Automatic Salient Object Extraction Based on Locally Adaptive Thresholding to Generate Tactile Graphics

Automatic extraction of salient regions is beneficial for various computer vision applications, such as image segmentation and object recognition. The salient visual information across images is very useful and plays a significant role for the visually impaired in identifying tactile information. In this paper, we introduce a novel saliency cuts method using local adaptive thresholding to obtain four regions from a given saliency map. First, we produced four regions for image segmentation using a saliency map as an input image and local adaptive thresholding. Second, the four regions were used to initialize an iterative version of the GrabCuts algorithm and to produce a robust and high-quality binary mask with a full resolution. Finally, salient objects’ outer boundaries and inner edges were detected using the solution from our previous research. Experimental results showed that local adaptive thresholding using integral images can produce a more robust binary mask compared to the results from previous works that make use of global thresholding techniques for salient object segmentation. The proposed method can extract salient objects with a low-quality saliency map, achieving a promising performance compared to existing methods. The proposed method has advantages in extracting salient objects and generating simple, important edges from natural scene images efficiently for delivering visually salient information to the visually impaired.

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Saliency Cuts: Salient Region Extraction based on Local Adaptive Thresholding for Image Information Recognition of the Visually Impaired

The International Arab Journal of Information Technology ◽

10.34028/iajit/17/5/4 ◽

2020 ◽

Vol 17 (5) ◽

pp. 713-720

Author(s):

Mukhriddin Mukhiddinov ◽

Rag-Gyo Jeong ◽

Jinsoo Cho

Keyword(s):

Visually Impaired ◽

Saliency Map ◽

Input Image ◽

Adaptive Thresholding ◽

Binary Mask ◽

Salient Object ◽

Full Resolution ◽

Visually Impaired People ◽

Region Extraction ◽

Edge Detection Method

In recent years, there has been an increased scope for assistive software and technologies, which help the visually impaired to perceive and recognize natural scene images. In this article, we propose a novel saliency cuts approach using local adaptive thresholding to obtain four regions from a given saliency map. The saliency cuts approach is an effective tool for salient object detection. First, we produce four regions for image segmentation using a saliency map as an input image and applying an automatic threshold operation. Second, the four regions are used to initialize an iterative version of the Grab Cut algorithm and to produce a robust and high-quality binary mask with a full resolution. Lastly, based on the binary mask and extracted salient object, outer boundaries and internal edges are detected by Canny edge detection method. Extensive experiments demonstrate that the proposed method correctly detects and extracts the main contents of the image sequences for delivering visually salient information to the visually impaired people compared to the results of existing salient object segmentation algorithms

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SALIENT AREA DISCERNMENT VIA HIGH DIMENSIONAL COLOR TRANSFORM AND LOCAL SPECIAL PLATFORM

International Journal of Engineering Technologies and Management Research ◽

10.29121/ijetmr.v5.i10.2018.298 ◽

2020 ◽

Vol 5 (10) ◽

pp. 17-24

Author(s):

P. Santhiya ◽

S. Selvi

Keyword(s):

Color Space ◽

Human Perception ◽

Saliency Map ◽

Estimation Methods ◽

High Dimensional ◽

Color Contrast ◽

Salient Object ◽

Salient Region Detection ◽

Region Detection ◽

Salient Regions

Detecting visually salient regions in images is fundamental problems and it is useful for applications like image segmentation, adaptive compression, and object recognition. A salient object region is a soft decomposition of foreground and background image elements. To detect salient regions in an image in terms of the saliency maps. To create a saliency map by using a linear combination of colors in high-dimensional color space. To improve the performance of saliency estimation, utilize the relative location and color contrast between superpixels. To resolve the saliency estimation from trimap by using learning based algorithm. This is based on an examination that salient regions frequently have individual colors’ compared with backgrounds in human sensitivity however, human perception is complicated and extremely nonlinear. The tentative outcome on three benchmark datasets show that our approach is valuable in assessment with the prior state-of-the-art saliency estimation methods. Finally, salient region detection that outputs full resolution saliency map with well-defined boundaries of the salient object.

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Efficient Vanishing Point Detection for Driving Assistance Based on Visual Saliency Map and Image Segmentation from a Vehicle Black-Box Camera

Symmetry ◽

10.3390/sym11121492 ◽

2019 ◽

Vol 11 (12) ◽

pp. 1492 ◽

Cited By ~ 2

Author(s):

JongBae Kim

Keyword(s):

Image Segmentation ◽

Visual Attention ◽

Support Systems ◽

Single Point ◽

Saliency Map ◽

Input Image ◽

Vanishing Point ◽

Safe Driving ◽

The Road ◽

Feature Information

Techniques for detecting a vanishing point (VP) which estimates the direction of a vehicle by analyzing its relationship with surrounding objects have gained considerable attention recently. VPs can be used to support safe vehicle driving in areas such as for autonomous driving, lane-departure avoidance, distance estimation, and road-area detection, by detecting points in which parallel extension lines of objects are concentrated at a single point in a 3D space. In this paper, we proposed a method of detecting the VP in real time for applications to intelligent safe-driving support systems. In order to support safe driving of autonomous vehicles, it is necessary to drive the vehicle with the VP in center of the road image in order to prevent the vehicle from moving out of the road area while driving. Accordingly, in order to detect the VP in the road image, a method of detecting a point where straight lines intersect in an area where edge directional feature information is concentrated is required. The visual attention model and image segmentation process are applied to quickly identify candidate VPs in the area where the edge directional feature-information is concentrated and the intensity contrast difference is large. In the proposed method, VPs are detected by analyzing the edges, visual-attention regions, linear components using the Hough transform, and image segmentation results in an input image. Our experimental results have shown that the proposed method could be applied to safe-driving support systems.

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The Study of Randomized Visual Saliency Detection Algorithm

Computational and Mathematical Methods in Medicine ◽

10.1155/2013/380245 ◽

2013 ◽

Vol 2013 ◽

pp. 1-9

Author(s):

Yuantao Chen ◽

Weihong Xu ◽

Fangjun Kuang ◽

Shangbing Gao

Keyword(s):

Image Segmentation ◽

Detection Method ◽

Saliency Detection ◽

Visual Saliency ◽

Detection Algorithm ◽

Saliency Map ◽

Input Image ◽

Memory Space ◽

Segmentation Process ◽

Visual Saliency Detection

Image segmentation process for high quality visual saliency map is very dependent on the existing visual saliency metrics. It is mostly only get sketchy effect of saliency map, and roughly based visual saliency map will affect the image segmentation results. The paper had presented the randomized visual saliency detection algorithm. The randomized visual saliency detection method can quickly generate the same size as the original input image and detailed results of the saliency map. The randomized saliency detection method can be applied to real-time requirements for image content-based scaling saliency results map. The randomization method for fast randomized video saliency area detection, the algorithm only requires a small amount of memory space can be detected detailed oriented visual saliency map, the presented results are shown that the method of visual saliency map used in image after the segmentation process can be an ideal segmentation results.

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Face Image Segmentation Using Color Information and Saliency Map

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.55-57.77 ◽

2011 ◽

Vol 55-57 ◽

pp. 77-81

Author(s):

Hui Ming Huang ◽

He Sheng Liu ◽

Guo Ping Liu

Keyword(s):

Image Segmentation ◽

Human Skin ◽

Skin Color ◽

Saliency Map ◽

Face Image ◽

Color Information ◽

Attention Model ◽

Face Area ◽

Face Region ◽

The Face

In this paper, we proposed an efficient method to address the problem of color face image segmentation that is based on color information and saliency map. This method consists of three stages. At first, skin colored regions is detected using a Bayesian model of the human skin color. Then, we get a chroma chart that shows likelihoods of skin colors. This chroma chart is further segmented into skin region that satisfy the homogeneity property of the human skin. The third stage, visual attention model are employed to localize the face region according to the saliency map while the bottom-up approach utilizes both the intensity and color features maps from the test image. Experimental evaluation on test shows that the proposed method is capable of segmenting the face area quite effectively，at the same time, our methods shows good performance for subjects in both simple and complex backgrounds, as well as varying illumination conditions and skin color variances.

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An Object-Based Image Reducing Approach

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.1044-1045.1049 ◽

2014 ◽

Vol 1044-1045 ◽

pp. 1049-1052 ◽

Cited By ~ 1

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.

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A novel approach of adaptive thresholding for image segmentation on GPU

2016 Fourth International Conference on Parallel, Distributed and Grid Computing (PDGC) ◽

10.1109/pdgc.2016.7913203 ◽

2016 ◽

Author(s):

Pawan Kumar Upadhyay ◽

Satish Chandra ◽

Arun Sharma

Keyword(s):

Image Segmentation ◽

Adaptive Thresholding ◽

Novel Approach

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Optimal Scale of Hierarchical Image Segmentation with Scribbles Guidance for Weakly Supervised Semantic Segmentation

International Journal of Pattern Recognition and Artificial Intelligence ◽

10.1142/s0218001421540264 ◽

2021 ◽

pp. 2154026

Author(s):

Zaid Al-Huda ◽

Donghai Zhai ◽

Yan Yang ◽

Riyadh Nazar Ali Algburi

Keyword(s):

Image Segmentation ◽

Graphical Model ◽

Semantic Segmentation ◽

Saliency Map ◽

Training Data ◽

Deep Convolutional Neural Networks ◽

High Quality ◽

Optimal Scale ◽

Supervised Segmentation ◽

Weakly Supervised

Deep convolutional neural networks (DCNNs) trained on the pixel-level annotated images have achieved improvements in semantic segmentation. Due to the high cost of labeling training data, their applications may have great limitation. However, weakly supervised segmentation approaches can significantly reduce human labeling efforts. In this paper, we introduce a new framework to generate high-quality initial pixel-level annotations. By using a hierarchical image segmentation algorithm to predict the boundary map, we select the optimal scale of high-quality hierarchies. In the initialization step, scribble annotations and the saliency map are combined to construct a graphic model over the optimal scale segmentation. By solving the minimal cut problem, it can spread information from scribbles to unmarked regions. In the training process, the segmentation network is trained by using the initial pixel-level annotations. To iteratively optimize the segmentation, we use a graphical model to refine segmentation masks and retrain the segmentation network to get more precise pixel-level annotations. The experimental results on Pascal VOC 2012 dataset demonstrate that the proposed framework outperforms most of weakly supervised semantic segmentation methods and achieves the state-of-the-art performance, which is [Formula: see text] mIoU.

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