AM going to start an Android project quite soon, as my latest posts probably helped show. The drawback is that it will be less research-oriented and more implementation-oriented. On the other hand, Android is becoming de facto OS in the mobile market and it increasingly seems like Dalvik development is a valuable skill. My three siblings all use Eclipse and my fiancée studied Java back in the days as well, so it only makes sense for me to come back to Java. Currently we explore what the project will involve, with questions arising such as:

• What features are simpler to implement for baseline functionality, e.g. collision detection?
• What framework, e.g. opencv, is available for rapid development?
• Possible technical collaborations of relevance?
• What is the innovation worth exploring/adding?
• Is stereovision (deriving 3-D) an option or just 2-D?
• What products are already out there and how competitive are they?

illow Garage was created by former Google employees with a talent in robotics. The Willow Garage Web site links to this new page on OpenCV for Android, bringing together detailed instructions. OpenCV is used widely in research labs and on mobile platform it will be interesting to test some cutting-edge methods. It is possible that I will soon explore this path of work. But first, starting tomorrow, I shall spend 8 days with my fiancée.

Clinical and exercise studies have always been a passion of mine, not just computer vision and graphics. I am currently hooking up with some good folks who are eager to start some projects and perhaps companies (based around computer science, which is what everyone in my family does), meaning that there will not be much free time for my activism. I will be away from Techrights until the end of next week.

s a bit of a dinosaur in technology (I still use a Palm PDA and single- or dual-core AMD), backward compatibility and long-term support are important to me. I am not a fan of PHP even though many programs that I like (the latest being Roundcube) use it almost exclusively.

Many problems seem to occur for those who use old versions of Gallery with the latest PHP, which has become notorious for its backward compatibility deficiencies. One bit of software that I use which is not compatible with PHP 5.3 is Gallery 1.x. It’s a version that I hacked a bit to suit my purposes, so upgrading would flush all my customisations away. Whether a sandboxed compatibility mode is available (such that, e.g., PHP 5.2 is run for specified paths) I do not know yet, but based on what people are saying suppressing the warnings and errors should be possible. It’s not a real solution but a cosmetic hack. If your Web host undergoes a PHP upgrade to 5.3 it can lead to lots of issues associated with out-of-date software. “A short time ago,” wrote my host (with which I host about 10 domains), “we emailed you to let you know that we were upgrading all our servers to the latest version of PHP. This is now complete. We therefore recommend you have a quick check of your site and ensure everything is working as it should.”

The bottom line is, from my personal point of view, is that PHP yet again proves that backward compatibility is too much for it to handle and, as such, one oughtn’t rely on long-term usage of programs written in PHP. Other authors pointed this out before. It’s quite the blunder. In Web-based environments in particular, a case of “lose compatibility or get cracked” may become more common if we become dependent on PHP.

ar navigation using computer vision algorithms/programs (as opposed to GPS/maps) is scarcely explored in the form of mobile applications. With many built-in cameras and increasing processing power/RAM it would be desirable to exploit — to the extent possible — whatever general-purpose devices have to offer while idle; single-purpose appliances like TomTom make less business sense nowadays and development frameworks for mobile platforms have become versatile enough to empower third-party developers. Based on conversations with colleagues, OpenCV and its many plugins should be somehow available for Android as well, albeit it may require some hacking and adaptation to the hardware at hand (high-end ARM for the most part).

If the goal is to make vehicles with cameras mounted onto them interpret a scene like humans do, then analysis of video sequences on mobile hardware (efficient applications) ought to be explored, with special emphasis on performance. C++ has little memory footprint and high efficiency. Contemporarily, resolution at a high capture rate is satisfactory enough for the task, but it is unclear whether a good algorithm that segments and tracks a scenes can keep up. A GPU-like processing power is available on some phones, but not all (drivers for non-x86 architectures are poor or scarce, too). MobileEye offers peripheral and assistive hardware for this reason, recognising the known caveats.Vuforia does augmented reality for mobile platforms and a company called ThirdSight also makes mobile applications with computer vision methodologies. Not so long ago (April 2010) it was reported that “development of new automobile safety features and military applications [...] could save lives.” The hardware is not specified in the report. To quote, “Snyder and his co-authors have written a program that uses algorithms to sort visual data and make decisions related to finding the lanes of a road, detecting how those lanes change as a car is moving, and controlling the car to stay in the correct lane.”

While purely automatic driving is currently verboten, computer-aided driving is legal and forms a growing trend. It need not involve any mechanics either, as it’s most about message-passing to a human (HCI).

HE YEAR 2010 had me do research on cardiac activity (image sequences) and in 2011 is was mostly 3-D surface geometry. Soon I might be switching to sequences again and also contribute to Android in one way or another, hence the past 3 posts.

he following video demostrates the accumulation and fusion of images with GPS/map data, but there is no synthesis of new data about the scene.

omputer Vision is definitely made possible on Android using OpenCV. Here is an android-opencv demo app [via] which may come handy for programming in C/C++. This further and latest exploration complements the earlier post as car navigation-targeted open source code is absent; what we currently have out there mostly uses maps, not image/video, so there is a gap that would augment an open source car, e.g. with open source navigation that incorporates widely-researched methods. Dashboard Cam, an Android application which is demostrated here, uses GPS and also uses photo overlays, but there is no computer vision/pattern recognition work being done.