Video of a classroom lecture has been shown to be a versatile learning resource comparable to a textbook. Captions in videos are highly valued by students, especially those with hearing disability and those whose first language is not English. Captioning by automatic speech recognition (ASR) tools is of limited use because of low and variable accuracy. Manual captioning with existing tools is a slow, tedious and expensive task. In this work, we present a web-based crowdsourcing editor to add or correct captions for video lectures. The editor allows a group, e.g., students in a class, to correct the captions for different parts of a video lecture simultaneously. Users can review and correct each other's work. The caption editor has been successfully employed to caption STEM coursework videos. Our findings based on survey results and interviews indicate that this innovative crowdsourcing tool is effective and efficient for captioning lecture videos and has considerable value in educational practice. The caption editor is integrated with Indexed Captioned Searchable (ICS) Videos framework at University of Houston that has been used by dozens of courses and 1000s of students. The ICS Videos framework including the captioning tool is open source software available to educational institutions.978-1-4799-3922-0/14/$31.00 ©2014 IEEE

No abstract

No abstract

No abstract

In spite of the advantages of ease of imaging and low acquisition cost, ultrasound images are noisier and have poorer image quality than other imaging modalities like CT or MRI and hence require an experienced clinician for interpretation. Processing by automated segmentation assists clinicians and improves the accuracy of assessment by minimizing its subjective nature. Various methods for the segmentation of ultrasound images exist, but there is not much literature on the processing of 2D ultrasound images of the foot. This work aimed at developing a novel method for image processing in order to achieve automated segmentation of ultrasound images of the plantar soft tissue. Preprocessing of the ultrasound images was performed using the anisotropic diffusion filter followed by contrast enhancement. The Chan-Vese active contour method was used for segmentation. Our method took into account the difficulty of visualization of the tissues and bony structures in the foot and used an additional curvature parameter for segmentation. Assessing the changes in the biomechanical properties of the plantar soft tissue can be a potential application of this method especially in case of the diabetic foot.