Salient Region Guided Blind Image Sharpness Assessment

Salient regions provide important cues for scene understanding to the human vision system. However, whether the detected salient regions are helpful in image blur estimation is unknown. In this study, a salient region guided blind image sharpness assessment (BISA) framework is proposed, and the effect of the detected salient regions on the BISA performance is investigated. Specifically, three salient region detection (SRD) methods and ten BISA models are jointly explored, during which the output saliency maps from SRD methods are re-organized as the input of BISA models. Consequently, the change in BISA metric values can be quantified and then directly related to the difference in BISA model inputs. Finally, experiments are conducted on three Gaussian blurring image databases, and the BISA prediction performance is evaluated. The comparison results indicate that salient region input can help achieve a close and sometimes superior performance to a BISA model over the whole image input. When using the center region input as the baseline, the detected salient regions from the saliency optimization from robust background detection (SORBD) method lead to consistently better score prediction, regardless of the BISA model. Based on the proposed hybrid framework, this study reveals that saliency detection benefits image blur estimation, while how to properly incorporate SRD methods and BISA models to improve the score prediction will be explored in our future work.

Download Full-text

Multi-Resolution Salient Region Detection in Frequency Domain

International Journal of Advanced Pervasive and Ubiquitous Computing ◽

10.4018/ijapuc.2014010105 ◽

2014 ◽

Vol 6 (1) ◽

pp. 60-70

Author(s):

Yingchun Guo ◽

Yanhong Feng ◽

Gang Yan ◽

Shuo Shi

Keyword(s):

Frequency Domain ◽

Euclidean Distance ◽

Detection Method ◽

Saliency Map ◽

Normal Distribution Function ◽

Salient Region Detection ◽

Salient Region ◽

Region Detection ◽

Proposed Model ◽

Salient Regions

Salient region detection is a challenge problem in computer vision, which is useful in image segmentation, region-based image retrieval, and so on. In this paper we present a multi-resolution salient region detection method in frequency domain which can highlight salient regions with well-defined boundaries of object. The original image is sub-sampled into three multi-resolution layers, and for each layer the luminance and color salient features are extracted in frequency domain. Then, the significant values are calculated by using invariant laws of Euclidean distance in Lab space and the normal distribution function is used to specify the salient map in each layer in order to remove noise and enhance the correlation among the vicinity pixels. The final saliency map is obtained by normalizing and merging the multi-resolution salient maps. Experimental evaluation depicts the promising results from the proposed model by outperforming the state-of-art frequency-tuned model.

Download Full-text

Movie Video Summarization- Generating Personalized Summaries Using Spatiotemporal Salient Region Detection

International Journal of Multimedia Data Engineering and Management ◽

10.4018/ijmdem.2019070101 ◽

2019 ◽

Vol 10 (3) ◽

pp. 1-26

Author(s):

Rajkumar Kannan ◽

Sridhar Swaminathan ◽

Gheorghita Ghinea ◽

Frederic Andres ◽

Kalaiarasi Sonai Muthu Anbananthen

Keyword(s):

Optical Flow ◽

Video Summarization ◽

Video Frame ◽

Color Contrast ◽

Color Distribution ◽

Salient Region Detection ◽

Salient Region ◽

Region Detection ◽

Video Summary ◽

Salient Regions

Video summarization condenses a video by extracting its informative and interesting segments. In this article, a novel video summarization approach is proposed based on spatiotemporal salient region detection. The proposed approach first segments a video into a set of shots which are ranked with spatiotemporal saliency scores. The score for a shot is computed by aggregating the frame level spatiotemporal saliency scores. This approach detects spatial and temporal salient regions separately using different saliency theories related to objects present in a visual scenario. The spatial saliency of a video frame is computed using color contrast and color distribution estimations and center prior integration. The temporal saliency of a video frame is estimated as an integration of local and global temporal saliencies computed using patch level optical flow abstractions. Finally, top ranked shots with the highest saliency scores are selected for generating the video summary. The objective and subjective experimental results demonstrate the efficacy of the proposed approach.

Download Full-text

Salient Region Detection for Biometric Watermarking

Computer Vision for Multimedia Applications ◽

10.4018/978-1-60960-024-2.ch013 ◽

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.

Download Full-text

Salient Region Detection for Biometric Watermarking

Image Processing ◽

10.4018/978-1-4666-3994-2.ch012 ◽

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

Download Full-text

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

Applied Computational Intelligence and Soft Computing ◽

10.1155/2018/1014595 ◽

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.

Download Full-text

Military Object Real-Time Detection Technology Combined with Visual Salience and Psychology

Electronics ◽

10.3390/electronics7100216 ◽

2018 ◽

Vol 7 (10) ◽

pp. 216 ◽

Cited By ~ 2

Author(s):

Xia Hua ◽

Xinqing Wang ◽

Dong Wang ◽

Jie Huang ◽

Xiaodong Hu

Keyword(s):

Visual Information ◽

Feature Fusion ◽

Visual Salience ◽

Salient Region Detection ◽

Salient Region ◽

Detection Model ◽

Region Detection ◽

Accurate Detection ◽

Detection Technology ◽

Salient Regions

This paper presents a method of military object detection through the combination of human visual salience and visual psychology, so as to achieve rapid and accurate detection of military objects on the vast and complex battlefield. Inspired by the process of human visual information processing, this paper establishes a salient region detection model based on double channel and feature fusion. In this model the pre-attention channel is to process information on the position and contrast of images, and the sub-attention channel is to integrate information on primary visual features first and then merges results of the two channels to determine the salient region. The main theory of Gestalt visual psychology is then used as the constraint condition to integrate the candidate salient regions and to obtain the object figure with overall perception. After that, the efficient sub-window search method is used to detect and filter the object in order to determine the location and range of objects. The experimental results show that, when compared with the existing algorithms, the algorithm proposed in this paper has prominent advantages in precision, effectiveness, and simplicity, which not only significantly reduces the effectiveness of battlefield camouflage and deception but also achieves the rapid and accurate detection of military objects, thus promoting its application prospect.

Download Full-text

Efficient 3D mesh salient region detection using local homogeneity measure

IET Image Processing ◽

10.1049/iet-ipr.2017.0598 ◽

2018 ◽

Vol 12 (9) ◽

pp. 1663-1672 ◽

Cited By ~ 1

Author(s):

Abdul Rahman El Sayed ◽

Abdallah El Chakik ◽

Hassan Alabboud ◽

Adnan Yassine

Keyword(s):

Salient Region Detection ◽

3D Mesh ◽

Local Homogeneity ◽

Salient Region ◽

Region Detection

Download Full-text

Concurrent scheduling and layout of virtual manufacturing cells considering assembly aspects

Proceedings of the Institution of Mechanical Engineers Part B Journal of Engineering Manufacture ◽

10.1177/0954405420980685 ◽

2020 ◽

pp. 095440542098068

Author(s):

Kamran Forghani ◽

S. M. T. Fatemi Ghomi ◽

Reza Kia

Keyword(s):

Mathematical Model ◽

Simulated Annealing ◽

Model Material ◽

Virtual Manufacturing ◽

Superior Performance ◽

Shop Floor ◽

Processing Route ◽

Manufacturing Cells ◽

Comparison Results ◽

Virtual Manufacturing Cells

Cell formation, scheduling, and facility layout are three main decisions in designing manufacturing cells. In this paper, we address the integration of these decisions in virtual manufacturing cells considering assembly aspects and process routing. We develop a mathematical model to determine the machine cells, the layout of machines and workstations on the shop floor, the processing route of parts, and the production sequence of operations on the machines. In this mathematical model, material handling costs and cycle time are minimized. To the best of our knowledge, this is the first paper that concurrently addresses the scheduling and layout of virtual manufacturing cells with assembly aspects and so-called criteria. To effectively solve the problem, a Population-based Simulated Annealing (PSA) combined with linear programming is proposed. The practical usability of the developed model is demonstrated in a case study. Finally, instances from the literature are solved to evaluate the performance of the PSA. The comparison results showed the superior performance of the PSA in comparison with CPLEX solver and standard simulated annealing.

Download Full-text