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Automated recognition of spacecraft and space debris using imaging plays an important role in securing space safety and space exploration. Although deep learning is now the most successful solution for image-based object classification, it requires a myriad number of training data, which are not available for most real applications. In this paper, we investigate different single and hybrid data augmentation...
In order to reduce the effects caused by complex environments and ambient light conditions, a fast, robust and effective obstacles detection method of vehicles based on image analysis of multi-feature is proposed. Firstly, regions of interest (ROI) which contain lanes, vehicles and few parts of interference background are extracted in the input image by detecting gradient feature in rows. Secondly,...
Multiple view data with different feature representations have widely arisen in various practical applications. Due to the information diversity, fusing multiview features is very valuable for classification purpose. In this paper, we propose a new multifeature fusion method called fractional-order discriminative multiview correlation projection (FDMCP), which is based on fractional-order scatter...
Automotive technology has been recently challenged with the issue of ensuring and improving road safety. Several academic institutions and automobile manufacturers are making efforts to develop technology for automotive safety. This study proposes an object recognition system based on the adaptive boosting algorithm that integrates a laser range finder and a camera. The laser range finder is used...
Feature fusion plays an important role in target recognition, especially when single sensor's recognition capability is limited under severe situations. In view of shortcomings of Multi-set Canonical Correlation Analysis (MCCA) and its supervised modified methods in using category information in fusion projection rule learning, a generalized discriminative learning version of MCCA, termed as GDMCCA,...
In multiple-target tracking problem, data association technique plays an significant role. When targets move closely or crosswise, performances of conventional data association algorithms which use kinematic information only may be degraded. Actually, beside the kinematic information, sensors always can obtain feature information about the target, and incorporating the features into data association...
Multispectral face recognition systems are widely used in various access control applications. The vulnerability of multispectral face recognition sensors towards low-cost Presentation Attack Instrument (PAI) such as printed photos used in attacks has emerged as a serious security threat. In this paper, we present a novel framework to detect presentation attacks against an extended multispectral face...
With the availability of sensor technology across the broad electromagnetic spectrum, multi-spectral imaging is increasingly used in biometric systems. Especially for face recognition, multi-spectral imaging has gained a lot of attention due to it's invariant property against variation caused by unknown illumination. However, obtaining best performance using multi-spectral imaging is still a challenge...
A new change detection method for heterogeneous remote sensing images (i.e. SAR & optics) has been proposed via pixel transformation. It is difficult to directly compare the pixels from heterogeneous images for detecting changes. We propose to transfer the pixels in different images to a common feature space for convenience of comparison. For each pixel in the 1st image, it will be transferred...
An autonomous navigation scheme for unmanned aerial vehicles is presented based on visual and inertial measurement information fusion without the known ground cooperative target. The UAV relative translation and rotation motion parameters are estimated by inter-frame image feature detection and tracking. Then the relative motion parameters are considered to be the relative pose measurements of two...
Atrial Fibrillation (AF) is the most common chronic arrhythmia. Effective detection of the AF would avoid serious consequences like stroke. Conventional AF detection methods need heuristic or hand-craft feature extraction. In this paper, A deep neural network named multi-scale convolutional neural networks (MCNN) based AF detector is proposed. Instant heart rate sequence is extracted from ECG signal,...
It is critical to classify the landing terrain from aerial images when an unmanned aerial vehicle lands at an unprepared site autonomously by using a vision sensor. Owing to the interference of illumination variations and noises, different terrains may show a similar image feature and the same terrain may have a different image feature, which brings great difficulties to image classification. To address...
The ball state tracking and detection technology plays a significant role in volleyball game analysis for volleyball team supporting and tactics development. This paper proposes a ball event detection method to achieve high detection rate by solving challenges including: the great variety of event length, the large intra-class difference of one event and the influence caused by ball trajectories....
Periocular characteristics has gained substantial importance in recent times to supplement the performance of facial biometrics or as a stand-alone characteristics. While most of the current biometric systems for authentication or surveillance operate either in NIR spectrum or visible spectrum, the ocular information can be well utilized if a comparison of images from different spectra has to be conducted...
Advanced driver assistance systems rely on the availability of robust information on the driving situation and the driver's needs and intentions to operate reasonably and safely. For this, they have to be enabled to identify and assess both the driving situation and the driver's intentions on the basis of features that can be measured by the vehicle. In case of the prediction of lane change maneuvers...
With the exponential growth of web meta-data, exploiting multimodal online sources via standard search engine has become a trend in visual recognition as it effectively alleviates the shortage of training data. However, the web meta-data such as text data is usually not as cooperative as expected due to its unstructured nature. To address this problem, this paper investigates the numerical representation...
A multiple hypothesis tracking (MHT) algorithm based on multi-feature fusion is presented in this paper to counter range deception jammings. Sparse decomposition coefficients and bispectrum features are extracted to distinguish the targets and the jammings. A two-stage fusion structure using neural network and Dempster-Shafer evidence theory is designed to implement multi-feature fusion so as to get...
Several models based on deep neural networks have applied to single image super-resolution and obtained great improvements in terms of both reconstruction accuracy and computational performance. All these methods focus either on performing the super-resolution (SR) reconstruction operation in the high resolution (HR) space after upscaling with a single filter, usually bicubic interpolation, or optimizing...
Detection of surface water in natural environment via multi-spectral imagery has been widely utilized in many fields, such land cover identification. However, due to the similarity of the spectra of water bodies, built-up areas, approaches based on high-resolution satellites sometimes confuse these features. A popular direction to detect water is spectral index, often requiring the ground truth to...
In this paper, a new face detection method is proposed based on skin color and an image feature called Locally Adaptive Regression Kernels (LARK). A novel preprocessing is applied in this method, which includes skin segmentation and the estimation of the scale and rotation. To segment the skins from the background, a compound color space called H-CgCr is proposed based on both HSV and YCgCr color...
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