19,960 research outputs found
Are all the frames equally important?
In this work, we address the problem of measuring and predicting temporal
video saliency - a metric which defines the importance of a video frame for
human attention. Unlike the conventional spatial saliency which defines the
location of the salient regions within a frame (as it is done for still
images), temporal saliency considers importance of a frame as a whole and may
not exist apart from context. The proposed interface is an interactive
cursor-based algorithm for collecting experimental data about temporal
saliency. We collect the first human responses and perform their analysis. As a
result, we show that qualitatively, the produced scores have very explicit
meaning of the semantic changes in a frame, while quantitatively being highly
correlated between all the observers. Apart from that, we show that the
proposed tool can simultaneously collect fixations similar to the ones produced
by eye-tracker in a more affordable way. Further, this approach may be used for
creation of first temporal saliency datasets which will allow training
computational predictive algorithms. The proposed interface does not rely on
any special equipment, which allows to run it remotely and cover a wide
audience.Comment: CHI'20 Late Breaking Work
Hierarchical Salient Object Detection for Assisted Grasping
Visual scene decomposition into semantic entities is one of the major
challenges when creating a reliable object grasping system. Recently, we
introduced a bottom-up hierarchical clustering approach which is able to
segment objects and parts in a scene. In this paper, we introduce a transform
from such a segmentation into a corresponding, hierarchical saliency function.
In comprehensive experiments we demonstrate its ability to detect salient
objects in a scene. Furthermore, this hierarchical saliency defines a most
salient corresponding region (scale) for every point in an image. Based on
this, an easy-to-use pick and place manipulation system was developed and
tested exemplarily.Comment: Accepted for ICRA 201
Toward a model of computational attention based on expressive behavior: applications to cultural heritage scenarios
Our project goals consisted in the development of attention-based analysis of human expressive behavior and the implementation of real-time algorithm in EyesWeb XMI in order to improve naturalness of human-computer interaction and context-based monitoring of human behavior. To this aim, perceptual-model that mimic human attentional processes was developed for expressivity analysis and modeled by entropy. Museum scenarios were selected as an ecological test-bed to elaborate three experiments that focus on visitor profiling and visitors flow regulation
Artificially created stimuli produced by a genetic algorithm using a saliency model as its fitness function show that Inattentional Blindness modulates performance in a pop-out visual search paradigm
Salient stimuli are more readily detected than less salient stimuli, and individual differences in such detection may be relevant to why some people fail to notice an unexpected stimulus that appears in their visual field whereas others do notice it. This failure to notice unexpected stimuli is termed 'Inattentional Blindness' and is more likely to occur when we are engaged in a resource-consuming task. A genetic algorithm is described in which artificial stimuli are created using a saliency model as its fitness function. These generated stimuli, which vary in their saliency level, are used in two studies that implement a pop-out visual search task to evaluate the power of the model to discriminate the performance of people who were and were not Inattentionally Blind (IB). In one study the number of orientational filters in the model was increased to check if discriminatory power and the saliency estimation for low-level images could be improved. Results show that the performance of the model does improve when additional filters are included, leading to the conclusion that low-level images may require a higher number of orientational filters for the model to better predict participants' performance. In both studies we found that given the same target patch image (i.e. same saliency value) IB individuals take longer to identify a target compared to non-IB individuals. This suggests that IB individuals require a higher level of saliency for low-level visual features in order to identify target patches
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