TweetPinShare0 Shares TORONTO — John Tavares again felt right at home on the road in Toronto.With 30 family members and friends watching from the stands, the New York Islanders captain continued his career mastery at Air Canada Centre and extended his lead in the NHL scoring race.And for good measure, Tavares scored the winning goal in overtime.Tavares netted his 33rd goal of the season 4:38 into the extra session, capping the Islanders’ two-goal comeback in a 4-3 victory over the Toronto Maple Leafs on March 9.Tavares, an Ontario native, went around several defenders to score the winning goal during a delayed Toronto penalty. With a goal and assist, he stretched his advantage in the scoring race to three points over Washington’s Alex Ovechkin.“He’s one of the best players in the world,” said Maple Leafs center Peter Holland, whose job for the second and third periods was to defend Tavares.“He’s so skilled. He’s very good at making passes out there, and he’s so strong on the puck down low in the offensive zone that it’s tough to contain him.”Tavares, who was born in and lives in nearby Mississauga and grew up in Oakville, has the most points of any visiting player at Air Canada Centre since he entered the NHL in the 2009-10 season.“I just try to prepare like every other game,” Tavares said. “I don’t try to put too much pressure on myself or look at it any differently because I’m from here and I have a lot of friends and family (in attendance).“But you have them here, and you certainly want to play well.” Tavares has four overtime goals this season.“I just tried to gain some speed,” Tavares said. “I saw their defenseman kind of backed off and kind of gave me the lane to the net. I just was able to hesitate enough to open up Bernier, and I was just happy it trickled in.”Tyler Kennedy — in his Islanders debut — Frans Nielson and Casey Cizikas all scored in the third period for New York (43-21-4), which has a three-point lead over the New York Rangers atop the Metropolitan Division.Michal Neuvirth made 22 saves for the Islanders, who will host the Rangers on March 10.David Booth, Holland and James van Riemsdyk scored for the Maple Leafs (26-35-6). Jonathan Bernier made 39 saves.New York trailed 2-0 and 3-1 in the third period. “It’s a much better feeling than it could have been,” Tavares said. “We were disappointed with our first two periods.”Two days after Maple Leafs interim coach Peter Horachek criticized his team for a lack of effort in a blowout loss to St. Louis, Toronto showed plenty but couldn’t complete the victory.“We did do some good things, but when you’re up by two goals in the third on home ice, you should never lose the game,” said van Riemsdyk, whose goal was just his second in 18 games.With Nazem Kadri scratched as punishment for missing a team meeting, Horachek changed up the lines and broke up the trio of van Riemsdyk, Tyler Bozak and Phil Kessel. It paid off for much of the night.Booth opened the scoring 18:23 into the first period by deking and lifting a backhander past Neuvirth. The goal was Booth’s third in three games after he scored only one in his first 41 this season.Less than a minute after Bernier made a big stop on Tavares, Holland followed up on a loose puck at the side of the net to give the Maple Leafs a 2-0 lead at 10:27 of the third.Kennedy cut the deficit in half 1:32 into the third period off a pass from Tavares. After van Riemsdyk restored Toronto’s two-goal lead with a goal off the rush at 6:06, the Islanders continued their comeback.With Booth in the penalty box, Nielsen scored at 9:58. Just 30 seconds later, Cizikas tipped a shot by Islanders defenseman Travis Hamonic past Bernier to tie it.As the Islanders buzzed around him, Bernier made two saves in the final seconds of regulation to get the game to overtime.
Traditionally, computer vision applications have relied on highly specialized algorithms painstakingly designed for each specific application and use case. This meant that designing for computer vision was hard, and this significantly slowed the adoption of vision-based applications. Additionally, this made new applications very expensive and time consuming.However, there has been a democratization of computer vision. By that we mean it’s becoming much easier to develop computer vision-based algorithms, systems and applications, as well as to deploy these solutions at scale – enabling many more developers and organizations to incorporate vision into their systems.Deep learning is one of the drivers of this trend. Because of the generality of deep learning algorithms, there’s less of a need to develop specialized algorithms. Instead, developer focus can shift to selecting among available algorithms, and then to obtaining the necessary quantities of training data.Deep neural networks (DNNs) have transformed computer vision, delivering superior results on tasks such as recognizing objects, localizing objects within a frame and determining which pixels belong to which object. Even problems previously considered solved with conventional techniques are now finding better solutions using deep learning techniques.As a result, computer vision developers are increasingly adopting deep learning techniques. In the Alliance’s most recent survey, 59% of vision system developers are already using DNNs (an increase from 34% two years ago). Another 28% are planning to use DNNs for visual intelligence in the near future.Another critical factor in simplifying computer vision development and deployment is the rise of cloud computing and much better development tools. For example, rather than spending days or weeks installing and configuring development tools, today engineers can get instant access to pre-configured development environments in the cloud. Likewise, when large amounts of compute power are required to train or validate a neural network algorithm, this compute power can be quickly and economically obtained in the cloud.Cloud computing offers an easy path for initial deployment of many vision-based systems, even in cases where ultimately developers will switch to edge-based computing to reduce costs. Our most recent survey found that 75% of respondents using deep neural networks for visual understanding in their products deploy those neural networks at the edge, while 42% use the cloud. These figures total to more than 100% because some survey respondents use both approaches.The world of practical computer vision is changing very fast – opening up many exciting technical and business opportunities. You can learn about the latest developments in computer vision at the Embedded Vision Summit, May 20-23, 2019, in Santa Clara, California. The event attracts a global audience of more than one thousand people who are developing and using computer vision technology. — Jeff Bier is the founder if the Embedded Vision Alliance. >> This article was originally published on our sister site, EE Times: “Tools, Algorithms Drive Embedded Vision.” For more articles related to embedded vision, see: – Embedded vision builds on specialized co-processors – Open-source software meets broad needs of robot-vision developers – Computer vision for the masses: bringing computer vision to the open web platform Can be deployed at low cost and with low power consumption Is usable by non-specialists Since we started the Embedded Vision Alliance in 2011, there has been an unprecedented growth in investment, innovation, and use of practical vision technology across a broad range of markets. To help understand technology choices and trends, the Embedded Vision Alliance conducts an annual survey of product developers.In the most recent iteration of this survey, completed in November 2018, 93% of respondents reported that they expect an increase in their organization’s vision-related activity over the coming year (61% expect a large increase). This increase would not be possible without extensive work on new algorithms and development tools to speed adoption of vision-based systems.Three fundamental factors are driving the proliferation of visual perception. It increasingly Share this:TwitterFacebookLinkedInMoreRedditTumblrPinterestWhatsAppSkypePocketTelegram Leave a Reply Cancel reply You must Register or Login to post a comment. This site uses Akismet to reduce spam. Learn how your comment data is processed. Continue Reading Previous Syslogic: rugged computers and HMI systems for construction machineryNext How smart sensors enhance ADAS designs Works well enough for diverse, real-world applications