Modification of YAPE keypoint detection algorithm for wide local contrast range images

2018 ◽  
Author(s):  
Dmitry P. Nikolaev ◽  
Artem Lukoyanov ◽  
Ivan Konovalenko
Keyword(s):  
Detection Algorithm ◽  
Local Contrast ◽  
Range Images ◽  
Keypoint Detection ◽  
Contrast Range

scholarly journals Salient Region Detection by Fusing Foreground and Background Cues Extracted from Single Image

2016 ◽  
Vol 2016 ◽  
pp. 1-18 ◽  
Author(s):  
Qiangqiang Zhou ◽  
Weidong Zhao ◽  
Lin Zhang ◽  
Zhicheng Wang
Keyword(s):  
Saliency Detection ◽  
Computational Cost ◽  
Detection Algorithm ◽  
Saliency Map ◽  
Local Contrast ◽  
Feature Maps ◽  
Color Distribution ◽  
Salient Region Detection ◽  
Region Detection ◽  
Application Fields

Saliency detection is an important preprocessing step in many application fields such as computer vision, robotics, and graphics to reduce computational cost by focusing on significant positions and neglecting the nonsignificant in the scene. Different from most previous methods which mainly utilize the contrast of low-level features, various feature maps are fused in a simple linear weighting form. In this paper, we propose a novel salient object detection algorithm which takes both background and foreground cues into consideration and integrate a bottom-up coarse salient regions extraction and a top-down background measure via boundary labels propagation into a unified optimization framework to acquire a refined saliency detection result. Wherein the coarse saliency map is also fused by three components, the first is local contrast map which is in more accordance with the psychological law, the second is global frequency prior map, and the third is global color distribution map. During the formation of background map, first we construct an affinity matrix and select some nodes which lie on border as labels to represent the background and then carry out a propagation to generate the regional background map. The evaluation of the proposed model has been implemented on four datasets. As demonstrated in the experiments, our proposed method outperforms most existing saliency detection models with a robust performance.

Investigation of infrared dim and small target detection algorithm based on the visual saliency feature

2020 ◽  
pp. 095441002098095
Author(s):  
Shaoyi Li ◽  
Xiaotian Wang ◽  
Xi Yang ◽  
Kai Zhang ◽  
Saisai Niu
Keyword(s):  
Optical Flow ◽  
Target Detection ◽  
Tracking System ◽  
Detection Algorithm ◽  
Saliency Map ◽  
Target Area ◽  
Local Contrast ◽  
Small Target ◽  
Small Target Detection ◽  
Detection Effect

Infrared dim and small target detection has an important role in the infrared thermal imaging seeker, infrared search and tracking system, space-based infrared system and other applications. Inspired by human visual system (HVS), based on the fusion of significant features of targets, the present study proposes an infrared dim and small target detection algorithm for complex backgrounds. Firstly, in order to calculate the target saliency map, the proposed algorithm initially uses the difference of Gaussian (DoG) and the contourlet filters for the preprocessing and fusion, respectively. Then the multi-scale improved local contrast measure (ILCM) method is applied to obtain the interested target area, effectively suppress the background clutter and improve the target signal-to-clutter ratio. Secondly, the optical flow method is used to estimate motion regions in the saliency map, which matches with the interested target region to achieve the initial target screening. Finally, in order to reduce the false alarm rate, forward pipeline filtering and optical flow estimation information are applied to achieve the multi-frame target recognition and achieve continuous detection of dim and small targets in image sequences. Experimental results show that compared with the conventional Tophat (TOP-HAT) and ILCM algorithms, the proposed algorithm can achieve stable, continuous and adaptive target detection for multiple backgrounds. The area under curve (AUC) and the harmonic average measure F1 are used to measure the overall performance and optimal performance of the target detection effect. For sky, sea and ground backgrounds, the test results of proposed algorithm for most sequences are 1. It is concluded that the proposed algorithm significantly improves the detection effect.

scholarly journals Infrared Small Target Detection via Modified Random Walks

2018 ◽  
Vol 10 (12) ◽  
pp. 2004 ◽  
Author(s):  
Chaoqun Xia ◽  
Xiaorun Li ◽  
Liaoying Zhao
Keyword(s):  
Random Walks ◽  
Target Detection ◽  
Detection Algorithm ◽  
Local Contrast ◽  
Small Target ◽  
Global Uniqueness ◽  
Dimensional Image ◽  
Small Target Detection ◽  
Small Targets ◽  
Infrared Small Target

Infrared small target detection under intricate background and heavy noise is one of the crucial tasks in the field of remote sensing. Conventional algorithms can fail in detecting small targets due to the low signal-to-noise ratios of the images. To solve this problem, an effective infrared small target detection algorithm inspired by random walks is presented in this paper. The novelty of our contribution involves the combination of the local contrast feature and the global uniqueness of the small targets. Firstly, the original pixel-wise image is transformed into an multi-dimensional image with respect to the local contrast measure. Secondly, a reconstructed seeds selection map (SSM) is generated based on the multi-dimensional image. Then, an adaptive seeds selection method is proposed to automatically select the foreground seeds potentially placed in the areas of the small targets in the SSM. After that, a confidence map is constructed using a modified random walks (MRW) algorithm to represent the global uniqueness of the small targets. Finally, we segment the targets from the confidence map by utilizing an adaptive threshold. Extensive experimental evaluation results on a real test dataset demonstrate that our algorithm is superior to the state-of-the-art algorithms in both target enhancement and detection performance.

scholarly journals A Curvature-Based Multidirectional Local Contrast Method for Star Detection of a Star Sensor

Photonics ◽  
2021 ◽  
Vol 9 (1) ◽  
pp. 13
Author(s):  
Kaili Lu ◽  
Enhai Liu ◽  
Rujin Zhao ◽  
Hui Zhang ◽  
Ling Lin ◽  
...  
Keyword(s):  
Detection Algorithm ◽  
Stray Light ◽  
Star Sensor ◽  
Local Contrast ◽  
Star Image ◽  
Light Interference ◽  
Contrast Method ◽  
Complex Background ◽  
The Difference ◽  
Lower False Alarm Rate

Stray light, such as sunlight, moonlight, and earth-atmosphere light, can bring about light spots in backgrounds, and it affects the star detection of star sensors. To overcome this problem, this paper proposes a star detection algorithm (CMLCM) with multidirectional local contrast based on curvature. It regards the star image as a spatial surface and analyzes the difference in the curvature between the star and the background. It uses a facet model to represent the curvature and calculate the second-order derivatives in four directions. According to the characteristic of the star and the complex background, it enhances the target and suppresses the complex background by a new calculation method of a local contrast map. Finally, it divides the local contrast map into multiple 256 × 256 sub-regions for a more effective threshold segmentation. The experimental results indicated that the CMLCM algorithm could effectively detect a large number of accurate stars under stray light interference, and the detection rate was higher than other compared algorithms with a lower false alarm rate.

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