Activity guided multi-scales collaboration based on scaled-CNN for saliency prediction

Saliency prediction is a very important and challenging task within the computer vision community. Many models exist that try to predict the salient regions on a scene from its RGB image values. Several new models are developed, and spectral imaging techniques may potentially overcome the limitations found when using RGB images. However, the experimental study of such models based on spectral images is difficult because of the lack of available data to work with. This article presents the first eight-channel multispectral image database of outdoor urban scenes together with their gaze data recorded using an eyetracker over several observers performing different visualization tasks. Besides, the information from this database is used to study whether the complexity of the images has an impact on the saliency maps retrieved from the observers. Results show that more complex images do not correlate with higher differences in the saliency maps obtained.

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The multi-scales nonlinear enhancement method of THz image

2013 IEEE INTERNATIONAL CONFERENCE ON MICROWAVE TECHNOLOGY & COMPUTATIONAL ELECTROMAGNETICS ◽

10.1109/icmtce.2013.6812451 ◽

2013 ◽

Author(s):

Zhang Peng ◽

Hu Weiliang ◽

Luo Wenjian ◽

Zhang Zhihui

Keyword(s):

Enhancement Method ◽

Multi Scales

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COMPARISON OF ICTAL AND INTERICTAL EEG SIGNALS USING FRACTAL FEATURES

International Journal of Neural Systems ◽

10.1142/s0129065713500287 ◽

2013 ◽

Vol 23 (06) ◽

pp. 1350028 ◽

Cited By ~ 34

Author(s):

YU WANG ◽

WEIDONG ZHOU ◽

QI YUAN ◽

XUELI LI ◽

QINGFANG MENG ◽

...

Keyword(s):

Feature Analysis ◽

Support Vector ◽

Eeg Analysis ◽

Eeg Signals ◽

Eeg Classification ◽

Significant Difference ◽

Interictal Eeg ◽

Nonlinear Features ◽

The Difference ◽

Multi Scales

The feature analysis of epileptic EEG is very significant in diagnosis of epilepsy. This paper introduces two nonlinear features derived from fractal geometry for epileptic EEG analysis. The features of blanket dimension and fractal intercept are extracted to characterize behavior of EEG activities, and then their discriminatory power for ictal and interictal EEGs are compared by means of statistical methods. It is found that there is significant difference of the blanket dimension and fractal intercept between interictal and ictal EEGs, and the difference of the fractal intercept feature between interictal and ictal EEGs is more noticeable than the blanket dimension feature. Furthermore, these two fractal features at multi-scales are combined with support vector machine (SVM) to achieve accuracies of 97.58% for ictal and interictal EEG classification and 97.13% for normal, ictal and interictal EEG classification.

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