In the real world, a realistic setting for computer vision or multimedia recognition problems is that we have some classes containing lots of training data and many classes contain a small amount of training data. Therefore, how to use frequent classes to help learning rare classes for which it is harder to collect the training data is an open question. Learning with Shared Information is an emerging topic in machine learning, computer vision and multimedia analysis. There are different level of components that can be shared during concept modeling and machine learning stages, such as sharing generic object parts, sharing attributes, sharing transformations, sharing regularization parameters and sharing training examples, etc. Regarding the specific methods, multi-task learning, transfer learning and deep learning can be seen as using different strategies to share information. These learning with shared information methods are very effective in solving real-world large-scale problems. This special issue aims at gathering the recent advances in learning with shared information methods and their applications in computer vision and multimedia analysis. Both state-of-the-art works, as well as literature reviews, are welcome for submission. Papers addressing interesting real-world computer vision and multimedia applications are especially encouraged. Topics of interest include, but are not limited to:  • Multi-task learning or transfer learning for large-scale computer vision and multimedia analysis • Deep learning for large-scale computer vision and multimedia analysis • Multi-modal approach for large-scale computer vision and multimedia analysis • Different sharing strategies, e.g., sharing generic object parts, sharing attributes, sharing transformations, sharing regularization parameters and sharing training examples, • Real-world computer vision and multimedia applications based on learning with shared information, e.g., event detection, object recognition, object detection, action recognition, human head pose estimation, object tracking, location-based services, semantic indexing. • New datasets and metrics to evaluate the benefit of the proposed sharing ability for the specific computer vision or multimedia problem. • Survey papers regarding the topic of learning with shared information.  Authors who are unsure whether their planned submission is in scope may contact the guest editors prior to the submission deadline with an abstract, in order to receive feedback.

IEEE transactions on pattern analysis and machine intelligence

Course Description This is an advanced course in which we explore the field of Statistical Optics. Topics covered include such subjects as the statistical properties of natural (thermal) and laser light, spatial and temporal coherence, effects of partial coherence on optical imaging instruments, effects on imaging due to randomly inhomogeneous media, and a statistical treatment of the detection of light. Development of this more comprehensive model of the behavior of light draws upon the use of tools traditionally available to the electrical engineer, such as linear system theory and the theory of stochastic processes.

http://ieeexplore.ieee.org/iel5/3/23094/01073023.pdf

Statistical optics

Organic (opto)electronic materials have received considerable attention due to their applications in thin-film-transistors, light-emitting diodes, solar cells, sensors, photorefractive devices, and many others. The technological promises include low cost of these materials and the possibility of their room-temperature deposition from solution on large-area and/or flexible substrates. The article reviews the current understanding of the physical mechanisms that determine the (opto)electronic properties of high-performance organic materials. The focus of the review is on photoinduced processes and on electronic properties important for optoelectronic applications relying on charge carrier photogeneration. Additionally, it highlights the capabilities of various experimental techniques for characterization of these materials, summarizes top-of-the-line device performance, and outlines recent trends in the further development of the field. The properties of materials based both on small molecules and on conjug...

Organic Optoelectronic Materials: Mechanisms and Applications

생체의 모델링과 Simulation

Photoacoustic imaging is a promising method for visualizing biological tissues with optical absorbers. This article provides an overview of the rapidly developing field of photoacoustic imaging. Photoacoustics, the physical basis of photoacoustic imaging, is analyzed briefly. The merits of photoacoustic technology, compared with optical imaging and ultrasonic imaging, are described. Various imaging techniques are also discussed, including scanning tomography, computed tomography and original detection of photoacoustic imaging. Finally, some biomedical applications of photoacoustic imaging are summarized.

Photoacoustic imaging in biomedicine

Non-invasively focusing light into strongly scattering media, such as biological tissue, is highly desirable but challenging. Recently, ultrasonically guided wavefront shaping technologies have been developed to address this limitation. So far, the focusing resolution of most implementations has been limited by acoustic diffraction. Here, we introduce nonlinear photoacoustically guided wavefront shaping (PAWS), which achieves optical diffraction-limited focusing in scattering media. We develop an efficient dual-pulse excitation approach to generate strong nonlinear photoacoustic (PA) signals based on the Grueneisen relaxation effect. These nonlinear PA signals are used as feedback to guide iterative wavefront optimization. As a result, light is effectively focused to a single optical speckle grain on the scale of 5-7 µm, which is ~10 times smaller than the acoustic focus with an enhancement factor of ~6,000 in peak fluence. This technology has the potential to benefit many applications that desire highly confined strong optical focus in tissue.

/pdf/photoacoustically-guided-wavefront-shaping-for-enhanced-2ejc1yqvyy.pdf

Photoacoustically guided wavefront shaping for enhanced optical focusing in scattering media

Focusing light deep inside living tissue has not been achieved despite its promise to play a central role in biomedical imaging, optical manipulation and therapy. To address this challenge, internal-guide-star-based wavefront engineering techniques—for example, time-reversed ultrasonically encoded (TRUE) optical focusing—were developed. The speeds of these techniques, however, were limited to no greater than 1 Hz, preventing them from in vivo applications. Here we improve the speed of optical focusing deep inside scattering media by two orders of magnitude, and focus diffuse light inside a dynamic scattering medium having a speckle correlation time as short as 5.6 ms, typical of living tissue. By imaging a target, we demonstrate the first focusing of diffuse light inside a dynamic scattering medium containing living tissue. Since the achieved focusing speed approaches the tissue decorrelation rate, this work is an important step towards in vivo deep tissue noninvasive optical imaging, optogenetics and photodynamic therapy.

/pdf/optical-focusing-deep-inside-dynamic-scattering-media-with-1f0kn0mnc6.pdf

Optical focusing deep inside dynamic scattering media with near-infrared time-reversed ultrasonically encoded (TRUE) light

The delineation of brain gliomas margins still poses challenges to precise imaging and targeted therapy, mainly due to strong light attenuation of the skull and high background interference. With deep penetration and high sensitivity, photoacoustic (PA) imaging (PAI) in the second near-infrared (NIR II) window holds great potential for brain gliomas imaging. Herein, mesoionic dye A1094 encapsulated in Arg-Gly-Asp-modified hepatitis B virus core protein (RGD-HBc) is designed and synthesized for effective NIR II PAI of brain gliomas. An aggregation-induced absorption enhancement mechanism is discovered in vitro and in vivo. It is also demonstrated that A1094@RGD-HBc, with an enhanced absorption in the NIR II window, displays ninefold PA signal amplification in vivo, allowing for precise PAI of the brain gliomas at a depth up to 5.9 mm. In addition, with the application of abovementioned agent, high-resolution PAI and ultrasensitive single photon emission computed tomography images of brain gliomas are acquired with accurate co-localization. Collectively, the results suggest great promise of A1094@RGD-HBc for diagnostic imaging and precise delineation of brain gliomas in clinical applications.

https://onlinelibrary.wiley.com/doi/epdf/10.1002/advs.201801615

Aggregation-Induced Absorption Enhancement for Deep Near-Infrared II Photoacoustic Imaging of Brain Gliomas In Vivo

Intralipid is widely used as an optical scattering agent in tissue-mimicking phantoms. Accurate control when using Intralipid is critical to match the optical diffusivity of phantoms to the prescribed value. Currently, most protocols of Intralipid-based hydrogel phantom fabrication focus on factors such as Intralipid brand and concentration. In this note, for the first time to our knowledge, we explore the dependence of the optical reduced scattering coefficient (at 532 nm optical wavelength) on the temperature and the time of mixing Intralipid with gelatin-water solution. The studied samples contained 1% Intralipid and were measured with oblique-incidence reflectometry. It was found that the reduced scattering coefficient increased when the Intralipid-gelatin-water mixture began to solidify at room temperature. For phantoms that had already solidified completely, the diffusivity was shown to be significantly influenced by the temperature and the duration of the mixing course. The dependence of the measured diffusivity on the mixing conditions was confirmed by experimental observations. Moreover, the mechanism behind the dependence behavior is discussed.

/pdf/dependence-of-optical-scattering-from-intralipid-in-gelatin-xke801qcln.pdf

Dependence of optical scattering from Intralipid in gelatin-gel based tissue-mimicking phantoms on mixing temperature and time.

Phase distortions due to scattering in random media restrict optical focusing beyond one transport mean free path. However, scattering can be compensated for by applying a correction to the illumination wavefront using spatial light modulators. One method of obtaining the wavefront correction is by iterative determination using an optimization algorithm. In the past, obtaining a feedback signal required either direct optical access to the target region, or invasive embedding of molecular probes within the random media. Here, we propose using ultrasonically encoded light as feedback to guide the optimization dynamically and non-invasively. In our proof-of-principle demonstration, diffuse light was refocused to the ultrasound focal zone, with a focus-to-background ratio of more than one order of magnitude after 600 iterations. With further improvements, especially in optimization speed, the proposed method should find broad applications in deep tissue optical imaging and therapy.

/pdf/ultrasonically-encoded-wavefront-shaping-for-focusing-into-s87x5r5o6f.pdf

Puxiang Lai

Papers

Photoacoustically guided wavefront shaping for enhanced optical focusing in scattering media

Optical focusing deep inside dynamic scattering media with near-infrared time-reversed ultrasonically encoded (TRUE) light

Aggregation-Induced Absorption Enhancement for Deep Near-Infrared II Photoacoustic Imaging of Brain Gliomas In Vivo

Dependence of optical scattering from Intralipid in gelatin-gel based tissue-mimicking phantoms on mixing temperature and time.

Ultrasonically encoded wavefront shaping for focusing into random media