Histogram of oriented gradients

About: Histogram of oriented gradients is a research topic. Over the lifetime, 2037 publications have been published within this topic receiving 55881 citations. The topic is also known as: HOG.

Experimentation using short-term spectral features for secure mobile internet voting authentication

Abstract: We propose a secure mobile Internet voting architecture based on the Sensus reference architecture and report the experiments carried out using short-term spectral features for realizing the voice biometric based authentication module of the architecture being proposed. The short-term spectral features investigated are Mel-Frequency Cepstral Coefficients (MFCCs), Mel-Frequency Discrete Wavelet Coefficients (MFDWC), Linear Predictive Cepstral Coefficients (LPCC), and Spectral Histogram of Oriented Gradients (SHOGs). The MFCC, MFDWC, and LPCC usually have higher dimensions that oftentimes lead to high computational complexity of the pattern matching algorithms in automatic speaker recognition systems. In this study, higher dimensions of each of the short-term features were reduced to an 81-element feature vector per Speaker using Histogram of Oriented Gradients (HOG) algorithm while neural network ensemble was utilized as the pattern matching algorithm. Out of the four short-term spectral features investigated, the LPCC-HOG gave the best statistical results with statistic of 0.9127 and mean square error of 0.0407. These compact LPCC-HOG features are highly promising for implementing the authentication module of the secure mobile Internet voting architecture we are proposing in this paper.

A survey of Haar-Like feature representation

Abstract: Object detection has been one of the most studied topics in the computer vision literature. To detect an object in an image, the detector must have knowledge of the characteristics of the object. Various approaches have been utilized such as Haar-Like features, color information, texture, edge orientation, etc. While numerous methods have been proposed to extract information using Haar-like feature, we are unaware of any surveys on this particular topic. For this reason we wrote this paper for surveying the different researches on Haar-Like features.

Training based cell detection from bright-field microscope images

Abstract: This paper proposes a framework for cell detection from bright-field microscope images. The method is trained using manually annotated images, and it uses Support Vector Machine classifiers with Histogram of Oriented Gradient features. The performance of the method is evaluated using 16 training and 12 test images with altogether 10736 human prostate cancer cells. Both the implementation and the annotated image database are released for download. The experiments consider various parameters and their effect on performance, and reaches accurate detection results with cross-validated AUC over 0.98, and mean relative deviation of 9 % from manually counted annotations in the growth curve over six days.

Automated detection of diabetic retinopathy in fundus images using fused features

Abstract: Diabetic retinopathy (DR) is one of the severe eye conditions due to diabetes complication which can lead to vision loss if left untreated. In this paper, a computationally simple, yet very effective, DR detection method is proposed. First, a segmentation independent two-stage preprocessing based technique is proposed which can effectively extract DR pathognomonic signs; both bright and red lesions, and blood vessels from the eye fundus image. Then, the performance of Local Binary Patterns (LBP), Local Ternary Patterns (LTP), Dense Scale-Invariant Feature Transform (DSIFT) and Histogram of Oriented Gradients (HOG) as a feature descriptor for fundus images, is thoroughly analyzed. SVM kernel-based classifiers are trained and tested, using a 5-fold cross-validation scheme, on both newly acquired fundus image database from the local hospital and combined database created from the open-sourced available databases. The classification accuracy of 96.6% with 0.964 sensitivity and 0.969 specificity is achieved using a Cubic SVM classifier with LBP and LTP fused features for the local database. More importantly, in out-of-sample testing on the combined database, the model gives an accuracy of 95.21% with a sensitivity of 0.970 and specificity of 0.932. This indicates the proposed model is very well-fitted and generalized which is further corroborated by the presented train-test curves.

A Gabor Odd Filter-Based Ratio Operator for SAR Image Matching

Abstract: Synthetic aperture radar (SAR) image matching is a challenging issue in remote sensing as the images contain significant multiplicative speckle noise. Histogram of oriented gradients (HOG)-based descriptors have been used popularly for the matching of the SAR images. An appropriate gradient computation of the SAR images plays a significant role in the matching performance of these HOG-based descriptors. The ratio operators are preferred for computing the gradients in the SAR images as these are usually corrupted by the multiplicative noise. Recently, a multiscale Gabor odd filter-based ratio operator (GOFRO) has been proposed for the edge detection of the SAR images. In this letter, we utilize the GOFRO for SAR image matching. We propose a Gabor odd filter (GOF)-based descriptor, where the gradients are computed by the GOFRO. At first, scale-invariant feature transform (SIFT) features are extracted from the SAR images. A novel approach is presented to select the more repeatable SIFT features in SAR images. Then, the proposed GOF-based descriptor is formed for the SIFT features and, finally, feature matching is performed. Experiments on three sets of simulated and real SAR image pairs demonstrate the effectiveness of the proposed GOF-based descriptor for SAR image matching.