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Computer vision

Laura Ruotsalainen, Martti Kirkko-Jaakkola, Jesperi Rantanen, Maija Mäkelä
The long-term objective of our research is to develop a method for infrastructure-free simultaneous localization and mapping (SLAM) and context recognition for tactical situational awareness. Localization will be realized by propagating motion measurements obtained using a monocular camera, a foot-mounted Inertial Measurement Unit (IMU), sonar, and a barometer. Due to the size and weight requirements set by tactical applications, Micro-Electro-Mechanical (MEMS) sensors will be used. However, MEMS sensors suffer from biases and drift errors that may substantially decrease the position accuracy...
February 14, 2018: Sensors
Roslyn Dakin, Paolo S Segre, Andrew D Straw, Douglas L Altshuler
How does agility evolve? This question is challenging because natural movement has many degrees of freedom and can be influenced by multiple traits. We used computer vision to record thousands of translations, rotations, and turns from more than 200 hummingbirds from 25 species, revealing that distinct performance metrics are correlated and that species diverge in their maneuvering style. Our analysis demonstrates that the enhanced maneuverability of larger species is explained by their proportionately greater muscle capacity and lower wing loading...
February 9, 2018: Science
Amirhossein Aghamohammadi, Mei Choo Ang, Elankovan A Sundararajan, Ng Kok Weng, Marzieh Mogharrebi, Seyed Yashar Banihashem
Visual tracking in aerial videos is a challenging task in computer vision and remote sensing technologies due to appearance variation difficulties. Appearance variations are caused by camera and target motion, low resolution noisy images, scale changes, and pose variations. Various approaches have been proposed to deal with appearance variation difficulties in aerial videos, and amongst these methods, the spatiotemporal saliency detection approach reported promising results in the context of moving target detection...
2018: PloS One
Lijun He, Xiaoya Qiao, Shuai Wen, Fan Li
Object tracking is an important research direction in computer vision and is widely used in video surveillance, security monitoring, video analysis and other fields. Conventional tracking algorithms perform poorly in specific scenes, such as a target with fast motion and occlusion. The candidate samples may lose the true target due to its fast motion. Moreover, the appearance of the target may change with movement. In this paper, we propose an object tracking algorithm based on motion consistency. In the state transition model, candidate samples are obtained by the target state, which is predicted according to the temporal correlation...
February 13, 2018: Sensors
Junxing Shi, Haiguang Wen, Yizhen Zhang, Kuan Han, Zhongming Liu
The human visual cortex extracts both spatial and temporal visual features to support perception and guide behavior. Deep convolutional neural networks (CNNs) provide a computational framework to model cortical representation and organization for spatial visual processing, but unable to explain how the brain processes temporal information. To overcome this limitation, we extended a CNN by adding recurrent connections to different layers of the CNN to allow spatial representations to be remembered and accumulated over time...
February 12, 2018: Human Brain Mapping
Javier Ortells, María Trinidad Herrero-Ezquerro, Ramón A Mollineda
Gait is a firsthand reflection of health condition. This belief has inspired recent research efforts to automate the analysis of pathological gait, in order to assist physicians in decision-making. However, most of these efforts rely on gait descriptions which are difficult to understand by humans, or on sensing technologies hardly available in ambulatory services. This paper proposes a number of semantic and normalized gait features computed from a single video acquired by a low-cost sensor. Far from being conventional spatio-temporal descriptors, features are aimed at quantifying gait impairment, such as gait asymmetry from several perspectives or falling risk...
February 12, 2018: Medical & Biological Engineering & Computing
Matias I Maturana, Nicholas V Apollo, David J Garrett, Tatiana Kameneva, Shaun L Cloherty, David B Grayden, Anthony N Burkitt, Michael R Ibbotson, Hamish Meffin
Implantable retinal stimulators activate surviving neurons to restore a sense of vision in people who have lost their photoreceptors through degenerative diseases. Complex spatial and temporal interactions occur in the retina during multi-electrode stimulation. Due to these complexities, most existing implants activate only a few electrodes at a time, limiting the repertoire of available stimulation patterns. Measuring the spatiotemporal interactions between electrodes and retinal cells, and incorporating them into a model may lead to improved stimulation algorithms that exploit the interactions...
February 12, 2018: PLoS Computational Biology
Yigong Zhang, Yingna Su, Jian Yang, Jean Ponce, Hui Kong
In this paper, we propose a vanishing-point constrained Dijkstra road model for road detection in a stereo-vision paradigm. First, the stereo-camera is used to generate the u- and v-disparity maps of road image, from which the horizon can be extracted. With the horizon and ground region constraints, we can robustly locate the vanishing point of road region. Second, a weighted graph is constructed using all pixels of the image, and the detected vanishing point is treated as the source node of the graph. By computing a vanishing-point constrained Dijkstra minimum-cost map, where both disparity and gradient of gray image are used to calculate cost between two neighbor pixels, the problem of detecting road borders in image is transformed into that of finding two shortest paths that originate from the vanishing point to two pixels in the last row of image...
May 2018: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
Hyunwoo J Kim, Nagesh Adluru, Heemanshu Suri, Baba C Vemuri, Sterling C Johnson, Vikas Singh
Statistical machine learning models that operate on manifold-valued data are being extensively studied in vision, motivated by applications in activity recognition, feature tracking and medical imaging. While non-parametric methods have been relatively well studied in the literature, efficient formulations for parametric models (which may offer benefits in small sample size regimes) have only emerged recently. So far, manifold-valued regression models (such as geodesic regression) are restricted to the analysis of cross-sectional data, i...
July 2017: Proceedings
Vivek Nityananda, Ghaith Tarawneh, Sid Henriksen, Diana Umeton, Adam Simmons, Jenny C A Read
Stereopsis is the ability to estimate distance based on the different views seen in the two eyes [1-5]. It is an important model perceptual system in neuroscience and a major area of machine vision. Mammalian, avian, and almost all machine stereo algorithms look for similarities between the luminance-defined images in the two eyes, using a series of computations to produce a map showing how depth varies across the scene [3, 4, 6-14]. Stereopsis has also evolved in at least one invertebrate, the praying mantis [15-17]...
February 2, 2018: Current Biology: CB
Charalambos Strouthopoulos, George Anifandis
PURPOSE: Evaluation of human embryos is one of the most important challenges in vitro fertilization (IVF) programs. The morphology and the morphokinetic parameters of the early cleaving embryo are of critical clinical importance. This stage spans the first 48 h post-fertilization, in which the embryo is dividing in smaller blastomeres at specific time-points. The morphology, in combination with the symmetry of the blastomeres seems to be powerful features with strong prognostic value for embryo evaluation...
March 2018: Computer Methods and Programs in Biomedicine
G Zaharchuk, E Gong, M Wintermark, D Rubin, C P Langlotz
Deep learning is a form of machine learning using a convolutional neural network architecture that shows tremendous promise for imaging applications. It is increasingly being adapted from its original demonstration in computer vision applications to medical imaging. Because of the high volume and wealth of multimodal imaging information acquired in typical studies, neuroradiology is poised to be an early adopter of deep learning. Compelling deep learning research applications have been demonstrated, and their use is likely to grow rapidly...
February 1, 2018: AJNR. American Journal of Neuroradiology
Won Hwa Kim, Mona Jalal, Seongjae Hwang, Sterling C Johnson, Vikas Singh
The adoption of "human-in-the-loop" paradigms in computer vision and machine learning is leading to various applications where the actual data acquisition (e.g., human supervision) and the underlying inference algorithms are closely interwined. While classical work in active learning provides effective solutions when the learning module involves classification and regression tasks, many practical issues such as partially observed measurements, financial constraints and even additional distributional or structural aspects of the data typically fall outside the scope of this treatment...
July 2017: Proceedings
Vamsi K Ithapu, Risi Kondor, Sterling C Johnson, Vikas Singh
Multiresolution analysis and matrix factorization are foundational tools in computer vision. In this work, we study the interface between these two distinct topics and obtain techniques to uncover hierarchical block structure in symmetric matrices - an important aspect in the success of many vision problems. Our new algorithm, the incremental multiresolution matrix factorization, uncovers such structure one feature at a time, and hence scales well to large matrices. We describe how this multiscale analysis goes much farther than what a direct "global" factorization of the data can identify...
July 2017: Proceedings
Pawan Sinha
An interview with Pawan Sinha, a computational neuroscientist interested in vision, particularly visual object discovery, something he has been investigating via 'Project Prakash' on children in rural India born with treatable blindness.
May 8, 2017: Current Biology: CB
Beatriz Antona, Ana Rosa Barrio, Adriana Gascó, Ana Pinar, Mariano González-Pérez, María C Puell
Asthenopia symptoms were investigated in visually-normal subjects without computer-related vision symptoms after prolonged reading from: smartphone versus hardcopy under photopic conditions, and smartphone in conditions of ambient versus dark room illumination. After reading from the smartphone, total symptom scores and nine out of ten questionnaire symptoms were significantly worse than for the hardcopy ("blurred vision while viewing the text, "blurred distance vision after the task", "difficulty in refocusing from one distance to another", "irritated or burning eyes", "dry eyes", "eyestrain", "tired eyes", "sensitivity to bright lights" and "eye discomfort")...
April 2018: Applied Ergonomics
Ariel Botwin, Adam Engel, Christopher Wasyliw
Retinal detachment is an ophthalmologic emergency that requires immediate medical attention as it can potentially lead to permanent vision loss. The gold standard for diagnosing retinal detachment is dilated funduscopic exam. However, when this exam is not feasible such as in an emergency room setting or if visualization of the posterior portion of the eye is not possible due to vitreous hemorrhage or dense cataracts, ocular ultrasound provides a readily available and effective alternative. We present the sonographic appearance of chronic retinal detachment in a 24-year-old female with a longstanding history of poorly controlled type 1 diabetes who could not undergo dilated funduscopic exam due to intra-ocular hemorrhage...
February 5, 2018: Emergency Radiology
Ying Xuan Zhi, Michelle Lukasik, Michael H Li, Elham Dolatabadi, Rosalie H Wang, Babak Taati
Robotic stroke rehabilitation therapy can greatly increase the efficiency of therapy delivery. However, when left unsupervised, users often compensate for limitations in affected muscles and joints by recruiting unaffected muscles and joints, leading to undesirable rehabilitation outcomes. This paper aims to develop a computer vision system that augments robotic stroke rehabilitation therapy by automatically detecting such compensatory motions. Nine stroke survivors and ten healthy adults participated in this study...
2018: IEEE Journal of Translational Engineering in Health and Medicine
Jae-Sang Hyun, George T-C Chiu, Song Zhang
This paper presents a method to achieve high-speed and high-accuracy 3D surface measurement using a custom-designed mechanical projector and two high-speed cameras. We developed a computational framework that can achieve absolute shape measurement in sub-pixel accuracy through: 1) capturing precisely phase-shifted fringe patterns by synchronizing the cameras with the projector; 2) generating a rough disparity map between two cameras by employing a standard stereo-vision method using texture images with encoded statistical patterns; and 3) utilizing the wrapped phase as a constraint to refine the disparity map...
January 22, 2018: Optics Express
Chengfei Guo, Jietao Liu, Tengfei Wu, Lei Zhu, Xiaopeng Shao
Tracking moving targets behind a scattering medium is a challenge, and it has many important applications in various fields. Owing to the multiple scattering, instead of the object image, only a random speckle pattern can be received on the camera when light is passing through highly scattering layers. Significantly, an important feature of a speckle pattern has been found, and it showed the target information can be derived from the speckle correlation. In this work, inspired by the notions used in computer vision and deformation detection, by specific simulations and experiments, we demonstrate a simple object tracking method, in which by using the speckle correlation, the movement of a hidden object can be tracked in the lateral direction and axial direction...
February 1, 2018: Applied Optics
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