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.

Segmental HOG: new descriptor for glomerulus detection in kidney microscopy image

Abstract: The detection of the glomeruli is a key step in the histopathological evaluation of microscopic images of the kidneys. However, the task of automatic detection of the glomeruli poses challenges owing to the differences in their sizes and shapes in renal sections as well as the extensive variations in their intensities due to heterogeneity in immunohistochemistry staining. Although the rectangular histogram of oriented gradients (Rectangular HOG) is a widely recognized powerful descriptor for general object detection, it shows many false positives owing to the aforementioned difficulties in the context of glomeruli detection. A new descriptor referred to as Segmental HOG was developed to perform a comprehensive detection of hundreds of glomeruli in images of whole kidney sections. The new descriptor possesses flexible blocks that can be adaptively fitted to input images in order to acquire robustness for the detection of the glomeruli. Moreover, the novel segmentation technique employed herewith generates high-quality segmentation outputs, and the algorithm is assured to converge to an optimal solution. Consequently, experiments using real-world image data revealed that Segmental HOG achieved significant improvements in detection performance compared to Rectangular HOG. The proposed descriptor for glomeruli detection presents promising results, and it is expected to be useful in pathological evaluation.

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

Descriptors for image-based fingerprint matchers

Abstract: This paper focuses on the use of image-based techniques in fingerprint verification. A detailed review of the existing literature is provided by classifying existing methods on the basis of their alignment procedure and discussing the most salient approaches and their pros and cons. Even if, at present, the image-based techniques do not gain performance comparable with that obtained by the best minutiae-based approaches, several good reasons can be listed to support the research on image-based approaches: the possibility of using additional features in combination with minutiae to improve verification performance, the availability of a fixed length feature vector which makes these approaches suitable to be indexed, to be coupled with a learning system or to be combined with tokenised random number in a two factor authentication system (Biohashing). In this work we compare several texture-based descriptors for fingerprints and propose a novel image-based fingerprint matcher based on the minutiae alignment. In this approach, the feature extraction is performed locally on a decomposition of the fingerprint in several overlapping sub-windows considering the following measures: Gabor filters descriptors, invariant local binary patterns and histogram of gradients. Moreover, we propose to perform a supervised selection of a small subset of descriptors, in order to reduce the dimensionality of the feature set and discarding the less discriminative features. Extensive experiments conducted over the four FVC2002 fingerprint databases using a blind testing protocol show that the proposed system dramatically outperforms the other image-based fingerprint matchers proposed in the literature. Moreover, a further experiment conducted on a set of images reconstructed from ISO templates show that, differently to the minutiae-based approaches, our image-based matcher cannot be faked with the sole knowledge of the minutiae position and orientation, at least the original orientation image is required in order have a chance of performing a successful attack.