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.

Ship Detection from Optical Remote Sensing Images Using Multi-Scale Analysis and Fourier HOG Descriptor

Abstract: Automatic ship detection by Unmanned Airborne Vehicles (UAVs) and satellites is one of the fundamental challenges in maritime research due to the variable appearances of ships and complex sea backgrounds. To address this issue, in this paper, a novel multi-level ship detection algorithm is proposed to detect various types of offshore ships more precisely and quickly under all possible imaging variations. Our object detection system consists of two phases. First, in the category-independent region proposal phase, the steerable pyramid for multi-scale analysis is performed to generate a set of saliency maps in which the candidate region pixels are assigned to high salient values. Then, the set of saliency maps is used for constructing the graph-based segmentation, which can produce more accurate candidate regions compared with the threshold segmentation. More importantly, the proposed algorithm can produce a rather smaller set of candidates in comparison with the classical sliding window object detection paradigm or the other region proposal algorithms. Second, in the target identification phase, a rotation-invariant descriptor, which combines the histogram of oriented gradients (HOG) cells and the Fourier basis together, is investigated to distinguish between ships and non-ships. Meanwhile, the main direction of the ship can also be estimated in this phase. The overall algorithm can account for large variations in scale and rotation. Experiments on optical remote sensing (ORS) images demonstrate the effectiveness and robustness of our detection system.

Skin Cancer Classification Using Image Processing and Machine Learning

Abstract: One of the most rapidly spreading cancers among various other types of cancers known to humans is skin cancer. Melanoma is the worst and the most dangerous type of skin cancer that appears usually on the skin surface and then extends deeper into the layers of skin. However, if diagnosed at an early stage; the survival rate of Melanoma patients is 96% with simple and economical treatments. The conventional method of diagnosing Melanoma involves expert dermatologists, equipment, and Biopsies. To avoid the expensive diagnosis, and to assist dermatologists, the field of machine learning has proven to provide state of the art solutions for skin cancer detection at an earlier stage with high accuracy. In this paper, a method for skin lesion classification and segmentation as benign or malignant is proposed using image processing and machine learning. A novel method of contrast stretching of dermoscopic images based on the methods of mean values and standard deviation of pixels is proposed. Then the OTSU thresholding algorithm is applied for image segmentation. After the segmentation, features including Gray level Co-occurrence Matrix (GLCM) features for texture identification, the histogram of oriented gradients (HOG) object, and color identification features are extracted from the segmented images. Principal component analysis (PCA) reduction of HOG features is performed for dimensionality reduction. Synthetic minority oversampling technique (SMOTE) sampling is performed to deal with the class imbalance problem. The feature vector is then standardized and scaled. A novel approach of feature selection based on the wrapper method is proposed before classification. Classifiers including Quadratic Discriminant, SVM (Medium Gaussian), and Random Forest are used for classification. The proposed approach is verified on the publicly accessible dataset of ISIC-ISBI 2016. Maximum accuracy is achieved using the Random Forest classifier. The classification accuracy of the proposed system with the Random Forest classifier on ISIC-ISBI 2016 is 93.89%. The proposed approach of contrast stretching before the segmentation gives satisfactory results of segmentation. Further, the proposed wrapper-based approach of feature selection in combination with the Random Forest classifier gives promising results as compared to other commonly used classifiers.

Pedestrian Detection in Far-Infrared Daytime Images Using a Hierarchical Codebook of SURF

Abstract: One of the main challenges in intelligent vehicles concerns pedestrian detection for driving assistance. Recent experiments have showed that state-of-the-art descriptors provide better performances on the far-infrared (FIR) spectrum than on the visible one, even in daytime conditions, for pedestrian classification. In this paper, we propose a pedestrian detector with on-board FIR camera. Our main contribution is the exploitation of the specific characteristics of FIR images to design a fast, scale-invariant and robust pedestrian detector. Our system consists of three modules, each based on speeded-up robust feature (SURF) matching. The first module allows generating regions-of-interest (ROI), since in FIR images of the pedestrian shapes may vary in large scales, but heads appear usually as light regions. ROI are detected with a high recall rate with the hierarchical codebook of SURF features located in head regions. The second module consists of pedestrian full-body classification by using SVM. This module allows one to enhance the precision with low computational cost. In the third module, we combine the mean shift algorithm with inter-frame scale-invariant SURF feature tracking to enhance the robustness of our system. The experimental evaluation shows that our system outperforms, in the FIR domain, the state-of-the-art Haar-like Adaboost-cascade, histogram of oriented gradients (HOG)/linear SVM (linSVM) and MultiFtrpedestrian detectors, trained on the FIR images.

Feature selection for facial emotion recognition using late hill-climbing based memetic algorithm

Abstract: Facial Emotion Recognition (FER) is an important research domain which allows us to provide a better interactive environment between humans and computers. Some standard and popular features extracted from facial expression images include Uniform Local Binary Pattern (uLBP), Horizontal-Vertical Neighborhood Local Binary Pattern (hvnLBP), Gabor filters, Histogram of Oriented Gradients (HOG) and Pyramidal HOG (PHOG). However, these feature vectors may contain some features that are irrelevant or redundant in nature, thereby increasing the overall computational time as well as recognition error of a classification system. To counter this problem, we have proposed a new feature selection (FS) algorithm based on Late Hill Climbing and Memetic Algorithm (MA). A novel local search technique called Late Acceptance Hill Climbing through Redundancy and Relevancy (LAHCRR) has been used in this regard. It combines the concepts of Local Hill-Climbing and minimal-Redundancy Maximal-Relevance (mRMR) to form a more effective local search mechanism in MA. The algorithm is then evaluated on the said feature vectors extracted from the facial images of two popular FER datasets, namely RaFD and JAFFE. LAHCRR is used as local search in MA to form Late Hill Climbing based Memetic Algorithm (LHCMA). LHCMA is compared with state-of-the-art methods. The experimental outcomes show that the proposed FS algorithm reduces the feature dimension to a significant amount as well as increases the recognition accuracy as compared to other methods.