Histogram Of Gradient Descriptor Research Articles

Optimized Scale-Invariant Hog Descriptors for Tobacco Plant Detection

The histogram of gradient (HOG) descriptor is being employed in this research work to demonstrate the technique of scale variant to identify the plant in surveillance videos. In few scenarios, the discrepancies in the histogram of gradient descriptors along with scale as well as variation in illumination are considered as one of the major hindrances. This research work introduces a unique SIO-HOG descriptor that is approximated to be scale-invariant. With the help of the footage that is captured from the tobacco plant identification process, the system can integrate adoptive bin selections as well as sample resizing. Further, this research work explores the impact of a PCA transform that is based on the process of feature selection on the performance of overall recognition and thereby considering finite scale range, adoptive orientation binning in non-overlapping descriptors, as well as finite scale range are all essential for a high detection rate. The feature vector of HOG over a complete search window is computationally intensive. However, suitable frameworks for classification can be developed by maintaining a precise range of attributes with finite Euclidean distance. Experimental results prove that the proposed approach for detecting tobacco from other weeds has resulted in an improved detection rate. And finally, the robustness of the complete plant detection system was evaluated on a video sequence with different non-linearity's that is quite common in a real-world environment and its performance metrics are evaluated

Optimized Scale Invariant HOG Descriptors for Object and Human Detection

This article presents a scale variation approach to identify objects and humans in video sequences using histogram of gradient descriptor. A significant restriction in HOG descriptors is its variations with scale changes and illumination changes, as is frequently the considered case. We recommend unique SIO-HOG descriptors that are figured to be invariant to scale changes. The system associates the benefits of adoptive bin selections and sample resizing in the object recognition process. We analyze the effect of PCA transform based feature selection process on object detection performance, ultimately the finite scale range, adoptive orientation binning in non-overlapping descriptors is all main thing for nominal detection rate. HOG feature vector over complete search window is computationally more exclusive, to acquire more precise set of features with finite Euclidean distance to classify them using KNN classifier. This new approach provides near-perfect ways of separating humans from other objects. The whole object detection system was assessed on a few test samples from real-world data sets and compared beside a publicly available pedestrian detection data base.

Catch them alive: A malware detection approach through memory forensics, manifold learning and computer vision

The everlasting increase in usage of information systems and online services have triggered the birth of the new type of malware which are more dangerous and hard to detect. In particular, according to the recent reports, the new type of fileless malware infect the victims’ devices without a persistent trace (i.e. file) on hard drives. Moreover, existing static malware detection methods in literature often fail to detect sophisticated malware utilizing various obfuscation and encryption techniques. Our contribution in this study is two-folded. First, we present a novel approach to recognize malware by capturing the memory dump of suspicious processes which can be represented as a RGB image. In contrast to the conventional approaches followed by static and dynamic methods existing in the literature, we aimed to obtain and use memory data to reveal visual patterns that can be classified by employing computer vision and machine learning methods in a multi-class open-set recognition regime. And second, we have applied a state of art manifold learning scheme named UMAP to improve the detection of unknown malware files through binary classification. Throughout the study, we have employed our novel dataset covering 4294 samples in total, including 10 malware families along with the benign executables. Lastly, we obtained their memory dumps and converted them to RGB images by applying 3 different rendering schemes. In order to generate their signatures (i.e. feature vectors), we utilized GIST and HOG (Histogram of Gradients) descriptors as well as their combination. Moreover, the obtained signatures were classified via machine learning algorithms of j48, RBF kernel-based SMO, Random Forest, XGBoost and linear SVM. According to the results of the first phase, we have achieved prediction accuracy up to 96.39% by employing SMO algorithm on the feature vectors combined with GIST+HOG. Besides, the UMAP based manifold learning strategy has improved accuracy of the unknown malware recognition models up to 12.93%, 21.83%, 20.78% on average for Random Forest, linear SVM and XGBoost algorithms respectively. Moreover, on a commercially available standard desktop computer, the suggested approach takes only 3.56 s for analysis on average. The results show that our vision based scheme provides an effective protection mechanism against malicious applications.

G2P: a new descriptor for pedestrian detection

Pedestrian detection plays an important role in many applications. Since its birth 13 years ago, Histogram Of Gradient (HOG) descriptor has become a popular descriptor for pedestrian detection. Besides its original instantiation, the HOG also reflects a general methodology of constructing descriptors based on histograms of gradients of certain image sub-blocks. Following this general methodology, a number of HOG-style descriptors have been reported in the literature. The generation process of these descriptors is summarized in this work, and a new descriptor is presented for pedestrian detection. Three contributions are made in this work. First, a general model called descriptor generation model (DGM) is proposed, which can be used to systematically construct a wide range of HOG-style descriptors for pedestrian detection. Second, based on the DGM, a pedestrian detection experimental framework (PDEF) is introduced to find the optimal HOG-style descriptor. In the PDEF, the performance of each descriptor can be evaluated. At last, the genetic algorithm is employed to search the optimal (or semi-optimal) HOG-style descriptor in the descriptor space. And a new descriptor named Second-order Gradient for Pedestrian detection (G2P) is presented. Experimental results demonstrate the advantage of the G2P descriptor over the standard HOG descriptor with ETH, CVC-02-system, NITCA and KITTI dataset, which also reflects the effectiveness of the DGM-based PDEF in finding better descriptors for pedestrian detection.

An ensemble shape gradient features descriptor based nodule detection paradigm: a novel model to augment complex diagnostic decisions assistance

Primarily, there are three basic operational constituents of Nodule Detection Systems namely nodule candidate detection, classification of nodule and extraction of features. Thresholding is one of the most important factor for nodule detection. To segment the lungs and nodules, Gaussian approximation based Particle Swarm Optimization (PSO) is used to determine the optimal threshold value. After extracting lungs part, 2D and 3D region of interests (ROI’s) are used to detect nodules with area and volume information of nodules and then distinguish between wall and vessels by using fuzzy C-mean. There are three key objects namely wall, nodule and vessel in the lugs volume with specific shape. Shape-based features with Histogram of Oriented Surface Normal Vectors (HOSNV) are used as a feature descriptor. A scaled and rotation invariant multi-coordinate histogram of thegradient is used to identify nodules with different sizes and directionless shapes. So, a Novel Ensemble Shape Gradient Features (NESGF) descriptor for pulmonary nodule classification is proposed using the Histogram of Oriented Surface Normal Vectors and Multi-Coordinate Histogram of Gradient descriptor. The random forest has been used to classify the nodules through intelligent usage of the ensemble concepts to learn weak classifiers. A standard benchmark database Lung Image Consortium Database (LICD) is used for testing and validation purposes. In order to show the performance of segmentation quality, the proposed model is compared through three quantitative measures inclusive of Variation of Information (VoI), Probabilistic Rand Index (PRI) and Jaccard Measure. The methods Area Under Curve, Sensitivity, Specificity and Sensitivity have are used for classification. For classification, accuracy, sensitivity, specificity and Area under curve (AUC) has been used.

An approach through gene-engineered animals

The everlasting increase in usage of information systems and online services have triggered the birth of the new type of malware which are more dangerous and hard to detect. In particular, according to the recent reports, the new type of fileless malware infect the victims’ devices without a persistent trace (i.e. file) on hard drives. Moreover, existing static malware detection methods in literature often fail to detect sophisticated malware utilizing various obfuscation and encryption techniques. Our contribution in this study is two-folded. First, we present a novel approach to recognize malware by capturing the memory dump of suspicious processes which can be represented as a RGB image. In contrast to the conventional approaches followed by static and dynamic methods existing in the literature, we aimed to obtain and use memory data to reveal visual patterns that can be classified by employing computer vision and machine learning methods in a multi-class open-set recognition regime. And second, we have applied a state of art manifold learning scheme named UMAP to improve the detection of unknown malware files through binary classification. Throughout the study, we have employed our novel dataset covering 4294 samples in total, including 10 malware families along with the benign executables. Lastly, we obtained their memory dumps and converted them to RGB images by applying 3 different rendering schemes. In order to generate their signatures (i.e. feature vectors), we utilized GIST and HOG (Histogram of Gradients) descriptors as well as their combination. Moreover, the obtained signatures were classified via machine learning algorithms of j48, RBF kernel-based SMO, Random Forest, XGBoost and linear SVM. According to the results of the first phase, we have achieved prediction accuracy up to 96.39% by employing SMO algorithm on the feature vectors combined with GIST+HOG. Besides, the UMAP based manifold learning strategy has improved accuracy of the unknown malware recognition models up to 12.93%, 21.83%, 20.78% on average for Random Forest, linear SVM and XGBoost algorithms respectively. Moreover, on a commercially available standard desktop computer, the suggested approach takes only 3.56 s for analysis on average. The results show that our vision based scheme provides an effective protection mechanism against malicious applications.