Feature synergy depends on feature contrast and objecthood

نویسندگان

  • Günter Meinhardt
  • Max Schmidt
  • Malte Persike
  • Bodo Röers
چکیده

Pairs of texture figures, defined by contrast in spatial frequency, orientation or both cues (redundant texture definition) had to be detected within a homogeneous Gabor field. In line with expectation we find better detection performance for arrangements with higher feature contrast along the border where the figures abut. Redundantly defined figures show synergy, a significant performance increase compared to the prediction of independent processing of orientation and spatial frequency cues. As found in previous studies [Spatial Vision 16 (2003) 459; Vision Research (submitted for publication)] this performance advantage is negatively correlated with visibility. In particular, figures with high border feature contrast are easily detectable but show weak synergy whereas figures with low border feature contrast are barely detectable but remarkably benefit from redundant texture definition. Closer analysis reveals that the form of the figures is also crucial: As long as they maintain a clear two dimensional shape the synergy effect is only marginally affected by variation figure size and border length. But when they degrade to one dimensional Gabor element arrays, synergy almost completely vanishes. The results imply that both factors, low visibility and objecthood, are critical for feature synergy. We conclude that facilitation across feature domains serves to segregate figure from ground when the signal from a single domain is too weak to enable object detection and vanishes under conditions of stable object vision.

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

A review on EEG based brain computer interface systems feature extraction methods

The brain – computer interface (BCI) provides a communicational channel between human and machine. Most of these systems are based on brain activities. Brain Computer-Interfacing is a methodology that provides a way for communication with the outside environment using the brain thoughts. The success of this methodology depends on the selection of methods to process the brain signals in each pha...

متن کامل

A review on EEG based brain computer interface systems feature extraction methods

The brain – computer interface (BCI) provides a communicational channel between human and machine. Most of these systems are based on brain activities. Brain Computer-Interfacing is a methodology that provides a way for communication with the outside environment using the brain thoughts. The success of this methodology depends on the selection of methods to process the brain signals in each pha...

متن کامل

Automatic Prostate Cancer Segmentation Using Kinetic Analysis in Dynamic Contrast-Enhanced MRI

Background: Dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) provides functional information on the microcirculation in tissues by analyzing the enhancement kinetics which can be used as biomarkers for prostate lesions detection and characterization.Objective: The purpose of this study is to investigate spatiotemporal patterns of tumors by extracting semi-quantitative as well as w...

متن کامل

Feature selection using genetic algorithm for breast cancer diagnosis: experiment on three different datasets

Objective(s): This study addresses feature selection for breast cancer diagnosis. The present process uses a wrapper approach using GA-based on feature selection and PS-classifier. The results of experiment show that the proposed model is comparable to the other models on Wisconsin breast cancer datasets. Materials and Methods: To evaluate effectiveness of proposed feature selection method, we ...

متن کامل

A Real-Time Electroencephalography Classification in Emotion Assessment Based on Synthetic Statistical-Frequency Feature Extraction and Feature Selection

Purpose: To assess three main emotions (happy, sad and calm) by various classifiers, using appropriate feature extraction and feature selection. Materials and Methods: In this study a combination of Power Spectral Density and a series of statistical features are proposed as statistical-frequency features. Next, a feature selection method from pattern recognition (PR) Tools is presented to e...

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:
  • Vision Research

دوره 44  شماره 

صفحات  -

تاریخ انتشار 2004