CSS image filters are a quick way to tweak images in the browser without resorting to Photoshop. This simple reference gives you 9 CSS filter shorthands that provide an excellent way to maintain style consistency across visual content on your site, or just add a little fun to image hovers. CSS filters are most commonly […]

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You can do everything today. There are certainly no limits to what the mind can achieve, and that includes floating. With simply manipulating layers using Photoshop, a floating image has never been...

An overwhelming majority of websites incorporate the “hero image” design pattern. This is where a large, visually impactful image is used at the top of the page along with key messaging to emotionally engage the target audience. As one of the first elements one sees, the actual imagery used is often subject of attention during […]

The post Does the hero image matter? appeared first on Psychology of Web Design | 3.7 Blog.

They say that a picture is worth a thousand words, but have you ever stopped to think what your Ecommerce images are saying about the products you’re trying to sell online? Are your photos helping your products to jump off the screen and convince shoppers to buy them? Or, are your product images quite simply […]

Original post: 5 Product Image Tips For High Converting Landing Pages

The post 5 Product Image Tips For High Converting Landing Pages appeared first on Daily Blog Tips.

Astronomers have produced a remarkable new image of Jupiter, tracing the glowing regions of warmth that lurk beneath the gas giant's cloud tops. The picture was captured in infared by the Gemini North Telescope in Hawaii, and is one of the sharpest observations of the planet ever made from the ground.

My book Creative Calling is out! Thanks for all your love, support, and help getting it out into the world. We kicked off celebrations in Seattle with over 700 people in attendance to talk about Creativity with my good buddy, Humans of New York creator, Brandon Stanton. I recorded the session for you. Hope you enjoy! FOLLOW HUMANS OF NEW YORK: instagram | twitter | website Listen to the Podcast Subscribe   This podcast is brought to you by CreativeLive. CreativeLive is the world’s largest hub for online creative education in photo/video, art/design, music/audio, craft/maker, money/life and the ability to make a living in any of those disciplines. They are high quality, highly curated classes taught by the world’s top experts — Pulitzer, Oscar, Grammy Award winners, New York Times best selling authors and the best entrepreneurs of our times.

The post Imagine What’s Possible – On Stage /w Humans of New York Creator Brandon Stanton appeared first on Chase Jarvis Photography.

Visualization turns data into images, but are images themselves data? There are often claims that they are, but then you mostly see the images themselves without much additional data. In this video, I look at image browsers, a project classifying selfies along a number of criteria, and the additional information stored in HEIC that makes […]

Input transformation based defense strategies fall short in defending against strong adversarial attacks. Some successful defenses adopt approaches that either increase the randomness within the applied transformations, or make the defense computationally intensive, making it substantially more challenging for the attacker. However, it limits the applicability of such defenses as a pre-processing step, similar to computationally heavy approaches that use retraining and network modifications to achieve robustness to perturbations. In this work, we propose a defense strategy that applies random image corruptions to the input image alone, constructs a self-correlation based subspace followed by a projection operation to suppress the adversarial perturbation. Due to its simplicity, the proposed defense is computationally efficient as compared to the state-of-the-art, and yet can withstand huge perturbations. Further, we develop proximity relationships between the projection operator of a clean image and of its adversarially perturbed version, via bounds relating geodesic distance on the Grassmannian to matrix Frobenius norms. We empirically show that our strategy is complementary to other weak defenses like JPEG compression and can be seamlessly integrated with them to create a stronger defense. We present extensive experiments on the ImageNet dataset across four different models namely InceptionV3, ResNet50, VGG16 and MobileNet models with perturbation magnitude set to {epsilon} = 16. Unlike state-of-the-art approaches, even without any retraining, the proposed strategy achieves an absolute improvement of ~ 4.5% in defense accuracy on ImageNet.

The connection between visual input and tactile sensing is critical for object manipulation tasks such as grasping and pushing. In this work, we introduce the challenging task of estimating a set of tactile physical properties from visual information. We aim to build a model that learns the complex mapping between visual information and tactile physical properties. We construct a first of its kind image-tactile dataset with over 400 multiview image sequences and the corresponding tactile properties. A total of fifteen tactile physical properties across categories including friction, compliance, adhesion, texture, and thermal conductance are measured and then estimated by our models. We develop a cross-modal framework comprised of an adversarial objective and a novel visuo-tactile joint classification loss. Additionally, we develop a neural architecture search framework capable of selecting optimal combinations of viewing angles for estimating a given physical property.

Device fingerprints like sensor pattern noise (SPN) are widely used for provenance analysis and image authentication. Over the past few years, the rapid advancement in digital photography has greatly reshaped the pipeline of image capturing process on consumer-level mobile devices. The flexibility of camera parameter settings and the emergence of multi-frame photography algorithms, especially high dynamic range (HDR) imaging, bring new challenges to device fingerprinting. The subsequent study on these topics requires a new purposefully built image dataset. In this paper, we present the Warwick Image Forensics Dataset, an image dataset of more than 58,600 images captured using 14 digital cameras with various exposure settings. Special attention to the exposure settings allows the images to be adopted by different multi-frame computational photography algorithms and for subsequent device fingerprinting. The dataset is released as an open-source, free for use for the digital forensic community.

Event-related desynchronization and synchronization (ERD/S) and movement-related cortical potential (MRCP) play an important role in brain-computer interfaces (BCI) for lower limb rehabilitation, particularly in standing and sitting. However, little is known about the differences in the cortical activation between standing and sitting, especially how the brain's intention modulates the pre-movement sensorimotor rhythm as they do for switching movements. In this study, we aim to investigate the decoding of continuous EEG rhythms during action observation (AO), motor imagery (MI), and motor execution (ME) for standing and sitting. We developed a behavioral task in which participants were instructed to perform both AO and MI/ME in regard to the actions of sit-to-stand and stand-to-sit. Our results demonstrated that the ERD was prominent during AO, whereas ERS was typical during MI at the alpha band across the sensorimotor area. A combination of the filter bank common spatial pattern (FBCSP) and support vector machine (SVM) for classification was used for both offline and pseudo-online analyses. The offline analysis indicated the classification of AO and MI providing the highest mean accuracy at 82.73$pm$2.38\% in stand-to-sit transition. By applying the pseudo-online analysis, we demonstrated the higher performance of decoding neural intentions from the MI paradigm in comparison to the ME paradigm. These observations led us to the promising aspect of using our developed tasks based on the integration of both AO and MI to build future exoskeleton-based rehabilitation systems.

Deep neural networks have exhibited promising performance in image super-resolution (SR). Most SR models follow a hierarchical architecture that contains both the cell-level design of computational blocks and the network-level design of the positions of upsampling blocks. However, designing SR models heavily relies on human expertise and is very labor-intensive. More critically, these SR models often contain a huge number of parameters and may not meet the requirements of computation resources in real-world applications. To address the above issues, we propose a Hierarchical Neural Architecture Search (HNAS) method to automatically design promising architectures with different requirements of computation cost. To this end, we design a hierarchical SR search space and propose a hierarchical controller for architecture search. Such a hierarchical controller is able to simultaneously find promising cell-level blocks and network-level positions of upsampling layers. Moreover, to design compact architectures with promising performance, we build a joint reward by considering both the performance and computation cost to guide the search process. Extensive experiments on five benchmark datasets demonstrate the superiority of our method over existing methods.

High-resolution remote sensing images (HRRSIs) contain substantial ground object information, such as texture, shape, and spatial location. Semantic segmentation, which is an important task for element extraction, has been widely used in processing mass HRRSIs. However, HRRSIs often exhibit large intraclass variance and small interclass variance due to the diversity and complexity of ground objects, thereby bringing great challenges to a semantic segmentation task. In this paper, we propose a new end-to-end semantic segmentation network, which integrates lightweight spatial and channel attention modules that can refine features adaptively. We compare our method with several classic methods on the ISPRS Vaihingen and Potsdam datasets. Experimental results show that our method can achieve better semantic segmentation results. The source codes are available at https://github.com/lehaifeng/SCAttNet.

For Convolutional Neural Network-based object detection, there is a typical dilemma: the spatial information is well kept in the shallow layers which unfortunately do not have enough semantic information, while the deep layers have a high semantic concept but lost a lot of spatial information, resulting in serious information imbalance. To acquire enough semantic information for shallow layers, Feature Pyramid Networks (FPN) is used to build a top-down propagated path. In this paper, except for top-down combining of information for shallow layers, we propose a novel network called Image Pyramid Guidance Network (IPG-Net) to make sure both the spatial information and semantic information are abundant for each layer. Our IPG-Net has two main parts: the image pyramid guidance transformation module and the image pyramid guidance fusion module. Our main idea is to introduce the image pyramid guidance into the backbone stream to solve the information imbalance problem, which alleviates the vanishment of the small object features. This IPG transformation module promises even in the deepest stage of the backbone, there is enough spatial information for bounding box regression and classification. Furthermore, we designed an effective fusion module to fuse the features from the image pyramid and features from the backbone stream. We have tried to apply this novel network to both one-stage and two-stage detection models, state of the art results are obtained on the most popular benchmark data sets, i.e. MS COCO and Pascal VOC.

Histopathology is a reflection of the molecular changes and provides prognostic phenotypes representing the disease progression. In this study, we introduced feature scores generated from hematoxylin and eosin histology images based on deep learning (DL) models developed for prostate pathology. We demonstrated that these feature scores were significantly prognostic for time to event endpoints (biochemical recurrence and cancer-specific survival) and had simultaneously molecular biologic associations to relevant genomic alterations and molecular subtypes using already trained DL models that were not previously exposed to the datasets of the current study. Further, we discussed the potential of such feature scores to improve the current tumor grading system and the challenges that are associated with tumor heterogeneity and the development of prognostic models from histology images. Our findings uncover the potential of feature scores from histology images as digital biomarkers in precision medicine and as an expanding utility for digital pathology.

Image dehazing is an ill-posed problem that has been extensively studied in the recent years. The objective performance evaluation of the dehazing methods is one of the major obstacles due to the lacking of a reference dataset. While the synthetic datasets have shown important limitations, the few realistic datasets introduced recently assume homogeneous haze over the entire scene. Since in many real cases haze is not uniformly distributed we introduce NH-HAZE, a non-homogeneous realistic dataset with pairs of real hazy and corresponding haze-free images. This is the first non-homogeneous image dehazing dataset and contains 55 outdoor scenes. The non-homogeneous haze has been introduced in the scene using a professional haze generator that imitates the real conditions of hazy scenes. Additionally, this work presents an objective assessment of several state-of-the-art single image dehazing methods that were evaluated using NH-HAZE dataset.

Convolution is an equivariant operation, and image position does not affect its result. A recent study shows that the zero-padding employed in convolutional layers of CNNs provides position information to the CNNs. The study further claims that the position information enables accurate inference for several tasks, such as object recognition, segmentation, etc. However, there is a technical issue with the design of the experiments of the study, and thus the correctness of the claim is yet to be verified. Moreover, the absolute image position may not be essential for the segmentation of natural images, in which target objects will appear at any image position. In this study, we investigate how positional information is and can be utilized for segmentation tasks. Toward this end, we consider {em positional encoding} (PE) that adds channels embedding image position to the input images and compare PE with several padding methods. Considering the above nature of natural images, we choose medical image segmentation tasks, in which the absolute position appears to be relatively important, as the same organs (of different patients) are captured in similar sizes and positions. We draw a mixed conclusion from the experimental results; the positional encoding certainly works in some cases, but the absolute image position may not be so important for segmentation tasks as we think.

This paper reviews the second challenge on spectral reconstruction from RGB images, i.e., the recovery of whole-scene hyperspectral (HS) information from a 3-channel RGB image. As in the previous challenge, two tracks were provided: (i) a "Clean" track where HS images are estimated from noise-free RGBs, the RGB images are themselves calculated numerically using the ground-truth HS images and supplied spectral sensitivity functions (ii) a "Real World" track, simulating capture by an uncalibrated and unknown camera, where the HS images are recovered from noisy JPEG-compressed RGB images. A new, larger-than-ever, natural hyperspectral image data set is presented, containing a total of 510 HS images. The Clean and Real World tracks had 103 and 78 registered participants respectively, with 14 teams competing in the final testing phase. A description of the proposed methods, alongside their challenge scores and an extensive evaluation of top performing methods is also provided. They gauge the state-of-the-art in spectral reconstruction from an RGB image.

Low-resolution text images are often seen in natural scenes such as documents captured by mobile phones. Recognizing low-resolution text images is challenging because they lose detailed content information, leading to poor recognition accuracy. An intuitive solution is to introduce super-resolution (SR) techniques as pre-processing. However, previous single image super-resolution (SISR) methods are trained on synthetic low-resolution images (e.g.Bicubic down-sampling), which is simple and not suitable for real low-resolution text recognition. To this end, we pro-pose a real scene text SR dataset, termed TextZoom. It contains paired real low-resolution and high-resolution images which are captured by cameras with different focal length in the wild. It is more authentic and challenging than synthetic data, as shown in Fig. 1. We argue improv-ing the recognition accuracy is the ultimate goal for Scene Text SR. In this purpose, a new Text Super-Resolution Network termed TSRN, with three novel modules is developed. (1) A sequential residual block is proposed to extract the sequential information of the text images. (2) A boundary-aware loss is designed to sharpen the character boundaries. (3) A central alignment module is proposed to relieve the misalignment problem in TextZoom. Extensive experiments on TextZoom demonstrate that our TSRN largely improves the recognition accuracy by over 13%of CRNN, and by nearly 9.0% of ASTER and MORAN compared to synthetic SR data. Furthermore, our TSRN clearly outperforms 7 state-of-the-art SR methods in boosting the recognition accuracy of LR images in TextZoom. For example, it outperforms LapSRN by over 5% and 8%on the recognition accuracy of ASTER and CRNN. Our results suggest that low-resolution text recognition in the wild is far from being solved, thus more research effort is needed.

Convolutional Neural Networks (CNNs) are generally prone to noise interruptions, i.e., small image noise can cause drastic changes in the output. To suppress the noise effect to the final predication, we enhance CNNs by replacing max-pooling, strided-convolution, and average-pooling with Discrete Wavelet Transform (DWT). We present general DWT and Inverse DWT (IDWT) layers applicable to various wavelets like Haar, Daubechies, and Cohen, etc., and design wavelet integrated CNNs (WaveCNets) using these layers for image classification. In WaveCNets, feature maps are decomposed into the low-frequency and high-frequency components during the down-sampling. The low-frequency component stores main information including the basic object structures, which is transmitted into the subsequent layers to extract robust high-level features. The high-frequency components, containing most of the data noise, are dropped during inference to improve the noise-robustness of the WaveCNets. Our experimental results on ImageNet and ImageNet-C (the noisy version of ImageNet) show that WaveCNets, the wavelet integrated versions of VGG, ResNets, and DenseNet, achieve higher accuracy and better noise-robustness than their vanilla versions.

Similarity measure, also called information measure, is a concept used to distinguish different objects. It has been studied from different contexts by employing mathematical, psychological, and fuzzy approaches. Image steganography is the art of hiding secret data into an image in such a way that it cannot be detected by an intruder. In image steganography, hiding secret data in the plain or non-edge regions of the image is significant due to the high similarity and redundancy of the pixels in their neighborhood. However, the similarity measure of the neighboring pixels, i.e., their proximity in color space, is perceptual rather than mathematical. This paper proposes an interval type 2 fuzzy logic system (IT2 FLS) to determine the similarity between the neighboring pixels by involving an instinctive human perception through a rule-based approach. The pixels of the image having high similarity values, calculated using the proposed IT2 FLS similarity measure, are selected for embedding via the least significant bit (LSB) method. We term the proposed procedure of steganography as IT2 FLS LSB method. Moreover, we have developed two more methods, namely, type 1 fuzzy logic system based least significant bits (T1FLS LSB) and Euclidean distance based similarity measures for least significant bit (SM LSB) steganographic methods. Experimental simulations were conducted for a collection of images and quality index metrics, such as PSNR, UQI, and SSIM are used. All the three steganographic methods are applied on datasets and the quality metrics are calculated. The obtained stego images and results are shown and thoroughly compared to determine the efficacy of the IT2 FLS LSB method. Finally, we have done a comparative analysis of the proposed approach with the existing well-known steganographic methods to show the effectiveness of our proposed steganographic method.

This paper reviews the Challenge on Image Demoireing that was part of the New Trends in Image Restoration and Enhancement (NTIRE) workshop, held in conjunction with CVPR 2020. Demoireing is a difficult task of removing moire patterns from an image to reveal an underlying clean image. The challenge was divided into two tracks. Track 1 targeted the single image demoireing problem, which seeks to remove moire patterns from a single image. Track 2 focused on the burst demoireing problem, where a set of degraded moire images of the same scene were provided as input, with the goal of producing a single demoired image as output. The methods were ranked in terms of their fidelity, measured using the peak signal-to-noise ratio (PSNR) between the ground truth clean images and the restored images produced by the participants' methods. The tracks had 142 and 99 registered participants, respectively, with a total of 14 and 6 submissions in the final testing stage. The entries span the current state-of-the-art in image and burst image demoireing problems.

Smart meters enable remote and automatic electricity, water and gas consumption reading and are being widely deployed in developed countries. Nonetheless, there is still a huge number of non-smart meters in operation. Image-based Automatic Meter Reading (AMR) focuses on dealing with this type of meter readings. We estimate that the Energy Company of Paran'a (Copel), in Brazil, performs more than 850,000 readings of dial meters per month. Those meters are the focus of this work. Our main contributions are: (i) a public real-world dial meter dataset (shared upon request) called UFPR-ADMR; (ii) a deep learning-based recognition baseline on the proposed dataset; and (iii) a detailed error analysis of the main issues present in AMR for dial meters. To the best of our knowledge, this is the first work to introduce deep learning approaches to multi-dial meter reading, and perform experiments on unconstrained images. We achieved a 100.0% F1-score on the dial detection stage with both Faster R-CNN and YOLO, while the recognition rates reached 93.6% for dials and 75.25% for meters using Faster R-CNN (ResNext-101).

In this paper, we present a novel method for line artefacts quantification in lung ultrasound (LUS) images of COVID-19 patients. We formulate this as a non-convex regularisation problem involving a sparsity-enforcing, Cauchy-based penalty function, and the inverse Radon transform. We employ a simple local maxima detection technique in the Radon transform domain, associated with known clinical definitions of line artefacts. Despite being non-convex, the proposed method has guaranteed convergence via a proximal splitting algorithm and accurately identifies both horizontal and vertical line artefacts in LUS images. In order to reduce the number of false and missed detection, our method includes a two-stage validation mechanism, which is performed in both Radon and image domains. We evaluate the performance of the proposed method in comparison to the current state-of-the-art B-line identification method and show a considerable performance gain with 87% correctly detected B-lines in LUS images of nine COVID-19 patients. In addition, owing to its fast convergence, which takes around 12 seconds for a given frame, our proposed method is readily applicable for processing LUS image sequences.

Coronavirus disease 2019 (Covid-19) is highly contagious with limited treatment options. Early and accurate diagnosis of Covid-19 is crucial in reducing the spread of the disease and its accompanied mortality. Currently, detection by reverse transcriptase polymerase chain reaction (RT-PCR) is the gold standard of outpatient and inpatient detection of Covid-19. RT-PCR is a rapid method, however, its accuracy in detection is only ~70-75%. Another approved strategy is computed tomography (CT) imaging. CT imaging has a much higher sensitivity of ~80-98%, but similar accuracy of 70%. To enhance the accuracy of CT imaging detection, we developed an open-source set of algorithms called CovidCTNet that successfully differentiates Covid-19 from community-acquired pneumonia (CAP) and other lung diseases. CovidCTNet increases the accuracy of CT imaging detection to 90% compared to radiologists (70%). The model is designed to work with heterogeneous and small sample sizes independent of the CT imaging hardware. In order to facilitate the detection of Covid-19 globally and assist radiologists and physicians in the screening process, we are releasing all algorithms and parametric details in an open-source format. Open-source sharing of our CovidCTNet enables developers to rapidly improve and optimize services, while preserving user privacy and data ownership.

The purpose of this container is to be able to use the Amazon ASK CLI and Amazon AWS CLI in a Docker container in DevOps pipelines.

Note: This is a fork from the martindsouza image with these changes:

[IMAGE-1] Before smartphones and Instagram, there were influencers, and they could be as shallow, overconfident and pejorative as they are today. This new adaptation of Jane Austen's Emma — the feature debuts of photographer and music-video director Autumn de Wilde and Man Booker Prize-winning novelist turned screenwriter Eleanor Catton — brings that sort of modern frisson to its retelling of the tale of a very rich young woman who amuses herself by interfering in the romantic lives of those around her.…

Systems and methods for generating a tuple of structured data files are described herein. In one example, a method includes detecting an expression that describes a structure of a structured image using a constructor. The method can also include using an inference-rule based search strategy to identify a hierarchical arrangement of bounding boxes in the structured image that match the expression. Furthermore, the method can include generating a first tuple of structured data files based on the identified hierarchical arrangement of bounding boxes in the structured image.

A method of identifying one or more particular images from an image collection, includes indexing the image collection to provide image descriptors for each image in the image collection such that each image is described by one or more of the image descriptors; receiving a query from a user specifying at least one keyword for an image search; and using the keyword(s) to search a second collection of tagged images to identify co-occurrence keywords. The method further includes using the identified co-occurrence keywords to provide an expanded list of keywords; using the expanded list of keywords to search the image descriptors to identify a set of candidate images satisfying the keywords; grouping the set of candidate images according to at least one of the image descriptors, and selecting one or more representative images from each grouping; and displaying the representative images to the user.

Heterocyclic compounds of formula (I) useful as imaging probes of Tau pathology in Alzheimer's disease are described. Compositions and methods of making such compounds are also described.

The active ray-curable inkjet ink comprises a gelling agent, photopolymerizable compounds and a photoinitiator, and reversibly transitions into a sol-gel phase according to the temperature. Therein: (1) a (meth)acrylate compound having a molecular weight of 300-1,500 and having 3-14 (—CH2—CH2—O—) structural units within a molecule is included as the first photopolymerizable compound at a proportion of 30-70 mass % relative to the total mass of the ink; (2) a (meth)acrylate compound having a molecular weight of 300-1,500 and a C log P value of 4.0-7.0 is included as the second photopolymerizable compound at a proportion of 10-40 mass % relative to the total mass of the ink; and (3) the gelling agent has a total of at least 12 carbon atoms, and has a straight or branched alkyl chain including at least three carbon atoms.

A system, method and computer program product for allocating shared resources. Upon receiving requests for resources, the cost of bundling software in a virtual machine (VM) image is automatically generated. Software is selected by the cost for each bundle according to the time required to install it where required, offset by the time to uninstall it where not required. A number of VM images having the highest software bundle value (i.e., highest cost bundled) is selected and stored, e.g., in a machine image store. With subsequent requests for resources, VMs may be instantiated from one or more stored VM images and, further, stored images may be updated selectively updated with new images.

A superconducting magnet device is configured to include: a refrigerant circulation flowpath in which a refrigerant (R) circulates; a refrigerator for cooling vapor of the refrigerant (R) in the refrigerant circulation flowpath; a superconducting coil cooled by the circulating refrigerant (R); a protective resistor thermally contacting the superconducting coil and having an internal space (S); a high-boiling-point refrigerant supply section for supplying a high-boiling-point refrigerant having a higher boiling point than the refrigerant (R) and frozen by the refrigerant (R) to the internal space (S) in the protective resistor; and a vacuum insulating container for at least accommodating the refrigerant circulation flowpath, the superconducting coil, and the protective resistor.

A self-shield open magnetic resonance imaging superconducting magnet comprises five pairs of coils: shim coils, first main magnetic coils, second main magnetic coils, third main magnetic coils, and shielding coils. The five pairs of coils are symmetric about the center. The shim coils are arranged closest to the center point; the first main magnetic coils, the second main magnetic coils, the third main magnetic coils, and the shielding coils are arranged in sequence outside. The first main magnetic coils are connected with reverse current. The second and third main magnetic coils are connected with positive current for providing the main magnetic field strength. The shim coils are connected with positive current for compensating the magnetic field in the central region. The shielding coils are connected with reverse current for creating a magnetic field opposite to the main magnetic field for compensating the stray magnetic field in the space.

A superconducting magnet device and a magnetic resonance imaging system not only avoid the need for costly aluminum alloy formers but also lower quench pressure effectively, have a baffle covering the former and the coil, with a gap between the baffle and the coil.

An image processing apparatus including: image processor which processes broadcasting signal, to display image based on processed broadcasting signal; communication unit which is connected to a server; a voice input unit which receives a user's speech; a voice processor which processes a performance of a preset corresponding operation according to a voice command corresponding to the speech; and a controller which processes the voice command corresponding to the speech through one of the voice processor and the server if the speech is input through the voice input unit. If the voice command includes a keyword relating to a call sign of a broadcasting channel, the controller controls one of the voice processor and the server to select a recommended call sign corresponding to the keyword according to a predetermined selection condition, and performs a corresponding operation under the voice command with respect to the broadcasting channel of the recommended call sign.

Various embodiments enable a device to perform tasks such as processing an image to recognize and locate text in the image, and providing the recognized text an application executing on the device for performing a function (e.g., calling a number, opening an internet browser, etc.) associated with the recognized text. In at least one embodiment, processing the image includes substantially simultaneously or concurrently processing the image with at least two recognition engines, such as at least two optical character recognition (OCR) engines, running in a multithreaded mode. In at least one embodiment, the recognition engines can be tuned so that their respective processing speeds are roughly the same. Utilizing multiple recognition engines enables processing latency to be close to that of using only one recognition engine.

Images are analyzed within a 3D environment that is generated based on spatial relationships of the images and that allows users to experience the images in the 3D environment. Image analysis may include ranking images based on user viewing information, such as the number of users who have viewed an image and how long an image was viewed. Image analysis may further include analyzing the spatial density of images within a 3D environment to determine points of user interest.

A data processing entity that includes a mass memory with a plurality of memory locations for storing memory blocks. Each of a plurality of software images includes a plurality of memory blocks with corresponding image addresses within the software image. The memory blocks of software images stored in boot locations of a current software image are relocated. The boot blocks of the current software image are stored into the corresponding boot locations. The data processing entity is booted from the boot blocks of the current software image in the corresponding boot locations, thereby loading the access function. Each request to access a selected memory block of the current software image is served by the access function, with the access function accessing the selected memory block in the associated memory location provided by the control structure.

A network storage server implements a method to discard sensitive data from a Persistent Point-In-Time Image (PPI). The server first efficiently identifies a dataset containing the sensitive data from a plurality of datasets managed by the PPI. Each of the plurality of datasets is read-only and encrypted with a first encryption key. The server then decrypts each of the plurality of datasets, except the dataset containing the sensitive data, with the first encryption key. The decrypted datasets are re-encrypted with a second encryption key, and copied to a storage structure. Afterward, the first encryption key is shredded.

An image forming apparatus is connected to a host device including first and second power domains which are separately supplied with power and includes first and second memories to be disposed in the second power domain, a main controller disposed in the first power domain and to perform a control operation using the first memory in a normal mode, and a sub-controller disposed in the second power domain and perform a control operation using the second memory in a power-saving mode, where when the normal mode is changed to the power-saving mode a power supply to the first power domain is shut off, the first memory operates in a self-refresh mode, and the main controller copies central processing unit (CPU) context information into a context storage unit, and when the power-saving mode is changed to the normal mode, the main controller is booted using the CPU context information stored in the context storage unit.

Systems and methods for processing magnetic resonance imaging (MRI) data are provided. A method includes receiving MRI data comprising a plurality of k-space points and deriving a plurality of image data sets based on the MRI data, each of the plurality of MRI image sets obtained by zeroing a different one of the plurality of k-space points. The method further includes computing image space metric values for each of the plurality of image data sets and adjusting a portion of the MRI data associated with ones of the image space metric values that fail to meet a threshold value to yield adjusted MRI data.

A magnetic resonance imaging apparatus includes an acoustic control unit and an image data acquisition unit. The acoustic control unit applies a gradient magnetic field for controlling a sound in synchronization with a signal representing a respiratory body motion. The image data acquisition unit acquires imaging data by subsequently imaging to control the sound and generate image data based on the imaging data.

Velocity of MR-imaged fluid flows is measured. Data representing a measure of distance traveled by flowing fluid appearing in at least two MR images of a subject's tissue taken at different respective imaging times is generated. Data representing at least one fluid velocity measurement of the flowing fluid is generated by calculating at least one instance of distance traveled by the fluid divided by elapsed time during travel based on different respective imaging times. Data representing at least one fluid velocity measurement is then output to at least one of: (a) a display screen, (b) a non-transitory data storage medium, and (c) a remotely located site.

Systems and methods for imaging within depth layers of a tissue include illuminating light rays at different changing wavelengths (frequencies), collimating illuminated light rays using a collimator, and splitting light rays using a beam splitter, such that some of the light rays are directed towards a reference mirror and some of the rays are directed towards the tissue. The systems and methods further include reflecting light rays from the reference mirror towards the imager, filtering out non-collimated light rays reflected off the tissue by using a telecentric optical system, and reflecting collimated light rays reflected off the tissue towards the imager, thus creating an image of an interference pattern based on collimated light rays reflected off the tissue and off the reference mirror. The method may further include creating full 2D images from the interference pattern for each depth layer of the tissue using Fast Fourier transform.

An intracorporeal marker, for marking a site within living tissue of a host, includes an outer body portion of biodegradable material. An inner body portion is located in the outer body portion. The inner body portion includes biological material that becomes calcified in the living tissue of the host over time. An agent interacts with the biological material to promote calcification of the biological material of the inner body portion in the living tissue of the host.

A three dimensional imaging system includes a first laser emitting light at a first wavelength, and a scanner for scanning the laser light in a pattern on the target area. A photo detector receives light reflected from the target area as a contrasted vein image, resulting from differential absorption and reflection therein of the first wavelength of light. The intensity of the first laser is incrementally increased, and the photo detector thereby receives a plurality of contrasted vein images, each being at incrementally distinct depths beneath the target skin surface. Image processing is performed on the plurality of vein images to successively layer the veins in the images according to their depth, to create a single processed vein image. A second laser emitting a second wavelength of light is used in combination with the scanner to project the processed vein image onto the target area to overlay the veins therein.

A device for fluorescence-based imaging and monitoring of a target comprising: a light source emitting light for illuminating the target, the emitted light including at least one wavelength or wavelength band causing at least one biomarker associated with the target to fluoresce; and a light detector for detecting the fluorescence.

Apparatus and methods are described including acquiring a first set of extraluminal images of a lumen, using an extraluminal imaging device. At least one of the first set of images is designated as a roadmap image. While an endoluminal device is being moved through the lumen, a second set of extraluminal images is acquired. A plurality of features that are visible within images belonging to the second set of extraluminal images are identified. In response to the identified features in the images belonging to the second set of extraluminal images, a plurality of local calibration factors associated with respective portions of the roadmap image are determined. Other applications are also described.

The present embodiments disclose the preparation of hyperpolarized 13C dialkyl succinate compounds and hyperpolarized 13C dialkyl fumarate compounds and their use in real time, in vivo metabolic imaging of the TCA cycle.