Burak Benligiray

Burak Benligiray, Cihan Topal, Cuneyt Akinlar

Fiducial markers provide better-defined features than the ones naturally available in the scene. For this reason, they are widely utilized in computer vision applications where reliable pose estimation is required. Factors such as imaging noise and subtle changes in illumination induce jitter on the estimated pose. Jitter impairs robustness in vision and robotics applications, and deteriorates the sense of presence and immersion in AR/VR applications. In this paper, we propose STag, a fiducial marker system that provides stable pose estimation. STag is designed to be robust against jitter factors, thus sustains pose stability better than the existing solutions. This is achieved by utilizing geometric features that can be localized more repeatably. The outer square border of the marker is used for detection and homography estimation. This is followed by a novel homography refinement step using the inner circular border. After refinement, the pose can be estimated stably and robustly across viewing conditions. These features are demonstrated with a comprehensive set of experiments, including comparisons with the state of the art fiducial marker systems.

Burak Benligiray, Cihan Topal, Cuneyt Akinlar

Jitter is an inevitable by-product of gaze detection. Because of this, gaze typing tends to be a slow and frustrating process. In this paper, we propose SliceType, a soft keyboard that is optimized for gaze input. Our main design objective is to use the screen area more efficiently by allocating a larger area to the target keys. We achieve this by determining the keys that will not be used for the next input, and allocating their space to the adjacent keys with a merging animation. Larger keys are faster to navigate towards, and easy to dwell on in the presence of eye tracking jitter. As a result, the user types faster and more comfortably. In addition, we employ a word completion scheme that complements gaze typing mechanics. A character and a related prediction is displayed at each key. Dwelling at a key enters the character, and double-dwelling enters the prediction. While dwelling on a key to enter a character, the user reads the related prediction effortlessly. The improvements provided by these features are quantified using the Fitts' law. The performance of the proposed keyboard is compared with two other soft keyboards designed for gaze typing, Dasher and GazeTalk. 37 novice users gaze-typed a piece of text using all three keyboards. The results of the experiment show that the proposed keyboard allows faster typing, and is more preferred by the users.

Caner Gacav, Burak Benligiray, Cihan Topal

Facial expression recognition methods use a combination of geometric and appearance-based features. Spatial features are derived from displacements of facial landmarks, and carry geometric information. These features are either selected based on prior knowledge, or dimension-reduced from a large pool. In this study, we produce a large number of potential spatial features using two combinations of facial landmarks. Among these, we search for a descriptive subset of features using sequential forward selection. The chosen feature subset is used to classify facial expressions in the extended Cohn-Kanade dataset (CK+), and delivered 88.7% recognition accuracy without using any appearance-based features.

Burak Benligiray, Cihan Topal

Nonlinear lens distortion rectification is a common first step in image processing applications where the assumption of a linear camera model is essential. For rectifying the lens distortion, forward distortion model needs to be known. However, many self-calibration methods estimate the inverse distortion model. In the literature, the inverse of the estimated model is approximated for image rectification, which introduces additional error to the system. We propose a novel distortion rectification method that uses the inverse distortion model directly. The method starts by mapping the distorted pixels to the rectified image using the inverse distortion model. The resulting set of points with subpixel locations are triangulated. The pixel values of the rectified image are linearly interpolated based on this triangulation. The method is applicable to all camera calibration methods that estimate the inverse distortion model and performs well across a large range of parameters.