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.

Exploiting colour information for better scene text detection and recognition

Abstract: This paper presents an approach for text detection and recognition in scene images. The main contribution of this paper is to demonstrate that the colour information within the images if efficiently exploited is good enough to identify text regions from the surrounding noise. In the same way, the colour information present in character and word images can be used to achieve significant performance improvement in the recognition of characters and words. The proposed pipeline makes use of the colour information and low-level image processing operations to enhance text information that improves the overall performance of text detection and recognition in the wild. The proposed method offers two main advantages. First, it enhances the text regions up to a level of clarity where a simple off-the-shelf feature representation and classification method achieves state-of-the-art recognition performance. Second, the proposed framework is computationally fast as compared to other text detection and recognition techniques that offer good accuracy at the cost of significantly high latency. We performed extensive experimentation to evaluate our method on challenging benchmark datasets (Chars74K, ICDAR03, ICDAR11 and SVT), and the results show a considerable performance improvement.

Parallel deep convolutional neural network for content based medical image retrieval

Abstract: DICOM images which helps in diagnosis and prognosis would be critical component in health care systems. Speedy recovery of past historic DICOM images based on the given query image is becoming a critical requirement for the Laboratories and Doctors for quick inference and accurate analogy of the patient conditions. In existing, It is also identified that there is a presence of imbalanced data set which degrade the retrieval accuracy of the model which may reduce by using extract the different kinds of features. The DCNN classifiers are trained by datasets whose data distributions of individual classes are not even or similar, they have always suffered from imbalanced classification performance against classes. Through DCNN can be used to minimize the gaps in terms of accuracy and retrieval but still efficiency parallelization would be essential for faster training and retrieval time. Time complexity is always been a major issue in DCNN, to overcome the above complexity the parallelization of model or data dimension need to be adapted. In this paper, parallel deep convolutional neural network (PDCNN) model is proposed by hyper parameter optimimzation for CBMIR system. The proposed model incorporating the low level content features, high level semantic features and compact features along with DCNN features to tackle the imbalanced dataset problem and reducing the DCNN training time for DICOM images. The high-level and compact features are extracted to resolve the imbalanced dataset problem by using the following algorithms: (a) local binary pattern (LBP), (b) histogram of oriented gradients (HOG) and (c) radon. The data parallelism was adopted in the proposed DCNN model to reduce the network training time by execution of DCNN layers across multiple CPU cores on a single PC. The implementation results for the proposed model in terms of Precision, Recall and F measure values are 87%, 87% and 92% respectively.

Convolutional Neural Network-Based Human Identification Using Outer Ear Images

Abstract: This paper presents a deep learning approach for ear localization and recognition. The comparable complexity between human outer ear and face in terms of its uniqueness and permanence has increased interest in the use of ear as a biometric. But similar to face recognition, it poses challenges such as illumination, contrast, rotation, scale, and pose variation. Most of the techniques used for ear biometric authentication are based on traditional image processing techniques or handcrafted ensemble features. Owing to extensive work in the field of computer vision using convolutional neural networks (CNNs) and histogram of oriented gradients (HOG), the feasibility of deep neural networks (DNNs) in the field of ear biometrics has been explored in this research paper. A framework for ear localization and recognition is proposed that aims to reduce the pipeline for a biometric recognition system. The proposed framework uses HOG with support vector machines (SVMs) for ear localization and CNN for ear recognition. CNNs combine feature extraction and ear recognition tasks into one network with an aim to resolve issues such as variations in illumination, contrast, rotation, scale, and pose. The feasibility of the proposed technique has been evaluated on USTB III database. This work demonstrates 97.9% average recognition accuracy using CNNs without any image preprocessing, which shows that the proposed approach is promising in the field of biometric recognition.

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

Abstract: 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

Scene Classification Using Multi-Resolution WAHOLB Features and Neural Network Classifier

Abstract: This article approaches scene classification problem by proposing an enhanced bag of features (BoF) model and a modified radial basis function neural network (RBFNN) classifier. The proposed BoF model integrates the image features extracted by histogram of oriented gradients, local binary pattern and wavelet coefficients. The extracted features are obtained in a hierarchical multi-resolution manner. The proposed approach is able to capture multi-level (the pixel-, patch-, and image-level) features. The histograms of features constructed by BoF model are then used for training a modified RBFNN classifier. As a modification, we propose using a new variant of particle swarm optimization, in which the parameters are updated adaptively, for determining the center of Gaussian functions in RBFNN. Experimental results demonstrate that our proposed approach significantly outperforms the state-of-the-art methods on scene classification of OT, FP, and LSP benchmark datasets.