Histogram Of Oriented Gradients Feature Vectors Research Articles

HMM-Based Action Recognition System for Elderly Healthcare by Colorizing Depth Map.

Addressing the problems facing the elderly, whether living independently or in managed care facilities, is considered one of the most important applications for action recognition research. However, existing systems are not ready for automation, or for effective use in continuous operation. Therefore, we have developed theoretical and practical foundations for a new real-time action recognition system. This system is based on Hidden Markov Model (HMM) along with colorizing depth maps. The use of depth cameras provides privacy protection. Colorizing depth images in the hue color space enables compressing and visualizing depth data, and detecting persons. The specific detector used for person detection is You Look Only Once (YOLOv5). Appearance and motion features are extracted from depth map sequences and are represented with a Histogram of Oriented Gradients (HOG). These HOG feature vectors are transformed as the observation sequences and then fed into the HMM. Finally, the Viterbi Algorithm is applied to recognize the sequential actions. This system has been tested on real-world data featuring three participants in a care center. We tried out three combinations of HMM with classification algorithms and found that a fusion with Support Vector Machine (SVM) had the best average results, achieving an accuracy rate (84.04%).

Bone age assessment of iranian children in an automatic manner

Background:Bone age assessment (BAA) is a radiological process with the aim of identifying growth disorders in children. The objective of this study is to assess the bone age of Iranian children in an automatic manner.Methods:In this context, three computer vision techniques including histogram of oriented gradients (HOG), local binary pattern (LBP), and scale-invariant feature transform (SIFT) are applied to extract appropriate features from the carpal and epiphyseal regions of interest. Two different datasets are applied here: the University of Southern California hand atlas for training this computer-aided diagnosis (CAD) system and Iranian radiographs for evaluating the performance of this system for BAA of Iranian children. In this study, the concatenation of HOG, LBP, and dense SIFT feature vectors and background subtraction are applied to improve the performance of this approach. Support vector machine (SVM) and K-nearest neighbor are used here for classification and the better results yielded by SVM.Results:The accuracy of female radiographs is 90% and of male is 71.42%. The mean absolute error is 0.16 and 0.42 years for female and male test radiographs, respectively. Cohen's kappa coefficients are 0.86 and 0.6, P < 0.05, for female and male radiographs, respectively. The results indicate that this proposed approach is in substantial agreement with the bone age reported by the experienced radiologist.Conclusion:This approach is easy to implement and reliable, thus qualified for CAD and automatic BAA of Iranian children.

Occlusion-Aware Correlation Particle Filter Target Tracking Based on RGBD Data

In recent decades, there have been considerable improvements in target-tracking algorithms. However, aspects such as target occlusion, scale variation, and illumination changes still present significant challenges to existing algorithms. In this paper, we describe an occlusion-aware correlation particle filter target-tracking method based on RGBD data. First, we derive a target occlusion judgment mechanism based on a depth image and the histogram of oriented gradients (HOG) feature. We then formulate the tracking mechanism for the target prediction–tracking–optimization–redetection process using a correlation maximum likelihood estimation particle filter algorithm. We propose an adaptive update strategy whereby the system saves a well-tracked model when no occlusion occurs, and then uses this saved model to replace poorly tracked models in the event of occlusion. Furthermore, we consider the scale variation and adjust the target size according to the depth image, but we leave the HOG feature vector dimension of the target area unchanged. Thus, the problems such as model offset, scale variation, and loss of features are corrected over time. The experimental results demonstrate that the proposed target-tracking algorithm can detect target occlusion and track targets well, requires fewer calculations to perform target prediction–tracking–optimization–redetection, reduces the impact of illumination changes, and achieves better real-time performance and accuracy than many existing algorithms.

The Excellent Properties of a Dense Grid-Based HOG Feature on Face Recognition Compared to Gabor and LBP

To effectively represent facial features in complex environments, a face recognition method based on dense grid histograms of oriented gradients (HOG) is proposed. First, the face image is divided by numerous dense grids from which the HOG features are extracted. Then, all the grid HOG feature vectors are composed to realize the feature expression of the whole face, and the nearest neighbor classifier is used for recognition. In the FERET face database with complex changes in illumination, time, and environment, we test the gamma illumination correction, the spatial gradient direction, the size of the block, the standardization, and the face image resolution to find and analyze the optimal HOG parameters for face recognition. Finally, we compare our dense grid HOG with the two famous local facial feature extraction methods: the Gabor wavelet and the local binary pattern (LBP) on face recognition. The experimental results show that the dense grid HOG method is more suitable for the variations in time and environment. The feature extraction times of the dense grid HOG and LBP are similar. However, the dense grid HOG method uses fewer dimensions to obtain a better recognition rate than the LBP. Moreover, the dense grid HOG feature extraction time greatly outperforms the Gabor wavelet feature.

Analysis of reduced‐set construction using image reconstruction from a HOG feature vector

Recently, several methods have been published that demonstrate how to reconstruct an image from a discriminative feature vector. This study explains that previous approaches minimising the histogram‐of‐oriented‐gradient (HOG) feature error in the principal component analysis (PCA) domain of the learning database have a disadvantage in that they cannot reflect the different dynamic range of each PCA dimension, and proposes an improved method to exploit the eigenvalue as the weighting factor of each PCA dimension. Experimental results using pedestrian and vehicle image databases quantitatively show that the proposed method improves the quality of reconstructed images. Additionally, the proposed method is applied to the image reconstruction of the resultant support vectors (SVs) of reduced‐set construction which showed the best performance among SV number reduction methods. As the resultant SVs of reduced‐set construction are not corresponding to any image of the learning database, it is hard to analyse the problem and performance of the method. By observing the images of the resultant SVs, one potential problem regarding the database used is newly considered and the direction of further study can be established in order to address the problem.

Robust lip detection based on histogram of oriented gradient features and convolutional neural network under effects of light and background

Detection of the lip area is essential pre-processing for several applications, such as lip reading and visual information services. In this paper, we propose a lip detection method that finds the lip area in an image using the histogram of oriented gradient (HOG) features and convolutional neural network (CNN). We find the face area from an input image, divide the face image in half, and apply sliding window detection to the bottom half of the image. We obtain the HOG feature vector from the image that corresponds to the window, and use it as the input to a pre-trained support vector machine (SVM). HOG and SVM are used for coarse detection. If SVM determines that the image is not the lip, we reapply sliding window detection. Otherwise, the image is used as input to CNN, which is employed for fine detection and to determine whether the image is the lip. If CNN determines that the image is the lip, we apply canny edge detection to the image to obtain the mouth contour. We use MATLAB to confirm the effectiveness of our method, and can find the mouth area with over 94% accuracy and over 98% precision.

Local learned dictionaries optimized to edge orientation for inverse halftoning.

A method is proposed for fully restoring local image structures of an unknown continuous-tone patch from an input halftoned patch with homogenously distributed dot patterns, based on a locally learned dictionary pair via feature clustering. First, many training sets consisting of paired halftone and continuous-tone patches are collected, and then histogram-of- oriented-gradient (HOG) feature vectors that describe the edge orientations are calculated from every continuous-tone patch, to group the training sets. Next, a dictionary learning algorithm is separately conducted on the categorized training sets, to obtain the halftone and continuous-tone dictionary pairs, optimized to edge-oriented patch representation. Finally, an adaptively smoothing filter is applied to the input halftone patch, to predict the HOG feature vector of an unknown continuous-tone patch, and to select one of the previously learned dictionary pairs, based on the Euclidean distance between the HOG mean feature vectors of the grouped training sets and the predicted HOG vector. In addition to using the local dictionary pairs, a patch fusion technique is used to reduce some artifacts, such as color noise and overemphasized edges on smooth regions. Experimental results show that the use of the paired dictionary selected by the local edge orientation and patch fusion technique not only reduced the artifacts in smooth regions, but also provided well expressed fine details and outlines, especially in the areas of textures, lines, and regular patterns.

Moving object localization using optical flow for pedestrian detection from a moving vehicle.

This paper presents a pedestrian detection method from a moving vehicle using optical flows and histogram of oriented gradients (HOG). A moving object is extracted from the relative motion by segmenting the region representing the same optical flows after compensating the egomotion of the camera. To obtain the optical flow, two consecutive images are divided into grid cells 14 × 14 pixels; then each cell is tracked in the current frame to find corresponding cell in the next frame. Using at least three corresponding cells, affine transformation is performed according to each corresponding cell in the consecutive images, so that conformed optical flows are extracted. The regions of moving object are detected as transformed objects, which are different from the previously registered background. Morphological process is applied to get the candidate human regions. In order to recognize the object, the HOG features are extracted on the candidate region and classified using linear support vector machine (SVM). The HOG feature vectors are used as input of linear SVM to classify the given input into pedestrian/nonpedestrian. The proposed method was tested in a moving vehicle and also confirmed through experiments using pedestrian dataset. It shows a significant improvement compared with original HOG using ETHZ pedestrian dataset.

Image Retrieval using Harris Corners and Histogram of Oriented Gradients

based image retrieval is the technique to retrieve similar images from a database that are visually similar to a given query image. It is an active and emerging research field in computer vision. In our proposed system, the Interest points based Histogram of Oriented Gradients (HOG) feature descriptor is used to retrieve the relevant images from the database. The dimensionality of the HOG feature vector is reduced by Principle Component analysis (PCA). To improve the retrieval accuracy of the system the Colour Moments along with HOG feature descriptor are used in this system. The Interest points are detected using the Harris-corner detector in order to extract the image features. The KD-tree is used for matching and indexing the features of the query image with the database images.

PEDESTRIAN DETECTION SYSTEM USING CASCADED BOOSTING WITH INVARIANCE OF ORIENTED GRADIENTS

This study presents a novel learning-based pedestrian detection system capable of automatically detecting individuals of different sizes and orientations against a wide variety of backgrounds, including crowds, even when the individual is partially occluded. To render the detection performance robust toward the effects of geometric and rotational variations in the original image, the feature extraction process is performed using both rectangular- and circular-type blocks of various sizes and aspect ratios. The extracted blocks are rotated in accordance with their dominant orientation(s) such that all the blocks extracted from the input images are rotationally invariant. The pixels within the cells in each block are then voted into rectangular- and circular-type 9-bin histograms of oriented gradients (HOGs) in accordance with their gradient magnitudes and corresponding multivariate Gaussian-weighted windows. Finally, four cell-based histograms are concatenated using a tri-linear interpolation technique to form one 36-dimensional normalized HOG feature vector for each block. The experimental results show that the use of the Gaussian-weighted window approach and tri-linear interpolation technique in constructing the HOG feature vectors improves the detection performance from 91% to 94.5%. In the proposed scheme, the detection process is performed using a cascaded detector structure in which the weak classifiers and corresponding weights of each stage are established using the AdaBoost self-learning algorithm. The experimental results reveal that the cascaded structure not only provides a better detection performance than many of the schemes presented in the literature, but also achieves a significant reduction in the computational time required to classify each input image.