Director of Product Marketing, CEVA, Imaging & Vision DSP core product line.
Smartphones have pretty much eliminated the need for point-and-shoot pocket cameras. For a while now, they have even been threatening to replace professional rigs. As early as 2012, Time Magazine featured a cover shot snapped by an iPhone 4S, and recently a Sports Illustrated cover photo was shot with a Motorola Z and Billboard Magazine’s cover was taken using an iPhone 7 plus. As smartphones cameras improve, the market has come to expect DSLR-like image and video quality as well as DSLR-like features. But, until now, DSLRs have still been irreplaceable in certain cases.
Demand for computer vision has never been stronger. Propelled by advances in deep learning, a staggering number of devices and applications utilizing vision and artificial intelligence (AI) have hit the market. While employing vision and deep learning in embedded systems poses a challenge, it is becoming a requirement. Here is a look at the variety of markets and use cases in which embedded intelligent vision has become a must-have component.
The successful spread of artificial intelligence (AI) into everyday applications will be dependent on how easy it is to deploy deep neural networks in small, low-power devices rather than large server networks
In this post we look at ways to deal with those challenges.
Googlenet deep convolutional neural network
In 2014, Google made an entry to the ImageNet large-scale visual recognition challenge (ILSVRC), titled GoogLeNet. It is an interesting case study because it is a 22-layer deep convolutional network, and includes nine inceptions, creating a very rich and complex topology.
It’s no secret that CEVA has been pushing the envelope for over a decade to make intelligent machine vision a viable possibility in mass market embedded devices. With four generation of successful, widely adopted DSP cores behind us, including the award-winning CEVA-XM4, we have established our position as the industry leader in low-power, high-performance programmable IP imaging and vision engines.
Today, we are especially excited to unveil our fifth generation imaging and vision platform, delivering unprecedented performance thanks to cutting edge enhancements and innovations. Based on the new CEVA-XM6 DSP core, our latest platform makes it easier, faster, and lower-risk than ever to efficiently harness the power of neural networks and machine vision for smartphones, autonomous vehicles, surveillance, robots, drones and other camera-enabled smart devices.
The latest Google I/O conference highlighted the fact that artificial intelligence (AI) is one of Google’s main focuses. And not just in reference to research-oriented, futuristic projects, like delivery drones. Google has made it quite clear that AI is coming into consumer homes and handheld devices now, in 2016, as my colleague expressed in this recent post. Certainly, Google isn’t the only company trying to take advantage of the power of AI. Microsoft, Facebook, Amazon, Baidu, and others are all competing to create the framework that will make this possible.
From drones to handheld devices, the rising demand for video cameras has made them ubiquitous, constantly driving down size and cost while pushing up resolution and overall quality. One of the main challenges in this field is stabilizing the image to generate clear, smooth footage. In this post, I would like to discuss the challenges that stabilization poses, and the pros and cons of existing solutions. I'll also give a brief technical overview of CEVA's software-based stabilization solution here.
Read the full blog here
We are proud to share with you that our fourth generation imaging and vision DSP, the CEVA-XM4 intelligent vision processor, has been named “Best Processor IP of 2015” by semiconductor industry technology analyst, The Linley Group.
Computer vision technology is complementing GPS sensors in visually smarter drones in a quest to have autonomous features like object tracking, environment sensing, collisions avoiding and more. At the same time, 4K video, now a must-have feature, and other functions like 3D depth map creation pose significant computational challenges for the high-quality image- and video-processing pipeline in drone electronics.