Computing the Stereo Matching Cost with a Convolutional Neural Network

We present a method for extracting depth information from a rectified image pair. We train a convolutional neural network to predict how well two image patches match and use it to compute the stereo matching cost. The cost is refined by cross-based cost aggregation and semiglobal matching, followed by a left-right consistency check to eliminate errors in the occluded regions. Our stereo method achieves an error rate of 2.61 % on the KITTI stereo dataset and is currently (August 2014) the top performing method on this dataset.Comment: Conference on Computer Vision and Pattern Recognition (CVPR), June 201

Training deep neural networks for stereo vision

We present a method for extracting depth information from a rectified image pair. Our approach focuses on the first stage of many stereo algorithms: the matching cost computation. We approach the problem by learning a similarity measure on small image patches using a convolutional neural network. Training is carried out in a supervised manner by constructing a binary classification data set with examples of similar and dissimilar pairs of patches. We examine two network architectures for learning a similarity measure on image patches. The first architecture is faster than the second, but produces disparity maps that are slightly less accurate. In both cases, the input to the network is a pair of small image patches and the output is a measure of similarity between them. Both architectures contain a trainable feature extractor that represents each image patch with a feature vector. The similarity between patches is measured on the feature vectors instead of the raw image intensity values. The fast architecture uses a fixed similarity measure to compare the two feature vectors, while the accurate architecture attempts to learn a good similarity measure on feature vectors. The output of the convolutional neural network is used to initialize the stereo matching cost. A series of post-processing steps follow: cross-based cost aggregation, semiglobal matching, a left-right consistency check, subpixel enhancement, a median filter, and a bilateral filter. We evaluate our method on the KITTI 2012, KITTI 2015, and Middlebury stereo data sets and show that it outperforms other approaches on all three data sets

Training deep neural networks for stereo vision

V pričujoči doktorski disertaciji predstavimo metodo za izračun cene ujemanja za problem stereo vida. Stereo podatkovne množice, na primer KITTI in Middlebury, so v zadnjih nekaj letih postale dovolj velike, da se lahko problema lotimo z metodami, ki temeljijo na učenju. Naš pristop temelji na uporabi globoke konvolucijske nevronske mreže in algoritma za nadzorovano strojno učenje. Učno množico zgradimo iz javno dostopnih stereo podatkovnih množic. Učni primer sestoji iz para slikovnih zaplat in pripada enemu izmed dveh razredov: pozitivnemu, ko sta slikovni zaplati v korespondenci in negativnemu, ko nista. Predstavljeni sta dve arhitekturi konvolucijskih nevronskih mrež za učenje podobnosti. Prva arhitektura je hitrejša od druge, vendar je izračunana globinska slika v povprečju manj natančna. V obeh primerih je vhod v nevronsko mrežo par slikovnih zaplat, izhod pa mera podobnosti med njima. Obe arhitekturi vsebujeta konvolucijski nevronski mreži, ki slikovni zaplati predstavita z vektorjem značilk. Podobnost med slikovnima zaplatama je izračunana na vektorju značilk, namesto na svetlostih posameznih slikovnih elementov. Prva arhitektura vektorja značilk primerja s kosinusno podobnostjo, medtem ko druga arhitektura vektorja primerja z naučeno večnivojsko nevronsko mrežo. Razvito metodo primerjamo z uveljavljenimi metodami na treh podatkovnih množicah -- KITTI 2012, KITTI 2015 in Middlebury -- in ugotovimo, da je naša metoda najnatančnejša na vse treh podatkovnih množicah.We present a method for extracting depth information from a rectified image pair. Our approach focuses on the first stage of many stereo algorithms: the matching cost computation. We approach the problem by learning a similarity measure on small image patches using a convolutional neural network. Training is carried out in a supervised manner by constructing a binary classification data set with examples of similar and dissimilar pairs of patches. We examine two network architectures for learning a similarity measure on image patches. The first architecture is faster than the second, but produces disparity maps that are slightly less accurate. In both cases, the input to the network is a pair of small image patches and the output is a measure of similarity between them. Both architectures contain a trainable feature extractor that represents each image patch with a feature vector. The similarity between patches is measured on the feature vectors instead of the raw image intensity values. The fast architecture uses a fixed similarity measure to compare the two feature vectors, while the accurate architecture attempts to learn a good similarity measure on feature vectors. The output of the convolutional neural network is used to initialize the stereo matching cost. A series of post-processing steps follow: cross-based cost aggregation, semiglobal matching, a left-right consistency check, subpixel enhancement, a median filter, and a bilateral filter. We evaluate our method on the KITTI 2012, KITTI 2015, and Middlebury stereo data sets and show that it outperforms other approaches on all three data sets

Training deep neural networks for stereo vision

We present a method for extracting depth information from a rectified image pair. Our approach focuses on the first stage of many stereo algorithms: the matching cost computation. We approach the problem by learning a similarity measure on small image patches using a convolutional neural network. Training is carried out in a supervised manner by constructing a binary classification data set with examples of similar and dissimilar pairs of patches. We examine two network architectures for learning a similarity measure on image patches. The first architecture is faster than the second, but produces disparity maps that are slightly less accurate. In both cases, the input to the network is a pair of small image patches and the output is a measure of similarity between them. Both architectures contain a trainable feature extractor that represents each image patch with a feature vector. The similarity between patches is measured on the feature vectors instead of the raw image intensity values. The fast architecture uses a fixed similarity measure to compare the two feature vectors, while the accurate architecture attempts to learn a good similarity measure on feature vectors. The output of the convolutional neural network is used to initialize the stereo matching cost. A series of post-processing steps follow: cross-based cost aggregation, semiglobal matching, a left-right consistency check, subpixel enhancement, a median filter, and a bilateral filter. We evaluate our method on the KITTI 2012, KITTI 2015, and Middlebury stereo data sets and show that it outperforms other approaches on all three data sets

Linker and Loader for the HIP Processor

In this work a collection of programs, called hiputils is presented. The toolchain constitutes an assembler, linker, dynamic loader, simulator and a static library creation utility for the HIP processor. A precise description of the process of creating, linking and loading of static and dynamic libraries in hiputils is given. A format for object files, static and dynamic libraries is also defined. Beside hiputils, linking and loading of programs and libraries is also described. Several object file formats, including COM, a.out and ELF are studied and compared. The three main tasks of linkers: storage allocation, symbol management and relocation are detailed. A description of libraries - static as well as dynamic - is also given, along with a description of dynamic loading and relocation. A mechanism, which allows code to run at an arbitrary start address is also depicted (position independent code)

Orange: data mining toolbox in Python

Orange is a machine learning and data mining suite for data analysis through Python scripting and visual programming. Here we report on the scripting part, which features interactive data analysis and component-based assembly of data mining procedures. In the selection and design of components, we focus on the flexibility of their reuse: our principal intention is to let the user write simple and clear scripts in Python, which build upon C++ implementations of computationally-intensive tasks. Orange is intended both for experienced users and programmers, as well as for students of data mining