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- 摘要
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- 实验方案
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Stroke diagnosis from retinal fundus images using multi texture analysis
摘要： Stroke is a cerebrovascular disease which is one of the significant causes of adult impairment. Research shows that retinal fundus images carry vital information for the prediction of various cardiovascular diseases like Stroke. This work investigates a multi-texture description for the computer aided diagnosis of Stroke from retinal fundus images. Texture of the retinal background is analyzed, thereby eliminating the need for segmentation. Gabor Filter (GF), Local Binary Pattern (LBP) and Histogram of Oriented gradients (HOG) are the texture descriptors implemented in this work. The texture descriptors are applied to the second Eigen channel obtained by Principal Component Analysis (PCA). Extracted features are concatenated to form a multi-texture representation and dimensionality reduction is done by ReliefF feature selection method. The compact feature vector is given to Na?ve Bayes classifier and performance metrics are evaluated. We have evaluated the performance of individual feature descriptors and multiple feature descriptors in retinal fundus images for stroke diagnosis. Multi-texture description outperforms individual texture descriptors by an accuracy of 95.1 %.
关键词： Gabor filter,ReliefF,histogram of oriented gradients,principal component analysis,local binary pattern,Stroke
更新于2025-09-23 15:22:29
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[IEEE 2018 IEEE/ACS 15th International Conference on Computer Systems and Applications (AICCSA) - Aqaba, Jordan (2018.10.28-2018.11.1)] 2018 IEEE/ACS 15th International Conference on Computer Systems and Applications (AICCSA) - Number of Texture Unit as Feature to Breast's Disease Classification from Thermal Images
摘要： This paper presents the use of the Number of Texture Unit as a feature extractor for classification of breast images. The Number of Texture Unit served as the basis for the idealization of the Local Binary Pattern a technique that is widely used in facial recognition. We compared the proposed strategy with the Gray Level Co-occurrence Matrix which is the most used texture analysis technique in the literature. With this work we have been able to show that the combination of the two techniques of feature extraction improves the final result of classification. To perform the tests we used the Support Vectors Machine classifier and obtained a result of 96.15% Area Under the Curve (Receiver Operating Characteristic Curve).
关键词： computer aided diagnosis,machine learning,support vector machine,feature extraction,infrared images,Local Binary Pattern
更新于2025-09-19 17:15:36
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Face recognition using depth and infrared pictures
摘要： This paper proposes the face recognition method using both the depth and infrared pictures. The conventional face recognition methods based on color picture recognize the human faces accurately, but are easily a?ected by the illumination and are vulnerable to the attempts to steal user’s face information through fake face such as the photograph or the sculpture. On the other hand, the methods based on the depth or infrared picture are less a?ected by the illumination, and prevent an attempt to recognize a false face. This paper utilizes the depth picture to reduce the recognition time and the infrared picture to increase the recognition performance. In the face detection, this paper ?nds the nose of a person using the captured depth picture for reducing detection time, and it detects the region of the face. In the feature extraction, it extracts the feature pictures from the infrared picture by 3D Local Binary Pattern. In the face identi?cation, this paper compares between the features of the captured face and features of faces that are pre-stored in the DB, and obtains the face similarity. If the similarity of the face is larger than the certain threshold, the face recognition succeeds. Simulation results show that the face recognition performance is good not only in the normal environment but also in the little illumination.
关键词： local binary pattern,depth picture,face recognition,infrared picture
更新于2025-09-12 10:27:22
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[IEEE 2017 14th IEEE India Council International Conference (INDICON) - Roorkee (2017.12.15-2017.12.17)] 2017 14th IEEE India Council International Conference (INDICON) - Non-parametric Iris Localization Using Pupil's Uniform Intensities and Adaptive Masking
摘要： Iris localization is one of the vigorous components of any iris recognition system. It deals with the separation of annular iris from the acquired eye image. Accuracy of the iris segmentation module directly affects the overall system accuracy. In order to localize the pupillary boundary, this work utilizes local binary pattern (LBP) to exploit the uniform intensities present in the pupil region, to detect its boundaries. LBP aids in reducing the adverse effects of eyelashes in pupil localization. Moreover, an adaptive mask is also developed for localizing the limbus boundary. This mask provides a mean to combat with the varying sizes of iris due to variation in illumination. Outcomes of experiments performed with two benchmark iris databases (i.e. CASIA-IrisV1 and IITD iris database) support the efficacy of the proposed approach.
关键词： adaptive masks.,iris recognition,iris localization,local binary pattern (LBP)
更新于2025-09-11 14:15:04
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[IEEE 2018 International Conference on Smart Computing and Electronic Enterprise (ICSCEE) - Shah Alam (2018.7.11-2018.7.12)] 2018 International Conference on Smart Computing and Electronic Enterprise (ICSCEE) - Face recognition and detection using Random forest and combination of LBP and HOG features
摘要： the effective facial recognition method should perform well in unregulated environments based on video broadcast to satisfy the demands of applications in real-world However, this still remains a big challenge for most current face recognition algorithms that will affect the accuracy of the system. This study was conducted to develop face recognition method based on video broadcast under illumination variation, facial expressions, different pose, orientation, occlusion, nationality variation and motion. Viola-Jones algorithm was applied to improve face detection which is these method have proven to detect the faces in an uncontrolled environment in the real world simply and high accuracy. A combination of Histograms of Oriented Gradients (HOG) and Local Binary Pattern (LBP) descriptors was conducted for faces features extraction purpose. These descriptors have proven to be lower computational time. The latest and accurate technique was applied for face classification based on Random Forest classifier (RF). To evaluate the efficiency of the Random Forest classifier, compared it with Support Vector Machine classifiers (SVM) is done with different existing feature extraction methods. Four experiments were implemented on Mediu staff database and excellent results have reported the efficiency of proposed algorithm average recognition accuracy 97.6% The Computer Vision and Image Processing MAT LAB 2016b Toolboxes was used for coding the desired system, dataset based on videos.
关键词： Viola &Jones,Face Recognition,Mediu Staff,Local Binary Pattern (LBP),Histograms of Oriented Gradients (HOG),Random Forest classifier (RF)
更新于2025-09-09 09:28:46
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Multi-scale LBP and SVM Classification to Identify Diabetic Retinopathy in Lesions
摘要： Diabetic Retinopathy (DR) is the most common disease induced by the complication of diabetes, causing blindness. In many rural areas, the contributions of ophthalmologists are predicatively less to treat the disease. Detection of lesions in the early stage is a progressive measure to diagnose DR. Initially, a preprocessing method is performed to detect the Optic Nerve Head (ONH) in the lesion. Based on the degree of reflectance in ONH, feature extraction is computed using multi-scale Local Binary Pattern (LBP) algorithm. Here, Gabor convolution is estimated and the structure of ONH is encoded. This extends to a statistical computation in terms of the moment and standard deviation. A Support Vector Machine (SVM) classification is formulated to locate the hemorrhages and exudates and an effective probabilistic multi-label lesion classification is performed to acquire five sets of results representing the diabetic retinopathy: 1) Grade-1 Exudates, 2) Grade-2 Exudates, 3) Micro aneurysms, 4) Hemorrhages, 5) Neovascularization. Finally, the affected area of lesions is used to diagnose the disease.
关键词： statistical computation,Gabor convolution,local binary pattern,optic nerve head,support vector machine
更新于2025-09-09 09:28:46
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Blind Image Quality Assessment Using Multiscale Local Binary Patterns
摘要： This article proposes a new no-reference image quality assessment method that is able to blindly predict the quality of an image. The method is based on a machine learning technique that uses texture descriptors. In the proposed method, texture features are computed by decomposing images into texture information using multiscale local binary pattern (MLBP) operators. In particular, the parameters of local binary pattern operators are varied, which generates MLBP operators. The features used for training the prediction algorithm are the histograms of these MLBP channels. The results show that, when compared with other state-of-the-art no-reference methods, the proposed method is competitive in terms of prediction precision and computational complexity.
关键词： MLBP,machine learning,multiscale local binary pattern,texture descriptors,no-reference image quality assessment
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
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Feature extraction using sequential cumulative bin and overlap mean intensity for iris classification
摘要： This paper examines an approach generalizing a variant of the local binary pattern (LBP) method for iris feature extraction. The proposed method employs two different LBP variants called the sequential cumulative bin and overlap mean intensity for projecting the one-dimensional local iris textures into a binary bit pattern. The assigned bit, either 1 or 0 as a bit code, replaces the original intensity value using a specific condition for the respective reference element. The ratio value from the total transition of 1 to 0 along the row axis represents the feature of each iris image. The extraction only utilizes a small area of interest on the iris image that covers parts of the iris textures with minimum eyelid and eyelashes. The assessment employs the support vector machines classifier and the result demonstrates a good classification rate with average accuracy of 94.0% for the individual mode. However, the classification rate has improved to reach 96.5% accuracy if the assessment uses a concatenated mode set of features. Besides that, increasing the amount of samples in the training data by using the synthetic together with the original samples has also been able to improve the classification rate.
关键词： iris classification,1D-Local binary pattern,histogram equalization,support vector machines
更新于2025-09-04 15:30:14
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Robust pedestrian detection in infrared images using rotation and scale invariant-based structure element descriptor
摘要： Pedestrian detection is a signi?cant problem in infrared (IR) images that ?nd varieties of applications in defense systems. The performance of the state-of-the-art of pedestrian detection methods in IR images still have abundant space for improvement towards accuracy. In this paper, a three-level ?ltering-based pedestrian block detection method is proposed. In addition, a rotation and scale invariant structure element descriptor (RSSED) is proposed for pedestrian detection in infrared (IR) images. To extract RSSED features, the pedestrian block detection result is encoded using local binary pattern (LBP). The LBP encoded image is quantised adaptively to four levels. Further, the proposed RSSED is used to generate the feature descriptor from the quantised image. Finally, support vector machine (SVM) is used to classify the objects in given IR image into pedestrian and non-pedestrian. The experimental results demonstrate that the proposed method performs effectively in pedestrian detection than the other methods.
关键词： local binary pattern,infrared imagery,pedestrian detection,structure element descriptor,adaptive quantisation,rotation and scale descriptor
更新于2025-09-04 15:30:14