Visual Attention Guided Object Detection and Tracking

2016 ◽  
pp. 99-114
Author(s):  
Debi Prosad Dogra
Keyword(s):  
Computer Vision ◽  
Object Recognition ◽  
Visual Attention ◽  
Visual Saliency ◽  
Video Object ◽  
Salient Region Detection ◽  
Region Detection ◽  
Video Object Tracking ◽  
Detection And Tracking ◽  
Pros And Cons

Scene understanding and object recognition heavily depend on the success of visual attention guided salient region detection in images and videos. Therefore, summarizing computer vision techniques that take the help of visual attention models to accomplish video object recognition and tracking, can be helpful to the researchers of computer vision community. In this chapter, it is aimed to present a philosophical overview of the possible applications of visual attention models in the context of object recognition and tracking. At the beginning of this chapter, a brief introduction to various visual saliency models suitable for object recognition is presented, that is followed by discussions on possible applications of attention models on video object tracking. The chapter also provides a commentary on the existing techniques available on this domain and discusses some of their possible extensions. It is believed that, prospective readers will benefit since the chapter comprehensively guides a reader to understand the pros and cons of this particular topic.

Computer Vision Based Asset Surveillance for Smart Buildings

2020 ◽  
Vol 17 (1) ◽  
pp. 456-463
Author(s):  
K. S. Gautam ◽  
Latha Parameswaran ◽  
Senthil Kumar Thangavel
Keyword(s):  
Computer Vision ◽  
Correlation Coefficient ◽  
Real World ◽  
Vision System ◽  
Region Of Interest ◽  
Detection And Tracking ◽  
Novel Approach ◽  
Ip Camera ◽  
Pros And Cons ◽  
Real World Problems

Unraveling meaningful pattern form the video offers a solution to many real-world problems, especially surveillance and security. Detecting and tracking an object under the area of video surveillance, not only automates the security but also leverages smart nature of the buildings. The objective of the manuscript is to detect and track assets inside the building using vision system. In this manuscript, the strategies involved in asset detection and tracking are discussed with their pros and cons. In addition to it, a novel approach has been proposed that detects and tracks the object of interest across all the frames using correlation coefficient. The proposed approach is said to be significant since the user has an option to select the object of interest from any two frames in the video and correlation coefficient is calculated for the region of interest. Based on the arrived correlation coefficient the object of interest is tracked across the rest of the frames. Experimentation is carried out using the 10 videos acquired from IP camera inside the building.

scholarly journals Learning-Based Visual Saliency Model for Detecting Diabetic Macular Edema in Retinal Image

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Xiaochun Zou ◽  
Xinbo Zhao ◽  
Yongjia Yang ◽  
Na Li
Keyword(s):  
Macular Edema ◽  
Diabetic Macular Edema ◽  
Retinal Image ◽  
Visual Saliency ◽  
Saliency Map ◽  
Support Vector ◽  
Salient Region Detection ◽  
Region Detection ◽  
Saliency Model ◽  
Visual Saliency Model

This paper brings forth a learning-based visual saliency model method for detecting diagnostic diabetic macular edema (DME) regions of interest (RoIs) in retinal image. The method introduces the cognitive process of visual selection of relevant regions that arises during an ophthalmologist’s image examination. To record the process, we collected eye-tracking data of 10 ophthalmologists on 100 images and used this database as training and testing examples. Based on analysis, two properties (Feature Property and Position Property) can be derived and combined by a simple intersection operation to obtain a saliency map. The Feature Property is implemented by support vector machine (SVM) technique using the diagnosis as supervisor; Position Property is implemented by statistical analysis of training samples. This technique is able to learn the preferences of ophthalmologist visual behavior while simultaneously considering feature uniqueness. The method was evaluated using three popular saliency model evaluation scores (AUC, EMD, and SS) and three quality measurements (classical sensitivity, specificity, and Youden’sJstatistic). The proposed method outperforms 8 state-of-the-art saliency models and 3 salient region detection approaches devised for natural images. Furthermore, our model successfully detects the DME RoIs in retinal image without sophisticated image processing such as region segmentation.

Salient Region Detection for Biometric Watermarking

2011 ◽  
pp. 218-236
Author(s):  
Ma Bin ◽  
Li Chun-lei ◽  
Wang Yun-hong ◽  
Bai Xiao
Keyword(s):  
Vision System ◽  
Region Of Interest ◽  
Visual Saliency ◽  
Saliency Map ◽  
Human Vision ◽  
Identification Accuracy ◽  
Biometric Data ◽  
Salient Region Detection ◽  
Salient Region ◽  
Region Detection

Visual saliency, namely the perceptual significance to human vision system (HVS), is a quality that differentiates an object from its neighbors. Detection of salient regions which contain prominent features and represent main contents of the visual scene, has obtained wide utilization among computer vision based applications, such as object tracking and classification, region-of-interest (ROI) based image compression, etc. Specially, as for biometric authentication system, whose objective is to distinguish the identification of people through biometric data (e.g. fingerprint, iris, face etc.), the most important metric is distinguishability. Consequently, in biometric watermarking fields, there has been a great need of good metrics for feature prominency. In this chapter, we present two salient-region-detection based biometric watermarking scenarios, in which robust annotation and fragile authentication watermark are respectively applied to biometric systems. Saliency map plays an important role of perceptual mask that adaptively select watermarking strength and position, therefore controls the distortion introduced by watermark and preserves the identification accuracy of biometric images.

Salient Region Detection for Biometric Watermarking

2013 ◽  
pp. 201-219
Author(s):  
Bin Ma ◽  
Chun-lei Li ◽  
Yun-hong Wang ◽  
Xiao Bai
Keyword(s):  
Vision System ◽  
Region Of Interest ◽  
Visual Saliency ◽  
Saliency Map ◽  
Human Vision ◽  
Identification Accuracy ◽  
Biometric Data ◽  
Salient Region Detection ◽  
Salient Region ◽  
Region Detection

Visual saliency, namely the perceptual significance to human vision system (HVS), is a quality that differentiates an object from its neighbors. Detection of salient regions which contain prominent features and represent main contents of the visual scene, has obtained wide utilization among computer vision based applications, such as object tracking and classification, region-of-interest (ROI) based image compression, etc. Specially, as for biometric authentication system, whose objective is to distinguish the identification of people through biometric data (e.g. fingerprint, iris, face etc.), the most important metric is distinguishability. Consequently, in biometric watermarking fields, there has been a great need of good metrics for feature prominency. In this chapter, we present two salient-region-detection based biometric watermarking scenarios, in which robust annotation and fragile authentication watermark are respectively applied to biometric systems. Saliency map plays an important role of perceptual mask that adaptively select watermarking strength and position, therefore controls the distortion introduced by watermark and preserves the identification accuracy of biometric images.

scholarly journals Saliency Aggregation: Multifeature and Neighbor Based Salient Region Detection for Social Images

2018 ◽  
Vol 2018 ◽  
pp. 1-16
Author(s):  
Ye Liang ◽  
Congyan Lang ◽  
Jian Yu ◽  
Hongzhe Liu ◽  
Nan Ma
Keyword(s):  
Saliency Detection ◽  
Visual Saliency ◽  
Salient Region Detection ◽  
Salient Region ◽  
Region Detection ◽  
Image Appearance ◽  
Deep Convolution Neural Network ◽  
Salient Regions ◽  
Social Images ◽  
Visual Saliency Models

The popularity of social networks has brought the rapid growth of social images which have become an increasingly important image type. One of the most obvious attributes of social images is the tag. However, the sate-of-the-art methods fail to fully exploit the tag information for saliency detection. Thus this paper focuses on salient region detection of social images using both image appearance features and image tag cues. First, a deep convolution neural network is built, which considers both appearance features and tag features. Second, tag neighbor and appearance neighbor based saliency aggregation terms are added to the saliency model to enhance salient regions. The aggregation method is dependent on individual images and considers the performance gaps appropriately. Finally, we also have constructed a new large dataset of challenging social images and pixel-wise saliency annotations to promote further researches and evaluations of visual saliency models. Extensive experiments show that the proposed method performs well on not only the new dataset but also several state-of-the-art saliency datasets.

Global-Based Salient Region Detection for Microscopic Image

2012 ◽  
Vol 151 ◽  
pp. 607-611
Author(s):  
Xiang Jun Liu ◽  
Cuixia Bai ◽  
Yi Gang Wang
Keyword(s):  
Visual Saliency ◽  
Spatial Location ◽  
Low Complexity ◽  
Microscopic Image ◽  
Color Information ◽  
Salient Region Detection ◽  
Salient Region ◽  
Region Detection ◽  
Important Study ◽  
Detection Effect

The salient region detection has been a very important study in machine vision and image analysis. Reliable estimation of visual saliency allows appropriate processing of images without prior knowledge of their contents, and thus remains an important step in many computer vision tasks including image segmentation, object recognition, adaptive compression and image retrieval. This paper presents a global-based contrast region detection method. The color information and the relevance of spatial location were taken into account. Experimental results show that the proposed method compared with the existed methods, our method yielded better detection effect, more precise and low complexity, at the same time, the method was more applicable for salient region detection of microscopic image.

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