Crater detection algorithm with part PHOG features for safe landing

Liu, An; Chen, Maoyin; Pan, Weiquan

doi:10.1109/icsai.2012.6223214

Cited by 11 publications

(7 citation statements)

References 13 publications

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“…20 The strength of the gradient at the point (X, Y) is computed as follows, PHOG features PHOG features are spatial shape descriptors that describe an image by its spatial layout and local shape.…”

Section: Extraction Of Shape Featuresmentioning

confidence: 99%

“…From the local region with 16 × 16 pixels, PHOG features are extracted. 20 The strength of the gradient at the point (X, Y) is computed as follows,…”

Section: Extraction Of Shape Featuresmentioning

confidence: 99%

“…For liver disease classification, the following features are extracted after the preprocessing stage. The textural features such as Gabor filters, local binary patterns (LBP), 16 acutance, 17 energy, contrast, inverse difference moment (IDM), 18 Gray level cooccurrence matrix (GLCM)-based features, 19 gray scale features, 18 pyramid histogram of oriented gradient (PHOG) features, 20 shape based features, 21,22 and morphological features. 23 Then the extracted features with high dimensionality are reduced by utilizing principal component analysis (PCA) 24 method.…”

Section: Introductionmentioning

confidence: 99%

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Optimizing deep belief network parameters using grasshopper algorithm for liver disease classification

Renukadevi¹,

Karunakaran²

2019

Int J Imaging Syst Tech

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Image processing plays a vital role in many areas such as healthcare, military, scientific and business due to its wide variety of advantages and applications. Detection of computed tomography (CT) liver disease is one of the difficult tasks in the medical field. Hand crafted features and classifications are the two types of methods used in the previous approaches, to classify liver disease. But these classification results are not optimal. In this article, we propose a novel method utilizing deep belief network (DBN) with grasshopper optimization algorithm (GOA) for liver disease classification. Initially, the image quality is enhanced by preprocessing techniques and then features like texture, color and shape are extracted. The extracted features are reduced by utilizing the dimensionality reduction method like principal component analysis (PCA). Here, the DBN parameters are optimized using GOA for recognizing liver disease. The experiments are performed on the real time and open source CT image datasets which embraces normal, cyst, hepatoma, and cavernous hemangiomas, fatty liver, metastasis, cirrhosis, and tumor samples. The proposed method yields 98% accuracy, 95.82% sensitivity, 97.52% specificity, 98.53% precision, and 96.8% F-1 score in simulation process when compared with other existing techniques. K E Y W O R D S deep belief network (DBN), grasshopper optimization algorithm (GOA), liver disease classification, principal component analysis (PCA)

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Section: Extraction Of Shape Featuresmentioning

confidence: 99%

“…From the local region with 16 × 16 pixels, PHOG features are extracted. 20 The strength of the gradient at the point (X, Y) is computed as follows,…”

Section: Extraction Of Shape Featuresmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Optimizing deep belief network parameters using grasshopper algorithm for liver disease classification

Renukadevi¹,

Karunakaran²

2019

Int J Imaging Syst Tech

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“…HOG rely on spatial edge orientations to represent different sections of an image. The gradient operator is convolved with every pixel of the image I(x,y) to obtain the gradient of the image [8]. The gradient of the image is given as follows.…”

Section: B Spatial Pyramid Of Hog Featuresmentioning

confidence: 99%

A vision based human - elepahant collision detection system

Dua

Shukla

Mittal

2015

2015 Third International Conference on Image Information Processing (ICIIP)

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Elephant intrusion in areas with high human movement can prove lethal for both human beings and elephants.The proposed system seeks to identify elephants with the aid of a Video Camera. The suggested methodology was applied to zones having high intervention of human beings and elephants. Regions with higher human movements like roads were extracted from the initial video frames. This process is followed by detecting motion in the video frame. The objects in the area of motion are then identified as elephant or non-elephant with the help of PHOG features and Support Vector Machines (SVM) classifiers.A dataset constituting of images of elephants and other objects was used for training the proposed algorithm. An overall accuracy of 85.29% was attained when static images containing elephants and other objects were classified. The same approach for detection was applied to identify moving elephants in the video frames. The proposed system endeavors to prevent casualties that occur in areas having high human elephant interaction.

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“…For crater pictures, an proficient element extraction strategy ought to be powerful in diverse scales, brightening points, and landscapes. Numerous components have been created, for example, standardized pixel-based elements [1], Haar-like elements [7], [8], and other neighborhood descriptors [9], [10]. For pixel-based element, all the first pixels are standardized to dispense with the impact of splendor and differentiation on unique crater pictures by a complexity standardization method.…”

Section: Introductionmentioning

confidence: 99%

A Survey Paper on Crater Detection

2015

IJSR

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Craters are the most plenteous landform on the planet surface, which could give key intimations to planetary science. Because of varieties in the landscape, brightening, and scale, it is trying to distinguish Craters through remote detecting pictures and it requires an effective crater feature extraction method. In this paper, we address this issue utilizing Gist features, which can give exceedingly compelling depictions on crater's nearby edges and worldwide structure. The proposed crater's discovery technique contains three key steps. To begin with, we separate all candidate craters on a planet picture utilizing edge based technique and Hough transform. Second, Gist elements are created from chose preparing tests. Third, Craters discovery is led utilizing Gist features vectors with random forest classification. Contrasted with pixel based also, Haar-like components, our strategy indicates more exact craters acknowledgment, and accomplishes fulfilled results in the analyses directed on the Mars Orbiter Camera (MOC) database.

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Crater detection algorithm with part PHOG features for safe landing

Cited by 11 publications

References 13 publications

Optimizing deep belief network parameters using grasshopper algorithm for liver disease classification

Optimizing deep belief network parameters using grasshopper algorithm for liver disease classification

A vision based human - elepahant collision detection system

A Survey Paper on Crater Detection

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