Detection of Small Target in Infrared Image Based on Background Predication by FLS-SVM

Yiquan, 吴一全 Wu; Danyan, 尹丹艳 Yin

doi:10.3788/aos20103010.2806

Cited by 2 publications

(2 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Another change is the fuzzy SVM (FSVM) [56], which uses a fuzzy math function to overcome the influence of noisy data on the SVM. Later on, the fuzzy least square SVM (FLS-SVM) [57] based on the least square function and FSVM, mainly to solve the unclassifiable part. To overcome the shortcomings of the mixed noise, such as singular points and Gaussian noise, a new type of FSVM, called a fuzzy robust v-SVM (FRv-SVM) [58], the combination of triangle fuzzy theory, v-SVM, and robustness can effectively punish these mixed noises.…”

Section: Ga As An Intelligent Computational Accelerator For Geospatial Data Processing a Ga With A Support Vector Machinementioning

confidence: 99%

Geometric Algebra Applications in Geospatial Artificial Intelligence and Remote Sensing Image Processing

Bhatti

Yuan

et al. 2020

IEEE Access

View full text Add to dashboard Cite

With the increasing demand for multidimensional data processing, Geometric algebra (GA) has attracted more and more attention in the field of geographical information systems. GA unifies and generalizes real numbers and complex, quaternion, and vector algebra, and converts complicated relations and operations into intuitive algebra independent of coordinate systems. It also provides a solution for solving multidimensional information processing with a high correlation among the dimensions and avoids the loss of information. Traditional methods of computer vision and artificial intelligence (AI) provide robust results in multidimensional processing after being combined with GA and give additional feature analysis facility to remote sensing images. In this paper, we provide a detailed review of GA in different fields of AI and computer vision regarding its applications and the current developments in geospatial research. We also discuss the Clifford-Fourier transform (CFT) and quaternions (sub-algebra of GA) because of their necessity in remote sensing image processing. We focus on how GA helps AI and solves classification problems, as well as improving these methods using geometric algebra processing. Finally, we discuss the issues, challenges, and future perspectives of GA with regards to possible research directions.INDEX TERMS Geometric algebra, Clifford algebra, geometric algebra, computer vision, artificial intelligence, quaternions.

show abstract

Section: Ga As An Intelligent Computational Accelerator For Geospatial Data Processing a Ga With A Support Vector Machinementioning

confidence: 99%

Geometric Algebra Applications in Geospatial Artificial Intelligence and Remote Sensing Image Processing

Bhatti

Yuan

et al. 2020

IEEE Access

View full text Add to dashboard Cite

show abstract

“…Moving target detection algorithm based on the video mainly use frames information of a video sequence and frames information between the sequence to extract the motion information [1] . Optical flow method, frame-difference method and background subtraction method are mainly three detection methods [2] . Optical flow algorithm is complex, time-consuming, poor real-time, and not suitable for embedded hardware system; frame-difference method, which is one of the most commonly used method, extracts the region of the moving target by comparing different point between adjacent frames of continuous image sequences [3,4] ; The back ground subtraction method， which is another important goal， extracts the region of the moving target by comparing current image and background image.…”

Section: Introductionmentioning

confidence: 99%

Improved Moving Target Detection Technology

Jin

Yang

Liu

et al. 2014

AMR

View full text Add to dashboard Cite

In order to detect target clearly, a detection system based on DM642 was designed. The system used improved frame-difference method combined with the background subtraction to detect target. First, the CCD camera scanned the surroundings step by step, then the background model was built, and improved three-frame-difference method was used to get the three-frame-difference image. The target image was the difference of target region extracted by three-frame-difference method and the target region extracted by background subtraction method. Experiments showed that the target image had less interference and a clear profile.

show abstract

Detection of Small Target in Infrared Image Based on Background Predication by FLS-SVM

Cited by 2 publications

References 0 publications

Geometric Algebra Applications in Geospatial Artificial Intelligence and Remote Sensing Image Processing

Geometric Algebra Applications in Geospatial Artificial Intelligence and Remote Sensing Image Processing

Improved Moving Target Detection Technology

Contact Info

Product

Resources

About