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Motion blur is the apparent streaking of moving objects in a photograph or a sequence of frames, such as a film or animation. It results when the image being recorded changes during the recording of a single exposure, due to rapid movement or long exposure. Usages / Effects of motion blur Photography When a camera creates an image, that image does not represent a single instant of time. Because of technological constraints or artistic requirements, the image may represent the scene over a period of time. Most often this exposure time is brief enough that the image captured by the camera appears to capture an instantaneous moment, but this is not always so, and a fast moving object or a longer exposure time may result in blurring artifacts which make this apparent. As objects in a scene move, an image of that scene must represent an integration of all positions of those objects, as well as the camera's viewpoint, over the period of exposure determined by the shu

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CS-503: Visual intelligence : machines and minds

The course will discuss classic material as well as recent advances in computer vision and machine learning relevant to processing visual data with a primary focus on embodied intelligence and vision for active agents.

MICRO-511: Image processing I

Introduction to the basic techniques of image processing. Introduction to the development of image-processing software and to prototyping in JAVA. Application to real-world examples in industrial vision and biomedical imaging.

MICRO-452: Basics of mobile robotics

The course teaches the basics of autonomous mobile robots. Both hardware (energy, locomotion, sensors) and software (signal processing, control, localization, trajectory planning, high-level control) will be tackled. The students will apply the knowledge to program and control a real mobile robot.

Person Retrieval in Surveillance Videos Via Deep Attribute Mining and Reasoning

Peixia Li, Zhiye Wang, Zhen Wei

Person retrieval largely relies on the appearance features of pedestrians. This task is rather more difficult in surveillance videos due to the limitations of extracting robust appearance features brought by the cross-view and cross-camera data with lower image resolution, motion blur, occlusion and other kinds of image degradation. To build up a more reliable person retrieval system, recent works introduced appearance attribute models to describe and distinguish different persons with high-level semantic concepts. Despite the progress of previous works, the value of utilizing appearance attributes is still under-explored. On one hand, existing methods lack for concise and precise attribute representations that are specific for each attribute category and, in the meantime, are able to filter noisy information in irrelevant spatial locations and useless patterns. On the other hand, correlation and reasoning between different attributes are neglected, which could generate more useful information and add more robustness to the retrieval system. In this paper, we propose an Attribute Mining and Reasoning (AMR) framework which is capable to handle the issues in question. The AMR makes better use of appearance attributes with two main components. First, the AMR disentangles the representations of different attributes by localizing their spatial positions and identifying their effective patterns in a weakly supervised manner. To achieve more reliable localization, we propose the Attribute Localization Ensemble (ALE) module that is consisted of multiple localization heads and a voting mechanism. Second, we introduce the Attribute Reasoning (AR) module to correlate different attributes together with the global appearance features and discover their latent relations to generate more comprehensive descriptions of pedestrians. Extensive experiments on DukeMTMC-ReID and Market-1501 datasets demonstrate the effectiveness of the proposed AMR framework as well as its superiority over the existing state-of-the-art methods. The AMR model also shows great generalization ability on the unseen CUHK03 dataset when it is only trained on Market-1501 dataset.

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2021

Multi-planar geometry and latent image recovery from a single motion-blurred image

Subeesh Vasu

Existing works for depth estimation and image deblurring in the presence of depth-dependent blur work with the assumption of a multi-layered scene wherein each layer is modeled in the form of a fronto-parallel plane. In this work, we attempt to relax these constraints by considering more generalized settings of a 3D scene with piecewise planar structure, i.e., a scene that can be modeled as a combination of multiple planes with arbitrary orientations. To this end, we first propose a novel approach to estimate the normal of a planar surface from a single motion-blurred image. We then extend this idea and develop an algorithm for automatic recovery of the number of planes, the parameters corresponding to each plane, and camera motion from a single motion-blurred image of a multi-planar 3D scene. Finally, we propose a first-of-its-kind approach to recover the planar geometry and latent image of the scene by adopting an alternating minimization framework built on our findings. Experiments on synthetic and real data reveal that our proposed method achieves state-of-the-art results on the dual problem of depth recovery and image deblurring.

SPRINGER2022

This thesis addresses the problem of recovering the 3-D shape of a deformable object in single images, or image sequences acquired by a monocular video camera, given that a 3-D template shape and a template image of the object are available. While being a very challenging problem in computer vision, being able to reconstruct and track 3-D deformable objects in videos allows us to develop many potential applications ranging from sports and entertainments to engineering and medical imaging. This thesis extends the scope of deformable object modeling to real-world applications of fully 3-D modeling of deformable objects from video streams with a number of contributions. We show that by extending the Laplacian formalism, which was first introduced in the Graphics community to regularize 3-D meshes, we can turn the monocular 3-D shape reconstruction of a deformable object given correspondences with a reference image into a much better-posed problem with far fewer degrees of freedom than the original one. This has proved key to achieving real-time performance while preserving both sufficient flexibility and robustness. Our real-time 3-D reconstruction and tracking system of deformable objects can very quickly reject outlier correspondences and accurately reconstruct the object shape in 3D. Frame-to-frame tracking is exploited to track the object under difficult settings such as large deformations, occlusions, illumination changes, and motion blur. We present an approach to solving the problem of dense image registration and 3-D shape reconstruction of deformable objects in the presence of occlusions and minimal texture. A main ingredient is the pixel-wise relevancy score that we use to weigh the influence of the image information from a pixel in the image energy cost function. A careful design of the framework is essential for obtaining state-of-the-art results in recovering 3-D deformations of both well- and poorly-textured objects in the presence of occlusions. We study the problem of reconstructing 3-D deformable objects interacting with rigid ones. Imposing real physical constraints allows us to model the interactions of objects in the real world more accurately and more realistically. In particular, we study the problem of a ball colliding with a bat observed by high speed cameras. We provide quantitative measurements of the impact that are compared with simulation-based methods to evaluate which simulation predictions most accurately describe a physical quantity of interest and to improve the models. Based on the diffuse property of the tracked deformable object, we propose a method to estimate the environment irradiance map represented by a set of low frequency spherical harmonics. The obtained irradiance map can be used to realistically illuminate 2-D and 3-D virtual contents in the context of augmented reality on deformable objects. The results compare favorably with baseline methods. In collaboration with Disney Research, we develop an augmented reality coloring book application that runs in real-time on mobile devices. The app allows the children to see the coloring work by showing animated characters with texture lifted from their colors on the drawing. Deformations of the book page are explicitly modeled by our 3-D tracking and reconstruction method. As a result, accurate color information is extracted to synthesize the character's texture.

EPFL2016

Animation

Animation is the method by which are manipulated to create moving images. In traditional animation, images are drawn or painted by hand on transparent celluloid sheets to be photographed and exhibite

Exposure (photography)

In photography, exposure is the amount of light per unit area (the 's illuminance times the exposure time) reaching a frame of photographic film or the surface of an electronic , as determined by sh

Stop motion

Stop motion is an animated filmmaking technique in which objects are physically manipulated in small increments between individually photographed frames so that they will appear to exhibit independe