You are reading the article Tile Matches Expected Feature Of Apple’s ‘Airtags’ Item Trackers, Smart Alerts updated in December 2023 on the website Cancandonuts.com. We hope that the information we have shared is helpful to you. If you find the content interesting and meaningful, please share it with your friends and continue to follow and support us for the latest updates. Suggested January 2024 Tile Matches Expected Feature Of Apple’s ‘Airtags’ Item Trackers, Smart Alerts
Smart Alerts when you leave something behind in a public place are Tile’s latest attempt to stay ahead of Apple’s anticipated launch of its own item tracker, which may be named AirTag.
We exclusively reported back in April that Apple is working on its own competitor to Tile, integrating with the iOS Find My app. One of the expected features is automatic alerts when you leave behind a protected item, and that’s something Tile is now adding to its own products…
Tile has currently offered three forms of protection. First, if you simply misplace something in your own home, you can use the app to make it sound an audible alert. The Apple Watch offers similar functionality, making your iPhone ping so you can work out where you left it.
Second, you can define safe zones, like your home, and be automatically alerted when an item leaves that area.
Third, if you mark a Tile as lost, anyone running the app will automatically and anonymously alert you when they come into range.
Engadget reports that the company is now adding the ability to warn you whenever you leave any protected item behind anywhere.
The company is relaunching Smart Alerts in beta to automatically warn when you’ve left a tracked item behind if it’s been there for at least five minutes. If you rush out of the coffee shop without your bag, you’ll ideally get an alert before you’ve reached your car. You’ll need a Premium subscription ($30 per year or $3 per month), but it might be a small price to pay if you can’t bear the thought of leaving a valuable item alone for more than a few minutes. The beta will be available in December.
Tile offers a range of products to electronically tag your valuables, ranging from a waterproof sticker to a wallet-sized Slim tracker to the Pro, with a 400-foot range and loud audible alert.
We recently rounded up everything we know so far about Apple’s upcoming product. The key selling point will be that, if you lose something, anyone’s iOS device within range will be used to help track it down.
The true selling point of Apple’s item tracker will be its integration with iOS. It is believed that you will be able to access your item trackers via the “Find My” device on your iPhone, iPad, or Mac. Once the item trackers are available, there will be a new “Items” tab in the Find My application for all of the things you choose to track.
The item tracker itself will be paired to a user’s iCloud account by proximity to an iPhone, much like AirPods. Users will also be able to receive notifications when their iPhone gets too far away from the tag. For instance, your iPhone could alert you if you get too far away from your keys or wallet. Certain locations can be added to a list of ignored locations so that the item can be left at those locations without you receiving a notification.
Furthermore, you’ll be able to put Apple’s item tracker in “Lost Mode” – which means the tag will store your contact information, allowing other Apple users to read that information. In such instances, you’ll receive a notification when your item has been found.
It’s not yet known when Apple plans to launch AirTag, or what it will cost. Tile products can cost less than $12.50 each when bought in multipacks. Smart Alerts will launch in beta shortly.
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There’s bad news for Apple’s AirPods Studio headphones and AirTags trackers
Apple’s big iPhone 12 event may have four phones, but some of the most hotly-anticipated accessories – including AirPods Studio headphones and Apple’s first tracking tags – could end up MIA, new leaks suggest. The “Hi, Speed” event on October 13 is expected to cover a number of bases, with the iPhone 12 range adding 5G and ceding a little stage time to a new HomePod mini. Don’t expect a full accessories-fest, though.
Indeed, some of the items we’ve heard rumors about for much of 2023 could end up missing next Tuesday’s reveal. Manufacturing issues during this time of pandemic are being blamed for some iPhone 12 variants following on in November, but it’s also being cited as a cause of delay for accessories like Apple’s first over-the-ear headphones to bear the AirPods brand.
Believed to be launching as AirPods Studio, the new headphones will come in a variety of forms, with both sports and luxury fittings. Renders shared by leaker Jon Prosser show what’s believed to be the high-end iteration, with a $599 sticker price reflecting their metal and leather construction. However there’ll also be a “sport-like” version that will come in at $350, he claims.
AirPods Studio 🎧
The renders that I shared last month seem to be the luxury variant — made of leather/metal.
I’m being told they’ll retail for…. $599 😳
There will be another sport-like variant made of cheaper materials for $350.
— jon prosser (@jon_prosser) October 9, 2023
That would follow Apple’s strategy with Apple Watch, though we’ll probably have to wait until later in the year to see the full line-up. AirPods Studio aren’t expected to be at the October 13 event, and nor it’s said are AirTags.
Apple’s take on the tracking tag market currently dominated by Tile have long been a point of leaks and discussion. Indeed the UWB (ultrawideband) radio that Apple added in the iPhone 11 using its new U1 chip was generally seen as paving the way for direction-aware localization. With the U1 and the AirTags, it’s believed, you’d be able to track down whatever had been tagged with precise directions shown on the iPhone’s display.
What hasn’t been discovered, though, is a launch date for Apple AirTags themselves. First expected earlier in 2023, now Prosser says that the unveil has been pushed back to March 2023.
So, about AirTags.
This one hurts my heart…
— jon prosser (@jon_prosser) October 9, 2023
It’s a huge setback, and it’s not entirely clear what might have prompted it. One possibility is that manufacturing has proved to be an issue: after all, there’s a fair amount of technology that needs to be squeezed into each AirTag, which is believed to have a polished metal back and a white fascia. Alternatively, it could be waiting on more devices with UWB and the U1 chipset to arrive, so that the flagship tracking features are actually more usable.
With four new iPhone 12 variants, 5G to explain, and new camera tech, it’s clear that Apple will have plenty to occupy itself with on October 13. Given previous rumors that the company plans to leave out bundled EarPods from the new iPhone box, however, what will arguably be just as interesting is seeing how Apple justifies the accessory omission.
Follow these steps to configure iOS 15’s Conversation Boost feature on your AirPods Pro and learn how to adjust ambient noise reduction to help you hear a conversation in a noisy area.
What is Conversation Boost?
Conversation Boost is Apple’s marketing name for an ambient noise reduction feature for folks with hearing problems that’s currently restricted to a single AirPods model—AirPods Pro.
Apple says this accessibility feature can help people with mild hearing challenges stay more connected in conversations. It boosts the voice of the person speaking in front of you, and you can toggle the amount of ambient noise cancellation. Of course, you don’t need to suffer from hearing problems just to use Conversation Boost for enhancing face-to-face interactions.
How to turn on Conversation Boost
Conversation Boost is an accessibility feature that people who never visit the Accessibility section in the Settings app would have a hard time discovering on their own.
Connect your AirPods to your iPhone
Open Settings on your iPhone running iOS 15.0+
Choose Accessibility from the root list
Hit Audio/Visual underneath the heading Hearing
Touch Headphone Accommodations underneath the Audio heading
Slide the Headphone Accommodations switch to the ON position
Choose the option labeled Transparency Mode at the bottom
Switch on the Custom Transparency Mode option
Scroll down and toggle on Conversation Boost
The screenshot below illustrates where the Conversation Boost switch is located.
You won’t see this option unless the earbuds are connected to your phone.
How to add Conversation Boost control to Control Panel
With the Conversation Boost feature turned on, you’d be wise to add its button to the Control Center overlay for fast access to the feature from anywhere in iOS, including the lock screen.
Open Settings on your iPhone powered by iOS 15.0+
Choose Control Center from the main list
Hit the (+) plus sign next to Hearing to add the feature to your Control Center
You should now see the option Hearing on the Included Controls list.
And now you can boost face-to-face conversations anytime you want.
How to use Conversation Boost
With Conversation Boost turned on in your accessibility settings, use the Control Center toggle to activate Conversation Boost on your AirPods Pro when needed.
Open Control Center on your iPhone with iOS 15.0+
Select the Hearing option from your Control Center
Touch Phone & Media underneath Headphone Accommodations
On the next screen, choose Transparency Mode
You’ll return to the previous screen, now set Conversation Boost to “On” at the bottom
The Hearing control in Control Center lets you toggle Conversation Boost on the fly.
You can also adjust the amount of ambient noise reduction and other audio filters.
Adjusting ambient noise reduction and other Conversation Boost features
You can set the amount of ambient noise reduction and adjust other audio filters for Conversation Boost either through Control Center or via your accessibility settings.
Open Control Center on your iPhone with iOS 15.0+
On the Control Center overlay, touch the Hearing control
Set ambient noise reduction and other audio settings to your liking
Amplification: Amplify the audio picked by your AirPods Pro microphone
Transparency Balance: Set to left (L), right (R) or leave in the middle for neutral balance
Tone: Set the overall audio feeling between Darker and Brighter
Ambient Noise Reduction: Control the amount of noise cancelation
Alternatively, go to Settings → Accessibility → Audio/Visual → /Headphone Accommodations → Transparency Mode to set the amount of ambient noise reduction and configure other audio filters. Keep in mind that changing the values for amplification, transparency balance, tone and ambient noise reduction won’t just affect Conversation Boost but also the regular noise-canceling Transparency Mode feature on your AirPods Pro.
As we mentioned earlier, Conversation Boost isn’t strictly for folks who are a little hard of hearing. Everyone can use Conversation Boost. You may be having issues hearing the person in front of you talking. Or you may be conducting a conversation in a particularly noisy area.
Whichever the case, Conversation Boost is ready to boost your conversation.
Conversation Boost FAQ: Your questions, answered
Can I use Conversation Boost with other headphones?
Conversation Boost is currently supported on Apple’s own AirPods Pro wireless earbuds. Other models are incompatible with this feature, including the pricey AirPods Max headphones.
What are the system requirements for Conversation Boost?
Conversation Boost requires that your AirPods Pro be updated to firmware version 4A400, which launched on October 6, 2023. You’ll also need an iPhone or iPod touch with at least the iOS 15.0 software or an iPad running iPadOS 15.0 or newer.
Do I have the right AirPods firmware for Conversation Boost?
If you have older firmware, you’ll need to update your AirPods Pro. Apple doesn’t provide a specific mechanism to download and install these updates. Rather, the new firmware is installed automatically when your AirPods Pro are in their charging case.
The AirPods Pro case needs to be connected to power and within Bluetooth range of its paired iPhone to kickstart the update process. Read: How to use Live Listen on iPhone
Where do I find the version number for AirPods firmware?
Before you can set up Conversation Boost on your AirPods Pro, verify that you have the required firmware by venturing into Settings → Bluetooth, then touch the “i” next to your listed AirPods Pro. The version number of the current firmware is printed next to “Version”.
We’ll update this article if Apple adds Conversation Boost to other AirPods models.
Conversation Boost vs. Live Listen: What’s the difference?
You could argue that Conversation Boost sounds an awful lot like Live Listen, another accessibility feature from Apple, and you’d be right. While at the core both features make it easier to hear conversations in noisy situations, they’re not the same.
With Live Listen, your iPhone acts as a remote mic that sends audio wirelessly to your AirPods. As noted by a support document on the Apple website, Live Listen used to be only available for MFi-certified hearing aids. As of iOS 12, this feature can be used with your AirPods.
Watch Apple’s “Tap to Pay” feature, which lets the iPhone accept contactless payments without using additional hardware, in use at Apple Park’s visitor center.
The “Tap to Pay” feature was announced in February 2023, with the company’s visitor center at the Apple Park headquarters already supporting the feature.
A video included right ahead shows a customer paying for their goods with Apple Pay simply by tapping their iPhone against a retail employee’s iPhone.
It’s not clear when support for the feature was implemented, but it would appear that Apple has begun a controlled rollout of “Tap to Pay”.Watch “Tap to Pay” in use at the Apple Park visitor center
A developer shared on his Twitter a video showcasing the feature in action. As you can see for yourself, this new Apple Pay feature works just as you’d expect. An app on a retail employee’s phone which runs the Isaac payment system acts as a contactless NFC terminal. A customer then makes the payment by tapping the top of their iPhone gently against the other device. Apple says it created “Tap to Pay” to help small businesses and individuals accept payments on the go without having to use additional hardware such as dongles from Square. The feature effectively turns your NFC-enabled iPhone into a payment terminal.
— Michael (@NTFTWT) May 15, 2023
Apple has said that “Tap to Pay” will be used across its retail stores, and this is the first known use of the feature in the wild. The Apple Park visitor center is a retail store located on the company’s campus. It has a cantilevered carbon fiber roof that appears to float (it’s only supported by stone-clad cores and no other extraneous columns for support). The store is open only to Apple Park visitors and neighbors.Your iPhone is now a payment terminal
Accepting credit card payments on a phone via NFC without requiring any additional external hardware is nothing new. What’s new is Apple’s willingness to permit third-party developers to implement this feature in their apps. Previously, no apps could use contactless payments features that require NFC hardware.
“Tap to Pay” should especially cater to small businesses and individuals such as hairstylists, who will soon be able to process tap-to-pay contactless payments right out of the box. Apple announced the Tap to Pay feature earlier in 2023 but didn’t provide a firm launch date beyond a vague “later in 2023” target. But with the Apple Park visitor center now using the tech, it shouldn’t be long before the first “Tap to Pay” apps start arriving. No software update should be required to use this feature because support for “Tap to Pay” is reportedly present in iOS 15.4.Tap to Pay requirements
“Tap to Pay” will first launch in the United States
The feature requires an iPhone XS or later and a partner-enabled app. The first non-Apple payment platforms to offer “Tap to Pay” to customers will be Stripe and Dutch payment processing company Adyen, with additional partnerships and apps arriving later this year. Read: How to fix iPhone widgets not working or updating
Because Google’s free Android software is available in many shapes, price points and across a variety of carriers and manufacturers, the search giant has relatively easy and early on taken the lead in terms of device activations. But even with its clear lead in terms of unit sales, Android has always lagged behind iOS in terms of quantity of the apps found on its store, dubbed the Google Play Store. Today, Google has announced that its store now carries 700,000 third-party apps, which means that the Play Store has officially matched the App Store in sheer number of apps available…
Brian Womack, reporting for Bloomberg:
By luring software developers to its Android platform, Google is attempting to eliminate a key selling point Apple has used for the iPhone and iPad.
Applications have become a battleground as the two companies look for an edge in the $219.1 billion smartphone market, akin to how Microsoft Corp. dominated the personal-computer business by getting other companies to write programs for its Windows operating system.
The report doesn’t mention the number of tablet-optimized apps on the Android store, though Google executives mentioned at yesterday’s Nexus media event that any application written for an Ice Cream Sandwich-driven seven-inch tablet should scale up nicely to eight, nine and ten-inch tablets and beyond.
Rather than run blown up iPhone apps, Apple prides itself with more than 275,000 apps that have been written specifically for the iPad. Apple’s press release from last week updates us with the latest statistics about its platform:
iPad runs over 700,000 apps available on the App Store, including more than 275,000 apps designed specifically for iPad, from a wide range of categories including books, games, business, news, sports, health, reference and travel. iPad also supports the more than 5,000 newspapers and magazines offered in Newsstand and the more than 1.5 million books available on the iBookstore.
The iTunes Store puts the world’s most popular online music, TV and movie store at your fingertips with a catalog of over 26 million songs, over 190,000 TV episodes and over 45,000 films. The new iBooks app for iPad lets users read ebooks in over 40 languages.
Of course, quantity is one thing and quality is an entirely different thing. People more often part with their hard-earned money to buy hardware and software in the Apple ecosystem compared to Android. Contrast this to the ad-supported nature of the Android platform, with users not buying apps as often as their Apple counterparts do.
This has helped Apple pay $6.5 billion to developers since the App Store’s inception in the summer of 2008 – and that’s after Apple’s customary 30 percent cut. Whichever way you look at it, the breadth and quality of apps available on Apple’s App Store still makes Apple’s platform the most lucrative playground for both developers and users out there.
We will see if Google matches to beat Apple on two other important metrics – the quality and breadth of Android apps and its app store’s revenue.
What do you think?
How long until Android becomes a more lucrative ecosystem for developers than Apple’s iOS platform?
This article was published as a part of the Data Science BlogathonIntroduction
In this article, I am gonna discuss various algorithms of image feature detection, description, and feature matching using OpenCV.
First of all, let’s see what is computer vision because OpenCV is an Open source Computer Vision library.
What happens when a human sees this image?
He will be able to recognize the faces which are there inside the images. So, in a simple form, computer vision is what allows computers to see and process visual data just like humans. Computer vision involves analyzing images to produce useful information.
What is a feature?
When you see a mango image, how can you identify it as a mango?
By analyzing the color, shape, and texture you can say that it is a mango.
The clues which are used to identify or recognize an image are called features of an image. In the same way, computer functions, to detect various features in an image.
We will discuss some of the algorithms of the OpenCV library that are used to detect features.1. Feature Detection Algorithms 1.1 Harris Corner Detection
Harris corner detection algorithm is used to detect corners in an input image. This algorithm has three main steps.
Determine which part of the image has a large variation in intensity as corners have large variations in intensities. It does this by moving a sliding window throughout the image.
For each window identified, compute a score value R.
Apply threshold to the score and mark the corners.
Here is the Python implementation of this algorithm.import cv2 import numpy as np imput_img = 'det_1.jpg' ori = cv2.imread(imput_img) image = cv2.imread(imput_img) gray = cv2.cvtColor(image,cv2.COLOR_BGR2GRAY) gray = np.float32(gray) dst = cv2.cornerHarris(gray,2,3,0.04) dst = cv2.dilate(dst,None) cv2.imshow('Original',ori) cv2.imshow('Harris',image) if cv2.waitKey(0) & 0xff == 27: cv2.destroyAllWindows()
Here is the output.1.2 Shi-Tomasi Corner Detector
This is another corner detection algorithm. It works similar to Harris Corner detection. The only difference here is the computation of the value of R. This algorithm also allows us to find the best n corners in an image.
Let’s see the Python implementation.import numpy as np import cv2 from matplotlib import pyplot as plt img = cv2.imread('det_1.jpg') ori = cv2.imread('det_1.jpg') gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY) corners = cv2.goodFeaturesToTrack(gray,20,0.01,10) corners = np.int0(corners) for i in corners: x,y = i.ravel() cv2.circle(img,(x,y),3,255,-1) cv2.imshow('Original', ori) cv2.imshow('Shi-Tomasi', img) cv2.waitKey(0) cv2.destroyAllWindows()
This is the output of the Shi-Tomasi algorithm. Here the top 20 corners are detected.
The next one is Scale-Invariant Feature Transform.1.3 Scale-Invariant Feature Transform (SIFT)
SIFT is used to detect corners, blobs, circles, and so on. It is also used for scaling an image.
Consider these three images. Though they differ in color, rotation, and angle, you know that these are the three different images of mangoes. How can a computer be able to identify this?
Both Harris corner detection and Shi-Tomasi corner detection algorithms fail in this case. But SIFT algorithm plays a vital role here. It can detect features from the image irrespective of its size and orientation.
Let’s implement this algorithm.import numpy as np import cv2 as cv ori = cv.imread('det_1.jpg') img = cv.imread('det_1.jpg') gray = cv.cvtColor(img,cv.COLOR_BGR2GRAY) sift = cv.SIFT_create() kp, des = sift.detectAndCompute(gray,None) img=cv.drawKeypoints(gray,kp,img,flags=cv.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS) cv.imshow('Original',ori) cv.imshow('SIFT',image) if cv.waitKey(0) & 0xff == 27: cv.destroyAllWindows()
The output is shown below.
You can see that there are some lines and circles in the image. The size and orientation of the feature are indicated by the circle and line inside the circle respectively.
We will see the next algorithm of feature detection.1.4 Speeded-up Robust Features (SURF)
SURF algorithm is simply an upgraded version of SIFT.
Let’s implement this.import numpy as np import cv2 as cv ori =cv.imread('/content/det1.jpg') img = cv.imread('/content/det1.jpg') surf = cv.xfeatures2d.SURF_create(400) kp, des = surf.detectAndCompute(img,None) img2 = cv.drawKeypoints(img,kp,None,(255,0,0),4) cv.imshow('Original', ori) cv.imshow('SURF', img2)
Next, we will see how to extract another feature called bob.2. Detection of blobs
implement this one.import cv2 import numpy as np; ori = cv2.imread('det_1.jpg') im = cv2.imread("det_1.jpg", cv2.IMREAD_GRAYSCALE) detector = cv2.SimpleBlobDetector_create() keypoints = detector.detect(im) im_with_keypoints = cv2.drawKeypoints(im, keypoints, np.array(), (0,0,255), cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS) cv2.imshow('Original',ori) cv2.imshow('BLOB',im_with_keypoints) if cv2.waitKey(0) & 0xff == 27: cv2.destroyAllWindows()
Let’s see the output. Here, the blobs are detected very well.
Now, let’s jump into feature descriptor algorithms.3. Feature Descriptor Algorithms
Features are typically distinct points in an image and the descriptor gives a signature, so it describes the key point that is considered. It extracts the local neighborhood around that point so a local image patch is created and a signature from this local patch is computed.3.1 Histogram of Oriented Gradients (HoG)
detection applications. HoG is a technique that is used to count the occurrence of gradient orientation in localized portions of an image.
Let’s implement this algorithm.from skimage.feature import hog import cv2 ori = cv2.imread('/content/det1.jpg') img = cv2.imread("/content/det1.jpg") _, hog_image = hog(img, orientations=8, pixels_per_cell=(16, 16), cells_per_block=(1, 1), visualize=True, multichannel=True) cv2.imshow('Original', ori) cv2.imshow('HoG', hog_image)
The next one is BRIEF.3.2 Binary Robust Independent Elementary Features (BRIEF)
BRIEF is an alternative to the popular SIFT descriptor and they are faster to compute and more compact.
Let’s see its implementation.import numpy as np import cv2 as cv ori = cv.imread('/content/det1.jpg') img = cv.imread('/content/det1.jpg',0) star = cv.xfeatures2d.StarDetector_create() brief = cv.xfeatures2d.BriefDescriptorExtractor_create() kp = star.detect(img,None) print( brief.descriptorSize() ) print( des.shape ) img2 = cv.drawKeypoints(img, kp, None, color=(0, 255, 0), flags=0) cv.imshow('Original', ori) cv.imshow('BRIEF', img2)
Here is the result.3.3 Oriented FAST and Rotated BRIEF (ORB)
ORB is a one-shot facial recognition algorithm. It is currently being used in your mobile phones and apps like Google photos in which you group the people stab you see the images are grouped according to the people. This algorithm does not require any kind of major computations. It does not require GPU. Here, two algorithms are involved. FAST and BRIEF. It works on keypoint matching. Key point matching of distinctive regions in an image like the intensity variations.
Here is the implementation of this algorithm.import numpy as np import cv2 ori = cv2.imread('/content/det1.jpg') img = cv2.imread('/content/det1.jpg', 0) orb = cv2.ORB_create(nfeatures=200) kp = orb.detect(img, None) img2 = cv2.drawKeypoints(img, kp, None, color=(0, 255, 0), flags=0) cv2.imshow('Original', ori) cv2.imshow('ORB', img2)
Here is the output.
Now, let’s see about feature matching.4. Feature Matching
Feature matching is like comparing the features of two images which may be different in orientations, perspective, lightening, or even differ in sizes and colors. Let’s see its implementation.import cv2 img1 = cv2.imread('/content/det1.jpg', 0) img2 = cv2.imread('/content/88.jpg', 0) orb = cv2.ORB_create(nfeatures=500) kp1, des1 = orb.detectAndCompute(img1, None) kp2, des2 = orb.detectAndCompute(img2, None) bf = cv2.BFMatcher(cv2.NORM_HAMMING, crossCheck=True) matches = bf.match(des1, des2) matches = sorted(matches, key=lambda x: x.distance) match_img = cv2.drawMatches(img1, kp1, img2, kp2, matches[:50], None) cv2.imshow('original image', img1) cv2.imshow('test image', img2) cv2.imshow('Matches', match_img) cv2.waitKey()
This is the result of this algorithm.Endnotes
I hope you enjoyed this article. I have given a brief introduction to various feature detection, description, and feature matching techniques. The above-mentioned techniques are used in object detection, object tracking, and object classification applications.
The real fun starts when you start practicing. So, start practicing these algorithms, implement them in real-world projects, and see the fun. Keep learning.
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