scholarly journals Superior colliculus neurons encode a visual saliency map during free viewing of natural dynamic video

2017 ◽  
Vol 8 (1) ◽  
Author(s):  
Brian J. White ◽  
David J. Berg ◽  
Janis Y. Kan ◽  
Robert A. Marino ◽  
Laurent Itti ◽  
...  
Keyword(s):  
Superior Colliculus ◽  
Visual Saliency ◽  
Saliency Map ◽  
Free Viewing ◽  
Superior Colliculus Neurons ◽  
Natural Dynamic

scholarly journals Superior colliculus encodes visual saliency before the primary visual cortex

2017 ◽  
Vol 114 (35) ◽  
pp. 9451-9456 ◽  
Author(s):  
Brian J. White ◽  
Janis Y. Kan ◽  
Ron Levy ◽  
Laurent Itti ◽  
Douglas P. Munoz
Keyword(s):  
Visual Cortex ◽  
Superior Colliculus ◽  
Primary Visual Cortex ◽  
Visual Processing ◽  
Visual Saliency ◽  
Saliency Map ◽  
Stimulus Onset ◽  
Brain Areas ◽  
V1 Neurons ◽  
Processing Stream

Models of visual attention postulate the existence of a bottom-up saliency map that is formed early in the visual processing stream. Although studies have reported evidence of a saliency map in various cortical brain areas, determining the contribution of phylogenetically older pathways is crucial to understanding its origin. Here, we compared saliency coding from neurons in two early gateways into the visual system: the primary visual cortex (V1) and the evolutionarily older superior colliculus (SC). We found that, while the response latency to visual stimulus onset was earlier for V1 neurons than superior colliculus superficial visual-layer neurons (SCs), the saliency representation emerged earlier in SCs than in V1. Because the dominant input to the SCs arises from V1, these relative timings are consistent with the hypothesis that SCs neurons pool the inputs from multiple V1 neurons to form a feature-agnostic saliency map, which may then be relayed to other brain areas.

An Object-Based Image Reducing Approach

2014 ◽  
Vol 1044-1045 ◽  
pp. 1049-1052 ◽  
Author(s):  
Chin Chen Chang ◽  
I Ta Lee ◽  
Tsung Ta Ke ◽  
Wen Kai Tai
Keyword(s):  
Visual Saliency ◽  
Saliency Map ◽  
Input Image ◽  
Image Size ◽  
Target Image ◽  
Feature Maps ◽  
Object Based ◽  
Wide Range

Common methods for reducing image size include scaling and cropping. However, these two approaches have some quality problems for reduced images. In this paper, we propose an image reducing algorithm by separating the main objects and the background. First, we extract two feature maps, namely, an enhanced visual saliency map and an improved gradient map from an input image. After that, we integrate these two feature maps to an importance map. Finally, we generate the target image using the importance map. The proposed approach can obtain desired results for a wide range of images.

Video Objective Quality Evaluation System Based on the Visual Saliency Map

2013 ◽  
Vol 411-414 ◽  
pp. 1362-1367 ◽  
Author(s):  
Qing Lan Wei ◽  
Yuan Zhang
Keyword(s):  
Evaluation System ◽  
Quality Evaluation ◽  
Objective Assessment ◽  
Visual Saliency ◽  
Objective Evaluation ◽  
Subjective Assessment ◽  
Saliency Map ◽  
Assessment Scores ◽  
Objective Quality ◽  
Quality Evaluation System

This paper presents the thoughts about application of saliency map to the video objective quality evaluation system. It computes the SMSE and SPSNR values as the objective assessment scores according to the saliency map, and compares with conditional objective evaluation methods as PSNR and MSE. Experimental results demonstrate that this method can well fit the subjective assessment results.

scholarly journals Ship Detection in Panchromatic Optical Remote Sensing Images Based on Visual Saliency and Multi-Dimensional Feature Description

2020 ◽  
Vol 12 (1) ◽  
pp. 152 ◽  
Author(s):  
Ting Nie ◽  
Xiyu Han ◽  
Bin He ◽  
Xiansheng Li ◽  
Hongxing Liu ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Fourier Transform ◽  
Detection Method ◽  
Saliency Detection ◽  
Visual Saliency ◽  
Saliency Map ◽  
False Alarms ◽  
Optical Remote Sensing ◽  
Remote Sensing Images ◽  
Ship Detection

Ship detection in panchromatic optical remote sensing images is faced with two major challenges, locating candidate regions from complex backgrounds quickly and describing ships effectively to reduce false alarms. Here, a practical method was proposed to solve these issues. Firstly, we constructed a novel visual saliency detection method based on a hyper-complex Fourier transform of a quaternion to locate regions of interest (ROIs), which can improve the accuracy of the subsequent discrimination process for panchromatic images, compared with the phase spectrum quaternary Fourier transform (PQFT) method. In addition, the Gaussian filtering of different scales was performed on the transformed result to synthesize the best saliency map. An adaptive method based on GrabCut was then used for binary segmentation to extract candidate positions. With respect to the discrimination stage, a rotation-invariant modified local binary pattern (LBP) description was achieved by combining shape, texture, and moment invariant features to describe the ship targets more powerfully. Finally, the false alarms were eliminated through SVM training. The experimental results on panchromatic optical remote sensing images demonstrated that the presented saliency model under various indicators is superior, and the proposed ship detection method is accurate and fast with high robustness, based on detailed comparisons to existing efforts.

scholarly journals SALIENCY-GUIDED CHANGE DETECTION OF REMOTELY SENSED IMAGES USING RANDOM FOREST

2018 ◽  
Vol XLII-3 ◽  
pp. 341-348
Author(s):  
W. Feng ◽  
H. Sui ◽  
X. Chen
Keyword(s):  
Random Forest ◽  
Change Detection ◽  
Learning Algorithm ◽  
Saliency Detection ◽  
Principal Component ◽  
Visual Saliency ◽  
Saliency Map ◽  
Difference Image ◽  
Fcm Clustering ◽  
Optimal Segmentation

Studies based on object-based image analysis (OBIA) representing the paradigm shift in change detection (CD) have achieved remarkable progress in the last decade. Their aim has been developing more intelligent interpretation analysis methods in the future. The prediction effect and performance stability of random forest (RF), as a new kind of machine learning algorithm, are better than many single predictors and integrated forecasting method. In this paper, we present a novel CD approach for high-resolution remote sensing images, which incorporates visual saliency and RF. First, highly homogeneous and compact image super-pixels are generated using super-pixel segmentation, and the optimal segmentation result is obtained through image superimposition and principal component analysis (PCA). Second, saliency detection is used to guide the search of interest regions in the initial difference image obtained via the improved robust change vector analysis (RCVA) algorithm. The salient regions within the difference image that correspond to the binarized saliency map are extracted, and the regions are subject to the fuzzy <i>c</i>-means (FCM) clustering to obtain the pixel-level pre-classification result, which can be used as a prerequisite for superpixel-based analysis. Third, on the basis of the optimal segmentation and pixel-level pre-classification results, different super-pixel change possibilities are calculated. Furthermore, the changed and unchanged super-pixels that serve as the training samples are automatically selected. The spectral features and Gabor features of each super-pixel are extracted. Finally, superpixel-based CD is implemented by applying RF based on these samples. Experimental results on Ziyuan 3 (ZY3) multi-spectral images show that the proposed method outperforms the compared methods in the accuracy of CD, and also confirm the feasibility and effectiveness of the proposed approach.

scholarly journals Behavioural evidence for the existence of a spatiotopic free-viewing saliency map

2019 ◽  
Vol 19 (10) ◽  
pp. 305a
Author(s):  
Matthias Kümmerer ◽  
Thomas S.A. Wallis ◽  
Matthias Bethge
Keyword(s):  
Saliency Map ◽  
Free Viewing ◽  
Behavioural Evidence

scholarly journals Normal correspondence of tectal maps for saccadic eye movements in strabismus

2016 ◽  
Vol 116 (6) ◽  
pp. 2541-2549 ◽  
Author(s):  
John R. Economides ◽  
Daniel L. Adams ◽  
Jonathan C. Horton
Keyword(s):  
Electrical Stimulation ◽  
Eye Movements ◽  
Superior Colliculus ◽  
Saccadic Eye Movements ◽  
Systematic Change ◽  
Free Viewing ◽  
Pattern Deviation ◽  
Ocular Deviation ◽  
The Right ◽  
Stem Structure

The superior colliculus is a major brain stem structure for the production of saccadic eye movements. Electrical stimulation at any given point in the motor map generates saccades of defined amplitude and direction. It is unknown how this saccade map is affected by strabismus. Three macaques were raised with exotropia, an outwards ocular deviation, by detaching the medial rectus tendon in each eye at age 1 mo. The animals were able to make saccades to targets with either eye and appeared to alternate fixation freely. To probe the organization of the superior colliculus, microstimulation was applied at multiple sites, with the animals either free-viewing or fixating a target. On average, microstimulation drove nearly conjugate saccades, similar in both amplitude and direction but separated by the ocular deviation. Two monkeys showed a pattern deviation, characterized by a systematic change in the relative position of the two eyes with certain changes in gaze angle. These animals' saccades were slightly different for the right eye and left eye in their amplitude or direction. The differences were consistent with the animals' underlying pattern deviation, measured during static fixation and smooth pursuit. The tectal map for saccade generation appears to be normal in strabismus, but saccades may be affected by changes in the strabismic deviation that occur with different gaze angles.

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