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The principal component (PC) approach offers compressions of an image sequence into fewer images and noise suppressing filters. Multiple MR images of the same tomographic slice obtained with different acquisition parameters (i.e., with different TR, TE, and flip angles), time sequences of images in nuclear medicine, and cardiac ultrasound image sequences are examples of such input image sets. In this paper noise relationships of original and linearly transformed image sequences in general, and specifically of original, PC, and PC-filtered images are discussed. As the spinoff, it introduces locally weighted PC transforms and filters, nonlinear PC's, and a single-image based filter for suppression of noise. Examples illustrate increased perceptibility of anatomical/functional structures in PC images and PC-filtered images, including extraction of physiological functional information by PC loading curves. Generally, the more correlated the original images are, the more effective is the PC approach.

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We conducted an eye-tracking study where 30 participants performed searches on the web. We measured their topical knowledge before and after each task. Their eye-fixations were labelled as "reading" or "scanning". The series of reading fixations in a line, called "reading-sequences" were characterized by their length in pixels, fixation duration, and the number of fixations making up the sequence. We hypothesize that differences in knowledge-change of participants are reflected in their eye-tracking measures related to reading. Our results show that the participants with higher change in knowledge differ significantly in terms of their total reading-sequence-length, reading-sequence-duration, and number of reading fixations, when compared to participants with lower knowledge-change. CCS CONCEPTS• Human-centered computing → Empirical studies in HCI; KEYWORDS eye-tracking, knowledge assessment, search as learning

The task of answering questions about images has garnered attention as a practical service for assisting populations with visual impairments as well as a visual Turing test for the artificial intelligence community. Our first aim is to identify the common vision skills needed for both scenarios. To do so, we analyze the need for four vision skills-object recognition, text recognition, color recognition, and counting-on over 27,000 visual questions from two datasets representing both scenarios. We next quantify the difficulty of these skills for both humans and computers on both datasets. Finally, we propose a novel task of predicting what vision skills are needed to answer a question about an image. Our results reveal (mis)matches between aims of real users of such services and the focus of the AI community. We conclude with a discussion about future directions for addressing the visual question answering task.