Defining Image Memorability Using the Visual Memory Schema.

Memorability of an image is a characteristic determined by the human observers' ability to remember images they have seen. Yet recent work on image memorability defines it as an intrinsic property that can be obtained independent of the observer. The current study aims to enhance our understanding and prediction of image memorability, improving upon existing approaches by incorporating the properties of cumulative human annotations. We propose a new concept called the Visual Memory Schema (VMS) referring to an organization of image components human observers share when encoding and recognizing images. The concept of VMS is operationalised by asking human observers to define memorable regions of images they were asked to remember during an episodic memory test. We then statistically assess the consistency of VMSs across observers for either correctly or incorrectly recognised images. The associations of the VMSs with eye fixations and saliency are analysed separately as well. Lastly, we adapt various deep learning architectures for the reconstruction and prediction of memorable regions in images and analyse the results when using transfer learning at the outputs of different convolutional network layers.

Silhouette Orientation Volumes for Efficient Fall Detection in Depth Videos.

A novel method to detect human falls in depth videos is presented in this paper. A fast and robust shape sequence descriptor, namely the Silhouette Orientation Volume (SOV), is used to represent actions and classify falls. The SOV descriptor provides high classification accuracy even with a combination of simple associated models, such as Bag-of-Words and the Naïve Bayes classifier. Experiments on the public SDU-Fall dataset show that this new approach achieves up to 91.89% fall detection accuracy with a single-view depth camera. The classification rate is about 5% higher than the results reported in the literature. An overall accuracy of 89.63% was obtained for the six-class action recognition, which is about 25% higher than the state of the art. Moreover, a perfect silhouette-based action recognition rate of 100% is achieved on the Weizmann action dataset.

Use of the dynamic volume spline method to predict facial soft tissue changes associated with orthognathic surgery.

OBJECTIVE: The shape of the face can be estimated before the surgery by using 3-dimensional computer programs that provide tools to guide skill modifications. The aim of this study was to present the dynamic volume spline method to predict facial soft tissue changes after the modification of the skull associated with orthognathic surgery. STUDY DESIGN: Soft tissue volume is modeled by a dynamic volume spline that includes the elastic behavior of the actual tissue. The model is a hybrid of spring-mass model and finite element model, and combines their advantageous properties. It provides fast and realistic soft tissue simulations. Postsurgical shape of the patient's face is estimated by reshaping the skull and letting the soft tissue model relax over the new boundary conditions formed by the new skull shape. Postsurgical estimations were compared with the conventional method's estimations, where the soft tissue is not modeled biomechanically. Also, postsurgical estimations were compared with the actual postsurgical data for 6 orthognathic surgery patients. RESULTS: The mean of the error between the estimated shapes and the actual postsurgical shapes was ∼1.8 mm when the whole face was considered. CONCLUSION: When the facial soft tissue is modeled by the dynamic volume spline, the postsurgical shape is estimated better than by the conventional method and previous methods in the literature.