Optimised Computational Visual Attention Model for Robotic Cognition

Amudha, J.; Chadalawada, Ravi Kiran; Subashini, V.; Kumar, B. B. S.

doi:10.1007/978-3-642-32063-7_27

Cited by 3 publications

(2 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Natural Language Processing (NLP) and then interfaced with vision using Language Perceptional Translator (LPT) for parsing the sentence and to extract the corresponding properties of an object like location, colour, size and shape for the object. Few features that influence the visual search task were context, features and background [12]. Context was unimportant for the detection of human due to its dynamic behaviour in surveillance scenes.…”

Section: Related Workmentioning

confidence: 99%

Feature Gate Computational Top-Down Model for Target Detection

Ramachandran¹,

Amudha²

2020

Adv. sci. technol. eng. syst. j.

View full text Add to dashboard Cite

Computer vision is a technique used for processing images and videos which are increasingly becoming ubiquitous day by day. Technologies developed are revolving around human needs and demands high computational power as volume of data increases. The extraction of the necessary information for processing, that is independent of various scene complexity is a challenging task. Computation visual attention methods channelize a way to select the information using psychological studies on the human visual system. This work aims to develop a computational visual attention method to select the target in a scene. Feature Gate Top-Down model is proposed to filter the significant region of a target in the scene. The proposed model is extended as a choice-based system to detect target or salient movement in surveillance videos. Experimental analysis is performed on various scenes for detecting human as a target followed by the analysis on surveillance videos is evaluated. The metrics such as Kullback-Leibler divergence (KL div), Normalized Scanpath Saliency (NSS), Correlation Coefficient (CC) and similarity reveals that the proposed model is more adaptive in identifying the target region by suppressing other dominant objects.

show abstract

Section: Related Workmentioning

confidence: 99%

Feature Gate Computational Top-Down Model for Target Detection

Ramachandran¹,

Amudha²

2020

Adv. sci. technol. eng. syst. j.

View full text Add to dashboard Cite

show abstract

“…Trough the central-surrounding structure, the contrast is increased between the target area and background area, so the target area will be noticed by the visual system. [15][16][17] Where + represents central excitement zone, and − represents central excitement zone.…”

Section: Related Theory Analysismentioning

confidence: 99%

Strip Steel Defect Detection Based on Saliency Map Construction Using Gaussian Pyramid Decomposition

Guan

2015

ISIJ Int.

View full text Add to dashboard Cite

A novel detection algorithm for strip steel defect image based on saliency map construction using Gaussian Pyramid decomposition is proposed in this paper. Firstly, the acquired gray image of strip steel is decomposed into strips steel sub-images with different resolution by Gaussian Pyramid. Secondly, the saliency map is constructed by the central-surround differences operation of strips steel sub-images and image fusion of difference sub-images. Finally, we respectively calculated mean values of maximum value in image rows and columns, in which small mean is chosen as the optimal threshold segmentation of strip image, and then to segment surface defects of steel strip. Experiment results show that the proposed method is valid for inhibition of the image background and can be realized complete segmentation and accurate detection for strip steel defect.

show abstract

Vιsual question answering models Evaluation

Sarath

Amudha

2020

2020 International Conference for Emerging Technology (INCET)

View full text Add to dashboard Cite

Optimised Computational Visual Attention Model for Robotic Cognition

Cited by 3 publications

References 8 publications

Feature Gate Computational Top-Down Model for Target Detection

Feature Gate Computational Top-Down Model for Target Detection

Strip Steel Defect Detection Based on Saliency Map Construction Using Gaussian Pyramid Decomposition

Vιsual question answering models Evaluation

Contact Info

Product

Resources

About