Estimation of a focused object using a corneal surface image for eye-based interaction

Researchers are considering the use of eye tracking in head-mounted camera systems, such as Google’s Project Glass. Typical methods require detailed calibration in advance, but long periods of use disrupt the calibration record between the eye and the scene camera. In addition, the focused object might not be estimated even if the point-of-regard is estimated using a portable eye-tracker. Therefore, we propose a novel method for estimating the object that a user is focused upon, where an eye camera captures the reflection on the corneal surface. Eye and environment information can be extracted from the corneal surface image simultaneously. We use inverse ray tracing to rectify the reflected image and a scale-invariant feature transform to estimate the object where the point-of-regard is located. Unwarped images can also be generated continuously from corneal surface images. We consider that our proposed method could be applied to a guidance system and we confirmed the feasibility of this application in experiments that estimated the object focused upon and the point-of-regard

ESTIMATION OF RUNNING INJURY RISKS USING WEARABLE SENSORS

This research estimates running pattern characteristics that relate to running injury risks quantitatively and simply from a real-environment running motion. Wearable inertial measurement unit (IMU) sensors are used to provide a simple measurement of the running patterns in a real environment. We then measure an experimental running motion in detail in the laboratory using both large-scale devices and wearable sensors, and build correlational models between the conventional parameters related to running injury risks and parameters from wearable sensors. These correlational models realize a quantitative and simple estimation of running pattern characteristics related to running injury risks from a real-environment running motion. Our models estimate that fatigue, grounding style, pronation, and grounding impact have a high correlation with injury risk by the conventional methods. A feedback of these parameters and shoe selection based on these information would contribute to a reduction of running injuries

Transferring CNNs to Multi-instance Multi-label Classification on Small Datasets

Image tagging is a well known challenge in image processing. It is typically addressed through multi-instance multi-label (MIML) classification methodologies. Convolutional Neural Networks (CNNs) possess great potential to perform well on MIML tasks, since multi-level convolution and max pooling coincide with the multi-instance setting and the sharing of hidden representation may benefit multi-label modeling. However, CNNs usually require a large amount of carefully labeled data for training, which is hard to obtain in many real applications. In this paper, we propose a new approach for transferring pre-trained deep networks such as VGG16 on Imagenet to small MIML tasks. We extract features from each group of the network layers and apply multiple binary classifiers to them for multi-label prediction. Moreover, we adopt an L1-norm regularized Logistic Regression (L1LR) to find the most effective features for learning the multi-label classifiers. The experiment results on two most-widely used and relatively small benchmark MIML image datasets demonstrate that the proposed approach can substantially outperform the state-of-the-art algorithms, in terms of all popular performance metrics