Learn Computer Vision and Image Processing in LabVIEW

In this lecture we going to discuss color processing as well introduction into image color-space. You shall learn the difference between RGB color space as well HSV color-space and why they both are important. We also discuss briefly the two apps that you will be building, related to color processing.

In this lecture we going to be creating our very first app!! You will learn how to count all the blue M&Ms in an image. This may be a simple task but when there are thousands of objects you want to count, then this algorithm will be really useful for counting autonomously.

So now that we know the basics of colors and color-space, we can go ahead and do some color tracking! This app shows you the basics of color tracking in real time.

The slides of Section 2 - Color Processing, are downloadable here.

In computer vision and image processing the concept of feature detection refers to methods that aim at computing abstractions of image information and making local decisions at every image point whether there is an image feature of a given type at that point or not. The resulting features will be subsets of the image domain, often in the form of isolated points, continuous curves or connected regions. This lecture teaches you the basics of feature detection.

In computer vision, blob detection methods are aimed at detecting regions in a digital image that differ in properties, such as brightness or color, compared to surrounding regions. Informally, a blob is a region of an image in which some properties are constant or approximately constant; all the points in a blob can be considered in some sense to be similar to each other.

In this app we shall learn how to use blob detection to segment coins into blobs and then extract information from these blobs.This is Vision-App 3.

In this lecture we shall be detecting range by using Blog Detection. In computer vision, blob detection methods are aimed at detecting regions in a digital image that differ in properties, such as brightness or color, compared to surrounding regions.

The slides of Section 3 - Basic Feature Detection, are downloadable here.

Edge detection is an image processing technique for finding the boundaries of objects within images. It works by detecting discontinuities in brightness. Edge detection is used for image segmentation and data extraction in areas such as image processing, computer vision, and machine vision.

In this lecture we shall discuss the important concepts behind Edge detection and why we should use it. If you have any questions, please feel free to contact me in the discussion area of this course.

How to do Edge detection And Simple Lane Detection in LabVIEW.

This Lecture shows you how to a simple measurement of a ruler as well on how to do simple lane detection in LabVIEW.

The first App shows you how to use the Caliper tool in LabVIEW Vision Assistant to measure width of an object in an image.

The lane detection algorithm (LDA) detects the driving lane boundaries and estimates the geometry of the lane in the 3D world coordinates relative to the vehicle.

The slides of Section 4 - Lines and Edges, are downloadable here.

In this lecture we discuss the basics of template or pattern matching. Template Matching is a method for searching and finding the location of a template image in a larger image.

Optical flow or optic flow is the pattern of apparent motion of objects, surfaces, and edges in a visual scene caused by the relative motion between an observer (an eye or a camera) and the scene. This lectures gives a brief introduction into optical flow

Optical Character Recognition, or OCR, is a technology that enables you to convert different types of documents, such as scanned paper documents, PDF files or images captured by a digital camera into editable and searchable data.

A barcode is an optical machine-readable representation of data relating to the object to which it is attached. Originally barcodes systematically represented data by varying the widths and spacings of parallel lines, and may be referred to as linear or one-dimensional (1D).

Feature correspondence is one of the fundamental problems of computer vision and is a key ingredient in a wide range of applications including object recognition, 3D reconstruction, mosaicing, motion segmentation, and image morphing.

In this app we apply what we learnt in Lecture 17 to create a pattern matching App. We use pattern matching to detect checker beads within an image.

A really fun and cool app to develop is the object tracking app. This app we shall use the object tracking function to detect our desired object in real time.

In this lecture you will learn how to create your own Barcode Recognition App

Using what we learnt in Lecture 19. We apply this knowledge to create our own Optical Character Recognition app to detect text within an image. You will learn how to train your classifier as well.

The slides of Section 5 - Advanced Feature detection, are downloadable here.

[Bonus Lecture] - IEEE Conference Paper Presentation - A Three-Step Vehicle Detection Framework for

Range Estimation Using a Single Camera

Abstract—This paper proposes and validates a real-time onroad vehicle detection system, which uses a single camera for the purpose of intelligent driver assistance. A three-step vehicle detection framework is presented to detect and track the target vehicle within an image. In the first step, probable vehicle locations are hypothesized using pattern recognition. The vehicle candidates are then verified in the hypothesis verification step. In this step, lane detection is used to filter vehicle candidates that are not within the lane region of interest. In the final step tracking and online learning are implemented to optimize the detection algorithm during misdetection and temporary occlusion. Good detection performance and accuracy was observed in highway driving environments with minimal shadows.

Delivering FPGA Vision to the Masses. National Instruments Vision development module and vision assistant take machine vision from idea to prototype to application deployment

This lecture teaches you the basics of the Kalman Filter using a simple pokemon example.