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akaturk Akademik ölçüm

OpenAlex konusu

Visual Attention and Saliency Detection

Bu sayfa OpenAlex konu etiketine göre çalışmaları ve o konuda görünen akademisyenleri listeler. YÖKSİS temel alan / yan dal değildir.

OpenAlex 463 eser 27 yazar konusu

Çalışmalar

463 eser

  1. OpenAlex üst %1 OpenAlex 99.7%

    Özet henüz yok.

  2. YÖKSİS SJR Q1 JCR Q1 OpenAlex üst %1 OpenAlex 99.9%

    To detect visually salient elements of complex natural scenes, computational bottom-up saliency models commonly examine several feature channels such as color and orientation in parallel. They compute a separate feature map for each channel and then linearly combine these maps to produce a master saliency map. However…

  3. YÖKSİS OpenAlex üst %1 OpenAlex 99.5%

    A new instrument, the Object-Spatial Imagery Questionnaire (OSIQ), was designed to assess individual differences in visual imagery preferences and experiences. The OSIQ consists of two scales: an object imagery scale that assesses preferences for representing and processing colourful, pictorial, and high-resolution im…

  4. YÖKSİS SJR Q1 JCR Q1 OpenAlex üst %1 OpenAlex 99.9%

    To detect visually salient elements of complex natural scenes, computational bottom-up saliency models commonly examine several feature channels such as color and orientation in parallel. They compute a separate feature map for each channel and then linearly combine these maps to produce a master saliency map. However…

  5. YÖKSİS SJR Q1 JCR Q1 OpenAlex üst %1 OpenAlex 99.5%

    Computational modeling of the primate visual system yields insights of potential relevance to some of the challenges that computer vision is facing, such as object recognition and categorization, motion detection and activity recognition, or vision-based navigation and manipulation. This paper reviews some functional…

  6. OpenAlex üst %10 OpenAlex 96.8%

    High dynamic range images may be created by capturing multiple images of a scene with varying exposures. Images created in this manner are prone to ghosting artifacts, which appear if there is movement in the scene at the time of capture. This paper describes a novel approach to removing ghosting artifacts from high d…

  7. YÖKSİS SJR Q1 JCR Q1 OpenAlex üst %10 OpenAlex 98.6%

    Computational saliency models for still images have gained significant popularity in recent years. Saliency prediction from videos, on the other hand, has received relatively little interest from the community. Motivated by this, in this paper, we study the use of deep learning for dynamic saliency prediction and prop…

  8. YÖKSİS SJR Q1 JCR Q1 OpenAlex üst %10 OpenAlex 96.3%

    Parallel pattern recognition requires great computational resources; it is NP-complete. From an engineering point of view it is desirable to achieve good performance with limited resources. For this purpose, we develop a serial model for visual pattern recognition based on the primate selective attention mechanism. Th…

  9. YÖKSİS SJR Q2 JCR Q1 OpenAlex 86.6%

    Despite the growing interest in fixation selection under natural conditions, there is a major gap in the literature concerning its developmental aspects. Early in life, bottom-up processes, such as local image feature - color, luminance contrast etc. - guided viewing, might be prominent but later overshadowed by more…

  10. OpenAlex üst %10 OpenAlex 91.2%

    We examined how observers discount perceived surface orientation in estimating perceived albedo (lightness). Observers viewed complex rendered scenes binocularly. The orientation of a test patch was defined by depth cues of binocular disparity and linear perspective. On each trial, observers first estimated the orient…

  11. OpenAlex üst %10 OpenAlex 98.7%

    Objects are integral to a robot's understanding of space. Various tasks such as semantic mapping, pick-and-carry missions or manipulation involve interaction with objects. Previous work in the field largely builds on the assumption that the object in question starts out within the ready sensory reach of the robot. In…

  12. YÖKSİS SJR Q1 JCR Q1 OpenAlex üst %10 OpenAlex 96.0%

    Özet henüz yok.

Akademisyenler

27 akademisyen