Abstract Whenever a patient needs to enter the operating room, in case the surgery requires general anesthesia, he/she must be intubated, and an anesthesiologist has to make a previous check to the patient in order to evaluate his/her airway. This process should be done to the patient to anticipate any problem, such as a difficult airway at the time of being anesthetized. In fact, the inadequate detection of a difficult airway can cause serious complications, even death. This...
28m
Abstract In this work, we explore the problems of recommending and visualising makeup products based on images of customers. Focusing on mascara, we propose a two-stage approach that first recommends products to a new customer based on the preferences of other customers with similar visual appearance and then visualises how the recommended products might look on the customer. For the initial product recommendation, we train a Siamese convolutional neural network, using our own...
28m
Abstract Plant image analysis plays an important role in agriculture. It is used to record the morphological plant traits regularly and accurately. The plant growth is one of the key traits to be analyzed, which relies on leaf area (i.e., leaf region or plant region) and leaf count. One of the ways to find the leaf count is counting the leaves using segmented plant region. In this paper, a new plant region segmentation scheme is proposed in the orthogonal transform domain based...
28m
Abstract Accelerated by the proliferation of small, affordable, and lightweight electronically scanning radar systems as well as advances in Unmanned Aircraft System (UAS) technology, Geo-Registered Radar Returns data are becoming an incredible source for geolocalization in GPS-denied UAS navigation. Most existing approaches match aerial images to pre-stored Digital Elevation Models (DEMs) through 3D terrain reconstruction or GPU-based terrain rendering techniques. However, these...
2w
Abstract There is an increase in consumption of agricultural produce as a result of the rapidly growing human population, particularly in developing nations. This has triggered high-quality plant phenotyping research to help with the breeding of high-yielding plants that can adapt to our continuously changing climate. Novel, low-cost, fully automated plant phenotyping systems, capable of infield deployment, are required to help identify quantitative plant phenotypes. The identification...
Dec 2019
Abstract Many target tracking tasks require high spatial and temporal precision. High frame rate imaging at high spatial resolution is commonly used in these applications, but this approach is expensive and generates large amounts of data which can complicate implementation. When tracking a single object in motion, almost all of this information is unused. A technique has been developed to exploit this sparsity and track motion with a long exposure where absolute timing is encoded...
Dec 2019
Abstract Convolutional neural networks (CNNs) have proven very effective for learning features in visual tracking. While working effectively, it is still very challenging due to the scale variations and deformation, which may cause inconsecutive tracking trajectory and distraction. In this paper, pre-train deep learning network architecture is adopted for visual tracking, by introducing a spectral pooling in the network. Then, we propose an algorithm which, by interpreting scale...
Sep 2019
Abstract A digital image is a rich medium of information. The development of user-friendly image editing tools has given rise to the need for image forensics. The existing methods for the investigation of the authenticity of an image perform well on a limited set of images or certain datasets but do not generalize well across different datasets. The challenge of image forensics is to detect the traces of tampering which distorts the texture patterns. A method for image forensics...
Sep 2019
Abstract This paper investigates the reconstruction of Van Gogh’s drawings which have been degraded in the course of time due to aging problems, like ink fading and discoloration. Learning to predict the past and original appearances of degraded drawings can help to envisage how the artist’s work may have looked at the time of creation. In this paper, we use reproductions as reference information for the past appearances of drawings and consider the reconstruction of drawings...
Sep 2019
Abstract Geometrical registration of a query image with respect to a 3D model, or pose estimation, is the cornerstone of many computer vision applications. It is often based on the matching of local photometric descriptors invariant to limited viewpoint changes. However, when the query image has been acquired from a camera position not covered by the model images, pose estimation is often not accurate and sometimes even fails, precisely because of the limited invariance of descriptors....
Sep 2019
Abstract Most existing systems for calibrating multi-projector display suffered from several important limitations such as dependence on point of view, restriction on the display surface and moreover the number of projectors and using obtrusive markers. In this paper, a new method for view-independent calibration for multi-projector displays is presented. Given that the calibration problem of a multi-projector display is an optimization problem, we compute the calibration parameters...
Aug 2019
Abstract Conventional autonomous unmanned air vehicle (UAV) autopilot systems use global navigation satellite system (GNSS) signal for navigation. However, autopilot systems fail to navigate due to lost or jammed GNSS signal. To solve this problem, information from other sensors such as optical sensors are used. Monocular simultaneous localization and mapping (SLAM) algorithms have been developed over the last few years and achieved state-of-the-art accuracy (e.g., visual SLAM...
Aug 2019
Abstract Though deep neural networks have played a very important role in the field of vision-based hand gesture recognition, however, it is challenging to acquire large numbers of annotated samples to support its deep learning or training. Furthermore, in practical applications it often encounters some case with only one single sample for a new gesture class so that conventional recognition method cannot be qualified with a satisfactory classification performance. In this paper,...
Aug 2019
Abstract This paper presents the advantages of a single-camera stereo omnidirectional system (SOS) in estimating egomotion in real-world environments. The challenge of applying omnidirectional stereo vision via a single camera is what separates our work from others. In practice, dynamic environments, deficient illumination, and poor textured surfaces result in the lack of features to track in the observable scene. As a consequence, this negatively affects the pose estimation of...
Jul 2019
Abstract As one of the leading killers of females, breast cancer has become one of the heated research topics in the community of clinical medical science and computer science. In the clinic, mammography is a publicly accepted technique to detect early abnormalities such as masses and distortions in breast leading to cancer. Interpreting the images, however, is time-consuming and error-prone for radiologists considering artificial factors including potential fatigue. To improve...
Jul 2019
Abstract In this paper, an early prediction of vehicle trajectories and turning movements are investigated using traffic cameras. A vision-based tracking system is developed to monitor intersection videos and collect vehicle trajectories with their labels known as turning movements. Firstly, two intersection videos are monitored for 2 h, and collected trajectories with their labels are used to train deep neural networks and obtain the turning models for the prediction task. Deep...
Jun 2019
Abstract This paper presents a new vision-based method for real-time assessment of upper-body postures of a subject who is sitting in front of a desk studying or operating a computer. Unlike most existing vision-based methods that perform offline assessment from human skeletons extracted from RGB video or depth maps, the proposed method analyses directly single images captured by a webcam in front of the subject without the prone-to-error process of extracting the skeleton data...
Jun 2019
Abstract Despite a big volume of research on action recognition, little attention has been given to individual action recognition in poor-quality spectator crowd scenes. It is an important scenario, because most of the surveillance systems generate poor-quality videos, though current state-of-the-art methods may not be effectively applicable. Therefore recognizing actions performed by individuals in poor-quality spectator crowd scenes is an unsolved problem. In such cases, the...
Jun 2019
Abstract Robust wide baseline pose estimation is an essential step in the deployment of smart camera networks. In this work, we highlight some current limitations of conventional strategies for relative pose estimation in difficult urban scenes. Then, we propose a solution which relies on an adaptive search of corresponding interest points in synchronized video streams which allows us to converge robustly toward a high-quality solution. The core idea of our algorithm is to build...
Jun 2019
Abstract Hand detection is an essential step to support many tasks including HCI applications. However, detecting various hands robustly under conditions of cluttered backgrounds, motion blur or changing light is still a challenging problem. Recently, object detection methods using CNN models have significantly improved the accuracy of hand detection yet at a high computational expense. In this paper, we propose a light CNN network, which uses a modified MobileNet as the feature...
Jun 2019
Abstract In this article, the authors propose a concise corner detection algorithm, which is called CCDA. A cascade classifier concept is used to derive a corner detector, which can quickly discard the most non-corner pixels. The ruler of gradient direction is used to get the corner, which can avoid the influence of the light change. The method of second derivative non-maximum suppression is used to get the location of the corner and can get the exact corner point. As a result,...
Jun 2019
Abstract Localization is among the most important prerequisites for autonomous navigation. Vision-based systems have got great attention in recent years due to numerous camera advantages over other sensors. Reducing the computational burden of such systems is an active research area making them applicable to resource-constrained systems. This paper aims to propose and compare a fast monocular approach, named ARM-VO, with two state-of-the-art algorithms, LibViso2 and ORB-SLAM2,...
May 2019
Unfortunately, Fig. 9 was incorrectly published in the online version. Correct figure is updated here.
May 2019
Abstract We propose a camera model for cameras with hypercentric lenses. Because of their geometry, hypercentric lenses allow to image the top and the sides of an object simultaneously. This makes them useful for certain inspections tasks, for which otherwise multiple images would have to be acquired and stitched together. After describing the projection geometry of hypercentric lenses, we derive a camera model for hypercentric lenses that is intuitive for the user. Furthermore,...
May 2019
Abstract Cerebral microbleeds (CMBs) are small perivascular hemosiderin deposits leaked from cerebral small vessels in normal (or near normal) tissue. It is important to detect CMBs accurately and reliably for diagnosing and researching some cerebrovascular diseases and cognitive dysfunctions. In the last decade, several approaches based on traditional machine learning and classical convolutional neural network (CNN) were developed for detecting CMBs semi-automatically and automatically....
Apr 2019
Abstract Large scenes such as building facades and other architectural constructions often contain repeating elements such as identical windows and brick patterns. In this paper, we present a novel approach that improves the resolution and geometry of 3D meshes of large scenes with such repeating elements. By leveraging structure from motion reconstruction and an off-the-shelf depth sensor, our approach captures a small sample of the scene in high resolution and automatically extends...
Apr 2019
Abstract We present a novel method for enhancing texture irregularities, both lesions and microcalcifications, in digital X-ray mammograms. It can be implemented in computer-aided diagnostic systems to help improve radiologists’ diagnosis precision. The method provides three different outputs aimed at enhancing three different sizes of mammogram abnormalities. Our approach uses a two-dimensional adaptive causal autoregressive texture model to represent local texture characteristics....
Apr 2019
Abstract Eye tracking (ET) for gaze interaction in wearable computing imposes harder constraints on computational efficiency and illumination conditions than remote ET. In this paper we present xSDL, an extended temporal support computer vision algorithm for accurate, robust, and efficient pupil detection and gaze estimation. The robustness and efficiency of xSDL partly come from the use of stroboscopic differential lighting (SDL), an extension of the differential lighting pupil...
Apr 2019
Abstract This paper presents a novel local surface descriptor for 3D object recognition in the presence of clutter and occlusion. For a keypoint and its local neighbor points, a unique and repeatable local reference frame is first constructed. Then the depth information of those local points is encoded into a vector using multi-view method. Finally we obtain a compact feature with length of 27. Besides its compactness in length, the generated feature is tested to be not only descriptive...
Apr 2019
Abstract Based on pairs of spatial symmetric patches, a novel efficient and distinctive binary descriptor is proposed in this paper for rotated circular image recognition. To achieve rotation invariance during feature computation, a local coordinate system is first found with radial transform technology. On the basis of that, local binary patterns against rotation can be extracted. Meanwhile, the circular image is divided into a set of overlapped annular regions, and pairs of patch...
Apr 2019
Abstract Graticule intersections in topographic maps are usually considered to be suitable candidates for reference points in geometric calibration because the corresponding geographical information can be directly retrieved from the maps or derived from sheet numbers. Previous research on automatic corner point detection relies on the assumption that scanned maps are not rotated, which is rarely practical. To address this issue, a semantic segmentation approach for accurate graticule...
Mar 2019
Abstract We address the problem of calibrating an embedded depth camera network designed for people tracking purposes. In our system, the nodes of the network are responsible for detecting the people moving in their view, and sending the observations to a centralized server for data fusion and tracking. We employ a plan-view approach where the depth camera views are transformed to top-view height maps where people are observed. As the server transforms the observations to a global...
Mar 2019
Abstract We propose a new topology-aware point set registration algorithm which can cope with multi-part articulated and non-rigid deformations. Point set registration is formulated as a maximum likelihood (ML) estimation problem where two topologically complementary constraints are jointly optimized in a probabilistic framework. The first is coherent point drift that keeps the overall spatial connectivity and associativity by moving the point set collectively and coherently. The...
Mar 2019
Small object segmentation with fully convolutional network based on overlapping domain decomposition
Abstract We propose a new segmentation algorithm based on deep learning. To segment ice hockey players, a fully convolutional network (FCN) is adopted and fine-tuned with our augmented training data. The original FCN has difficulty segmenting small-size objects. To solve this problem, our method divides an input image into four overlapping sub-images and each image is fed into the deep learning network. After obtaining segmentation results from all sub-images, we combine them into...
Mar 2019
Abstract It is easy to show that in computer vision, there is the closely coupled relation between feature matching and fundamental matrix estimation. The widely used robust methods such as RANSAC and its improved versions separately deal with feature matching and fundamental matrix estimation. Although these methods are simple to implement, their performance may be relatively low in the presence of gross outliers. By exploiting such coupled relation, the soft decision optimization...
Mar 2019
Abstract In this paper, an effective method is proposed for breast mass segmentation using a superpixel generation and curve evolution method. The simple linear iterative clustering method and density-based spatial clustering of applications with noise method are applied to generate superpixels in mammograms at first. Thereafter, a region of interesting (ROI) that contains the breast mass is built on the superpixel generation results. Finally, the image patch and the position of...
Mar 2019
Abstract Omnidirectional cameras cover more ground than perspective cameras, at the expense of resolution. Their comprehensive field of view makes omnidirectional cameras appealing for security and ambient intelligence applications. Person detection is usually a core part of such applications. Conventional methods fail for omnidirectional images due to different image geometry and formation. In this study, we propose a method for person detection in omnidirectional images, which...
Mar 2019
In the affiliation of the first author, Shandong University was omitted by mistake.
Mar 2019
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ΑπάντησηΔιαγραφήAutomated Vision Inspection Machines