time it should be possible to get a high degree of reliability by comparing a large number of searches for the same, or similar, combination of words and analysing the most popular resources chosen from the results.

Ok, so there are some synergies, but how can these be leveraged to create a more integrated search service across traditional and social search? There are two possibilities here...

Integrating the results into a single unified list

Given that we effectively have the same process for creating relationships operating in both traditional search and social search (albeit with the process steps in reverse for traditional search) we have a synergy which should be easy to leverage. By combining the social tag dataset with the search and preferred results data-set (both of which relate words to resources) we can create a unified single data-set. An issue presents itself here in that one search dataset will likely have a significantly different number of entries than the other. However this can be overcome by creating an aggregation of the data which represents it in a qualitative rather than quantative way (i.e. by using ratios to represent relationship strength in each case rather than absolute volumes based on frequency of usage). 

A further more serious issue with this approach, is that new sites which have not yet been actively chosen as the result of a traditional search will not appear in the results as the relationship has not yet been formed (social sesarch does not suffer from this problem). A method of incorporating these sites, a kind of process to nurture the new, is threfore required. 

Using the unified data-set to return search refinement possibilities to the user

Many searches typically don't point the user in the desired direction in a single iteration. However in a typical search process (e.g. using google) the user is required to figure out the correct words to use to successfully refine the search and remove irrelevant results. Using the unified data-set, it is now possible to send back a tag cloud of the most heavily related words which the user can then select to refine the search. This creates a faster and more precise search refinement process and allow a user to navigate to weaker relationships much more quickly if desired by a continual process of suggestive refinement.

These are coneptual ideas based around my perceived view of the strength of the semantic word relationships represented, as such I warmly welcome discussion and feedback which will help the develop these ideas.

A good command of your camera and a little compositional planning is 80% of the equation

I found a great Blog entry about why having the best camera is not overly relevant to good photography. I would merely temper this by saying that you need to like the 'style' that your camera brings to each shot. Good stuff tho', check it out here

http://digital-photography-school.com/stop-wishing-for-that-amazing-camera-and-appreciate-the-one-youve-got