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[Communications in Computer and Information Science] Advances in Signal Processing and Intelligent Recognition Systems Volume 968 (4th International Symposium SIRS 2018, Bangalore, India, September 19–22, 2018, Revised Selected Papers) || A Novel Method for Stroke Prediction from Retinal Images Using HoG Approach
摘要： Stroke is one of the principal reasons for adult impairment worldwide. Retinal fundus images are analyzed for the detection of various cardiovascular diseases like Stroke. Stroke is mainly characterized by soft and hard exudates, artery or vein occlusion and alterations in retinal vasculature. In this research work, Histogram of Oriented Gradients (HoG) has been implemented to extract features from the region of interest of retinal fundus images. This innovative method is assessed for the computer aided diagnosis of normal healthy and abnormal images of stroke patients. A comparative analysis has been made between the extracted HoG features and Haralick features. HoG features extracted from the region of interest, when given to a Na?ve Bayes classifier provides an accuracy of 93% and a Receiver Operating Characteristic (ROC) curve area of 0.979.
关键词： Haralick features,Na?ve Bayes classifier,Histogram of Oriented Gradients (HoG),Stroke
更新于2025-09-23 15:23:52
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia, Spain (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Weed Classification in Hyperspectral Remote Sensing Images Via Deep Convolutional Neural Network
摘要： Automatic weed detection and mapping are critical for site-speci?c weed control in order to reduce the cost of farming as well as the impact of herbicides on human health. In this paper, we investigate patch-based weed identi?cation using hyperspectral images. Convolutional Neural Network (CNN) is evaluated and compared with the Histogram of Oriented Gradients (HoG) for this purpose. Suitable patch sizes are investigated. The limitation of RGB imagery is demonstrated. The experimental results indicate that the overall accuracy of the weed classi?cation using CNN increases with the increasing number of bands used. With more bands, CNN extracts more powerful and discriminative features and leads to improved classi?cation as compared to the traditional HoG feature extraction method. The computational load of CNN, however, is slightly increased with the increasing number of bands.
关键词： Histogram of Oriented Gradients (HoG),weed mapping,Hyperspectral images,Convolutional Neural Network (CNN)
更新于2025-09-09 09:28:46
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A new thermal infrared and visible spectrum images-based pedestrian detection system
摘要： In this paper, we propose a hybrid system for pedestrian detection, in which both thermal and visible images of the same scene are used. The proposed method is achieved in two basic steps: (1) Hypotheses generation (HG) where the locations of possible pedestrians in an image are determined and (2) hypotheses verification (HV), where tests are done to check the presence of pedestrians in the generated hypotheses. HG step segments the thermal image using a modified version of OTSU thresholding technique. The segmentation results are mapped into the corresponding visible image to obtain the regions of interests (possible pedestrians). A post-processing is done on the resulting regions of interests to keep only significant ones. HV is performed using random forest as classifier and a color-based histogram of oriented gradients (HOG) together with the histograms of oriented optical flow (HOOF) as features. The proposed approach has been tested on OSU Color-Thermal, INO Video Analytics and LITIV data sets and the results justify its effectiveness.
关键词： Thermal images,Random forests,Local binary pattern (LBP),Pedestrian detection,Histograms of oriented optical flow (HOOF),Support vector machines (SVMs),Visible images,Histogram of oriented gradients (HOG)
更新于2025-09-04 15:30:14