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In this work, we present an appearance based human activity recognition system. It uses background modeling to segment the foreground object and extracts useful discriminative features for representing activities performed by humans and robots. Subspace based method like principal component analysis is used to extract low dimensional features from large voluminous activity images. These low dimensional...
In this paper we present a new 3D line alignment technique that can be used in limited resourced MAVs for performing loop closures as well as mutual localisation between MAVs. We identify pairs of 3D line matches from RGB-D frames and find the optimal transformation which aligns these two line sets onto each other in order to find the corresponding relative pose. The alignment of the two 3D line sets...
In object recognition techniques, specially feature-based methods, a fundamental step is to extract keypoints which are distinct and considerably interesting in the image. There are many different keypoint detectors already available, each with its own specific use and results vary enormously. It is widely agreed that evaluation of feature detectors is important. To our knowledge there is no comparative...
Most unsupervised learners such as Kmeans, Gaussian mixture model (GMM) or sparse coding do not use the structure information found from the neighbourhood of an image patch for parameter estimation. A recent image prior technique combine unsupervised learning with Markov Random Field (MRF) by replacing the MRF potential with an unsupervised learner. It uses a neighbourhood of image patches for learning...
In this paper, a panoramic image stitching algorithm is presented. The aim of image stitching is to detect several images of the same scene and merge them to create a larger image. This is achieved by first detecting the overlapping area of the acquired images, and then aligning and blending the seams of the images automatically to create a seamless panoramic image. The experimental testing into the...
In this paper, we introduce the application of generic multi-level Convolutional Neural Networks (CNN) approach into the scene understanding or image parsing task. Given an input image, first, a set of similar images from the training set are retrieved based on global-level CNN feature matching similarities. Then, the input test image and the similar images are overseg-mented into superpixels. Next,...
Body part segmentation and detection in videos is a useful analysis for many computer vision tasks such as action recognition and video search. Conventional methods mainly focus on body part detection assuming upright posture of the human body. Recently, a body part detection framework was proposed to include non-upright postures. This method consists of 2 parts, initial segmentation and computation...
We describe a technique for object detection that uses a combination of global shape descriptors and local point descriptors. Our system is able to represent pose using a global shape descriptor, rather than the commonly used part based representation. This approach considerably reduces computational complexity and achieves a significant performance improvement on an extensive dataset: CUB-200-2011...
This paper presents a Wavelet Transform (WT) based Artificial Neural Network (ANN) input data pre-processing scheme and outlines the results of localized rotor angular speed defect of a wind turbine system by employing this proposed methodology. The methodology consists of calculating Daubechies 4-order (DAUB-4) dilation WTs with the Multi-Resolution Analysis (MRA) of the data, and then extracting...
Ultrasonic inspection technique has been widely used in defect detection of carbon fiber reinforced polymer (CFRP) materials. Although a variety of signal processing methods have been applied to highlight the defect features contained in ultrasonic signals, the most usual way to identify the defective regions is still not automatic, which is not only time-consuming but also critically dependent upon...
Towards achieving lane-level localization, precision and accuracy plays an important role in vehicle localization efficiency. While Global Positioning System (GPS) is usually used for localization, it has low accuracy caused by signal degradation due to several reasons such as lack of well-positioned satellites, signal obstruction or multipath error. Thus, multi-sensor data fusion has been widely...
Autonomous UAV must rely on the navigation system to fly in the indoor environment. However, the traditional navigation system such as GPS cannot be used in the indoor environment. A method of Simultaneous Location and Mapping (SLAM) based on RGB-D cameras is used to solve this matter. We adopt the RGB-D SLAM algorithm to locate the camera and build the 3D map of the environment in this paper. The...
In this work, an autonomous surveillance system providing ultra wide angle (UWA) video information is presented using multi-UAV platform. Towards this end, a fleet of UAVs are initially deployed into appropriate formation (such as triangle, diamond, or line) with cameras pointing outwards to cover the region of interest (ROI). In aid of image processing technique, the UAVs formation will be further...
In this paper, we propose a discriminative and robust appearance model based on features extracted from a random partition image hashing algorithm to account for severe occlusion and disappearance. We divide the original image into multiple sub-blocks with random positions and scales. Hash functions are used to map blocks into compact binary codes, with which more effective target matching can be...
This paper is made up of a series of performance evaluations of computer vision algorithms, namely detectors and descriptors. The OpenCV 3.1 implementations of these algorithms were used for these evaluations. The main purpose behind these evaluations was to determine the best algorithms to use for a UAV guidance system.
In this paper, we propose an algorithm called Hessian ORB — Overlapped FREAK (HOOFR) to attack the feature matching problem in image processing. Our algorithm is based on the combination of the ORB detector and the FREAK bio-inspired descriptor. We address some modifications related to the detection and the description processes in order to enhance HOOFR reliability, speed and memory fingerprint....
This paper presents a vision based sign language gesture recognition framework that can assist people with impaired hearing and speech with their social interaction and interactive communications. Utilizing a low-cost sensor, such as Microsoft Kinect combined with advanced machine learning analysis, it aims to ease the challenging issue of increasing demand for professional sign language interpreting...
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