In cameras with radial distortion, straight lines in space are in general mapped to curves in the image. Although epipolar geometry also gets distorted, there is a set of special epipolar lines that remain straight, namely those that go through the distortion center. By finding these straight epipolar lines in camera pairs we can obtain constraints on the distortion center(s) without any calibration object or plumb line assumptions in the scene. Although this holds for all radial distortion models we conceptually prove this idea using the division distortion model and the radial fundamental matrix which allow for a very simple closed form solution of the distortion center from two views (same distortion) or three views (different distortions). The non-iterative nature of our approach makes it immune to local minima and allows finding the distortion center also for cropped images or those where no good prior exists. Besides this, we give comprehensive relations between different undistortion models and discuss advantages and drawbacks.

According to the World Health Organization, 85% of the world population suffers from back pain, which accounts for over 50% of physical incapacity, permanent or temporary, among individuals in working-age. In most situations, this is caused by an incorrect posture, which causes changes in the spine structure. This paper proposes an instrumented vest for postural reeducation to address this issue. The vest has a set of inertial measurement unit (IMU) sensors strategically placed to provide an accurate characterization of the spine profile. The sensor readings are classified by a central processing unit. In case of an incorrect posture, users are alerted by an audio signal and through vibration. The wearable system works in stand-alone mode, but can also communicate with external systems through an API. Two applications were developed to communicate with the device through this API, one intended to run on a desktop computer and the other one for Android devices. These applications monitor spine profiles in real time and notify the user of incorrect postures, among other functionalities. The device prototype and the applications have been tested by 10 individuals in two different settings, first without any kind of feedback and then with feedback enabled. The tests demonstrate the usability, accuracy and robustness of the system, proving its high level of reliability in classifying postures and effectiveness for postural reeducation. In the future, the system is expected to be used as a platform for a serious game, to promote posture reeducation in a real world scenario.