Cucumber Detection Based on Texture and Color in Greenhouse

2017 ◽  
Vol 31 (08) ◽  
pp. 1754016 ◽  
Author(s):  
Dahua Li ◽  
Hui Zhao ◽  
Xiangfei Zhao ◽  
Qiang Gao ◽  
Liang Xu
Keyword(s):  
Texture Analysis ◽  
Tree Canopy ◽  
Support Vector ◽  
Color Analysis ◽  
Gray Scale ◽  
Gray Scale Image ◽  
Detection And Counting ◽  
Color Similarity ◽  
Shape Irregularity ◽  
Candidate Regions

Agriculture robot by mechanical harvesting requires automatic detection and counting of fruits in tree canopy. Because of color similarity, shape irregularity, and background complex, fruit identification turns to be a very difficult task and not to mention to execute pick action. Therefore, green cucumber detection within complex background is a challenging task due to all the above-mentioned problems. In this paper, a technique based on texture analysis and color analysis is proposed for detecting cucumber in greenhouse. RGB image was converted to gray-scale image and HSI image to perform algorithm, respectively. Color analysis was carried out in the first stage to remove background, such as soil, branches, and sky, while keeping green fruit pixels presented cucumbers and leaves as many as possible. In parallel, MSER and HOG were applied to texture analysis in gray-scale image. We can obtain some candidate regions by MSER to obtain the candidate including cucumber. The support vector machine is the classifier used for the identification task. In order to further remove false positives, key points were detected by a SIFT algorithm. Then, the results of color analysis and texture analysis were merged to get candidate cucumber regions. In the last stage, the mathematical morphology operation was applied to get complete cucumber.

Application of image enhancement and pattern recognition algorithms to seismic reflection data

1983 ◽  
Vol 73 (1) ◽  
pp. 307-314
Author(s):  
George A. McMechan
Keyword(s):  
Pattern Recognition ◽  
Pacific Ocean ◽  
Image Enhancement ◽  
Seismic Reflection ◽  
Gray Scale ◽  
Seismic Reflection Data ◽  
Reflection Data ◽  
Recognition Algorithms ◽  
Gray Scale Image ◽  
Northeastern Pacific

abstract A digital seismic reflection section may be converted to a gray scale image composed of pixels and processed with techniques borrowed from the disciplines of image enhancement and pattern recognition. Types of processing include scaling, thresholding, density equalization, filtering, segmentation, and edge-finding. These are successfully applied to a migrated common mid-point seismic reflection line that traverses the Queen Charlotte fault (located in the northeastern Pacific Ocean). The result is the definition and enhancement of an elongated, near-vertical reflectivity anomaly associated with the Queen Charlotte fault.

scholarly journals Malware Classification with Improved Convolutional Neural Network Model

2020 ◽  
Vol 12 (6) ◽  
pp. 30-43
Author(s):  
Sumit S. Lad ◽  
◽  
Amol C. Adamuthe
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Personal Information ◽  
Support Vector ◽  
Svm Classifier ◽  
Gray Scale ◽  
Malware Classification

Malware is a threat to people in the cyber world. It steals personal information and harms computer systems. Various developers and information security specialists around the globe continuously work on strategies for detecting malware. From the last few years, machine learning has been investigated by many researchers for malware classification. The existing solutions require more computing resources and are not efficient for datasets with large numbers of samples. Using existing feature extractors for extracting features of images consumes more resources. This paper presents a Convolutional Neural Network model with pre-processing and augmentation techniques for the classification of malware gray-scale images. An investigation is conducted on the Malimg dataset, which contains 9339 gray-scale images. The dataset created from binaries of malware belongs to 25 different families. To create a precise approach and considering the success of deep learning techniques for the classification of raising the volume of newly created malware, we proposed CNN and Hybrid CNN+SVM model. The CNN is used as an automatic feature extractor that uses less resource and time as compared to the existing methods. Proposed CNN model shows (98.03%) accuracy which is better than other existing CNN models namely VGG16 (96.96%), ResNet50 (97.11%) InceptionV3 (97.22%), Xception (97.56%). The execution time of the proposed CNN model is significantly reduced than other existing CNN models. The proposed CNN model is hybridized with a support vector machine. Instead of using Softmax as activation function, SVM performs the task of classifying the malware based on features extracted by the CNN model. The proposed fine-tuned model of CNN produces a well-selected features vector of 256 Neurons with the FC layer, which is input to SVM. Linear SVC kernel transforms the binary SVM classifier into multi-class SVM, which classifies the malware samples using the one-against-one method and delivers the accuracy of 99.59%.

scholarly journals Video Colorization Methods: a Survey

2020 ◽  
pp. 675-686
Author(s):  
Mathel Emaduldeen A-Monem ◽  
Tareq Zaid Hammood
Keyword(s):  
Gray Scale ◽  
Huge Number ◽  
Gray Scale Image

Nowadays, there are a huge number of video colorization methods. This is because in the gray scale image one value (gray) must be converted into three corresponding values (RGB). In this paper, some of these methods have been presented and discussed. Then, different comparisons have been established between these methods and the results demonstrate the efficiency of each method.

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