Shadows remain segmented as selectable regions in object-based attention paradigms

نویسندگان

  • Lee de-Wit
  • David Milner
  • Robert Kentridge
چکیده

It is unclear how shadows are processed in the visual system. Whilst shadows are clearly used as an important cue to localise the objects that cast them, there is mixed evidence regarding the extent to which shadows influence the recognition of those objects. Furthermore experiments exploring the perception of shadows per se have provided evidence that the visual system has less efficient access to the detailed form of a region if it is interpreted as a shadow. The current study sought to clarify our understanding of the manner in which shadows are represented by the visual system by exploring how they influence attention in two different object-based attention paradigms. The results provide evidence that cues to interpret a region as a shadow do not reduce the extent to which that region will result in a within-'object' processing advantage. Thus, whilst there is evidence that shadows are processed differently at higher stages of object perception, the present result shows that they are still represented as distinctly segmented regions as far as the allocation of attention is concerned. This result is consistent with the idea that object-based attention phenomena result from region-based scene segmentation rather than from the representations of objects per se.

برای دانلود رایگان متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Shadow Detection and Removal Based on YCbCr Color Space

Shadows in an image can reveal information about the object’s shape and orientation, and even about the light source. Thus shadow detection and removal is a very crucial and inevitable task of some computer vision algorithms for applications such as image segmentation and object detection and tracking. This paper proposes a simple framework using the luminance, chroma: blue, chroma: red (YCbCr)...

متن کامل

Detection and Tracking of Moving

This paper presents a method for detection and tracking of moving cast shadows on a dominating scene background in a monocular video sequence. The method assumes moving shadows on a dominant smooth shaped background. The shadow causing light sources are assumed to be strong enough to cause visible temporal frame diierences by moving cast shadows. These diierences are detected and classiied into...

متن کامل

Separation and contrast enhancement of overlapping cast shadow components using polarization.

Shadow is an inseparable aspect of all natural scenes. When there are multiple light sources or multiple reflections several different shadows may overlap at the same location and create complicated patterns. Shadows are a potentially good source of information about a scene if the shadow regions can be properly identified and segmented. However, shadow region identification and segmentation is...

متن کامل

Adaptive Object Segmentation from Surveillance Video Sequences

Identifying moving objects from a video sequence is a fundamental and critical task in many computer vision applications. We develop an efficient adaptive object segmentation algorithm for color video surveillance sequences; background is modeled using Multiple Correlation Coefficient ( ) using pixel-level based approach. Segmented foreground generally includes self shadows as foreground object...

متن کامل

Shadow elimination for effective moving object detection by Gaussian shadow modeling

This paper presents a novel approach for eliminating unexpected shadows from multiple pedestrians from a static and textured background using Gaussian shadow modeling. First, a set of moving regions are segmented from the static background using a background subtraction technique. The extracted moving region may contain multiple shadows from various pedestrians. In order to remove these unwante...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

عنوان ژورنال:

دوره 3  شماره 

صفحات  -

تاریخ انتشار 2012