105,991 research outputs found
Rapid Visual Categorization is not Guided by Early Salience-Based Selection
The current dominant visual processing paradigm in both human and machine
research is the feedforward, layered hierarchy of neural-like processing
elements. Within this paradigm, visual saliency is seen by many to have a
specific role, namely that of early selection. Early selection is thought to
enable very fast visual performance by limiting processing to only the most
salient candidate portions of an image. This strategy has led to a plethora of
saliency algorithms that have indeed improved processing time efficiency in
machine algorithms, which in turn have strengthened the suggestion that human
vision also employs a similar early selection strategy. However, at least one
set of critical tests of this idea has never been performed with respect to the
role of early selection in human vision. How would the best of the current
saliency models perform on the stimuli used by experimentalists who first
provided evidence for this visual processing paradigm? Would the algorithms
really provide correct candidate sub-images to enable fast categorization on
those same images? Do humans really need this early selection for their
impressive performance? Here, we report on a new series of tests of these
questions whose results suggest that it is quite unlikely that such an early
selection process has any role in human rapid visual categorization.Comment: 22 pages, 9 figure
Radar and RGB-depth sensors for fall detection: a review
This paper reviews recent works in the literature on the use of systems based on radar and RGB-Depth (RGB-D) sensors for fall detection, and discusses outstanding research challenges and trends related to this research field. Systems to detect reliably fall events and promptly alert carers and first responders have gained significant interest in the past few years in order to address the societal issue of an increasing number of elderly people living alone, with the associated risk of them falling and the consequences in terms of health treatments, reduced well-being, and costs. The interest in radar and RGB-D sensors is related to their capability to enable contactless and non-intrusive monitoring, which is an advantage for practical deployment and users’ acceptance and compliance, compared with other sensor technologies, such as video-cameras, or wearables. Furthermore, the possibility of combining and fusing information from The heterogeneous types of sensors is expected to improve the overall performance of practical fall detection systems. Researchers from different fields can benefit from multidisciplinary knowledge and awareness of the latest developments in radar and RGB-D sensors that this paper is discussing
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