vincentmatthys/recvis17

Object recognition and computer vision @MVA 2017-2018

Object recognition and computer vision 2017/2018

ENS Paris-Saclay, MSc MVA

Webpage of the lecture

Jean Ponce, Ivan Laptev, Cordelia Schmid and Josef Sivic

Teaching assistants: Gul Varol and Ignacio Rocco

Course description

Automated object recognition -- and more generally scene analysis -- from photographs and videos is the grand challenge of computer vision. This course presents the image, object, and scene models, as well as the methods and algorithms, used today to address this challenge.

Homework 1

The goal of instance-level recognition is to match (recognize) a specific object or scene. Examples include recognizing a specific building, such as Notre Dame, or a specific painting, such as 'Starry Night' by Van Gogh. The object is recognized despite changes in scale, camera viewpoint, illumination conditions and partial occlusion. An important application is image retrieval - starting from an image of an object of interest (the query), search through an image dataset to obtain (or retrieve) those images that contain the target object.

The goal of this assignment is to experiment and get basic practical experience with the methods that enable specific object recognition. It includes: (i) using SIFT features to obtain sparse matches between two images; (ii) using affine co-variant detectors to cover changes in viewpoint; (iii) vector quantizing the SIFT descriptors into visual words to enable large scale retrieval; and (iv) constructing and using an image retrieval system to identify objects.

Homework 2

In image classification, an image is classified according to its visual content. For example, does it contain an airplane or not. An important application is image retrieval - searching through an image dataset to obtain (or retrieve) those images with particular visual content.

The goal of this exercise is to get basic practical experience with image classification. It includes: (i) training a visual classifier for five different image classes (aeroplanes, motorbikes, people, horses and cars); (ii) assessing the performance of the classifier by computing a precision-recall curve; (iii) varying the visual representation used for the feature vector, and the feature map used for the classifier; and (iv) obtaining training data for new classifiers using Google / Bing image search.