scholarly journals Point cloud saliency detection via local sparse coding

DYNA ◽  
2019 ◽  
Vol 86 (209) ◽  
pp. 238-247 ◽  
Author(s):  
Esmeide Alberto Leal Narvaez ◽  
German Sanchez Torres ◽  
John William Branch Bedoya
Keyword(s):  
Literature Review ◽  
Sparse Coding ◽  
Visual Information ◽  
Point Cloud ◽  
Direct Detection ◽  
Minimum Description Length ◽  
Saliency Detection ◽  
Visual Saliency ◽  
Point Clouds ◽  
Saliency Map

The human visual system (HVS) can process large quantities of visual information instantly. Visual saliency perception is the process of locating and identifying regions with a high degree of saliency from a visual standpoint. Mesh saliency detection has been studied extensively in recent years, but few studies have focused on 3D point cloud saliency detection. The estimation of visual saliency is important for computer graphics tasks such as simplification, segmentation, shape matching and resizing. In this paper, we present a method for the direct detection of saliency on unorganized point clouds. First, our method computes a set of overlapping neighborhoods and estimates adescriptor vector for each point inside it. Then, the descriptor vectors are used as a natural dictionary in order to apply a sparse coding process. Finally, we estimate a saliency map of the point neighborhoods based on the Minimum Description Length (MDL) principle.Experiment results show that the proposed method achieves similar results to those from the literature review and in some cases even improves on them. It captures the geometry of the point clouds without using any topological information and achieves an acceptable performance. The effectiveness and robustness of our approach are shown by comparing it to previous studies in the literature review.

scholarly journals Ship Detection in Panchromatic Optical Remote Sensing Images Based on Visual Saliency and Multi-Dimensional Feature Description

2020 ◽  
Vol 12 (1) ◽  
pp. 152 ◽  
Author(s):  
Ting Nie ◽  
Xiyu Han ◽  
Bin He ◽  
Xiansheng Li ◽  
Hongxing Liu ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Fourier Transform ◽  
Detection Method ◽  
Saliency Detection ◽  
Visual Saliency ◽  
Saliency Map ◽  
False Alarms ◽  
Optical Remote Sensing ◽  
Remote Sensing Images ◽  
Ship Detection

Ship detection in panchromatic optical remote sensing images is faced with two major challenges, locating candidate regions from complex backgrounds quickly and describing ships effectively to reduce false alarms. Here, a practical method was proposed to solve these issues. Firstly, we constructed a novel visual saliency detection method based on a hyper-complex Fourier transform of a quaternion to locate regions of interest (ROIs), which can improve the accuracy of the subsequent discrimination process for panchromatic images, compared with the phase spectrum quaternary Fourier transform (PQFT) method. In addition, the Gaussian filtering of different scales was performed on the transformed result to synthesize the best saliency map. An adaptive method based on GrabCut was then used for binary segmentation to extract candidate positions. With respect to the discrimination stage, a rotation-invariant modified local binary pattern (LBP) description was achieved by combining shape, texture, and moment invariant features to describe the ship targets more powerfully. Finally, the false alarms were eliminated through SVM training. The experimental results on panchromatic optical remote sensing images demonstrated that the presented saliency model under various indicators is superior, and the proposed ship detection method is accurate and fast with high robustness, based on detailed comparisons to existing efforts.

scholarly journals SALIENCY-GUIDED CHANGE DETECTION OF REMOTELY SENSED IMAGES USING RANDOM FOREST

2018 ◽  
Vol XLII-3 ◽  
pp. 341-348
Author(s):  
W. Feng ◽  
H. Sui ◽  
X. Chen
Keyword(s):  
Random Forest ◽  
Change Detection ◽  
Learning Algorithm ◽  
Saliency Detection ◽  
Principal Component ◽  
Visual Saliency ◽  
Saliency Map ◽  
Difference Image ◽  
Fcm Clustering ◽  
Optimal Segmentation

Studies based on object-based image analysis (OBIA) representing the paradigm shift in change detection (CD) have achieved remarkable progress in the last decade. Their aim has been developing more intelligent interpretation analysis methods in the future. The prediction effect and performance stability of random forest (RF), as a new kind of machine learning algorithm, are better than many single predictors and integrated forecasting method. In this paper, we present a novel CD approach for high-resolution remote sensing images, which incorporates visual saliency and RF. First, highly homogeneous and compact image super-pixels are generated using super-pixel segmentation, and the optimal segmentation result is obtained through image superimposition and principal component analysis (PCA). Second, saliency detection is used to guide the search of interest regions in the initial difference image obtained via the improved robust change vector analysis (RCVA) algorithm. The salient regions within the difference image that correspond to the binarized saliency map are extracted, and the regions are subject to the fuzzy <i>c</i>-means (FCM) clustering to obtain the pixel-level pre-classification result, which can be used as a prerequisite for superpixel-based analysis. Third, on the basis of the optimal segmentation and pixel-level pre-classification results, different super-pixel change possibilities are calculated. Furthermore, the changed and unchanged super-pixels that serve as the training samples are automatically selected. The spectral features and Gabor features of each super-pixel are extracted. Finally, superpixel-based CD is implemented by applying RF based on these samples. Experimental results on Ziyuan 3 (ZY3) multi-spectral images show that the proposed method outperforms the compared methods in the accuracy of CD, and also confirm the feasibility and effectiveness of the proposed approach.

Ant-Inspired Visual Saliency Detection in Image

2011 ◽  
pp. 263-272
Author(s):  
Jing Tian ◽  
Weiyu Yu
Keyword(s):  
Visual System ◽  
Human Visual System ◽  
Saliency Detection ◽  
Visual Saliency ◽  
Saliency Map ◽  
Experimental Results ◽  
Superior Performance ◽  
Local Phase ◽  
Visual Saliency Detection ◽  
Pheromone Information

Visual saliency detection aims to produce saliency map of images via simulating the behavior of the human visual system (HVS). An ant-inspired approach is proposed in this chapter. The proposed approach is inspired by the ant’s behavior to find the most saliency regions in image, by depositing the pheromone information (through ant’s movements) on the image to measure its saliency. Furthermore, the ant’s movements are steered by the local phase coherence of the image. Experimental results are presented to demonstrate the superior performance of the proposed approach.

scholarly journals Visual Saliency Detection for Over-Temperature Regions in 3D Space via Dual-Source Images

Sensors ◽  
2020 ◽  
Vol 20 (12) ◽  
pp. 3414
Author(s):  
Dawei Gong ◽  
Zhiheng He ◽  
Xiaolong Ye ◽  
Ziyun Fang
Keyword(s):  
Adaptive Sampling ◽  
High Performance ◽  
Dimensional Space ◽  
Saliency Detection ◽  
Visual Saliency ◽  
Point Clouds ◽  
Temperature Anomalies ◽  
Imaging Characteristics ◽  
Dual Source ◽  
Visual Saliency Detection

To allow mobile robots to visually observe the temperature of equipment in complex industrial environments and work on temperature anomalies in time, it is necessary to accurately find the coordinates of temperature anomalies and obtain information on the surrounding obstacles. This paper proposes a visual saliency detection method for hypertemperature in three-dimensional space through dual-source images. The key novelty of this method is that it can achieve accurate salient object detection without relying on high-performance hardware equipment. First, the redundant point clouds are removed through adaptive sampling to reduce the computational memory. Second, the original images are merged with infrared images and the dense point clouds are surface-mapped to visually display the temperature of the reconstructed surface and use infrared imaging characteristics to detect the plane coordinates of temperature anomalies. Finally, transformation mapping is coordinated according to the pose relationship to obtain the spatial position. Experimental results show that this method not only displays the temperature of the device directly but also accurately obtains the spatial coordinates of the heat source without relying on a high-performance computing platform.

scholarly journals The Study of Randomized Visual Saliency Detection Algorithm

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.

scholarly journals A New Approach on Skull Stripping of Brain MRI based on Saliency Detection using Dictionary Learning and Sparse Coding

Prospectiva ◽  
2019 ◽  
Vol 17 (2) ◽  
Author(s):  
Nallig Eduardo Leal Narváez ◽  
Eduardo Enrique Zurek Varela
Keyword(s):  
Dictionary Learning ◽  
Sparse Coding ◽  
Saliency Detection ◽  
Brain Mri ◽  
Saliency Map ◽  
Magnetic Resonance Images ◽  
Solution Approach ◽  
Skull Stripping ◽  
The Brain ◽  
Brain Tissues

In brain magnetic resonance images (brain MRI) analysis, for diagnosing certain brain conditions, it is necessary to quantify the brain tissue, which implies to separate the brain from extracranial or non-brain tissues through a process of isolation known as skull stripping. This is a non-trivial task since different types of tissues may have the same gray level, and during the separation process, some brain tissues could be removed. This paper presents a new solution approach for the skull stripping problem, based on saliency detection using dictionary learning and sparse coding, which can operate over T1 and T2 weighted axial brain MRI. Our method first subdivides the axial MRI into full overlapped patches and runs a dictionary learning over them for obtaining its sparse representation. Then, by analyzing the sparse coding matrix, we compute how many patches a dictionary atom affects to classify them as frequent or rare. Then, we calculate the saliency map of the axial MRI according to the composition of the image patches, i.e. an image patch is considered salient if it is mainly composed of frequent atoms, an atom is frequent whether it affects many patches. The non-salient pixels, corresponding to non-brain tissues, are eliminated from the MRI. Numerical results validate our method

scholarly journals H-RANSAC: A HYBRID POINT CLOUD SEGMENTATION COMBINING 2D AND 3D DATA

2018 ◽  
Vol IV-2 ◽  
pp. 1-8 ◽  
Author(s):  
A. Adam ◽  
E. Chatzilari ◽  
S. Nikolopoulos ◽  
I. Kompatsiaris
Keyword(s):  
Visual Information ◽  
Point Cloud ◽  
Structural Information ◽  
Hybrid Approach ◽  
Point Clouds ◽  
3D Segmentation ◽  
Plane Fitting ◽  
Fitting Algorithm ◽  
Segmentation Approach ◽  
2D Data

In this paper, we present a novel 3D segmentation approach operating on point clouds generated from overlapping images. The aim of the proposed hybrid approach is to effectively segment co-planar objects, by leveraging the structural information originating from the 3D point cloud and the visual information from the 2D images, without resorting to learning based procedures. More specifically, the proposed hybrid approach, H-RANSAC, is an extension of the well-known RANSAC plane-fitting algorithm, incorporating an additional consistency criterion based on the results of 2D segmentation. Our expectation that the integration of 2D data into 3D segmentation will achieve more accurate results, is validated experimentally in the domain of 3D city models. Results show that HRANSAC can successfully delineate building components like main facades and windows, and provide more accurate segmentation results compared to the typical RANSAC plane-fitting algorithm.

scholarly journals A study on recognizing multi-real world object and estimating 3D position in augmented reality

2021 ◽  
Author(s):  
Taemin Lee ◽  
Changhun Jung ◽  
Kyungtaek Lee ◽  
Sanghyun Seo
Keyword(s):  
Augmented Reality ◽  
Real World ◽  
Visual Information ◽  
Point Cloud ◽  
Three Dimensional ◽  
Point Clouds ◽  
Location Estimation ◽  
Virtual Space ◽  
Camera Tracking ◽  
The Real

AbstractAs augmented reality technologies develop, real-time interactions between objects present in the real world and virtual space are required. Generally, recognition and location estimation in augmented reality are carried out using tracking techniques, typically markers. However, using markers creates spatial constraints in simultaneous tracking of space and objects. Therefore, we propose a system that enables camera tracking in the real world and visualizes virtual visual information through the recognition and positioning of objects. We scanned the space using an RGB-D camera. A three-dimensional (3D) dense point cloud map is created using point clouds generated through video images. Among the generated point cloud information, objects are detected and retrieved based on the pre-learned data. Finally, using the predicted pose of the detected objects, other information may be augmented. Our system estimates object recognition and 3D pose based on simple camera information, enabling the viewing of virtual visual information based on object location.

Dynamic Zero-Parallax-Setting Techniques for Multi-View Autostereoscopic Display

2020 ◽  
Vol 2020 (2) ◽  
pp. 98-1-98-6
Author(s):  
Yuzhong Jiao ◽  
Mark Ping Chan Mok ◽  
Kayton Wai Keung Cheung ◽  
Man Chi Chan ◽  
Tak Wai Shen ◽  
...  
Keyword(s):  
Spatial Distribution ◽  
Deep Learning ◽  
Saliency Detection ◽  
Depth Map ◽  
Visual Saliency ◽  
Saliency Map ◽  
Detection Methods ◽  
3D Vision ◽  
Detection Techniques ◽  
Autostereoscopic Displays

The objective of this paper is to research a dynamic computation of Zero-Parallax-Setting (ZPS) for multi-view autostereoscopic displays in order to effectively alleviate blurry 3D vision for images with large disparity. Saliency detection techniques can yield saliency map which is a topographic representation of saliency which refers to visually dominant locations. By using saliency map, we can predict what attracts the attention, or region of interest, to viewers. Recently, deep learning techniques have been applied in saliency detection. Deep learning-based salient object detection methods have the advantage of highlighting most of the salient objects. With the help of depth map, the spatial distribution of salient objects can be computed. In this paper, we will compare two dynamic ZPS techniques based on visual attention. They are 1) maximum saliency computation by Graphic-Based Visual Saliency (GBVS) algorithm and 2) spatial distribution of salient objects by a convolutional neural networks (CNN)-based model. Experiments prove that both methods can help improve the 3D effect of autostereoscopic displays. Moreover, the spatial distribution of salient objects-based dynamic ZPS technique can achieve better 3D performance than maximum saliency-based method.

Saliency Detection Based on Multi-Scale Superpixel and Dictionary Learning

2014 ◽  
Vol 701-702 ◽  
pp. 348-351
Author(s):  
Gang Hou ◽  
He Xin Yan ◽  
Fan Zhang ◽  
Hui Rong Hou ◽  
Ming Zhang
Keyword(s):  
Dictionary Learning ◽  
Saliency Detection ◽  
Visual Saliency ◽  
Scale Space ◽  
Detection Algorithm ◽  
Saliency Map ◽  
Reconstruction Error ◽  
Multi Scale ◽  
Training Samples ◽  
Scale Spaces

In recent years, saliency detection has been gaining increasing attention since it could significantly boost many content-based multimedia applications. In this paper, we propose a visual saliency detection algorithm based on multi-scale superpixel and dictionary learning . Firstly, in each scale space, we extract the boundaries as the training samples to learn a dictionary through sparse coding and dictionary learning methods. Then, according to reconstruction error of each superpixel, the saliency map is generated for each scale of superpixel. Finally, some saliency maps from different scale spaces are fused together to generate the final saliency map. The experimental results show that the proposed algorithm can highlight the salient regions uniformly and performs better compared with the other five methods.

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