Showing papers on "Histogram of oriented gradients published in 2014"

Histograms of Oriented Gradients for Landmine Detection in Ground-Penetrating Radar Data

Abstract: Ground-penetrating radar (GPR) is a powerful and rapidly maturing technology for subsurface threat identification. However, sophisticated processing of GPR data is necessary to reduce false alarms due to naturally occurring subsurface clutter and soil distortions. Most currently fielded GPR-based landmine detection algorithms utilize feature extraction and statistical learning to develop robust classifiers capable of discriminating buried threats from inert subsurface structures. Analysis of these techniques indicates strong underlying similarities between efficient landmine detection algorithms and modern techniques for feature extraction in the computer vision literature. This paper explores the relationship between and application of one modern computer vision feature extraction technique, namely histogram of oriented gradients (HOG), to landmine detection in GPR data. The results presented indicate that HOG features provide a robust tool for target identification for both classification and prescreening and suggest that other techniques from computer vision might also be successfully applied to target detection in GPR data.

Histogram of Oriented Lines for Palmprint Recognition

Abstract: Subspace learning methods are very sensitive to the illumination, translation, and rotation variances in image recognition. Thus, they have not obtained promising performance for palmprint recognition so far. In this paper, we propose a new descriptor of palmprint named histogram of oriented lines (HOL), which is a variant of histogram of oriented gradients (HOG). HOL is not very sensitive to changes of illumination, and has the robustness against small transformations because slight translations and rotations make small histogram value changes. Based on HOL, even some simple subspace learning methods can achieve high recognition rates.

Rotation-Invariant HOG Descriptors Using Fourier Analysis in Polar and Spherical Coordinates

Abstract: The histogram of oriented gradients (HOG) is widely used for image description and proves to be very effective. In many vision problems, rotation-invariant analysis is necessary or preferred. Popular solutions are mainly based on pose normalization or learning, neglecting some intrinsic properties of rotations. This paper presents a method to build rotation-invariant HOG descriptors using Fourier analysis in polar/spherical coordinates, which are closely related to the irreducible representation of the 2D/3D rotation groups. This is achieved by considering a gradient histogram as a continuous angular signal which can be well represented by the Fourier basis (2D) or spherical harmonics (3D). As rotation-invariance is established in an analytical way, we can avoid discretization artifacts and create a continuous mapping from the image to the feature space. In the experiments, we first show that our method outperforms the state-of-the-art in a public dataset for a car detection task in aerial images. We further use the Princeton Shape Benchmark and the SHREC 2009 Generic Shape Benchmark to demonstrate the high performance of our method for similarity measures of 3D shapes. Finally, we show an application on microscopic volumetric data.

Emotion Recognition in the Wild with Feature Fusion and Multiple Kernel Learning

Abstract: This paper presents our proposed approach for the second Emotion Recognition in The Wild Challenge. We propose a new feature descriptor called Histogram of Oriented Gradients from Three Orthogonal Planes (HOG_TOP) to represent facial expressions. We also explore the properties of visual features and audio features, and adopt Multiple Kernel Learning (MKL) to find an optimal feature fusion. An SVM with multiple kernels is trained for the facial expression classification. Experimental results demonstrate that our method achieves a promising performance. The overall classification accuracy on the validation set and test set are 40.21% and 45.21%, respectively.

HSOG: A Novel Local Image Descriptor Based on Histograms of the Second-Order Gradients

Abstract: Recent investigations on human vision discover that the retinal image is a landscape or a geometric surface, consisting of features such as ridges and summits. However, most of existing popular local image descriptors in the literature, e.g., scale invariant feature transform (SIFT), histogram of oriented gradient (HOG), DAISY, local binary Patterns (LBP), and gradient location and orientation histogram, only employ the first-order gradient information related to the slope and the elasticity, i.e., length, area, and so on of a surface, and thereby partially characterize the geometric properties of a landscape. In this paper, we introduce a novel and powerful local image descriptor that extracts the histograms of second-order gradients (HSOGs) to capture the curvature related geometric properties of the neural landscape, i.e., cliffs, ridges, summits, valleys, basins, and so on. We conduct comprehensive experiments on three different applications, including the problem of local image matching, visual object categorization, and scene classification. The experimental results clearly evidence the discriminative power of HSOG as compared with its first-order gradient-based counterparts, e.g., SIFT, HOG, DAISY, and center-symmetric LBP, and the complementarity in terms of image representation, demonstrating the effectiveness of the proposed local descriptor.

One-shot-learning gesture recognition using HOG-HOF features

Abstract: The purpose of this paper is to describe one-shot-learning gesture recognition systems developed on the ChaLearn Gesture Dataset (ChaLearn). We use RGB and depth images and combine appearance (Histograms of Oriented Gradients) and motion descriptors (Histogram of Optical Flow) for parallel temporal segmentation and recognition. The Quadratic-Chi distance family is used to measure differences between histograms to capture cross-bin relationships. We also propose a new algorithm for trimming videos--to remove all the unimportant frames from videos. We present two methods that use a combination of HOG-HOF descriptors together with variants of a Dynamic Time Warping technique. Both methods outperform other published methods and help narrow the gap between human performance and algorithms on this task. The code is publicly available in the MLOSS repository.

Recognizing Gaits on Spatio-Temporal Feature Domain

Abstract: Gait has been known as an effective biometric feature to identify a person at a distance, e.g., in video surveillance applications. Many methods have been proposed for gait recognitions from various different perspectives. It is found that these methods rely on appearance (e.g., shape contour, silhouette)-based analyses, which require preprocessing of foreground–background segmentation (FG/BG). This process not only causes additional time complexity, but also adversely influences performances of gait analyses due to imperfections of existing FG/BG methods. Besides, appearance-based gait recognitions are sensitive to several variations and partial occlusions, e.g., caused by carrying a bag and varying a cloth type. To avoid these limitations, this paper proposes a new framework to construct a new gait feature directly from a raw video. The proposed gait feature extraction process is performed in the spatio-temporal domain. The space-time interest points (STIPs) are detected by considering large variations along both spatial and temporal directions in local spatio-temporal volumes of a raw gait video sequence. Thus, STIPs are allocated, where there are significant movements of human body in both space and time. A histogram of oriented gradients and a histogram of optical flow are computed on a 3D video patch in a neighborhood of each detected STIP, as a STIP descriptor. Then, the bag-of-words model is applied on each set of STIP descriptors to construct a gait feature for representing and recognizing an individual gait. When compared with other existing methods in the literature, it has been shown that the performance of the proposed method is promising for the case of normal walking, and is outstanding for the case of partial occlusion caused by walking with carrying a bag and walking with varying a cloth type.

Efficient Handwritten Digit Recognition based on Histogram of Oriented Gradients and SVM

Abstract: Automatic Handwritten Digits Recognition (HDR) is the process of interpreting handwritten digits by machines. There are several approaches for handwritten digits recognition. In this paper we have proposed an appearance feature-based approach which process data using Histogram of Oriented Gradients (HOG). HOG is a very efficient feature descriptor for handwritten digits which is stable on illumination variation because it is a gradient-based descriptor. Moreover, linear SVM has been employed as classifier which has better responses than polynomial, RBF and sigmoid kernels. We have analyzed our model on MNIST dataset and 97.25% accuracy rate has been achieved which is comparable with the state of the art. General Terms Image Processing, Computer Vision, Artificial Intelligence

Human Detection by Quadratic Classification on Subspace of Extended Histogram of Gradients

Abstract: This paper proposes a quadratic classification approach on the subspace of Extended Histogram of Gradients (ExHoG) for human detection. By investigating the limitations of Histogram of Gradients (HG) and Histogram of Oriented Gradients (HOG), ExHoG is proposed as a new feature for human detection. ExHoG alleviates the problem of discrimination between a dark object against a bright background and vice versa inherent in HG. It also resolves an issue of HOG whereby gradients of opposite directions in the same cell are mapped into the same histogram bin. We reduce the dimensionality of ExHoG using Asymmetric Principal Component Analysis (APCA) for improved quadratic classification. APCA also addresses the asymmetry issue in training sets of human detection where there are much fewer human samples than non-human samples. Our proposed approach is tested on three established benchmarking data sets - INRIA, Caltech, and Daimler - using a modified Minimum Mahalanobis distance classifier. Results indicate that the proposed approach outperforms current state-of-the-art human detection methods.

Moving Object Localization Using Optical Flow for Pedestrian Detection from a Moving Vehicle

Abstract: 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.

Face recognition based on the fusion of global and local HOG features of face images

Abstract: Histogram of oriented gradients (HOG) descriptor was initially applied to human detection and achieved great success. In recent years, HOG descriptor has also been applied to face recognition. However, comparing with other sophisticated feature descriptors such as LBP, Gabor and so on, there are still considerable research space on the application of HOG features for face recognition. There are two main contributions. On one hand, the main parameters are statistically analysed characterising HOG descriptor for face recognition, which seems to be not discussed clearly in literatures so far. On the other hand, a novel framework for face recognition based on the fusion of global and local HOG features has been proposed. Face images are first illumination normalised by the DoG filter. Secondly, global and local HOG features are extracted by PCA + LDA or LDA with different framework. Finally, in decision level, global and local classifiers are built by the nearest neighbour classifier, after that, two classifiers are fused by a weighted sum rule. Experimental results on two large-scale face databases FERET and CAS-PEAL-R1 show that, in comparison with 12 state-of-the-art approaches of face recognition, the proposed method achieves the highest average recognition rate.

2D view aggregation for lymph node detection using a shallow hierarchy of linear classifiers.

Abstract: Enlarged lymph nodes (LNs) can provide important information for cancer diagnosis, staging, and measuring treatment reactions, making automated detection a highly sought goal. In this paper, we propose a new algorithm representation of decomposing the LN detection problem into a set of 2D object detection subtasks on sampled CT slices, largely alleviating the curse of dimensionality issue. Our 2D detection can be effectively formulated as linear classification on a single image feature type of Histogram of Oriented Gradients (HOG), covering a moderate field-of-view of 45 by 45 voxels. We exploit both max-pooling and sparse linear fusion schemes to aggregate these 2D detection scores for the final 3D LN detection. In this manner, detection is more tractable and does not need to perform perfectly at instance level (as weak hypotheses) since our aggregation process will robustly harness collective information for LN detection. Two datasets (90 patients with 389 mediastinal LNs and 86 patients with 595 abdominal LNs) are used for validation. Cross-validation demonstrates 78.0% sensitivity at 6 false positives/volume (FP/vol.) (86.1% at 10 FP/vol.) and 73.1% sensitivity at 6 FP/vol. (87.2% at 10 FP/vol.), for the mediastinal and abdominal datasets respectively. Our results compare favorably to previous state-of-the-art methods.

Human vs. Computer in Scene and Object Recognition

Abstract: Several decades of research in computer and primate vision have resulted in many models (some specialized for one problem, others more general) and invaluable experimental data. Here, to help focus research efforts onto the hardest unsolved problems, and bridge computer and human vision, we define a battery of 5 tests that measure the gap between human and machine performances in several dimensions (generalization across scene categories, generalization from images to edge maps and line drawings, invariance to rotation and scaling, local/global information with jumbled images, and object recognition performance). We measure model accuracy and the correlation between model and human error patterns. Experimenting over 7 datasets, where human data is available, and gauging 14 well-established models, we find that none fully resembles humans in all aspects, and we learn from each test which models and features are more promising in approaching humans in the tested dimension. Across all tests, we find that models based on local edge histograms consistently resemble humans more, while several scene statistics or "gist" models do perform well with both scenes and objects. While computer vision has long been inspired by human vision, we believe systematic efforts, such as this, will help better identify shortcomings of models and find new paths forward.

On Effectiveness of Histogram of Oriented Gradient Features for Visible to Near Infrared Face Matching

Abstract: The advent of near infrared imagery and it's applications in face recognition has instigated research in cross spectral (visible to near infrared) matching. Existing research has focused on extracting textural features including variants of histogram of oriented gradients. This paper focuses on studying the effectiveness of these features for cross spectral face recognition. On NIR-VIS-2.0 cross spectral face database, three HOG variants are analyzed along with dimensionality reduction approaches and linear discriminant analysis. The results demonstrate that DSIFT with subspace LDA outperforms a commercial matcher and other HOG variants by at least 15%. We also observe that histogram of oriented gradient features are able to encode similar facial features across spectrums.

Plant Leaf Recognition Using Zernike Moments and Histogram of Oriented Gradients

Abstract: A method using Zernike Moments and Histogram of Oriented Gradients for classification of plant leaf images is proposed in this paper. After preprocessing, we compute the shape features of a leaf using Zernike Moments and texture features using Histogram of Oriented Gradients and then the Support Vector Machine classifier is used for plant leaf image classification and recognition. Experimental results show that using both Zernike Moments and Histogram of Oriented Gradients to classify and recognize plant leaf image yields accuracy that is comparable or better than the state of the art. The method has been validated on the Flavia and the Swedish Leaves datasets as well as on a combined dataset.

A Relative-Discriminative-Histogram-of-Oriented-Gradients-Based Particle Filter Approach to Vehicle Occlusion Handling and Tracking

Abstract: This paper presents a relative discriminative histogram of oriented gradients (HOG) (RDHOG)-based particle filter (RDHOGPF) approach to traffic surveillance with occlusion handling. Based on the conventional HOG, an extension known as RDHOG is proposed, which enhances the descriptive ability of the central block and the surrounding blocks. RDHOGPF can be used to predict and update the positions of vehicles in continuous video sequences. RDHOG was integrated with the particle filter framework in order to improve the tracking robustness and accuracy. To resolve multiobject tracking problems, a partial occlusion handling approach is addressed, based on the reduction of the particle weights within the occluded region. Using the proposed procedure, the predicted trajectory is closer to that of the real rigid body. The proposed RDHOGPF can determine the target by using the feature descriptor correctly, and it overcomes the drift problem by updating in low-contrast and very bright situations. An empirical evaluation is performed inside a tunnel and on a real road. The test videos include low viewing angles in the tunnel, low-contrast and bright situations, and partial and full occlusions. The experimental results demonstrate that the detection ratio and precision of RDHOGPF both exceed 90%.

Vehicle Model Recognition Using Geometry and Appearance of Car Emblems from Rear View Images

Abstract: In this paper a novel vehicle model recognition approach is presented modelling the geometry and appearance of car emblems (model, trim level, etc) from rear view images The proposed system is assisted by LPR and VMR modules Thus, a generic methodology is defined to build a hierarchical structure of car-make-dependent vehicle model classifiers The emblems location, size and variations are firstly learnt Then, the appearance of each badge is modelled using a linear SVM binary classifier with HOG (histogram of oriented gradients) features and the outputs of each individual classifier are converted to an estimate of posterior probabilities A specific probability is computed for each hypothesis (model) integrating the posterior probabilities of all the emblems using the geometric mean Inference about the most probable car model is finally carried out selecting the model with the maximum probability The authors evaluate this approach on a dataset composed of 1,342 images (910/432 for training/test) corresponding to 8 different car makes and 28 different car models (52 considering generations) achieving an overall accuracy of 9375%

Automatic target detection in GPR images using Histogram of Oriented Gradients (HOG)

Abstract: Ground Penetrating Radar (GPR) has proven itself to be one of the most popular and reliable geophysical device in subsurface investigation. However, human operators are required to manually interpret the GPR data. In a typical geophysical survey, collected GPR data sometimes can be enormously huge, causing issues such as time consuming and inaccuracy in results due to human errors. In this paper, we present an algorithm that automatically detects hyperbolic signatures in GPR data in B-scan model. This developed algorithm is able to mark potential regions that contain the reflections from target of buried objects. Histogram of Oriented Gradients (HOG) was initially developed to detect pedestrians, but it can be also well-adapted to detect particular shapes and objects. HOG descriptors are extracted from a set of training images and are trained using a linear SVM classifier. The main purpose of this algorithm is to narrow down the data into possible target reflection regions. After that, we implement Hough Transform to highlight the hyperbolic patterns in the reflection. The results shows that the developed system can perform target detection at an average of 93.75% detection rate for all four test sets.

Local descriptors to improve off-line handwriting-based gender prediction

Abstract: Gender prediction based on the handwritten text becomes to earn a considerable importance for the document analysis community Gender prediction based on the handwritten text becomes to earn a considerable importance for the document analysis community. It is helpful for person identification as well as in some situations where one needs to classify population according to women-men categories. However, only a few studies have been carried out in this field. In the present work, we propose the use of local descriptors in order to improve the gender classification based on offline handwritten text. Specifically, we employ Histogram of Oriented Gradients (HOG), Local Binary Patterns (LBP) as well as grid features, which are successful in various pattern recognition applications. The prediction task is achieved by SVM classifier. The results obtained on samples extracted from IAM dataset show that local descriptors provide quite promising results.

HOG Feature Extractor Hardware Accelerator for Real-Time Pedestrian Detection

Abstract: Histogram of oriented gradients (HOG) is considered as the most promising algorithm in human detection, however its complexity and intensive computational load is an issue for real-time detection in embedded systems. This paper presents a hardware accelerator for HOG feature extractor to fulfill the requirements of real-time pedestrian detection in driver assistance systems. Parallel and deep pipelined hardware architecture with special defined memory access pattern is employed to improve the throughput while maintaining the accuracy of the original algorithm reasonably high. Adoption of efficient memory access pattern, which provides simultaneous access to the required memory area for different functional blocks, avoids repetitive calculation at different stages of computation, resulting in both higher throughput and lower power. It does not impose any further resource requirements with regard to memory utilization. Our presented hardware accelerator is capable of extracting HOG features for 60 fps (frame per second) of HDTV (1080x1920) frame and could be employed with several instances of support vector machine (SVM) classifier in order to provide multiple object detection.

Learning Human Actions by Combining Global Dynamics and Local Appearance

Abstract: In this paper, we address the problem of human action recognition through combining global temporal dynamics and local visual spatio-temporal appearance features. For this purpose, in the global temporal dimension, we propose to model the motion dynamics with robust linear dynamical systems (LDSs) and use the model parameters as motion descriptors. Since LDSs live in a non-Euclidean space and the descriptors are in non-vector form, we propose a shift invariant subspace angles based distance to measure the similarity between LDSs. In the local visual dimension, we construct curved spatio-temporal cuboids along the trajectories of densely sampled feature points and describe them using histograms of oriented gradients (HOG). The distance between motion sequences is computed with the Chi-Squared histogram distance in the bag-of-words framework. Finally we perform classification using the maximum margin distance learning method by combining the global dynamic distances and the local visual distances. We evaluate our approach for action recognition on five short clips data sets, namely Weizmann, KTH, UCF sports, Hollywood2 and UCF50, as well as three long continuous data sets, namely VIRAT, ADL and CRIM13. We show competitive results as compared with current state-of-the-art methods.

Character Recognition in Natural Scenes Using Convolutional Co-occurrence HOG

Abstract: Recognition of characters in natural images is a challenging task due to the complex background, variations of text size and perspective distortion, etc. Traditional optical character recognition (OCR) engine cannot perform well on those unconstrained text images. A novel technique is proposed in this paper that makes use of convolutional co occurrence histogram of oriented gradient (ConvCoHOG), which is more robust and discriminative than both the histogram of oriented gradient (HOG) and the co-occurrence histogram of oriented gradients (CoHOG). In the proposed technique, a more informative feature is constructed by exhaustively extracting features from every possible image patches within character images. Experiments on two public datasets including the ICDAr 2003 Robust Reading character dataset and the Street View Text (SVT) dataset, show that our proposed character recognition technique obtains superior performance compared with state-of-the-art techniques.

Spontaneous Facial Expression Recognition Based on Histogram of Oriented Gradients Descriptor

Abstract: Automatically detecting facial expressions has become an important research area. It plays a significant role in security, human-computer interaction and health-care. Yet, earlier work focuses on posed facial expression. In this paper, we propose a spontaneous facial expression recognition method based on effective feature extraction and facial expression recognition for Micro Expression analysis. In feature extraction we used histogram of oriented gradients (HOG) descriptor to extract facial expression features. Expression recognition is performed by using a Support vector machine (SVM) classifier to recognize six emotions (happiness, anger, disgust, fear, sadness and surprise). Experiments show promising results of the proposed method with recognition accuracy of 95% on static images while 80% on videos.

Automatic Target Recognition in Infrared Imagery Using Dense HOG Features and Relevance Grouping of Vocabulary

Abstract: We study automatic target recognition (ATR) in infrared (IR) imagery by applying two recent computer vision techniques, Histogram of Oriented Gradients (HOG) and Bag-of-Words (BoW). We propose the idea of dense HOG features which are extracted from a set of high-overlapped local patches in a small IR chip and we apply a vocabulary tree that is learned from a set of training images to support efficient and scalable BoW-based ATR. We develop a relevance grouping of vocabulary (RGV) technique to improve the ATR performance by additional voting from grouped visual words. Different from traditional word grouping techniques, RGV groups visual words of the same dominant class to enhance the voting confidence in BoW-based classification. The proposed ATR algorithm is evaluated against recent sparse representation-based classification (SRC) approaches that reportedly outperform traditional methods. Experimental results on the COMANCHE IR dataset demonstrate the advantages of the newly proposed algorithm (BoW-RGV) over the recent SRC approaches.

Gaussian Mixture Model With Advanced Distance Measure Based on Support Weights and Histogram of Gradients for Background Suppression

Abstract: The proposed work is targeted toward improving the Gaussian mixture model (GMM) for the background suppression-based moving object detection. The GMM has been widely used for moving object detection due to its high applicability. However, the GMM cannot properly model noisy or nonstationary backgrounds and fails to discriminate between the foreground and background modes. The extensions to GMM provide increased accuracy in expense of complex implementation and reduced applicability. In response, this work proposes two simple improvements: 1) a novel distance measure based on local support weights and histogram of gradients to provide distinct cluster values; and 2) use of background layer concept to properly segment the foreground. The method also uses variable number of clusters for generalization. The main advantages of the method are implicit use of pixel relationships through distance measure with least modification to the conventional GMM and effective background noise removal through the use of background layer concept with no postprocessing involved. The extensive experimentations on various types of video sequences are performed to validate the improvement in accuracy compared to the GMM and a number of state-of-the-art methods.

Finger Vein Recognition Using Histogram of Competitive Gabor Responses

Abstract: Finger vein has been proved to be an effective biometric for personal identification in recent years. Inspired by the good power of Gabor filter in capturing specific texture characteristics from any orientation of an image, this paper proposes a simple, yet powerful and efficient local descriptor for finger vein recognition, called histogram of competitive Gabor responses (HCGR). Specially, HCGR is based on a set of competitive Gabor response (CGR) which consists of two components: competitive Gabor magnitude (CGM) and competitive Gabor orientation (CGO). A set of CGR includes the information on magnitude and orientation of the maximum responses of the Gabor filter bank with a number of different orientations. For a given image, we calculate its CGM image and CGO image and represent them in a concatenated histogram, called HCGR. This histogram can efficiently and effectively exploit the discriminative orientation and local features in a finger vein image. The experimental results obtained on our publically available finger vein image database MMCBNU_6000 demonstrate that the proposed HCGR outperforms the classical local operators such as Gabor, steerable, histogram of oriented gradients (HOG) and local binary pattern (LBP).

Histogram of oriented gradients front end processing: An FPGA based processor approach

Abstract: The Field Programmable Gate Array (FPGA) implementation of the commonly used Histogram of Oriented Gradients (HOG) algorithm is explored. The HOG algorithm is employed to extract features for object detection. A key focus has been to explore the use of a new FPGA-based processor which has been targeted at image processing. The paper gives details of the mapping and scheduling factors that influence the performance and the stages that were undertaken to allow the algorithm to be deployed on FPGA hardware, whilst taking into account the specific IPPro architecture features. We show that multi-core IPPro performance can exceed that of against state-of-the-art FPGA designs by up to 3.2 times with reduced design and implementation effort and increased flexibility all on a low cost, Zynq programmable system.