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Amazon’s Alexa system is a powerful smart assistant that lets you shop with only your voice, control your smart home, and much more–but a home isn’t truly smart until it is automated. With Alexa, you can set up routines that happen at a specific time each day or when pre-set conditions are met.

Table of Contents

How to Create an Alexa Routine

Making a new routine in Alexa is simple. There are three main steps involved. Once you learn how to do them, you can set up all the Alexa routines you want. Take a look. 

Open the Alexa app and tap the More tab in the lower-right corner to open the menu. Tap Routines, and then tap the plus sign in the top-right corner. You’ll see a new screen where you have three options:

Enter routine name

When this happens

Add reaction

The first option is self-explanatory. Just name the routine whatever you want, although it’s a good policy to name it something that identifies it at a glance.

The second option is where the fun begins. You can choose for the routine to begin with a specific voice command, on a schedule, after a smart device trigger, when you reach a location, when you set an alarm, after pressing the Echo button, or when you activate Guard mode on Ring, ADT, or other compatible systems.

Choose the command you want to use. In this example, we used the voice command trigger phrase “Game time.” 

Once you’ve chosen the trigger for your routine, choose the response. This will be the action that Alexa takes to set your routine in action. There are a series of options to choose from:

Alexa Says: Alexa will respond with a customized phrase that you set or choose from a list.

Briefings: Alexa provides a summary of the day’s events and reminders.

Calendar: Alexa provides a breakdown of the day’s events from your linked calendar.

Calling: Alexa will make a call through a linked account like Skype.

Date and Time: Alexa will tell you the current date and time.

Device Settings: Alexa will stop playing music, adjust the volume, or go into Do Not Disturb mode.

Drop in Notification: Alexa makes a drop-in call.

E-mail: Alexa will read your email summary.

Entertain Me: Alexa will sing a song or tell a joke.

Fire TV: Alexa will begin to play content on Fire TV devices.

Good News: Alexa will read good, uplifting news stories.

Guard: Alexa will activate Guard to protect your home.

IFTTT: Alexa will run specific the IFTTT applets you have set up.

Messaging: Alexa will send or receive announcements.

Music: Alexa will play a song, specific artist, playlist, or station.

News: Alexa will play the news from your Flash Briefing.

Skills: Alexa will activate a pre-set Skill.

Smart Home: Alexa will control a smart home device you choose. 

Sounds: Alexa will play sounds you choose from categories like animals, bells and buzzers, crowds, and more.

Traffic: Alexa will provide a traffic report.

Wait: Alexa will set a timer for a time you specify.

Weather: Alexa will report the weather.

Custom: Alexa will respond to a question you set. 

Almost every option has sub-commands once you select it. To continue the example from above, we chose the Smart Home reaction. This opens another screen that provides access to three options: All Devices, Control group, and Control scene. 

This means that when you say “Alexa, game time,” all of the lights in the loft will turn on–and because they are preset to the individual colors, the routine will automatically set the mood for an evening of binging on your favorite games. 

Alexa Skills

An Alexa Skill is a bit like an app. It expands the basic functionality of Alexa beyond what it can do by default. Access Skills by tapping More and choosing Skills and Games from the menu. 

This opens the Skills and Games Menu where you can scroll through available skills in the Discover tab, search for specific types of Skills from the Categories tab, and view any Skills you have saved through the Your Skills tab. Even if you’ve never chosen Skills before, you might already have some available through devices added to your Alexa app.

For example, if you have LIFX bulbs or an iRobot device you control through Alexa, those skills will already be available in the Your Skills tab. There are almost two dozen categories to pick from, with more than 100,000 different Skills to help you totally customize and control your life through Alexa. 

To enable a Skill, choose it from the menu and then tap “Enable to Use.” Once you’ve done this, you have access to any abilities the Skill allows. 

Alexa Routines and Skills enable use of Alexa-compatible smart home devices, but there are also other Skills that act as shortcuts. For example, IFTTTrigger grants access to any of the services on chúng tôi that can be integrated into Alexa routines. This makes it easier to find potential IFTTT integrations than manually searching through the website itself. 

In addition to Skills that grant more functionality or enable productivity, you can also play games through Alexa Skills. Most of these games are trivia or quiz-style games, but there are others available to users with an Echo Show. You can also play Skyrim. It’s not quite like it would be on PC, but it just shows that the game really is everywhere.

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Hard Skills Vs Soft Skills

Introduction to Hard Skills vs Soft Skills

You very well understand that skill is an innate or acquired ability to perform a task with expertise. We have different tasks to perform throughout our lives; we need to acquire different types of Hard Skills vs Soft Skills to meet our needs and requirements of the specific task.

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Let us now check out the different types of Hard Skills vs Soft Skills through the story of Peter.

Once, a huge merchant ship harbored on a port for trading. Despite the best efforts of numerous expert engineers and technicians, including top technicians deployed at the port for ship maintenance, the ship’s engine would not start, and the cause of the problem could not be determined.

Some local townspeople hired to load and unload the ships suggested to the captain that he should try the service of one of their local man, Peter, an old fisherman who also fixed fishing boats around the town. In a hurry to depart, the captain instructed Peter to be brought aboard the ship at their earliest convenience.

The captain’s desperation dissipated now that the ship had returned to life. He hesitated to pay a massive amount for tapping an engine with a small hammer. Since Peter had a huge fan following in the town, he could not offend him by refusing to pay the bill.

He came up with a brilliant idea and asked Peter politely to break up the charges and provide him with an itemized bill instead.

Without blinking for a second, Peter took back a scrap of the bill and added above $6000,

Tapping with hammer = $ 2.00

Knowing where to tap = $ 5998

Total = $ 6000

Peter’s skill for repairing ship engines was complex, which is required for a job. Being confident and walking in to fix a ship that experts had failed to repair was his life skill. People in the town vouching, trusting, and promoting him was his social skill. Using his creativity to itemize the bill was his soft skill. Thanks to Peter, now you must clearly understand different skills and their usefulness in our lives.

Now let us move ahead and explore Hard Skills vs Soft Skills, determining how important they are for our career development.

The article on Hard Skills vs Soft Skills is structured as below:

Hard Skills vs Soft Skills Infographics

Below infographics on Hard Skills vs Soft Skills throws light on major points of differences between the two skills.

What are Hard Skills?

Your workplace or business demands hard skills for performing specific tasks. They are teachable skills that can be tangible and measured with tests, exams, and interviews. For a person to acquire a complex skill, specific prerequisites should be met by them. For example, to be a surgeon, that particular individual should possess an above-average IQ (Intelligence Quotient). Your hard skills are often centered on the logical or left side of your brain.

A few examples of hard skills are:



Mathematics and Science skills




Operating certain machines

What are Soft Skills?

Soft skills are psychological and emotional competencies enabling a person to deal effectively with challenges in personal or professional life. Compared to hard skills, soft skills are not prerequisites for acquiring them and are not job specific. They promote the social, physical, and mental well-being of a person. They are also referred to as life skills.

Soft skills are closely linked to one’s Emotional Quotient (EQ) and are primarily based on the right side of the brain. Since they are interpersonal and people skills, though we can recognize a soft skill, it takes work to measure it.

The top ten soft skills or life skills identified by WHO are:

Decision making


Creative thinking

Critical thinking

Effective communication

Interpersonal relationship skills



Coping with emotions

Coping with stress

Which are important Hard Skills vs Soft Skills?

There is no competition between hard skills vs soft skills. They are both essential and play important roles in your career development. You may set up an interview with an employer with the strength of complex skills, but your soft skills will play an essential role in your being hired.

Again, your soft skills will play a massive part as you adapt to your new role, interact with colleagues, and handle challenges cropping up in your working environment. Your job performance relies heavily on your hard skills, which are necessary for carrying out the tasks assigned to you efficiently according to your job role. Your soft skills will show that you use your hard skills effectively for success.

Why do we Neglect Soft Skills?

Recently there has been a lot of stress on soft skills, even in schools and colleges, besides corporates or workplaces. Candidates work hard to acquire complex skills for higher pay but often ignore their soft skills. Since there is no certificate for good communication skills or the ability to cope with stress, we take such skills for granted.

Recently there has been a lot of stress on soft skills, even in schools and colleges, besides corporates or workplaces. Candidates work hard to acquire complex skills for higher pay but often ignore their soft skills. Since there is no certificate for good communication skills or the ability to cope with stress, we take such skills for granted.

Though hard skills remain the same in every company you work for, the soft skills requirement may change depending on the culture, nature, and professional attitude of the people you work with. It is common for people to undervalue the significance of soft skills, as it can be challenging to identify the specific skills required for a given situation.

Hard skills can be listed on a resume and cover letter for a job application. This makes it very lucrative for candidates. Though they mention soft skills in the CV, they know they are personal skills, making it challenging for the employer to quantify them. Hence, they need to pay more attention to this skill set and realize that they will be evaluated by how they interact and relate to the interviewer during an interview.

Neglecting your soft skills will be one of the biggest career blunders you will be committing since soft skills will help you to translate your hard skills, knowledge, and abilities into actual talents. You will have the edge over others by knowing what to do, how, and when to do it.

Careers and Skill Priorities

Even though we stress a lot today about the importance of soft skills, there is no doubt that there are specific careers where you may succeed with good hard skills, even if your soft skills are questionable. Physicists and Mathematicians are excellent examples of this category. Many top scientists, including Albert Einstein, needed to gain social and life skills, yet it did not affect their achievements. We will never know whether such scientists would have achieved more or less if they had soft skills and socialized quite often.

The Italian Renaissance painter Michelangelo had poor soft skills, avoided socializing, and neglected his hygiene. That, however, did not stop him from being a great artist of all time.

Careers in Mathematics, Physics, or mechanics may require fewer soft skills than hard skills. In some situations, the presence of other people may even hinder the work, one of the reasons why some people like to work alone.

Even though teachers, lawyers, or accountants need good hard skills, they can only be successful in their careers with soft skills. Though they need thorough knowledge about their subject matter, they will only make much progress if they build a good rapport with their clients. They need excellent communication, relationship, and social skills for success.

Marketing and sales, business, PRO, front desk management, and many more similar jobs are careers that need high soft skills and just little hard skills. Even though knowledge about the product is necessary, success in such careers depends on soft skills like communication, negotiation, persuasion, identifying potentials, and cracking and closing deals.

The Right Time to Learn

While there is often an emphasis on children acquiring hard skills at an early age, it is actually more important to focus on developing soft skills during the early stages of life. For example – even though it is easy for an adult to learn mathematics at any stage of life, it will not be as easy to change how he or she communicates so easily.

Soft skills play a crucial role in shaping an individual’s personality, as they become deeply ingrained and challenging to modify once developed. Soft skills, when taught to children, would help them acquire hard skills much more easily. They would also develop into individuals with a more positive attitude, self-confidence, leadership quality, good teamwork, decision-making ability, and creativity, further enhancing their hard skill acquisition throughout life.

Acquiring soft skills will also promote the mental well-being of an individual, bringing down incidences of unwanted and unhealthy coping mechanisms like living in denial, substance abuse to manage stress or grief, and violent behavior, to name just a few.

With the changing lifestyles, young people today lack the necessary soft skills to manage their lives effectively. The cultural and traditional mechanisms that subtly passed on soft skills to youngsters are now obsolete. Many schools worldwide have adopted soft skills/ life skills programs in their curriculum to deal with this social problem facing our generation.

What do Employers Look for?

Realizing the importance of soft skills, today, more and more employers are looking for candidates with both skill sets – hard skills vs soft skills for their companies. During the interview, they often observe how the applicant is dressed, how he/she walks in, shakes hands with the people present, introduces himself, communicates, and makes eye contact. All these will have a significant impact on the outcome of the interview.

Smart employers have realized that it is easy to teach simple hard skills to their employers, like typing or computer programming, whereas it is either impossible or tough to teach soft skills.

People do not change overnight, and learning soft skills is making a complete personality makeover. It is never too late to learn. If you have ignored your soft skills so far, take the initiative and start today. Begin with decision-making and goal setting, with the decision being that you will improve your soft skill and the goal being which areas you will touch in a year.


As mentioned, there is no competition between your hard and soft skills; they are crucial in determining your value to an employer. Just because soft skills are not tangible and measurable, do not make a list of skills from the internet to add to your resume. You should learn to identify your soft skills or lack thereof to make progress.

Do not use vague words which do not mean anything. If you believe in and specify your hard skills in a resume, do the same for your soft skills. If you mention that you have good command over a language, make sure you do. Your interviewer may check it out for you during the interview process. If found you have made false claims, the whole integrity of your resume will be lost.

Another challenge you will face is identifying your soft skill. You know your hard skills, but are you even aware of your soft skills? You may have to take some tests or conduct self-evaluation with feedback from friends and others to know your strong and weak soft skills. Emphasize your strong points while trying to improve over the weaker ones.

Recommended Articles

This is a guide to Hard Skills vs Soft Skills. Here we discuss the introduction, infographics, and what are important hard Skills or Soft Skills. You can also look at the following articles to learn more –

Computer Vision Tutorial: A Step

19 minutes


Rating: 5 out of 5.


What’s the first thing you do when you’re attempting to cross the road? We typically look left and right, take stock of the vehicles on the road, and make our decision. Our brain is able to analyze, in a matter of milliseconds, what kind of vehicle (car, bus, truck, auto, etc.) is coming towards us. Can machines do that?

Now, there are multiple ways of dealing with computer vision challenges. The most popular approach I have come across is based on identifying the objects present in an image, aka, object detection. But what if we want to dive deeper? What if just detecting objects isn’t enough – we want to analyze our image at a much more granular level?

As data scientists, we are always curious to dig deeper into the data. Asking questions like these is why I love working in this field!

In this article, I will introduce you to the concept of image segmentation. It is a powerful computer vision algorithm that builds upon the idea of object detection and takes us to a whole new level of working with image data. This technique opens up so many possibilities – it has blown my mind.

What Is Image Segmentation?

Let’s understand image segmentation using a simple example. Consider the below image:

There’s only one object here – a dog. We can build a straightforward cat-dog classifier model and predict that there’s a dog in the given image. But what if we have both a cat and a dog in a single image?

We can train a multi-label classifier, in that instance. Now, there’s another caveat – we won’t know the location of either animal/object in the image.

That’s where image localization comes into the picture (no pun intended!). It helps us to identify the location of a single object in the given image. In case we have multiple objects present, we then rely on the concept of object detection (OD). We can predict the location along with the class for each object using OD.

Before detecting the objects and even before classifying the image, we need to understand what the image consists of. Enter – Image Segmentation.

How Does Image Segmentation Work?

We can divide or partition the image into various parts called segments. It’s not a great idea to process the entire image at the same time as there will be regions in the image which do not contain any information. By dividing the image into segments, we can make use of the important segments for processing the image. That, in a nutshell, is how image segmentation works.

An image is a collection or set of different pixels. We group together the pixels that have similar attributes using image segmentation. Take a moment to go through the below visual (it’ll give you a practical idea of image segmentation):

Source :

Object detection builds a bounding box corresponding to each class in the image. But it tells us nothing about the shape of the object. We only get the set of bounding box coordinates. We want to get more information – this is too vague for our purposes.

Image segmentation creates a pixel-wise mask for each object in the image. This technique gives us a far more granular understanding of the object(s) in the image.

Why do we need to go this deep? Can’t all image processing tasks be solved using simple bounding box coordinates? Let’s take a real-world example to answer this pertinent question.

What Is Image Segmentation Used For?

The shape of the cancerous cells plays a vital role in determining the severity of the cancer. You might have put the pieces together – object detection will not be very useful here. We will only generate bounding boxes which will not help us in identifying the shape of the cells.

Image Segmentation techniques make a MASSIVE impact here. They help us approach this problem in a more granular manner and get more meaningful results. A win-win for everyone in the healthcare industry.

Source: Wikipedia

Here, we can clearly see the shapes of all the cancerous cells. There are many other applications where Image segmentation is transforming industries:

Traffic Control Systems

Self Driving Cars

Locating objects in satellite images

Different Types of Image Segmentation

We can broadly divide image segmentation techniques into two types. Consider the below images:

Can you identify the difference between these two? Both the images are using image segmentation to identify and locate the people present.

In image 1, every pixel belongs to a particular class (either background or person). Also, all the pixels belonging to a particular class are represented by the same color (background as black and person as pink). This is an example of semantic segmentation

Image 2 has also assigned a particular class to each pixel of the image. However, different objects of the same class have different colors (Person 1 as red, Person 2 as green, background as black, etc.). This is an example of instance segmentation

Let me quickly summarize what we’ve learned. If there are 5 people in an image, semantic segmentation will focus on classifying all the people as a single instance. Instance segmentation, on the other hand. will identify each of these people individually.

So far, we have delved into the theoretical concepts of image processing and segmentation. Let’s mix things up a bit – we’ll combine learning concepts with implementing them in Python. I strongly believe that’s the best way to learn and remember any topic.

Region-based Segmentation

One simple way to segment different objects could be to use their pixel values. An important point to note – the pixel values will be different for the objects and the image’s background if there’s a sharp contrast between them.

In this case, we can set a threshold value. The pixel values falling below or above that threshold can be classified accordingly (as an object or the background). This technique is known as Threshold Segmentation.

If we want to divide the image into two regions (object and background), we define a single threshold value. This is known as the global threshold.

If we have multiple objects along with the background, we must define multiple thresholds. These thresholds are collectively known as the local threshold.

Let’s implement what we’ve learned in this section. Download this image and run the below code. It will give you a better understanding of how thresholding works (you can use any image of your choice if you feel like experimenting!).

First, we’ll import the required libraries.

View the code on Gist.

Let’s read the downloaded image and plot it:

View the code on Gist.

It is a three-channel image (RGB). We need to convert it into grayscale so that we only have a single channel. Doing this will also help us get a better understanding of how the algorithm works.

Python Code:

Now, we want to apply a certain threshold to this image. This threshold should separate the image into two parts – the foreground and the background. Before we do that, let’s quickly check the shape of this image:


(192, 263)

The height and width of the image is 192 and 263 respectively. We will take the mean of the pixel values and use that as a threshold. If the pixel value is more than our threshold, we can say that it belongs to an object. If the pixel value is less than the threshold, it will be treated as the background. Let’s code this:

View the code on Gist.

Nice! The darker region (black) represents the background and the brighter (white) region is the foreground. We can define multiple thresholds as well to detect multiple objects:

View the code on Gist.

Calculations are simpler

Fast operation speed

When the object and background have high contrast, this method performs really well

But there are some limitations to this approach. When we don’t have significant grayscale difference, or there is an overlap of the grayscale pixel values, it becomes very difficult to get accurate segments.

Edge Detection Segmentation

What divides two objects in an image? There is always an edge between two adjacent regions with different grayscale values (pixel values). The edges can be considered as the discontinuous local features of an image.

We can make use of this discontinuity to detect edges and hence define a boundary of the object. This helps us in detecting the shapes of multiple objects present in a given image. Now the question is how can we detect these edges? This is where we can make use of filters and convolutions. Refer to this article if you need to learn about these concepts.

The below visual will help you understand how a filter colvolves over an image :

Here’s the step-by-step process of how this works:

Take the weight matrix

Put it on top of the image

Perform element-wise multiplication and get the output

Move the weight matrix as per the stride chosen

Convolve until all the pixels of the input are used

One such weight matrix is the sobel operator. It is typically used to detect edges. The sobel operator has two weight matrices – one for detecting horizontal edges and the other for detecting vertical edges. Let me show how these operators look and we will then implement them in Python.

Sobel filter (horizontal) =


Sobel filter (vertical) =


Edge detection works by convolving these filters over the given image. Let’s visualize them on this article.

View the code on Gist.

It should be fairly simple for us to understand how the edges are detected in this image. Let’s convert it into grayscale and define the sobel filter (both horizontal and vertical) that will be convolved over this image:

View the code on Gist.

Now, convolve this filter over the image using the convolve function of the ndimage package from scipy.

View the code on Gist.

Let’s plot these results:

View the code on Gist. View the code on Gist.

Here, we are able to identify the horizontal as well as the vertical edges. There is one more type of filter that can detect both horizontal and vertical edges at the same time. This is called the laplace operator:


Let’s define this filter in Python and convolve it on the same image:

View the code on Gist.

Next, convolve the filter and print the output:

View the code on Gist.

Here, we can see that our method has detected both horizontal as well as vertical edges. I encourage you to try it on different images and share your results with me. Remember, the best way to learn is by practicing!

Clustering-based Image Segmentation

This idea might have come to you while reading about image segmentation. Can’t we use clustering techniques to divide images into segments? We certainly can!

In this section, we’ll get an an intuition of what clustering is (it’s always good to revise certain concepts!) and how we can use of it to segment images.

Clustering is the task of dividing the population (data points) into a number of groups, such that data points in the same groups are more similar to other data points in that same group than those in other groups. These groups are known as clusters.

K-means Clustering

One of the most commonly used clustering algorithms is k-means. Here, the k represents the number of clusters (not to be confused with k-nearest neighbor). Let’s understand how k-means works:

First, randomly select k initial clusters

Randomly assign each data point to any one of the k clusters

Calculate the centers of these clusters

Calculate the distance of all the points from the center of each cluster

Depending on this distance, the points are reassigned to the nearest cluster

Calculate the center of the newly formed clusters

Finally, repeat steps (4), (5) and (6) until either the center of the clusters does not change or we reach the set number of iterations

Let’s put our learning to the test and check how well k-means segments the objects in an image. We will be using this image, so download it, read it and and check its dimensions:

View the code on Gist.

It’s a 3-dimensional image of shape (192, 263, 3). For clustering the image using k-means, we first need to convert it into a 2-dimensional array whose shape will be (length*width, channels). In our example, this will be (192*263, 3).

View the code on Gist.

(50496, 3)

We can see that the image has been converted to a 2-dimensional array. Next, fit the k-means algorithm on this reshaped array and obtain the clusters. The cluster_centers_ function of k-means will return the cluster centers and labels_ function will give us the label for each pixel (it will tell us which pixel of the image belongs to which cluster).

View the code on Gist.

I have chosen 5 clusters for this article but you can play around with this number and check the results. Now, let’s bring back the clusters to their original shape, i.e. 3-dimensional image, and plot the results.

View the code on Gist.

Amazing, isn’t it? We are able to segment the image pretty well using just 5 clusters. I’m sure you’ll be able to improve the segmentation by increasing the number of clusters.

k-means works really well when we have a small dataset. It can segment the objects in the image and give impressive results. But the algorithm hits a roadblock when applied on a large dataset (more number of images).

It looks at all the samples at every iteration, so the time taken is too high. Hence, it’s also too expensive to implement. And since k-means is a distance-based algorithm, it only applies to convex datasets and is unsuitable for clustering non-convex clusters.

Finally, let’s look at a simple, flexible and general approach for image segmentation.

Mask R-CNN

Data scientists and researchers at Facebook AI Research (FAIR) pioneered a deep learning architecture, called Mask R-CNN, that can create a pixel-wise mask for each object in an image. This is a really cool concept so follow along closely!

Mask R-CNN is an extension of the popular Faster R-CNN object detection architecture. Mask R-CNN adds a branch to the already existing Faster R-CNN outputs. The Faster R-CNN method generates two things for each object in the image:

Its class

The bounding box coordinates

Mask R-CNN adds a third branch to this which outputs the object mask as well. Take a look at the below image to get an intuition of how Mask R-CNN works on the inside:


We take an image as input and pass it to the ConvNet, which returns the feature map for that image

Region proposal network (RPN) is applied on these feature maps. This returns the object proposals along with their objectness score

A RoI pooling layer is applied on these proposals to bring down all the proposals to the same size

Finally, the proposals are passed to a fully connected layer to classify and output the bounding boxes for objects. It also returns the mask for each proposal

Mask R-CNN is the current state-of-the-art for image segmentation and runs at 5 fps.

Summary of Image Segmentation Techniques

I have summarized the different image segmentation algorithms in the below table.. I suggest keeping this handy next time you’re working on an image segmentation challenge or problem!

AlgorithmDescriptionAdvantagesLimitationsRegion-Based SegmentationSeparates the objects into different regions based on some threshold value(s).a. Simple calculations

b. Fast operation speed

c. When the object and background have high contrast, this method performs really well

When there is no significant grayscale difference or an overlap of the grayscale pixel values, it becomes very difficult to get accurate chúng tôi Detection SegmentationMakes use of discontinuous local features of an image to detect edges and hence define a boundary of the chúng tôi is good for images having better contrast between chúng tôi suitable when there are too many edges in the image and if there is less contrast between objects.Segmentation based on ClusteringDivides the pixels of the image into homogeneous clusters.Works really well on small datasets and generates excellent clusters.a. Computation time is too large and expensive.

b. k-means is a distance-based algorithm. It is not suitable for clustering non-convex clusters.

Mask R-CNNGives three outputs for each object in the image: its class, bounding box coordinates, and object maska. Simple, flexible and general approach

b. It is also the current state-of-the-art for image segmentation

High training time


This article is just the beginning of our journey to learn all about image segmentation. In the next article of this series, we will deep dive into the implementation of Mask R-CNN. So stay tuned!

I have found image segmentation quite a useful function in my deep learning career. The level of granularity I get from these techniques is astounding. It always amazes me how much detail we are able to extract with a few lines of code. I’ve mentioned a couple of useful resources below to help you out in your computer vision journey:

Frequently Asked Questions

Q1. What are the different types of image segmentation?

A. There are mainly 4 types of image segmentation: region-based segmentation, edge detection segmentation, clustering-based segmentation, and mask R-CNN.

Q2. What is the best image segmentation method?

A. Clustering-based segmentation techniques such as k-means clustering are the most commonly used method for image segmentation.

Q3. What is image segmentation?

A. Image segmentation is the process of filtering or categorizing a database of images into classes, subsets, or regions based on certain specific features or characteristics.


Amazon Echo Show Gives Alexa A Screen And Free Video Calls

Amazon Echo Show gives Alexa a screen and free video calls

We may receive a commission on purchases made from links.

The new Amazon Echo Show, the latest way for Jeff Bezos & Co. to sneak Alexa into your home, has been officially revealed, complete with both voice and touch. The first Echo device to include a display, the Echo Show has a 7-inch touchscreen and a 5-megapixel camera for video calls. There’s also a total of eight microphones packed into its fairly blocky housing.

They’re used for Amazon’s speech recognition array, the system which allows Echo to hear you from across the room and pinpoint your speech while ignoring other ambient sounds around the room. A microphone mute button stops the Echo Show from listening out for your wake-word trigger. [Update: Echo Show has eight microphones, not nine as reported earlier; the front microphone shown in Amazon’s diagram is in fact one of the eight in the main array, not an extra]

Alexa’s functionality remains the same as from the audio-only Echo devices. You’ll be able to ask questions and for specific tracks or genres, across not only Amazon Music but Pandora, Spotify, TuneIn, iHeartRadio, and other providers. Dual 2-inch stereo speakers are built-in under the touchscreen for playback.

As with other Echo devices, there’s broad integration with various services and smart home kit. Through Echo Show you’ll be able to command your WeMo, SmartThings, Hue, Insteon, Ring, Arlo, Wink, and Ecobee IoT devices, for instance. You’ll be able to ask for a daily briefing of news and updates from your Google Calendar, too.

Where Echo Show sets itself apart from its siblings is the extra functionality having a display enables. As well as requesting music, you’ll be able to ask for YouTube videos, along with flash briefings in video format. Echo Show will be able to display music lyrics for impromptu karaoke sessions, together with your photos and text versions of your to-do and shopping lists.

There’s also the ability to see connected camera feeds on the screen, such as from your smart doorbell or what’s going on in a kid’s nursery. New with Echo Show, however, is the ability to make video calls: again, you ask for a contact, and they’re called, either through their own Echo Show or via the free Alexa app for their smartphone. A feature called Drop In will allow for instant video connections; you’ll be able to whitelist who gets to instantly appear on your display.

Voice messaging is also supported. That works across all Echo devices, too, so if you’ve got an Echo Dot, Echo, or the Alexa app, you’ll be able to make and receive voice calls and leave messages through that, too. Amazon says all calls, audio and video, are free.

For connectivity, there’s dual-band WiFi a/b/g/n/a (2.4/5GHz), along with Bluetooth with A2DP stereo streaming. The Echo Show can play music back via external Bluetooth speakers, too, though only if those speakers don’t require a PIN code to pair them. Without batteries, you’ll need to keep the Echo Show plugged in via the included 6 foot adapter.

Preorders for the Echo Show begin today, priced at $229.99. It’ll begin shipping on June 28, Amazon says, and be available in both black and white. If you buy two, meanwhile – useful if you want to make two-way video calls – Amazon will cut $100 off the combined price.

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Ari Simon has been a writer with Make Tech Easier since August 2011. Ari loves anything related to technology and social media. When Ari isn’t working, he enjoys traveling and trying out the latest tech gadget.

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How To Create A Customized Alexa Skill Using Blueprints

The smart speaker market has plenty of great options, but the Amazon Echo with Alexa has a useful option the others haven’t rolled out yet. When you use Alexa, you have the possibility of creating your own skills with absolutely no coding required!

What are Alexa Blueprints?

Alexa Blueprints is a new method for customizing your Alexa experience. Simply put, Blueprints are templates to create skills of your own to be used in your home without coding. It’s as easy as filling in the blanks.

When you make a skill, the default setting makes the skill available in only your account. However, you can share the skill with friends and family if you want.

What can you do with Alexa Blueprints?

There are five different categories of Skills Blueprints: Greetings & Occasions, Fun & Games, Learning & Knowledge, At Home, and Storyteller.

In the Greetings & Occasions category, you will find what amounts to a list of greeting cards you can send to someone for their birthday or other events.

Fun & Games contains blueprints for general trivia games and birthday trivia for parties to see who knows the birthday boy or girl the best. There’s also a game show blueprint and a blueprint to create an inspirational quote generator.

For those of you with kids in school or going back to school yourself, the Learning & Knowledge category has tools to help! Just create a skill using the Flashcards or Quizzes blueprints to aid you in your studying.

The At Home category has useful templates to create skills for your house guests, babysitters, or petsitters. You can create your own custom question and answer skill and even create a chore chart.

Time for a bedtime story? Templates in the storyteller section include ones for fairy tales, sci-fi, and fables.

How do you use Alexa Blueprints?

So where do you go to create these blueprints? Follow these instructions, and you’ll have your first skill done in less time than you can imagine.

2. Log in to your Amazon account that you use for your Alexa.

4. Pick your template from more than twenty options. Check out Featured blueprints for inspiration.

7. Name your skill.

8. Hit “Create Skill.”

9. Wait a few minutes. There will be a notification when it is ready to use.

10. Test it by asking Alexa to open it: “Alexa, open (skill name).”

11. If you need to, edit it in “Skills You’ve Made” at the top of the Alexa Blueprints page.

Proper names can be tough for Alexa to understand, so you want to avoid specific family names in your titles and stay more generic with your names. She can understand common special characters like question marks and dashes. If you use a symbol that she can’t understand, you will receive an error message.

Share your Blueprints

You can share an Alexa skill with your family and friends, but just remember, if you share it with them, they will be able to share it too. Take that into consideration before sending it out to the masses. Don’t worry, though. You are the only one who will be able to edit the skill.

To share:

3. Choose Share with Others.

4. Indicate if the skill is meant for kids under 13.

You can see who is using your skill and revoke access whenever you want.

There is no limit as to how many skills you can create, so be creative and have fun with it!

Image credit: FlashCards vocabulary

Tracey Rosenberger

Tracey Rosenberger spent 26 years teaching elementary students, using technology to enhance learning. Now she’s excited to share helpful technology with teachers and everyone else who sees tech as intimidating.

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