PPT-Stereo Matching by Training a Convolutional Neural

Network to Compare Image Patches Jure Zbontar Yann LeCun Background Motivation Problem Formulation Methodology Training Data Suggested Net Architectures Sequential Steps Results Conclusion. Many slides adapted from Steve Seitz. Binocular stereo. Given a calibrated binocular stereo pair, fuse it to produce a depth image. image 1. image 2. Dense depth map. Binocular stereo. Given a calibrated binocular stereo pair, fuse it to produce a depth image. Chapter 11 Stereo Correspondence. Presented by: . 蘇唯誠. 0921679513. r02922114@ntu.edu.tw. 指導教授. : . 傅楸善 博士. Introduction. Stereo matching is the process of taking two or more images and estimating a 3D model of the scene by finding matching pixels in the images and converting their 2D positions into 3D depths.. Many slides adapted from Steve Seitz. Binocular stereo. Given a calibrated binocular stereo pair, fuse it to produce a depth image. image 1. image 2. Dense depth map. Binocular stereo. Given a calibrated binocular stereo pair, fuse it to produce a depth image. Xi Mo. 4/3/2017. x. y. z. x. y. xl. xr. Disparity=xl-. xr. Disparity space. Traditional way of stereo matching. Benchmark of. Middlebury. 3DMST(Rank 1). Error . Map. Disparity map of left view. Left view. Last time. Linear classifiers on pixels bad, need non-linear classifiers. Multi-layer . perceptrons. . overparametrized. Reduce parameters by local connections and shift invariance => Convolution. Noah . Snavely. , . Zhengqi. Li. Single image stereogram, by . Niklas. . Een. Mark Twain at Pool Table", no date, UCR Museum of Photography. Stereo. Given two images from different viewpoints. How can we compute the depth of each point in the image?. Convolutions. Reduce parameters. Capture shift-invariance: location of patch in image should not matter. Subsampling. Allows greater invariance to deformations. Allows the capture of large patterns with small filters. Article and Work by. : Justin . Salamon. and Juan Pablo Bello. Presented by . : . Dhara. Rana. Overall Goal of Paper. Create a way to classify environmental sound given an audio clip. Other methods of sound classification: (1) dictionary learning and (2) wavelet filter banks . Convolutional Codes COS 463 : Wireless Networks Lecture 9 Kyle Jamieson [Parts adapted from H. Balakrishnan ] So far, we’ve seen block codes Convolutional Codes: Simple design, especially at the transmitter Several slides from Larry . Zitnick. and Steve Seitz. Why do we perceive depth?. What do humans use as depth cues?. Convergence . When watching an object close to us, our eyes point slightly inward. This difference in the direction of the eyes is called convergence. This depth cue is effective only on short distances (less than 10 meters). . José Ignacio Orlando. 1,2. , Elena Prokofyeva. 3,4. , Mariana del Fresno. 1,5. and Matthew B. Blaschko. 6. 1 . Instituto. . Pladema. , UNCPBA, . Tandil. , Argentina. 2. . Consejo. Nacional de . Investigaciones. n,k. ) code by adding the r parity digits. An alternative scheme that groups the data stream into much smaller blocks k digits and encode them into n digits with order of k say 1, 2 or 3 digits at most is the convolutional codes. Such code structure can be realized using convolutional structure for the data digits.. Kannan . Neten. Dharan. Introduction . Alzheimer’s Disease is a kind of dementia which is caused by damage to nerve cells in the brain and the usual side effects of it are loss of memory or other cognitive impairments.. Several slides from Larry . Zitnick. and Steve Seitz. Why do we perceive depth?. What do humans use as depth cues?. Convergence . When watching an object close to us, our eyes point slightly inward. This difference in the direction of the eyes is called convergence. This depth cue is effective only on short distances (less than 10 meters). .