Detection and Classification of Apple Fruit Diseases Using Complete Local Binary Patterns

Dubey, Shiv Ram; Jalal, Anand Singh

doi:10.1109/iccct.2012.76

Cited by 168 publications

(70 citation statements)

References 11 publications

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“…Compared with the results of CLBP 24,3 in Guo et al [33] and CLBP 8,1 in Dubey and Jalal [39], our overall accuracy is about 85%, which is a little bit lower than that in previous studies [33,39]. One reason might be that those researchers used CLBP in Outex database and digital imaging, while we applied it in high spatial resolution satellite images.…”

Section: Classification Results By Combining Clbp Texture Featurescontrasting

confidence: 51%

Assessing Texture Features to Classify Coastal Wetland Vegetation from High Spatial Resolution Imagery Using Completed Local Binary Patterns (CLBP)

et al. 2018

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Abstract:Coastal wetland vegetation is a vital component that plays an important role in environmental protection and the maintenance of the ecological balance. As such, the efficient classification of coastal wetland vegetation types is key to the preservation of wetlands. Based on its detailed spatial information, high spatial resolution imagery constitutes an important tool for extracting suitable texture features for improving the accuracy of classification. In this paper, a texture feature, Completed Local Binary Patterns (CLBP), which is highly suitable for face recognition, is presented and applied to vegetation classification using high spatial resolution Pléiades satellite imagery in the central zone of Yancheng National Natural Reservation (YNNR) in Jiangsu, China. To demonstrate the potential of CLBP texture features, Grey Level Co-occurrence Matrix (GLCM) texture features were used to compare the classification. Using spectral data alone and spectral data combined with texture features, the image was classified using a Support Vector Machine (SVM) based on vegetation types. The results show that CLBP and GLCM texture features yielded an accuracy 6.50% higher than that gained when using only spectral information for vegetation classification. However, CLBP showed greater improvement in terms of classification accuracy than GLCM for Spartina alterniflora. Furthermore, for the CLBP features, CLBP_magnitude (CLBP_m) was more effective than CLBP_sign (CLBP_s), CLBP_center (CLBP_c), and CLBP_s/m or CLBP_s/m/c. These findings suggest that the CLBP approach offers potential for vegetation classification in high spatial resolution images.

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Section: Classification Results By Combining Clbp Texture Featurescontrasting

confidence: 51%

Assessing Texture Features to Classify Coastal Wetland Vegetation from High Spatial Resolution Imagery Using Completed Local Binary Patterns (CLBP)

et al. 2018

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“…Over indoor dataset of 5 activities, 93% accuracy is gained using the parametric model of human from image sequences using motion/texture based human detection and tracking [9]. Vega and Sarkar reported 90% accuracy using 3 actions over 71 subjects using the change in the relational statistics among the detected image features, without the need for object models, perfect segmentation, or part-level tracking [13]. Whereas, we are able to gain upto 95% and 91% accuracy using just gait analysis over KTH and Wiezmann datasets respectively.…”

Section: Results Analysismentioning

confidence: 99%

“…But, there method requires multiple cameras from different viewpoints to model multi-view recognition system which requires extra setup and also computation, whereas the proposed approach is able to achieve high recognition performance from only a single viewpoint. Several other approaches and features used in [13][14][15][16][17][18][19][20][21][22][23][24][25] may be tied with gait analysis to predict the human actions. Human activity recognition using smartphones is also studied [26] but its recognition rate can be improved using gait analysis with more time efficiently.…”

Section: Literature Reviewmentioning

confidence: 99%

Analysis of Gait Pattern to Recognize the Human Activities

Gupta¹,

Dixit²,

Semwal³

2014

IJIMAI

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-Human activity recognition based on the computer vision is the process of labelling image sequences with action labels. Accurate systems for this problem are applied in areas such as visual surveillance, human computer interaction and video retrieval. The challenges are due to variations in motion, recording settings and gait differences. Here we propose an approach to recognize the human activities through gait. Activity recognition through Gait is the process of identifying an activity by the manner in which they walk. The identification of human activities in a video, such as a person is walking, running, jumping, jogging etc are important activities in video surveillance. We contribute the use of Model based approach for activity recognition with the help of movement of legs only. Experimental results suggest that our method are able to recognize the human activities with a good accuracy rate and robust to shadows present in the videos.

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“…Basic steps were pre processing, dividing the image into windows, features collection, window elimination and classification or decision making step . Detection of apple fruit done in [17] was based on color and texture features and the results were compared in both RGB and HSV color spaces. Results showed that CLBP (Complete Local Binary Patterns) showed the highest accuracy.…”

Section: International Journal Of Computer Applications (0975 -8887) mentioning

confidence: 99%

Quality evaluation of apple fruit: A Survey

Sindhi¹,

Pandya²,

Vegad³

2016

IJCA

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Disease recognition has been huge research area nowadays because inspection of quality of fruits at an early stage prevents spreading of disease to the other areas of fruit as well as helps to reduce great economic losses in agricultural sectors and industries. Different types of diseases exist in different fruits. The focus of the present research work is on quality evaluation of apple fruit. The basic process for defect detection in fruits is basically divided into two major steps; feature extraction and classification .Feature extraction involves extracting features like color, texture and shape from fruit image. The output of this are feature vectors which are given as an input to the classifier. Finally, the classifier categorizes them into appropriate classes. The accuracy of this process depends on many factors like number of input images, method chosen for pre processing, features extracted, classifier chosen, etc.

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Detection and Classification of Apple Fruit Diseases Using Complete Local Binary Patterns

Cited by 168 publications

References 11 publications

Assessing Texture Features to Classify Coastal Wetland Vegetation from High Spatial Resolution Imagery Using Completed Local Binary Patterns (CLBP)

Assessing Texture Features to Classify Coastal Wetland Vegetation from High Spatial Resolution Imagery Using Completed Local Binary Patterns (CLBP)

Analysis of Gait Pattern to Recognize the Human Activities

Quality evaluation of apple fruit: A Survey

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