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

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

A target detection method from low contrast forward looking infrared (FLIR) images is proposed. It is known that detecting small targets in remotely sensed image is difficult and challenging work. The goal is to identify target areas with small number of false alarms in a thermal infrared scene of battlefield. The proposed method consists of following three stages. First, center-surround difference with local adaptive threshold is proposed in order to find salient areas in an input image. Second, local thresholding is proposed to the local region of interest (ROf) based on the result of first step. The second step is needed to segment target silhouettes precisely. Third, the extracted binary target silhouettes are compared with target template using size and affinity to remove clutters. In the experiments, many natural infrared images with high variability are used to prove performance the proposed method. It is compared with a morphological method using receiver operating characteristic (ROC) curve and execution time. The result shows that our method is superior to the morphological method and it can be applied to automatic target recognition (ATR) system.

Accurate localization of the salient object from an image is a difficult problem when the saliency map is noisy and incomplete. A fast approach to detect salient objects from images is proposed in this paper. To well balance the size of the object and the saliency it contains, the salient object detection is first formulated with the maximum saliency density on the saliency map. To obtain the global optimal solution, a branch-and-bound search algorithm is developed to speed up the detection process. Without any prior knowledge provided, the proposed method can effectively and efficiently detect salient objects from images. Extensive results on different types of saliency maps with a public dataset of five thousand images show the advantages of our approach as compared to some state-of-the-art methods.

Nowadays, ship detection in sea-sky background is not only useful in maritime visual surveillance, but also helpful in maritime search and rescue. Since ships are salient objects in infrared images with sea-sky background, we present a novel and effective algorithm based on saliency for ship detection in this situation. Our algorithm adopts global saliency, local saliency and background prior to generate saliency maps. Ships are finally segmented in saliency maps. Our algorithm is compared with four classic salient object detection algorithms. And experimental results show our algorithm outperforms the other four algorithms in qualitative and quantitative terms. Keyword list: ship detection ; salient object detection ; infrared image ; sea - sky background . 1. INTRODUCTION Since ships are important vehicles for sea transportation, ship detection in sea-sky background is extensively used in maritime visual surveillance and reconnaissance, avoidance of ship collision accidents, maritime search and rescue, etc. Infrared imaging systems can almost work in any environment and all-weather conditions with its visual capacity in darkness and frog, which makes it a very suitable tool in this application. In recent years, ship detection in infrared images with sea-sky background attracts increasing attention. Many methods are proposed in this field, including image segmentation methods [1-2] and morphological methods [3]. However, due to complex sea-sky environment and large variation of ship sizes, these methods cannot solve the issue completely. And since ships are often salient objects in infrared images with sea-sky background, some researchers have recently tried salient object detection ideas used in color images [4-6]. And the paper is focused on using salient object detection methods to solve the issue. Salient objects are objects which stand out in the image and attract most people ¶VLPPHGLDWHDWWHQWLRQ7KHVWDQGDUGway to detect salient ob MHFWVLVILUVWO\XVLQJVDOLHQF\GHWHFWLRQPHWKRGVWRJHQHUDWHVDOLHQF\PDSVLQZKLFKDSL[HO¶VJUH\value is proportional to its degree of saliency and then segmenting salient objects in saliency maps. Many salient detection methods can be divided into three categories: local contrast methods, global rarity methods and other methods. In local contrast methods, regions which have high contrast with local neighbouring regions are regarded as salient regions. Itti et al. [7] decompose an image into several feature maps . The saliency in each feature map is measured by local center-surround difference in several scales and saliency of different feature maps are integrated to a final saliency

This paper presents a computational method to extract optimum rectangular Regions of Interest (RoI) in images with an associated saliency map. Although saliency maps provide an individual relevance measure for each pixel, to find the sub-image (i.e., rectangular region) that contains the set of the most relevant pixels requires an optimisation procedure to define the boundaries of the best RoI. This is achieved by the method devised in the paper, by following an approach based on balancing the amount of relevant information that is included and excluded from the RoI. The results show that such method is capable of finding the most relevant rectangular RoI and thus to extract the optimum sub-images according to the relevance measure given by a generic saliency map. Since the method is not tied to any particular type of images, it finds application in quite different fields, such as salient object extraction and processing in industry and surveillance, image compression using attention modelling, biomedical imaging, etc.

In recent years, the object-level saliency detection has attracted much research attention, due to its usefulness in many high-level tasks. Existing methods are mostly based on the contrast hypothesis, which regards the regions with high contrast in a certain context as salient objects. Although the contrast hypothesis is effective in many scenarios, it cannot handle some difficult cases. As a remedy to address the weakness of contrast hypothesis, we propose a novel compactness hypothesis, which assumes salient regions are more compact than background from the perspectives of both color layout and texture layout. Based on the compactness hypotheses, we implement an effective object-level saliency detection method. In the proposed method, we first construct a weak saliency map based on the compact hypotheses, then collect samples from the weak saliency map to train a dedicated classifier. This classifier is applied on each individual pixel of the input image to produce a confidence score. Finally, the confidence scores are used to form a saliency map. This process is carried out at different scales, and the corresponding results are integrated into the formation of the final saliency map. The proposed approach is evaluated on eight benchmark data sets, where it delivers the competitive performance compared with the state-of-the-art methods.

Stripe noise is usually introduced into infrared images during acquisition, which seriously deteriorates the image quality and affects the subsequent analysis of the image. In this paper, the spatial mathematic model and the comb-like impulse spectrum characteristics in Fourier domain of stripe noise are given. On this basis, a local adaptive threshold noise detection linear interpolation filter (LALIF) for stripe noise removal in infrared images is designed. In the filter method, the first, we crop the original image into two sub-images with optimal size to make the comb-like impulse spectrum characteristics of the stripe noise most prominent. The second, we use a local adaptive threshold to distinguish noise frequency points from the useful frequency points in the peak regions of the amplitude spectrum, and reassign the value of noise frequency points by linear interpolation. At last, we amalgamate the sub-images to obtain the stripe noise removal image. Experimental results indicate that the proposed method has a good performance in adaptability and stability for complex stripe noise removal in infrared images.

Salient object detection aims to detect the most attractive objects in images, which has been widely used as a fundamental of various multimedia applications. In this paper, we propose a novel salient object detection method for RGB-D images based on evolution strategy. Firstly, we independently generate two saliency maps on color channel and depth channel of a given RGB-D image based on its super-pixels representation. Then, we fuse the two saliency maps with refinement to provide an initial saliency map with high precision. Finally, we utilize cellular automata to iteratively propagate saliency on the initial saliency map and generate the final detection result with complete salient objects. The proposed method is evaluated on two public RGB-D datasets, and the experimental results show that our method outperforms the state-of-the-art methods.

摘要: 提出了一种基于颜色空间的Local特征和Regional特征的自然图像显著性检测方法. 该方法将图像分成8×8的子块, 计算多个尺度下每一个子块的Local特征和Regional特征, 并将其加权组合来确定子块的显著程度, 从而得到整个图像的显著特征. 此外, 通过计算4个颜色通道上的色度对比度, 获得显著物体的边缘. 将图像的显著特征与显著物体的边缘综合后得到图像中的显著目标. 实验结果显示, 本文提出的方法能够快速、清晰而准确地提取出图像中的显著性目标. 关键词: 显著性检测 / Local特征 / Regional特征 / 多尺度

This paper describes a novel approach for extraction of multiple objects from a given image of a natural scene. In the proposed approach, multiple objects are extracted by the application of saliency detection on the image. We use two distinct approaches for object extraction. One approach uses superpixels on the saliency map. Then the intensity of saliency map in each superpixel is used to compute distance between the centres of superpixels. These act as constraints to extract the objects from the image. The other approach is the application of Active Contour model on the saliency map and estimating a bounding box on the intermediate binary image result extracts the objects in the image. The approach is unique in its way of extracting objects from a scene containing multiple objects as it does not use extensive image search like the existing algorithms and therefore leads to a fast and simple extraction.

The optical remote sensing images (RSIs) show various spatial resolutions and cluttered background, where salient objects with different scales, types, and orientations are presented in diverse RSI scenes. Therefore, it is inappropriate to directly extend cutting-edge saliency detection methods for conventional RGB images to optical RSIs. Besides, the existing saliency models targeting RSIs often render imperfect saliency maps, where some of them are with coarse boundary details. To solve this problem, this article attempts to introduce the edge information to precisely detect salient objects in RSIs. Accordingly, we propose an edge-aware multiscale feature integration network (EMFI-Net) for salient object detection by conducting multiscale feature integration under the explicit and implicit assistance of salient edge cues. Specifically, our network contains two parts including the encoder and decoder. First, the encoder extracts multiscale deep features from three RSIs with different resolutions, where the high-level deep semantic features from three RSIs are integrated using a cascaded feature fusion module. Second, the encoder explicitly enriches the multiscale deep features by integrating the salient edge cues extracted by a salient edge extraction module. Meanwhile, we also implicitly deploy an edge-aware constraint to the supervision of the saliency map prediction by introducing a hybrid loss function. Finally, the decoder integrates the enriched multiscale deep features in a coarse-to-fine way, yielding a high-quality saliency map. The experiments conducted on two public optical RSI datasets clearly prove the effectiveness and superiority of the proposed EMFI-Net against the state-of-the-art saliency models.

This paper proposes an effective salient object segmentation method via the graph-based integration of saliency and objectness. Based on the superpixel segmentation result of the input image, a graph is built to represent superpixels using regular vertex, background seed vertex with the addition of a terminal vertex. The edge weights on the graph are defined by integrating the difference of appearance, saliency, and objectness between superpixels. Then, the object probability of each superpixel is measured by finding the shortest path from the corresponding vertex to the terminal vertex on the graph, and the resultant object probability map can generally better highlight salient objects and suppress background regions compared to both saliency map and objectness map. Finally, the object probability map is used to initialize salient object and background, and effectively incorporated into the framework of graph cut to obtain the final salient object segmentation result. Extensive experimental results on three public benchmark datasets show that the proposed method consistently improves the salient object segmentation performance and outperforms the state-of-the-art salient object segmentation methods. Furthermore, experimental results also demonstrate that the proposed graph-based integration method is more effective than other fusion schemes and robust to saliency maps generated using various saliency models.

Background prior has been widely used in many salient object detection models with promising results. These methods assume that the image boundary is all background. Then, color feature based methods are used to extract the salient object. However, such assumption may be inaccurate when the salient object is partially cropped by the image boundary. Besides, using only color feature is also insufficient. We present a novel salient object detection model based on background selection and multi-features. Firstly, we present a simple but effective method to pick out more reliable background seeds. Secondly, we utilize multi-features enhanced graph-based manifold ranking to get the saliency maps. Finally, we also present the salient object segmentation via computed saliency map. Qualitative and quantitative evaluation results on three widely used data sets demonstrate significant appeal and advantages of our technique compared with many state-of-the art models.

MFENet: Multitype fusion and enhancement network for detecting salient objects in RGB-T images

Deep learning has been applied to saliency detection in recent years. The superior performance has proved that deep networks can model the semantic properties of salient objects. Yet it is difficult for a deep network to discriminate pixels belonging to similar receptive fields around the object boundaries, thus deep networks may output maps with blurred saliency and inaccurate boundaries. To tackle such an issue, in this work, we propose a deep Level Set network to produce compact and uniform saliency maps. Our method drives the network to learn a Level Set function for salient objects so it can output more accurate boundaries and compact saliency. Besides, to propagate saliency information among pixels and recover full resolution saliency map, we extend a superpixel-based guided filter to be a layer in the network. The proposed network has a simple structure and is trained end-to-end. During testing, the network can produce saliency maps by efficiently feedforwarding testing images at a speed over 12FPS on GPUs. Evaluations on benchmark datasets show that the proposed method achieves state-of-the-art performance.