Object Detection is a technique associated with computer vision and image processing that performs the task of detecting instances of certain objects such as a human, vehicle, banner, building from a digital image or a video. Object detection combined with other advanced technology integrations allows us to perform face detection or pedestrian detection, popularly known as person tracking from a video. Object detection is being used in a plethora of areas such as security, human resource, healthcare, marketing, logistics and so on. This includes a variety of applications like detecting a broken bone from X-ray images, detect brand logo from image or video, and player/ball tracking in a football match. Research in computer vision focusing on object detection is growing rapidly.
Apple has acquired a startup in the UK that develops technology designed to improve smartphone photography. According to a new report from Bloomberg, Apple has acquired Spectral Edge for an undisclosed sum. Spectral Edge uses machine learning to "make smartphone pictures crisper, with more accurate colors." This works primarily by taking an infrared image and blending it with a standard photo. "Spectral Edge combines patented Image Fusion tech with Deep Learning to reveal more of the color, detail and clarity in any image," the company explained in its pitch.
You're going to need a free video content marketing strategy template. Yep… one of the most advanced pet identification systems out there... PiP is a smartphone app created for pet owners who've lost their cat, dog, fish. Should you misplace your pet, its photo will be analyzed and matched with photos of pets that have been found wandering the streets. Image analysis is used to beat lost tags, outdated microchips, and fading tattoos. Teaching a computer to see, is no walk in the park.
When a doctor suspects a patient may have lung lesions or pulmonary nodules, the next step is usually a CT scan. If lesions show up, doctors often recommend biopsies to obtain an accurate diagnosis and determine if the lesions are benign or malignant. The biopsy procedure is common, albeit intricate and error prone. Imaging can produce false positives, for one thing, resulting in unnecessary intervention. When surgeons perform biopsies, they can accidentally damage border areas of the lungs.
A new research project in Australia is using motion detectors and muscle sensors to track sheep shearers in an effort to minimize on the-job-injuries. Sheep shearers are six times more likely to be injured in the workplace than the average Australian worker. Data from sensors attached to sheep shearers will be used to model worker movement throughout the workday and test new ways of doing the job without risking injury. The study, a joint project between University of Melbourne and the trade group Australian Wool Innovation, uses sensors to measure electrical activity in muscles. These sensors are placed directly on the skin of the lower back and upper thighs, the ABC reported, while motion detectors are placed around the joints to track a worker's posture and shearing motions.
The Food and Drug Administration (FDA) is announcing the following public workshop entitled "Evolving Role of Artificial Intelligence in Radiological Imaging." The intent of this public workshop is to discuss emerging applications of Artificial Intelligence (AI) in radiological imaging including AI devices intended to automate the diagnostic radiology workflow as well as guided image acquisition. The purpose of the workshop is to work with interested stakeholders to identify the benefits and risks associated with use of AI in radiological imaging. We also plan to discuss best practices for the validation of AI-automated radiological imaging software and image acquisition devices. Validation of device performance with respect to the intended use is critical to assess safety and effectiveness.
Artificial Intelligence, Machine Learning and High Velocity Analytic workloads are going mainstream. Enterprises of all types and sizes want to seize the opportunity their data presents. As these workloads move from development to production, organizations face a significant challenge with the supporting storage architecture. At the heart of the problem is the file system the organization will use to store the information. It needs to be fast, scalable, durable and cloud-ready.
We have a threefold approach. First, AI as a technological choice compared to more traditional heuristic approaches, treating it undogmatically with clear eyes, especially when the goal is at improving the performance of certain existing technologies. Typical areas where we are carrying out this work include sound and image processing, video compression, computer vision, and cognitive state prediction by measuring physiological signals. Second is the use of AI as an approach for developing a solution to a problem, for which modeling is complex. This approach is closely tied to the use of data, whether personal or corporate, with a major focus on health care.
The ability to automatically estimate the quality and coverage of the samples produced by a generative model is a vital requirement for driving algorithm research. We present an evaluation metric that can separately and reliably measure both of these aspects in image generation tasks by forming explicit, non-parametric representations of the manifolds of real and generated data. We demonstrate the effectiveness of our metric in StyleGAN and BigGAN by providing several illustrative examples where existing metrics yield uninformative or contradictory results. Furthermore, we analyze multiple design variants of StyleGAN to better understand the relationships between the model architecture, training methods, and the properties of the resulting sample distribution. In the process, we identify new variants that improve the state-of-the-art.
Radiological sciences in the last ten years have advanced in a revolutionary manner, especially when it comes about medical imaging and computerized medical image processing. These techniques help in the understanding of the disease as well as initiation and evaluation of ongoing treatment. Apart from this, the dataset of these images is used in further analysis of such diseases occurring around the world as a whole. Heather Landi, a senior editor at Fierce Healthcare, writes in an article that IBM researchers estimate that medical images, as the largest and fastest-growing data source in the healthcare industry, account for at least 90 percent of all medical data. We can use a computer to process and manipulate the multidimensional digital images of psychological structures in order to visualize hidden characteristic diagnostic features that are very difficult or perhaps impossible to see using planer imaging methods.