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Accurate estimation of detection/classification performance for sidescan sonar systems in Mine Counter-Measure (MCM) applications is important for informing mission tactics and adapting autonomous behaviors. The approach presented in this paper assumes that detection/classification performance can be estimated solely from historical data collected from similar surveys. This paper introduces an algorithm...
Face detection is already incorporated in many biometrics and surveillance applications. Therefore, the reduction of false detections is a priority in those systems. However, face detection is still challenging. Many factors, such as pose variation and complex backgrounds, contribute to false detections. Besides, the fidelity of a true detection, measured by precision rate, is a concern in content-based...
Flexible printed circuit board (FPC) is a popular substrate for packaging integrated circuits (ICs). Detecting the circles rapidly on FPCs by using computer vision is very important to assess the quality of FPCs during its manufacturing. In this paper, a fast circle detection approach based on a threshold segmentation method and a validation check is proposed. In the algorithm, the image is firstly...
Owing to the elevated intra/inter variation among the foreground and background text of various document images, the text segmentation from the poorly degraded document images is the difficult job. This paper presents the document image binarization method by adaptive image contrast which is the integration of the local image gradient and the local image contrast which is lenient to background and...
Instead of using HOG feature on cells or blocks, the extraction of HOG features on corner points is proposed for multiple object visual tracking system in which single or multiple moving objects could be classified. Background subtraction and extraction of corner feature are applied to track and classify the moving objects. Firstly, moving objects will be detected in the form of regions from background...
This paper is concerned with biometrie spoofing detection using the dynamics of natural facial movements as a feature. Facial muscle movement information can be extracted from video sequences and encoded using the Facial Action Coding System (FACS). The proposed feature constructs a Facial Action Units Histogram (FAUH) to encapsulate this information for the detection of biometric presentation attacks...
This paper addresses the problem of pedestrian detection in high-density crowd images, characterized by strong homogeneity and clutter. We propose an evidential fusion algorithm which is able to exploit multiple detectors based on different gradient, texture and orientation descriptors. The evidential framework allows us to model the spatial imprecision arising from each of the detectors. A first...
People with disabilities who cannot move their whole body need other people to control the smart wheelchair or track the moving of object interest, in this case people. In this paper, we have proposed new movement controller of smart wheelchair using object tracking for disabled people who cannot move their whole body. The proposed method for determining direction of moving object using object tracking...
One important function in assistive robotics for home applications is the detection of emergency cases, like falls. In this paper, we present a new detection system which can run on a mobile robot to detect persons after a fall event robustly. The system is based on 3D Normal Distributions Transform (NDT) maps on which a powerful segmentation is applied. Segments most likely belonging to a person...
Convolutional neural network (CNN) based face detectors are inefficient in handling faces of diverse scales. They rely on either fitting a large single model to faces across a large scale range or multi-scale testing. Both are computationally expensive. We propose Scale-aware Face Detection (SAFD) to handle scale explicitly using CNN, and achieve better performance with less computation cost. Prior...
Vision-based traffic light detection has been widely studied over the past decade. However, it is still a challenging task to build a real-time and robust classifier-based detector without a high dependency on prior knowledge. In this paper, we have a deep look at the design of features and detection mechanism in the domain of traffic light detection; propose a multi-scale and multi-phase detector...
In this work, we consider the problem of detecting target objects in remote sensing imagery; such as detecting rooftops, trees, or cars in color/hyperspectral imagery. Many detection algorithms for this problem work by assigning a decision statistic (or “confidence”) to all, or a subset, of spatial locations in the data. A threshold is then applied to the statistics to identify detections. The detection...
In today world the necessity for the autonomous mobile robots and vehicles is increasing. The safety autonomous moving demands the reliable and fast detection algorithms. The Histogram of Oriented Gradients (HOG) descriptors show significantly outperforms the existing feature sets for a human detection. Though the given method has a lot of type I errors. The amount of these errors can be decreased...
In this paper, we present a drone dual-frequency technique in order to detect thick oil slicks in ocean. From physical point of view, the problem is considered to be a multi-layers wave surface scattering model where we study the reflection of the electromagnetic (EM) waves from the sea layer covered by oil layer. The electrical properties and the physical characteristics are defined for the layers...
Multi-object tracking is often hindered by difficulties such as occlusion and illumination change. In this paper, we propose a novel multi-object tracking method based on main-parts model. Main-parts model is formulated by segmenting parts of object and accumulating variations of appearance from previous frames. We assume that parts with weaker appearance variations are main-parts of an object. By...
Examines the problem of allocation of small-scale extended objects in noisy images. The processing structure includes a pre-filter and multithreshold selection of isolated fragments of the specified size. For the setting of adaptive thresholds is used to assess the effectiveness of selection fragments.
This paper presents a novel appearance and shape feature, RISAS, which is robust to viewpoint, illumination, scale and rotation variations. RISAS consists of a keypoint detector and a feature descriptor both of which utilise texture and geometric information present in the appearance and shape channels. A novel response function based on the surface normals is used in combination with the Harris corner...
The proof of human parts has an imperative effect on pose evaluation, and can be effortlessly confused with difficult background due to indefinite part detector. This paper circumvents this predicament by performing a proof supporting approach, where each part also receives confidence from its neighborhood which uses the outline information between connect parts and mitigates the risk of being blindly...
The paper introduces an OFDM signal detector based on cyclic correlation function. The detector belongs to the class of blind SCD detectors. The paper presents the main relations connected with the idea of cyclostationarity and cyclostationary features of OFDM signals. Results of simulations are presented as well.
This paper proposes a novel inherently rotation invariant local descriptor which combined intensity information and gradient information of key feature. The CS-LBP shows a better performance than SIFT and do not need large computation. To further enhance its performance and robustness, we calculated the gradient of key feature and computed a combined histogram included intensity and gradient information...
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