Tugas 1 Computer Vision

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Computer Vision

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What is computer vision?

Computer vision

getting computer to

“see”

Objek Detection Pose Estimation

….

Tasks

The field of computer vision began to take shape with the

development of early image processing techniques. One

notable project was the "Summer Vision Project" at MIT,

which aimed to build a computer system capable of

identifying objects in images.

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What is computer vision

Computer vision starts from the observation that biological vision and human vision in a remarkable thing and wouldn’t it be great if we could

replicated this sense with a computer and a camera.

This turn out to be very difficult vision is an inverse problem:

● Deduce something about the 3D scene structure, object and properties from 2D observations (images/videos)

#note: biological vision is complex we’ll steal ideas, but we don’t need to replicate implementation details

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Computer vision

There is a general agreement that computer vision involves computer and image data beyond this, there isn’t a single universally accepted framework or taxonomy instead, there is a diverse array of related tasks that are studied from different perspectives since :

● Highly interdisciplinary field: neuroscience, machine learning, psychology, signal processing ..

● the technology is evolving at an absurd pace, so the task that can be tackled are also evolving

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Image Classification

Objective : assign a class label to the whole image. Start from an a image feed it to image a classifier which class the image belongs to the classes are defined.

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Image Retrieval

Objective : rank a pool of images according to how well they match a query. The inputs to the model are a query image and a pool of images that want to search through these are each feed into a retrieval model which produces a ranking among the image pool according to how similar each images is to query.

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Object Detection

Objective : locate and classify object within an image. The image is passed to a detector which outputs a set of spatial regions and their corresponding classes.

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Semantic Segmentation

Objective : assign a class label to every pixel location in the image starting from an image a semantic segmenter will produce an output that is the same size as the input and at every pixel it gives class table the name semantic segmentation comes from the fact are effectively segmenting the image into regions and giving each region a class label.

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Instance Segmentation

Objective :The task is to detect classify and segment object in the image given a image the segementer aims to find the object classify them and segment them from the background.

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Instance Segmentation

let’s observe an instant segmenter in action on a few different images to see the results we can segment zebras frisbee player elephants and crowded street scenes all to a fairly high degree of accuracy with modern computer vision.

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Action recognition In Video

Objective : to take in a video sequence and predict the dominant action.

so given a set of frames from a video an action classifier predicts a single class exactly like an image classifier.

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Action recognition In Video

example : for example given the video on the left an action classifier should predict show jumping as an appropriate action.

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Mask Tracking On video

Objective : given a mask region covering an object covering an object and object update the mask to ensure it keeps covering the object across frames.

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Image captioning

Objective :provide an accurate text description of an image so given image captioner aims to produce a text caption describing the image contents to make this a little less abstract.

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Computer Vision - A Whirlwind tour of further themes

De-noising: in which initial image has been exposed to some kind of noise and our task is to remove the noise to recover the original image.

Super-resolution: where start from a low resolution image and aim to enhance it to a high resolution image.

Inpainting: where we have corrupted regions of an image and we aim to paint over these corrupted regions with content that is consistent with the surroundings more recently we also have image.

Outpainting: where we start from an image and then try to produce larger version that expands it in manner that consistent with the original another focus.

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Computer Vision - A Whirlwind tour of further themes

Feature detection: for instance taking an image and extracting the edges in it building on these detected features.

Han-crafted description: which take an image and construct some kind of descriptor which perhaps the best known is shift that will allow us to reliably re-identify the same objects even when viewed from a different angle or under different lighting conditions with the voice of deep learning.

Deep features: these descriptors have been transitioning into deep features also start from an image but extract features using a deep neural network has been trained to perform some related tasks.