High Order Volumetric Directional Pattern for Video-Based Face Recognition

Describing the dynamic textures has attracted growing attention in the field of computer vision and pattern recognition. In this paper, a novel approach for recognizing dynamic textures, namely, high order volumetric directional pattern (HOVDP), is proposed. It is an extension of the volumetric directional pattern (VDP) which extracts and fuses the temporal information (dynamic features) from three consecutive frames. HOVDP combines the movement and appearance features together considering the nth order volumetric directional variation patterns of all neighboring pixels from three consecutive frames. In experiments with two challenging video face databases, YouTube Celebrities and Honda/UCSD, HOVDP clearly outperformed a set of state-of-the-art approaches

Video-to-Video Pose and Expression Invariant Face Recognition using Volumetric Directional Pattern

Face recognition in video has attracted attention as a cryptic method of human identification in surveillance systems. In this paper, we propose an end-to-end video face recognition system, addressing a difficult problem of identifying human faces in video due to the presence of large variations in facial pose and expression, and poor video resolution. The proposed descriptor, named Volumetric Directional Pattern (VDP), is an oriented and multi-scale volumetric descriptor that is able to extract and fuse the information of multi frames, temporal (dynamic) information, and multiple poses and expressions of faces in input video to produce feature vectors, which are used to match with all the videos in the database. To make the approach computationally simple and easy to extend, key-frame extraction method is employed. Therefore, only the frames which contain important information of the video can be used for further processing instead of analyzing all the frames in the video. The performance evaluation of the proposed VDP algorithm is conducted on a publicly available database (YouTube celebrities’ dataset) and observed promising recognition rates

A Modular Approach for Key-Frame Selection in Wide Area Surveillance Video Analysis

This paper presents an efficient preprocessing algorithm for big data analysis. Our proposed key-frame selection method utilizes the statistical differences among subsequent frames to automatically select only the frames that contain the desired contextual information and discard the rest of the insignificant frames. We anticipate that such key frame selection technique will have significant impact on wide area surveillance applications such as automatic object detection and recognition in aerial imagery. Three real-world datasets are used for evaluation and testing and the observed results are encouraging

Intrusion Detection in Aerial Imagery for Protecting Pipeline Infrastructure

We present an automated mechanism that can detect and issue warnings of machinery threat such as the presence of construction vehicles on pipeline right-of-way. The proposed scheme models the human visual perception concepts to extract fine details of objects by utilizing the corners and gradient histogram information in pyramid levels. Two real-world aerial image datasets are used for testing and evaluation

Automatic Building Change Detection in Wide Area Surveillance

We present an automated mechanism that can detect and characterize the building changes by analyzing airborne or satellite imagery. The proposed framework can be categorized into three stages: building detection, boundary extraction and change identification. To detect the buildings, we utilize local phase and local amplitude from monogenic signal to extract building features for addressing issues of varying illumination. Then a support vector machine with Radial basis kernel is used for classification. In the boundary extraction stage, a level-set function with self-organizing map based segmentation method is used to find the building boundary and compute physical area of the building segments. In the last stage, the change of the detected building is identified by computing the area differences of the same building that captured at different times. The experiments are conducted on a set of real-life aerial imagery to show the effectiveness of the proposed method

The geometry of large Arctic tundra lakes observed in historical maps and satellite images

The climate of the Arctic is warming rapidly and this is causing major changes to the cycling of carbon and the distribution of permafrost in this region. Tundra lakes are key components of the Arctic climate system because they represent a source of methane to the atmosphere. In this paper, we aim to analyze the geometry of the patterns formed by large (> 0.8 km2) tundra lakes in the Russian High Arctic. We have studied images of tundra lakes in historical maps from the State Hydrological Institute, Russia (date 1977; scale 0.21166 km/pixel) and in Landsat satellite images derived from the Google Earth Engine (G.E.E.; date 2016; scale 0.1503 km/pixel). The G.E.E. is a cloud-based platform for planetary-scale geospatial analysis on over four decades of Landsat data. We developed an image-processing algorithm to segment these maps and images, measure the area and perimeter of each lake, and compute the fractal dimension of the lakes in the images we have studied. Our results indicate that as lake size increases, their fractal dimension bifurcates. For lakes observed in historical maps, this bifurcation occurs among lakes larger than 100 km2 (fractal dimension 1.43 to 1.87). For lakes observed in satellite images this bifurcation occurs among lakes larger than ∼100 km2 (fractal dimension 1.31 to 1.95). Tundra lakes with a fractal dimension close to 2 have a tendency to be self-similar with respect to their area–perimeter relationships. Area–perimeter measurements indicate that lakes with a length scale greater than 70 km2 are power-law distributed. Preliminary analysis of changes in lake size over time in paired lakes (lakes that were visually matched in both the historical map and the satellite imagery) indicate that some lakes in our study region have increased in size over time, whereas others have decreased in size over time. Lake size change during this 39-year time interval can be up to half the size of the lake as recorded in the historical map

High Order Volumetric Directional Pattern for Robust Face Recognition

The texture of objects in digital images is an important property that has been utilized in many computer vision and image analysis applications, such as pattern recognition, object classification, and region segmentation. Despite its frequent usage and many attempts to describe it in general terms, the texture lacks a precise definition. This makes the development of new texture descriptors a big challenge. In addition, researchers interest has recently spread into the dynamic texture (video domain), where the problem becomes more challenging. The main goal of feature description and representation techniques is to extract features from the image that are distinct and stable under different conditions during the image acquisition process. Texture descriptors can be generally classified into structural and statistical approaches. The structural methods consider the texture as a repetition of some primitives, with a specific rule of placement, while the statistical techniques characterize the stochastic properties of the spatial distribution of gray levels in an image using the gray tone co-occurrence matrix. In this work, we propose a combination of the structural and statistical approaches that can be utilized to recognize a variety of different textures, named High Order Local Directional Pattern (HOLDP) for still image based feature extraction (static texture) as well as High Order Volumetric Directional Pattern (HOVDP) for video based feature extraction (dynamic texture). Recently, the conventional Local Directional Pattern (LDP) has received a great deal of attention in face recognition applications. However, it only describes the micro structures of the texture images because it considers only a small neighborhood size. In fact, our proposed HOLDP descriptor can capture more detailed discriminative information by not only extracting the micro structures but also the macro structures of the texture images, which can be done by the help of a pyramidal multi-structure approach. The pyramid based multi-structure presented in this dissertation research can be created by encoding the directional information from different neighborhood layers of the image for each pixel position, and then concatenating the feature vectors of each neighborhood layer to form the final HOLDP feature map. Identifying human faces in video is a challenging problem due to the presence of large variations in facial pose and expression, as well as poor video resolution. To address this, Volumetric Directional Pattern (VDP) is proposed [1]. VDP is an oriented volumetric descriptor that is able to extract and fuse the information of multiple frames, temporal (dynamic) information, and multiple poses and expressions of faces in input videos to produce strong feature vectors. Meanwhile, to demonstrate the generality and capability of the HOLDP method, we develop another novel video based feature extraction technique, namely High Order Volumetric Directional Pattern (HOVDP) as an extension of VDP. HOVDP combines the movement and appearance features together by considering the nth order directional variation patterns of all neighboring pixel layers from three consecutive frames. From extensive experiments on still image based and video based face recognition benchmarks, we demonstrate the excellent performance of our proposed techniques compared to the state-of-the-art approaches

High Order Volumetric Directional Pattern for Video-Based Face Recognition

Describing the dynamic textures has attracted growing attention in the field of computer vision and pattern recognition. In this paper, a novel approach for recognizing dynamic textures, namely, high order volumetric directional pattern (HOVDP), is proposed. It is an extension of the volumetric directional pattern (VDP) which extracts and fuses the temporal information (dynamic features) from three consecutive frames. HOVDP combines the movement and appearance features together considering the nth order volumetric directional variation patterns of all neighboring pixels from three consecutive frames. In experiments with two challenging video face databases, YouTube Celebrities and Honda/UCSD, HOVDP clearly outperformed a set of state-of-the-art approaches