R-CNN: R-CNN computes region proposals using the ‘selective search’ method. In selective search, segmentation maps of different objects in an image are calculated, and the smaller regions are recursively combined into more significant regions based on similarity. Further, a CNN based network is used for feature extraction. Finally, the regions are classified. The drawback of this method is that it is pretty slow as it computes nearly 2000 regions for every image during test time.

CMOSimagesensor

Deep learning for computer vision is a heavily researched domain over the past decade. There have been several advances in deep learning technology for vision problems such as Image Classification, Object Detection, Semantic Segmentation, Generative Adversarial Networks and Image Super-Resolution, to name just a few.

YOLO: It stands for ‘You Only Look Once’. In Yolo, the input image is divided into grid cells. For each grid cell, bounding boxes are calculated. If the centre of the bounding box falls within the grid cell, then the coordinates and the class confidence scores are calculated.

Along with the theory, we will also discuss different functions and functionalities provided by OpenCV for image filtering.

There are several algorithms for classification, both in machine learning and deep learning. Some machine learning algorithms are Support Vector Machine, Decision Trees and K Nearest Neighbour. In deep understanding, Convolutional Neural Networks (CNNs) are the most prominent architectures for classification as they can easily learn the various features and produce highly accurate results.

CMOS sensorvs CCD

SSD: The basic working of SSD is quite similar to that of YOLO. However, in SSD the grid comprises different sizes, unlike YOLO, to better detect objects of varying scales. Although SSD is quicker than YOLO and gives more accurate results, the performance slightly reduces when tiny objects are.

This kind of thresholding will categorise the edges into strong, suppressed and weak. Based on a threshold, stron, they,g edges are included in the final detection map, suppressed edges are removed, and if the weak edges are connected with the strong edges, then they are also included in the final detection map.

Computer vision can mainly be divided into low-level vision, mid and high-level vision. Here, we will be discussing both categories. Image processing comprises low-level vision, whereas deep learning is a part of high-level vision.

In two-stage methods, the first step involves creating tentative region proposals where you can find objects and the second step is classification after feature extraction. Popular region-based object detection algorithms are R-CNN, Fast R-CNN, and Faster R-CNN.

Computer Vision as a field, in its primitive form, arose right in the early 1960s after the term Artificial Intelligence (AI) was coined at the Dartmouth conference. Image classification of cats was one of the initial challenging problems which researchers tackled in AI. With the advent of more excellent computational capabilities of machines and broader access to data, AI became prominent after the 1980s. So, presently, why is Computer Vision one of the most crucial domains of AI? The reason being, it has a tremendous number of applications relevant in day to day life across multiple diverse industries. Consider saving someone’s life on time when a person has been diagnosed with a fatal disease or being able to get those perfect fitting clothes without actually trying them; sounds interesting, right? Well, all of this is possible with the use of Computer Vision. Some more interesting examples are listed below.

Now, let’s talk about the core of computer vision, i.e. image processing. We will be discussing various major topics of image processing using OpenCV in python.

The Sobel detector calculates the gradient of the pixels in an image. It identifies the rate at which the pixel intensity changes from light to dark and the direction of the change. This change will let us know the presence of an edge by judging how quickly or gradually the intensity changes. It contains two kernels, one for detecting horizontal edges and the other for vertical edges.

Some historical references state that the sensor size is 3/2 * Format Type then rounded, however, there are still exist many discontinuities. With modern day imaging, there exist a nearly infinite number of image sensor diagonal dimensions for which there was no original video tube size corresponding to.So, we've done our best to derive a "modern-day" equation that defines image sensor format type more precisely. We used commonly agreed upon datapoints of 1" = 16.0mm, 1/2" = 8.0mm, 1/3" = 6.0mm, 1/4" = 4.5mm and backed out a fitting equation, then cross-referenced as many other articles as possible.Unfortunately, the formula for the digital image sensor type has a discontinuity occurring between the 1/2" image sensor and 1/2.3" image sensor format size.

The components of hue and saturation remain majorly indifferent to lighting conditions. The value component will change as per the lighting. Since in HSV color space, sources of chrominance and luminance are separate, it becomes easier to perform color segmentation by specifying a threshold.

CMOS sensor

In one-stage methods, an image goes through a single pass in the neural network, unlike the two-stage methods. A fixed number of predictions for objects are made based on a predefined grid. The bounding boxes and class probabilities are calculated for each of the objects in the output. Some of the one-stage algorithms are:

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The basic strategy for object detection can be summarized as three steps - selection of target region, feature extraction, and performing classification or regression.

Image blurring also referred to as smoothening, is an essential step in any image processing application. Blurring is usually a preprocessing step when dealing with edge detection as it helps reduce the noise around the edges of the objects.

DeepLab: The previously discussed methods are computationally expensive and fail to generate perfect acceptable boundaries. DeepLab tackles these issues with a novel approach. It introduces three main improvements with Atrous Convolutions, Atrous Spatial Pyramid Pooling (ASPP), and Conditional Random Fields (CRF). Atrous convolutions, also known as dilated convolutions, help obtain a larger context of features by maintaining a similar number of parameters. ASPP captures multi-scale information just from a single image, unlike the earlier method, which required multiple images of different resolutions. With CRFs, the boundaries of the segmented image could be made finer and sharper.

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Image classification is the computer vision task of identifying the content of an image and then segregating the objects into classes. For example, looking at the above image, we can easily recognise that it’s a leopard, but how does a computer classify it as a leopard?

The format type of a digital camera sensor is one of the most confusing in the vision industry. The format type is a remanent from how video camera tubes were constructed prior to the advent of CCDs or CMOS sensors. These 'video tubes' had opaque regions outside the active area of the cathode tube, which reduced the light sensitive region. These resulted from the mechanics holding either the larger tube or cathodes. For example:

Erosion is helpful for noise removal. As the name implies, this operation erodes the boundaries of the foreground objects hence making them thinner. The idea is when a kernel convolves with the image, a particular pixel value of the image is kept one only if all pixels under the kernel are 1. Else, the pixel value becomes zero.

The detection of edges in an image enables us to identify the objects that are present. So, it is a significant use case in computer vision. The edges are formed by a significant variation in the adjacent pixel intensities of an image. Two popular edge detection algorithms are - Sobel filter and the Canny filter.

Computer Vision is also one of the most demanding domains in AI. According to a report presented by ‘Research and Markets’, the value of AI in the Computer Vision market is estimated to increase by USD 35.4 billion in the next five years from the value of USD 15.9 billion in 2021. Therefore, considering the given statistics, there will be a promising rise in the need for Computer Vision engineers. So if you want to become one, don’t worry; this computer vision tutorial will provide a perfect outline for you to get started.

CMOS sensorSamsung

A 2D convolution layer to perform convolution and extract the feature maps followed by a non-linear activation function.

This step calculates the intensity gradient of the image along with both the directions - X and Y. Similar to the Sobel filter; it outputs the direction and the rate of change of intensity gradient.

RGB images are constructed using a 3D matrix, while grayscale images consist of a 2D matrix. Videos are also images stitched together at a specific fps (frames per second) rate. Therefore, to process the images, it is necessary to have an understanding of operations on vectors and matrices. Dimensionality reduction is a crucial concept for presenting the data in a compressed space. Furthermore, algorithms such as Linear Regression, Principal Component Analysis, Singular Value Decomposition and Linear Discriminant Analysis are some excellent examples of extensive usage of Linear Algebra.

This is a preprocessing step with Gaussian Blur to smoothen the image and reduce the noise around the edges for accurate results.

Faster R-CNN: Although Fast R-CNN is more efficient than R-CNN, it still uses the selective search algorithm for region proposal, which is pretty slow. Hence, in Faster R-CNN, a Region Proposal Network (RPN) is used to predict the object regions and objectness scores. RPN is a CNN that effectively learns the proposals. All the further steps of the RoI Pooling layer and fully connected layers remain the same, just like Fast R-CNN. Thus, Faster R-CNN creates learned region proposals and works faster than R-CNN and Fast R-CNN.

Computer vision and deep learning revolve around dealing with uncertainty. Since the algorithms always predict better versions of results, probability plays a significant role in determining the outcome’s success. For example, in multiclass classification, the output of a particular object being predicted with a specific class solely depends on probability axioms. Moreover, the optimisation strategies based on Maximum Likelihood Estimation (MLE) or Maximum a Posteriori Estimation (MAP) briefly describe the usage of statistics.

Image sharpening is just the opposite of blurring. It emphasises the variation in the neighbouring pixels so that edges look more vivid. Here, we will be building a custom 2D kernel for sharpening. The kernel used is,

To understand the working of a neural network, one of the essential concepts is the backpropagation algorithm. The primary strategy used is the updation of gradients based on the value of the loss function. This concept requires a basic understanding of Differential Calculus, Partial Derivatives and Divergence/Convergence of a function.

This computer vision tutorial serves as a go-to guide for anyone wanting to become a computer vision engineer. Moreover, to develop a complete understanding of these problems, it is always the best idea to start implementing projects. Do check out the following blog post on 15 Computer Vision Project Ideas for Beginners for getting awesome ideas on computer vision projects. Also, the fantastic end-to-end solved projects provided by ProjectPro are a great way to enhance your learning in becoming a computer vision engineer.

The sensor format size and pixel size of digital camera impacts nearly every performance attribute of a camera. The format size is a key element that contributes to system constraints across the low-light performance, dynamic range, size, cost, power consumption, lens requirement, number of pixels resolution, etc. Each of these will change the form and function of camera. There are general rules of thumb for how the these will impact the image quality of a camera.  The tradeoffs tend to be continuous and very inter-related across many degrees of freedom.Every camera use-case has application-specific requirements, so the selection of a CMOS image sensor needs to be made on a case-by-case basis. We've done our best to categorize the usage of  sensor-type by industry.

This operation is just the opposite of opening, i.e. dilation followed by erosion. It helps in closing small gaps in the objects of an image.

This color space also encodes the information of luminance and chroma in separate channels. The L channel corresponds to lightness (lighting intensity). The A and B components store color details, with the former consisting of color components ranging from green to magenta and the latter blue to yellow. As mentioned, the change in illumination will affect the Y component. The A and B components will significantly show the difference of color information irrespective of lighting conditions.

In YCrCb color space, Y stands for luma (intensity of luminance), Cr represents the red component after subtracting the luma component (R - Y) and similarly, Cb represents the blue component after subtracting the luma component (B - Y). Again, the distinct components of chroma and luminance aid in the effective separation of the colors; specifically, this color space works best for distinguishing the red and blue colors from an image.

IsCMOS sensorfull frame

This operation is simply erosion followed by dilation. It is helpful in removing noises while retaining the object boundaries.

Computer vision deals with analysing the image/video data and providing computational capabilities to a machine using different machine learning algorithms. Abstractly, it involves imparting the skill of human-like visual inference to a machine. Knowledge of various aspects of computer science is required to get started with computer vision. This computer vision tutorial will cover your questions related to:

Lastly, the final fully connected layer will consist of a sigmoid/softmax activation function to output the probability of prediction.

We've created a simple calculator to help translate between engineers and hobbyists who use the "35mm Equivalent EFL" when discussing Field of View.If you are looking for more advanced functionality and calculations, please see our more advanced Field of View calculator which includes distortion. PLEASE NOTE THAT THE BELOW CALCULATOR IS INCORRECT FOR LENSES WITH DISTORTION.

Canny edge detector follows a multi-stage edge detection algorithm. It is robust and highly efficient as it incorporates the Sobel filter method along with some post-processing steps. The significant stages are listed below:

Output: 1. Sobel in the x-direction, 2. Sobel in the y-direction, 3. Sobel in x-y direction and 4. Canny detection [Left to right in all rows]

As discussed earlier, firstly, you will need some prior knowledge about the mathematical subjects involved. Next, to know about the gist of fundamental concepts required for deep learning, refer to the chart below.

This blurring technique uses a Gaussian filter. Instead of directly averaging all the values with a constant number, a weighted average will take place here. The image pixels’ distance from the kernel’s centre will determine their corresponding weight involved in the weighted average. Pixels nearby the centre have more weight as opposed to the farther pixels.

Unet: This is the most widely used network for semantic segmentation. Since in the previous method, the model could not learn the fine details, U-Net adds multiple skip connections between the encoder and decoder to retrieve the intermediate feature maps. This helps in restoring the lost fine details. The architecture is called ‘Unet’ because of U-structure formed by the encoder, decoder and skip connections.

OpenCV also provides a function called ‘blur()’, which does image blurring. In this function, only the kernel size needs to be mentioned, then, it will create a blur matrix on its own and perform blurring.

Dilation is usually performed after erosion to increase the object area. Since erosion removes noise and makes the boundaries thin, dilation does just the opposite, i.e. it makes the object boundaries thicker. When the convolution occurs, if there is at least one pixel under the kernel whose value is one then the pixel value of the original image becomes 1.

Well that doesn’t seem to make any sense, does it? 1" is 25.4mm and 1/3 of 1" is 8.5mm! Even 1/3 of the 1" Format Diagonal should be 5mm!Let's take the 1" format type as an example. Part of the cathode ray tube's diameter was the tube wall and was not used for imaging purposes. So, the area was reduced from 25.4mm to 16mm. So, a lens suitable for a 1" type cathode ray tube did not need an image circle that extended to the entire 25.4mm tube diameter.

Morphological operations are also some basic operations that can be performed by convolution of a kernel with an image. The transformations are performed on a binary image. Different kinds of operations are erosion, dilation, opening, closing and few others.

RGB stands for the three colours - Red, Green and Blue. The pixels of an RGB image are made up with a linear combination of the colour intensities of Red, Green and Blue. The color differentiation is heavily dependent on the lighting conditions. Under bright lighting, the segregation of colours can be more visible as opposed to dull lighting. Hence, to perform colour based segmentation, RGB color space is not a good choice as the information related to color and luminance is mixed.

cmos sensorvs full-frame

“filter2D()” is a function provided by OpenCV which helps perform convolution of an image with any predefined custom kernel. The function requires three parameters - src, ddepth and kernel.

Fast R-CNN: In this method, instead of passing region proposals through conv-net in the beginning, the whole image is passed for calculating feature maps. Later, the region proposals are generated from a proposal method. Further, an RoI Pooling layer is used, which reshapes all the regions for input to the model. Ultimately, the fully connected layers output the bounding boxes and the classification values.

Use cases of semantic segmentation in real-life scenarios are abundant. It is applied in the automobile industry for self-driving cars, textile industry for virtual try-on features, portrait mode on Google’s phone, cosmetics industry for virtual make-up and many others.

Object detection is a computer vision application that involves image classification to recognise objects and object localisation to know where they are present. A bounding box is drawn around the region of interest and a class label is associated with it for the input. Some examples of classes can be a person, a food item, a car, a bottle, a dog or a purse.

This kind of blurring involves the replacement of the pixel values in the original image with the median value of the pixels present in the area covered by the blurring kernel.

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ddepth represents the depth of the output image. Keeping a default value to -1 will maintain the same depth of input and output image.

Next, fully connected layers are present wherein every node of each layer is connected with every node of the other layer. They perform the job of classification based on the extracted features.

Most manufacturers are consistent and accurate with their Format Type classifications. However, we've run across numerous specification sheets which are mis-classified.To repeat, always use the exact output pixel count and pixel pitch (size) when finding a lens! If you've figured out which sensor you're using and want to calculate the 35mm Format Type Equivalent EFL, check out our Camera Field of View Calculator.

All the pixels in the output image will correspond to different labels based on the objects present in the input image to represent the task.

Output: 1. Original Image (Grayscale), 2. Inverted Binary Image, 3. Erosion, 4. Dilation, 5. Opening and 6. Closing [Left to right in all rows]

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The deep learning-based object detection can be classified into two categories - one-stage methods and two-stage methods. Let’s discuss each of the categories and the associated prominent algorithms.

The basic blurring simply involves averaging the pixel values of the image. For example, you can construct a custom 3*3 kernel consisting of ones and divide it with 9. Convolution of this kernel with the image will cause the blurring effect.

In addition to the above-discussed topics, you should also explore the below topics for gaining a deeper insight into a variety of computer vision problems -

While I discussed the most popular morphological transformations, you can also check out morphological gradient, top hat and black hat.

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The next most important concept that we would cover in this computer vision tutorial is the various techniques involved in computer vision. Let’s dive right into the different computer vision problems and their working.

Fully Convolutional Network (FCN): This approach follows a simple encoder-decoder architecture. Existing image classification networks can serve as encoders, and reconstruct the image, a decoder module consisting of transpose convolutions can be attached. The downside of this approach is that it cannot learn the fine details of the features hence producing a coarse output segmentation map.

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Image filtering can be used for many purposes, such as blurring, sharpening or detecting edges of an image. It involves performing convolution of the image with a 2D kernel matrix. Convolution involves sliding the kernel over the input image and performing element-wise multiplication followed by addition. Check out this link for understanding the basic convolution operations.

This subjective classification means that manufacturers can apply 'best judgement' when publishing a Format Type for their sensor.The means that engineers should use the exact output pixel count and pixel pitch (size) when considering a lens, such as a CS Mount Lens or M12 Lens.The majority of sensors on the market approximately adhere to the Format Type dimensions below, which can calculated from the formula above:

Blurring an entire image is not a good choice when information related to sharp edges needs to be preserved. In that case, bilateral filtering comes handy. It selectively blurs the image based on the similarity of pixel values in a neighbourhood. This filter contains the property of Gaussian filter, i.e. filtering based on the distance from the kernel centre and the pixel intensities present in a neighbourhood of the image. Hence, it helps to maintain the edge structure of an image.

What isCMOS sensorin camera

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Semantic segmentation is the classification of every pixel of an image to a corresponding label. This is unlike image classification, which performs classification on the whole image. For example, in the above image, there will be one label for the three cars, another label for two people riding bicycles and a third label for the pillar.

Multiple algorithms have been developed over the years for the semantic segmentation task. Some of which we will be discussing are Fully Convolutional Network (FCN), Unet and DeepLab.

Object Detection has a vast range of applications. Relevant to current times, you can use it to create a social distancing application that can detect masks and check the distance between people walking. Moreover, object tracking is crucial to surveillance systems. Gesture Recognition, Face Recognition, Vehicle Identification are some other real-life use cases of object detection.

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The function used is ‘medianBlur()’. It requires only the input image and the kernel size of the median filter as its arguments.

This technique is used to ignore the false edges generated. It checks whether the gradient value of the current pixel is greater than the neighbouring pixel or not. In the former case, the gradient value of the current pixel remains unchanged otherwise, the value is updated to zero.

cmos sensorvs aps-c

You can replicate the above module multiple times to create a deep network. The model learns primitive features such as edges, color textures, contrast, etc. Going deeper, more intricate features such as the structure of an object are recognized.

You might wonder why we need to know all the mathematics when we already have the libraries and in-built functions doing the magic for us. Although somewhere, you are right, a mathematical understanding of the underlying concepts is needed to dive deeper into the architectures and optimise them for improved performance. Also, the core fundamentals of all the machine learning, deep learning and computer vision algorithms comprise pure mathematics; hence it is essential to know the same.

Image classification is prevalent across many industries. You can use it in Machine Vision for classifying anomalous products, Self Driving Cars for recognising trees, streets or poles in the driving scene, Medical Imagery to diagnose diseases, Augmented Reality for gaming, Agriculture for sustainable farming, and many other applications.

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The four prominent color spaces provided by OpenCV are RGB, HSV, YCrCb and LAB. Each of the colour spaces have their properties and consideration of various factors affecting an image. Using the appropriate color space can help tackle problems involving color based segmentation.