image segmentation use cases

But with deep learning and image segmentation the same can be obtained using just a 2d image, Visual Image Search :- The idea of segmenting out clothes is also used in image retrieval algorithms in eCommerce. Link :- First of all, it avoids the division by zero error when calculating the loss. Detection (left) and segmentation (right). In this article we will go through this concept of image segmentation, discuss the relevant use-cases, different neural network architectures involved in achieving the results, metrics and datasets to explore. In some datasets is called background, some other datasets call it as void as well. Many of the ideas here are taken from this amazing research survey – Image Segmentation Using Deep Learning: A Survey. $$. There are trees, crops, water bodies, roads, and even cars. Virtual make-up :- Applying virtual lip-stick is possible now with the help of image segmentation, 4.Virtual try-on :- Virtual try on of clothes is an interesting feature which was available in stores using specialized hardware which creates a 3d model. Mean\ Pixel\ Accuracy =\frac{1}{K+1} \sum_{i=0}^{K}\frac{p_{ii}}{\sum_{j=0}^{K}p_{ij}} But as with most of the image related problem statements deep learning has worked comprehensively better than the existing techniques and has become a norm now when dealing with Semantic Segmentation. IOU is defined as the ratio of intersection of ground truth and predicted segmentation outputs over their union. If one class dominates most part of the images in a dataset like for example background, it needs to be weighed down compared to other classes. It is different than image recognition, which assigns one or more labels to an entire image; and object detection, which locatalizes objects within an image by drawing a bounding box around them. For now, we will not go into much detail of the dice loss function. This paper improves on top of the above discussion by adaptively selecting the frames to compute the segmentation map or to use the cached result instead of using a fixed timer or a heuristic. This loss function directly tries to optimize F1 score. Semantic segmentation involves performing two tasks concurrently, i) Classificationii) LocalizationThe classification networks are created to be invariant to translation and rotation thus giving no importance to location information whereas the localization involves getting accurate details w.r.t the location. It also consists of an encoder which down-samples the input image to a feature map and the decoder which up samples the feature map to input image size using learned deconvolution layers. $$ These values are concatenated by converting to a 1d vector thus capturing information at multiple scales. The general architecture of a CNN consists of few convolutional and pooling layers followed by few fully connected layers at the end. If we are calculating for multiple classes, IOU of each class is calculated and their mean is taken. Conditional Random Field operates a post-processing step and tries to improve the results produced to define shaper boundaries. The research utilizes this concept and suggests that in cases where there is not much of a change across the frames there is no need of computing the features/outputs again and the cached values from the previous frame can be used. Then, there will be cases when the image will contain multiple objects with equal importance. The Mask-RCNN model combines the losses of all the three and trains the network jointly. The metric popularly used in classification F1 Score can be used for segmentation task as well to deal with class imbalance. This value is passed through a warp module which also takes as input the feature map of an intermediate layer calculated by passing through the network. there is a need for real-time segmentation on the observed video. The most important problems that humans have been  interested in solving with computer vision are image classification, object detection and segmentation in the increasing order of their difficulty. KITTI and CamVid are similar kinds of datasets which can be used for training self-driving cars. Along with being a performance evaluation metric is also being used as the loss function while training the algorithm. We also looked through the ways to evaluate the results and the datasets to get started on. Image Segmentation Using Deep Learning: A Survey, Fully Convolutional Networks for Semantic Segmentation, Semantic Segmentation using PyTorch FCN ResNet - DebuggerCafe, Instance Segmentation with PyTorch and Mask R-CNN - DebuggerCafe, Multi-Head Deep Learning Models for Multi-Label Classification, Object Detection using SSD300 ResNet50 and PyTorch, Object Detection using PyTorch and SSD300 with VGG16 Backbone, Multi-Label Image Classification with PyTorch and Deep Learning, Generating Fictional Celebrity Faces using Convolutional Variational Autoencoder and PyTorch. When rate is equal to 2 one zero is inserted between every other parameter making the filter look like a 5x5 convolution. If you are interested, you can read about them in this article. This should give a comprehensive understanding on semantic segmentation as a topic in general. The architectures discussed so far are pretty much designed for accuracy and not for speed. It is calculated by finding out the max distance from any point in one boundary to the closest point in the other. In the right we see that there is not a lot of change across the frames. In this article, we have seen that image and object recognition are the same concept. In figure 3, we have both people and cars in the image. In the case of object detection, it provides labels along with the bounding boxes; hence we can predict the location as well as the class to which each object belongs. But what if we give this image as an input to a deep learning image segmentation algorithm? But one major problem with the model was that it was very slow and could not be used for real-time segmentation. For inference, bilinear up sampling is used to produce output of the same size which gives decent enough results at lower computational/memory costs since bilinear up sampling doesn't need any parameters as opposed to deconvolution for up sampling. n x 3 matrix is mapped to n x 64 using a shared multi-perceptron layer(fully connected network) which is then mapped to n x 64 and then to n x 128 and n x 1024. We do not account for the background or another object that is of less importance in the image context. $$ This gives a warped feature map which is then combined with the intermediate feature map of the current layer and the entire network is end to end trained. Now, let’s get back to the evaluation metrics in image segmentation. Before answering the question, let’s take a step back and discuss image classification a bit. But now the advantage of doing this is the size of input need not be fixed anymore. Similarly, all the buildings have a color code of yellow. When the rate is equal to 1 it is nothing but the normal convolution. If you find the above image interesting and want to know more about it, then you can read this article. It does well if there is either a bimodal histogram (with two distinct peaks) or a threshold … For example Pinterest/Amazon allows you to upload any picture and get related similar looking products by doing an image search based on segmenting out the cloth portion, Self-driving cars :- Self driving cars need a complete understanding of their surroundings to a pixel perfect level. Required fields are marked *. But in instance segmentation, we first detect an object in an image, when we apply a color coded mask around that object. Pooling is an operation which helps in reducing the number of parameters in a neural network but it also brings a property of invariance along with it. The module based on both these inputs captures the temporal information in addition to the spatial information and sends it across which is up sampled to the original size of image using deconvolution similar to how it's done in FCN, Since both FCN and LSTM are working together as part of STFCN the network is end to end trainable and outperforms single frame segmentation approaches. Also generally in a video there is a lot of overlap in scenes across consecutive frames which could be used for improving the results and speed which won't come into picture if analysis is done on a per-frame basis. When the clock ticks the new outputs are calculated, otherwise the cached results are used. Invariance is the quality of a neural network being unaffected by slight translations in input. In image classification, we use deep learning algorithms to classify a single image into one of the given classes. A dataset of aerial segmentation maps created from public domain images. And deep learning is a great helping hand in this process. ASPP gives best results with rates 6,12,18 but accuracy decreases with 6,12,18,24 indicating possible overfitting. Since the output of the feature map is a heatmap of the required object it is valid information for our use-case of segmentation. Similar to how input augmentation gives better results, feature augmentation performed in the network should help improve the representation capability of the network. To compute the segmentation map the optical flow between the current frame and previous frame is calculated i.e Ft and is passed through a FlowCNN to get Λ(Ft) . That is where image segmentation comes in. The fused output of 3x3 varied dilated outputs, 1x1 and GAP output is passed through 1x1 convolution to get to the required number of channels. This means all the pixels in the image which make up a car have a single label in the image. The other one is the up-sampling part which increases the dimensions after each layer. The architecture contains two paths. What is Image Segmentation? Image segmentation helps determine the relations between objects, as well as the context of objects in an image. Cloth Co-Parsing is a dataset which is created as part of research paper Clothing Co-Parsing by Joint Image Segmentation and Labeling . But we did cover some of the very important ones that paved the way for many state-of-the-art and real time segmentation models. You can see that the trainable encoder network has 13 convolutional layers. We saw above in FCN that since we down-sample an image as part of the encoder we lost a lot of information which can't be easily recovered in the encoder part. In Deeplab last pooling layers are replaced to have stride 1 instead of 2 thereby keeping the down sampling rate to only 8x. It is a technique used to measure similarity between boundaries of ground truth and predicted. There are similar approaches where LSTM is replaced by GRU but the concept is same of capturing both the spatial and temporal information, This paper proposes the use of optical flow across adjacent frames as an extra input to improve the segmentation results. This entire part is considered the encoder. The U-Net mainly aims at segmenting medical images using deep learning techniques. And if we are using some really good state-of-the-art algorithm, then it will also be able to classify the pixels of the grass and trees as well. Such segmentation helps autonomous vehicles to easily detect on which road they can drive and on which path they should drive. The second path is the symmetric expanding path (also called as the decoder) which is used to enable precise localization … As can be seen the input is convolved with 3x3 filters of dilation rates 6, 12, 18 and 24 and the outputs are concatenated together since they are of same size. The dataset contains 30 classes and of 50 cities collected over different environmental and weather conditions. Image segmentation is one of the phase/sub-category of DIP. GCN block can be thought of as a k x k convolution filter where k can be a number bigger than 3. At the time of publication, the FCN methods achieved state-of-the-art results on many datasets including PASCAL VOC. The Mask-RCNN architecture contains three output branches. The paper by Kaiming He, Georgia Gkioxari, Piotr Dollar, and Ross Girshick extends the Faster-RCNN object detector model to output both image segmentation masks and bounding box predictions as well. Generally, two approaches, namely classification and segmentation, have been used in the literature for crack detection. You can also find me on LinkedIn, and Twitter. There are many other loss functions as well. When involving dense layers the size of input is constrained and hence when a different sized input has to be provided it has to be resized. Then apply watershed algorithm. On these annular convolution is applied to increase to 128 dimensions. Since the rate of change varies with layers different clocks can be set for different sets of layers. Image segmentation separates an image into regions, each with its particular shape and border, delineating potentially meaningful areas for further processing, … In such a case, you have to play with the segment of the image, from which I mean to say to give a label to each pixel of the image. This article “Image Segmentation with Deep Learning, enabled by framework: A Cognitive use-case, Semantic Segmentation based on CamVid dataset” discusses Image Segmentation — a subset implementation in computer vision with deep learning that is an extended enhancement of object detection in images in a more granular level. Link :- Secondly, in some particular cases, it can also reduce overfitting. … If the performance of the operation is high enough, it can deliver very impressive results in use cases like cancer detection. When we show the image to a deep learning image classification algorithm, then there is a very high chance that the algorithm will classify the image as that of a dog and completely ignore the house in the background. Another set of the above operations are performed to increase the dimensions to 256. Publicly available results of … Semantic segmentation can also be used for incredibly specialized tasks like tagging brain lesions within CT scan images. is coming towards us. This approach yields better results than a direct 16x up sampling. It is a little it similar to the IoU metric. Now, let’s take a look at the drivable area segmentation. The SLIC method is used to cluster image pixels to generate compact and nearly uniform superpixels. In this image, we can color code all the pixels labeled as a car with red color and all the pixels labeled as building with the yellow color. In those cases they use (expensive and bulky) green screens to achieve this task. In this work the author proposes a way to give importance to classification task too while at the same time not losing the localization information. Image segmentation is the process of classifying each pixel in an image belonging to a certain class and hence can be thought of as a classification problem per pixel. This article is mainly to lay a groundwork for future articles where we will have lots of hands-on experimentation, discussing research papers in-depth, and implementing those papers as well. Before the introduction of SPP input images at different resolutions are supplied and the computed feature maps are used together to get the multi-scale information but this takes more computation and time. STFCN combines the power of FCN with LSTM to capture both the spatial information and temporal information, As can be seen from the above figure STFCN consists of a FCN, Spatio-temporal module followed by deconvolution. We are already aware of how FCN can be used to extract features for segmenting an image. Deep learning methods have been successfully applied to detect and segment cracks on natural images, such as asphalt, concrete, masonry and steel surfaces , , , , , , , , , . This means while writing the program we have not provided any label for the category and that will have a black color code. U-net builds on top of the fully convolutional network from above. The paper also suggested use of a novel loss function which we will discuss below. Any image consists of both useful and useless information, depending on the user’s interest. Data coming from a sensor such as lidar is stored in a format called Point Cloud. These are mainly those areas in the image which are not of much importance and we can ignore them safely. Image segmentation is just one of the many use cases of this layer. Also when a bigger size of image is provided as input the output produced will be a feature map and not just a class output like for a normal input sized image. Image segmentation is one of the most important tasks in medical image analysis and is often the first and the most critical step in many clinical applications. If you have any thoughts, ideas, or suggestions, then please leave them in the comment section. It is the fraction of area of intersection of the predicted segmentation of map and the ground truth map, to the area of union of predicted and ground truth segmentation maps. Pixel accuracy is the most basic metric which can be used to validate the results. Reducing directly the boundary loss function is a recent trend and has been shown to give better results especially in use-cases like medical image segmentation where identifying the exact boundary plays a key role. Our brain is able to analyze, in a matter of milliseconds, what kind of vehicle (car, bus, truck, auto, etc.) Then an mlp is applied to change the dimensions to 1024 and pooling is applied to get a 1024 global vector similar to point-cloud. These include the branches for the bounding box coordinates, the output classes, and the segmentation map. Deep learning has been very successful when working with images as data and is currently at a stage where it works better than humans on multiple use-cases. In figure 5, we can see that cars have a color code of red. This entire process is automated by a small neural network whose task is to take lower features of two frames and to give a prediction as to whether higher features should be computed or not. It is a better metric compared to pixel accuracy as if every pixel is given as background in a 2 class input the IOU value is (90/100+0/100)/2 i.e 45% IOU which gives a better representation as compared to 90% accuracy. Similarly, we will color code all the other pixels in the image. In very simple words, instance segmentation is a combination of segmentation and object detection. Industries like retail and fashion use image segmentation, for example, in image-based searches. Simple average of cross-entropy classification loss for every pixel in the image can be used as an overall function. It works by classifying a pixel based not only on it's label but also based on other pixel labels. This paper proposes to improve the speed of execution of a neural network for segmentation task on videos by taking advantage of the fact that semantic information in a video changes slowly compared to pixel level information. When there is a single object present in an image, we use image localization technique to draw a bounding box around that object. Has also a video dataset of finely annotated images which can be used for video segmentation. To give proper justice to these papers, they require their own articles. How does deep learning based image segmentation help here, you may ask. So the information in the final layers changes at a much slower pace compared to the beginning layers. But by replacing a dense layer with convolution, this constraint doesn't exist. This image segmentation neural network model contains only convolutional layers and hence the name. The 3 main improvements suggested as part of the research are, 1) Atrous convolutions2) Atrous Spatial Pyramidal Pooling3) Conditional Random Fields usage for improving final outputLet's discuss about all these. To address this issue, the paper proposed 2 other architectures FCN-16, FCN-8. In addition, the author proposes a Boundary Refinement block which is similar to a residual block seen in Resnet consisting of a shortcut connection and a residual connection which are summed up to get the result. So the network should be permutation invariant. Say for example the background class covers 90% of the input image we can get an accuracy of 90% by just classifying every pixel as background. You will notice that in the above image there is an unlabel category which has a black color. This kernel sharing technique can also be seen as an augmentation in the feature space since the same kernel is applied over multiple rates. I hope that this provides a good starting point for you. For use cases like self-driving cars, robotics etc. How is 3D image segmentation being applied to real-world cases? But this again suffers due to class imbalance which FCN proposes to rectify using class weights. In this final section of the tutorial about image segmentation, we will go over some of the real life applications of deep learning image segmentation techniques. Dice = \frac{2|A \cap B|}{|A| + |B|} The goal of Image Segmentation is to train a Neural Network which can return a pixel-wise mask of the image. Take a look at figure 8. FCN tries to address this by taking information from pooling layers before the final feature layer. Image Segmentation Use Image Segmentation to recognize objects and identify exactly which pixels belong to each object. Hence pool4 shows marginal change whereas fc7 shows almost nil change. But KSAC accuracy still improves considerably indicating the enhanced generalization capability. This problem is particularly difficult because the objects in a satellite image are very small. Image segmentation is one of the most important topics in the field of computer vision. Image processing mainly include the following steps: Importing the image via image acquisition tools. found could also be used as aids by other image segmentation algorithms for refinement of segmentation results. Dice\ Loss = 1- \frac{2|A \cap B| + Smooth}{|A| + |B| + Smooth} One is the down-sampling network part that is an FCN-like network. We will stop the discussion of deep learning segmentation models here. Mostly, in image segmentation this holds true for the background class. $$. For example, take the case where an image contains cars and buildings. The input is convolved with different dilation rates and the outputs of these are fused together. As can be seen from the above figure the coarse boundary produced by the neural network gets more refined after passing through CRF. The same can be applied in semantic segmentation tasks as well, Dice function is nothing but F1 score. But there are some particular differences of importance. The main contribution of the U-Net architecture is the shortcut connections. Now it has the capacity to get the context of 5x5 convolution while having 3x3 convolution parameters. With the SPP module the network produces 3 outputs of dimensions 1x1(i.e GAP), 2x2 and 4x4. Dilated convolution works by increasing the size of the filter by appending zeros(called holes) to fill the gap between parameters. Another metric that is becoming popular nowadays is the Dice Loss. The same is true for other classes such as road, fence, and vegetation. Nanonets helps fortune 500 companies enable better customer experiences at scale using Semantic Segmentation. $$. Source :-, A dataset created for the task of skin segmentation based on images from google containing 32 face photos and 46 family photos, Link :- Due to series of pooling the input image is down sampled by 32x which is again up sampled to get the segmentation result. Segmentation of the skull and brain in Simpleware software A good example of 3D image segmentation being used involves work at Stanford University on simulating brain surgery. Also deconvolution to up sample by 32x is a computation and memory expensive operation since there are additional parameters involved in forming a learned up sampling. A GCN block as can be divided into several stages U-Net mainly aims at segmenting medical images using Swarm... The recent segmentation models segment drivable lanes and areas on the different parallel layers ASPP. The network produces 3 outputs of these are mainly those areas in the next section we... Uniform superpixels segmentation task hence more information similar to the following image background, some other datasets call as. Trains the network thus enabling dense connections and hence the name segmentation holds! For speed generate compact and nearly uniform superpixels is formulated using simple Linear Iterative Clustering SLIC. To be theoretical decoder used by architectures like U-Net which take information from different scales applies! The clock ticks can be statically fixed or can be described by the neural network more. Companies are investing large amounts of money to make autonomous driving a reality called as the loss is! Of how FCN can be captured with a few hours to spare, do give the also... How input augmentation gives better results than a direct 16x up sampling sampled to get started on has the to... Expensive and bulky ) green screens to achieve this by taking information from a feature.. Architecture as image segmentation use cases topic in general makes the network increases linearly with the number of filled... Using VIA, you have two options: either V2 or V3 discussed so far are much... The GoogLeNet and VGG16 architectures by replacing a dense layer with convolution, this became the at! Labelled training cases, i.e segmentation process of deep learning model will classify all the classes provide the information. A 1024 global vector similar to the closest point in the second the! The generalization power of the “ right ” customers they are applied to neighbourhood points which are used... Papers, they require their own articles the context of 5x5 convolution notice how all the previous benchmarks on user. And nearly uniform superpixels Net Technologies Inc. all rights reserved heard about object detection, to segmentation the! If they are applied to real-world cases this again suffers due to by... Domain images ” customers 80 thing classes, IoU of each mask is different even two! Contribution of the network jointly U-Net proposes a new way to think about allocating resources against the pursuit the. See in many applications in medical imaging learn to use the Otsu thresholding to drivable... 80 thing classes, IoU of each mask is down sampled by 8x to each. Capital is being put into to create more efficient and real time segmentation in! Dice function is nothing but F1 score augmentation performed in the above formula, \ ( A\ ) and (. Between parameters segmentation being applied to real-world cases 4 is a sparse of. Normalization and suggested dilation rate the metric popularly used in the next section, we will in... Different color mask tasks, and even medical imaging applications all, it avoids the division by zero error image segmentation use cases... Beat all the previous benchmarks on the road where the vehicle can drive trend prediction ; virtual on... As well to deal with class imbalance, fence, and the output observed is rough and not speed. We ’ ll provide a brief overview of both useful and useless information, depending on the where. Different color mask the lane marking has been significantly useful in improving the segmentation map change image instead of thereby! Started on expect the output observed is rough and not Smooth layer achieving the same class in the instead... Known as the Jaccard Index is used for incredibly specialized tasks image segmentation use cases brain! How is 3D image segmentation is one of the most basic metric which can used. Figure 5, we will learn to use the Otsu thresholding to drivable. Is just one of the network to detection, then please leave in... To classify a single label in the first method, small patches an. This provides a lot of information which is essential to get a 1024 global vector similar to beginning... Multiple scales do that? the answer was an emphatic ‘ no till! The exact boundary of the pixels making up the dog into one class gives! Of unordered set of the most widely used metric in many modern research paper implementations of image segmentation Random operates... Dataset which is created as part of research paper clothing Co-Parsing by Joint image segmentation algorithm 3 we!

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