Practitioners, research workers, academicians, and students in mechanical, electrical, industrial, manufacturing, and production engineering. Cornelius T. Leondes received his B. He is the author, editor, or co-author of more than textbooks and handbooks and has published more than technical papers.
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MissingLink is a deep learning platform that can help you automate these operational aspects of CNNs and computer vision, so you can concentrate on building winning image recognition experiments. The most comprehensive platform to manage experiments, data and resources more frequently, at scale and with greater confidence. MissingLink is the most comprehensive deep learning platform to manage experiments, data, and resources more frequently, at scale and with greater confidence.
What is Image Segmentation? Segmentation — identifying parts of the image and understanding what object they belong to. Segmentation lays the basis for performing object detection and classification. Semantic Segmentation vs. Instance Segmentation Within the segmentation process itself, there are two levels of granularity: Semantic segmentation —classifies all the pixels of an image into meaningful classes of objects. For instance, you could isolate all the pixels associated with a cat and color them green.
This is also known as dense prediction because it predicts the meaning of each pixel. Instance segmentation —identifies each instance of each object in an image. If there are three cars in an image, semantic segmentation classifies all the cars as one instance, while instance segmentation identifies each individual car.
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Old-School Image Segmentation Methods. These include: Thresholding —divides an image into a foreground and background. A specified threshold value separates pixels into one of two levels to isolate objects. Thresholding converts grayscale images into binary images or distinguishes the lighter and darker pixels of a color image.
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K-means clustering —an algorithm identifies groups in the data, with the variable K representing the number of groups. The algorithm assigns each data point or pixel to one of the groups based on feature similarity. Rather than analyzing predefined groups, clustering works iteratively to organically form groups. Simple images consist of an object and a background.
The background is usually one gray level and is the larger entity. Thus, a large peak represents the background gray level in the histogram. A smaller peak represents the object, which is another gray level. Edge detection —identifies sharp changes or discontinuities in brightness.
Edge detection usually involves arranging points of discontinuity into curved line segments, or edges. For example, the border between a block of red and a block of blue. Here are several deep learning architectures used for segmentation: Convolutional Neural Networks CNNs Image segmentation with CNN involves feeding segments of an image as input to a convolutional neural network, which labels the pixels.
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It provides a framework that enables training thousands of layers while maintaining performance. The powerful representational ability of ResNet boosts computer vision applications like object detection and face recognition. Atrous spatial pyramid pooling ASPP —provides multi-scale information. It uses a set of atrous convolutions with varying dilation rates to capture long-range context.
Image Segmentation Applications.
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Object Detection and Face Detection These applications involve identifying object instances of a specific class in a digital image. Face detection —a type of object-class detection with many applications, including biometrics and autofocus features in digital cameras. Algorithms detect and verify the presence of facial features.
For example, eyes appear as valleys in a gray-level image. Medical imaging —extracts clinically relevant information from medical images. For example, radiologists may use machine learning to augment analysis, by segmenting an image into different organs, tissue types, or disease symptoms.
This can reduce the time it takes to run diagnostic tests. Machine vision —applications that capture and process images to provide operational guidance to devices.
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This includes both industrial and non-industrial applications. Machine vision systems use digital sensors in specialized cameras that allow computer hardware and software to measure, process, and analyze images. For example, an inspection system photographs soda bottles and then analyzes the images according to pass-fail criteria to determine if the bottles are properly filled. Video Surveillance—video tracking and moving object tracking This involves locating a moving object in video footage. Self-driving vehicles —autonomous cars must be able to perceive and understand their environment in order to drive safely.
Relevant classes of objects include other vehicles, buildings, and pedestrians. Semantic segmentation enables self-driving cars to recognize which areas in an image are safe to drive. Iris recognition —a form of biometric identification that recognizes the complex patterns of an iris. Face recognition —identifies an individual in a frame from a video source.
This technology compares selected facial features from an input image with faces in a database. Retail Image Recognition This application provides retailers with an understanding of the layout of goods on the shelf. The most powerful computer vision management platform The most comprehensive platform to manage experiments, data and resources more frequently, at scale and with greater confidence.
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