Publications by Bahar Aydemir | EPFL Graph Search

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Saliency models are image-based prediction models that estimate human visual attention. Such models, when applied to architectural spaces, could pave the way for design decisions where visual attention is taken into account. In this study, we tested the pe ...

Façades and light pattern composition have been shown to influence the spatial experience and physiological responses of humans [1,2]. The present study examines the effect of sunlight penetration and window size on fixations to the floor of the scene, and ...

Recent years have seen remarkable advancements in saliency estimation methods, mainly due to deep learning models leveraging the widespread availability of real-world images. However, saliency is profoundly shaped by the intricacies of the human visual att ...

Deep saliency prediction algorithms complement the object recognition features, they typically rely on additional information such as scene context, semantic relationships, gaze direction, and object dissimilarity. However, none of these models consider th ...

Saliency prediction has made great strides over the past two decades, with current techniques modeling low-level information, such as color, intensity and size contrasts, and high-level ones, such as attention and gaze direction for entire objects. Despite ...

Bahar Aydemir

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Saliency prediction in 360° architectural scenes: Performance and impact of daylight variations

Virtual reality to assess visual attraction and perceived interest to daylit scene variations

Visual Saliency Prediction for Natural Images, Comics Panels, and Comics Pages

TempSAL - Uncovering Temporal Information for Deep Saliency Prediction

Modeling Object Dissimilarity for Deep Saliency Prediction