Evaluation Of Computer Vision Algorithms Research Articles

Разработка систем контроля целостности зубьев ковша экскаватора с применением методов компьютерного зрения

he article describes the problem of monitoring the integrity and condition of the teeth of mining excavators based on computer vision. IN the article defined the possible evaluation of computer vision algorithms that can be applied to solve this problem. In the article presented the architecture of the software and hardware for the implementation of this class of systems.

A virtual environment for evaluation of computer vision algorithms under general airborne camera imperfections

AbstractThis paper describes a camera simulation framework for validating machine vision algorithms under general airborne camera imperfections. Lens distortion, image delay, rolling shutter, motion blur, interlacing, vignetting, image noise, and light level are modelled. This is the first simulation that considers all temporal distortions jointly, along with static lens distortions in an online manner. Several innovations are proposed including a motion tracking system allowing the camera to follow the flight log with eligible derivatives. A reverse pipeline, relating each pixel in the output image to pixels in the ideal input image, is developed. It is shown that the inverse lens distortion model and the inverse temporal distortion models are decoupled in this way. A short-time pixel displacement model is proposed to solve for temporal distortions (i.e. delay, rolling shutter, motion blur, and interlacing). Evaluation is done by several means including regenerating an airborne dataset, regenerating the camera path on a calibration pattern, and evaluating the ability of the time displacement model to predict other frames. Qualitative evaluations are also made.

VisFM: Visual Analysis of Image Feature Matchings

AbstractFeature matching is the most basic and pervasive problem in computer vision and it has become a primary component in big data analytics. Many tools have been developed for extracting and matching features in video streams and image frames. However, one of the most basic tools, that is, a tool for simply visualizing matched features for the comparison and evaluation of computer vision algorithms is not generally available, especially when dealing with a large number of matching lines. We introduce VisFM, an integrated visual analysis system for comprehending and exploring image feature matchings. VisFM presents a matching view with an intuitive line bundling to provide useful insights regarding the quality of matched features. VisFM is capable of showing a summarization of the features and matchings through group view to assist domain experts in observing the feature matching patterns from multiple perspectives. VisFM incorporates a series of interactions for exploring the feature data. We demonstrate the visual efficacy of VisFM by applying it to three scenarios. An informal expert feedback, conducted by our collaborator in computer vision, demonstrates how VisFM can be used for comparing and analysing feature matchings when the goal is to improve an image retrieval algorithm.

Some Fuzzy Tools for Evaluation of Computer Vision Algorithms

In this article, some issues related to the performance evaluation of computer vision algorithms within the version of direct empirical supervised evaluation method developed at SRISA RAS are considered. This approach partly relies on the elements defined by using the fuzzy set theory, in particular, fuzzy similarity measures and fuzzy reference ground truth images. Some known measures of segmentation quality are considered and their extensions, representing the fuzzy similarity measures, are offered. As an example, the author considers an application of fuzzy ground truth images and fuzzy similarity measures, including some newly introduced ones, to the evaluation of face recognition algorithms.

Generation of synthetic Kinect depth images based on empirical noise model

The development, training and evaluation of computer vision algorithms rely on the availability of a large number of images. The acquisition of these images can be time-consuming if they are recorded using real sensors. An alternative is to rely on synthetic images which can be rapidly generated. This Letter describes a novel method for the simulation of Kinect v1 depth images. The method is based on an existing empirical noise model from the literature. The authors show that their relatively simple method is able to provide depth images which have a high similarity with real depth images.

Introduction of New Editors

O support the continuing increase in submission arising from the popularity of TPAMI with authors, we are pleased to announce that Professor Zoubin Ghahramani will be joining David Fleet as an Associate Editor-in-Chief (AEIC) of TPAMI. He will help to maintain the high quality that readers expect and the timeliness and thoroughness of reviewing that authors’ demand. The AEIC works hand-in-hand with the Editor-in-Chief in all aspects of TPAMI’s editorial review process, including establishing policies, selecting special issues, selecting editors, helping the fl ow of papers through the review process, handling appeals, etc. Dr. Ghahramani is a leader in the fi eld of machine learning, an area of increasing importance to TPAMI, for both the quality of his research and his service to the community. We are also happy to announce that the TPAMI editorial board is expanding with the addition of four new Associate Editors, Dr. Sing Bing Kang, Professor Kevin Murphy. Dr. Salil Prabhakar, and Professor Dale Schuurmans. Dr. Kang will handle papers on image-based rendering, vision for graphics, image and video processing/enhancement, and stereopsis and structure from motion. Professor Murphy will oversee papers about graphical models, structure learning, causal inference, unsupervised learning, and Bayesian methods. Dr. Prabhakar will oversee the review process of papers in all aspects of biometric systems and theoretical and empirical evaluation of computer vision algorithms. Professor Schuurmans’ expertise includes machine learning techniques (support vector machines, kernel methods, clustering, dimensionality reduction), graphical models, optimization, and Monte Carlo methods, and he has begun to handle papers in these areas. Their brief biographies are below. Welcome to TPAMI’s editorial board!

Qualitative evaluation of computer vision algorithms in polar terrains

We address the issue of Computer Vision in Antarctica for robot navigation by analysing images collected at Patriot Hills, Antarctica in the Fall of 1998. Conditions produced by polar weather and terrain are unique and challenging for perception equipment and computer vision algorithms. The later aspect will be studied here through the evaluation of a colour segmentation algorithm, a stereo vision algorithm and a feature points detector and tracker algorithm.

Empirical Evaluation of Computer Vision Algorithms

——————————F—————————— omputer vision emerged as a subfield in computer science and in electrical engineering in the 1960s. Two main motivations for research in computer vision are to develop algorithms to solve vision problems and to understand and model the human visual system. It turns out that finding satisfactory answers to either motivation is significantly harder than common wisdom initially assumed. Research in computer vision has actively continued to the current time. Most of the research in the computer vision and pattern recognition community is focused on developing solutions to vision problems. With three decades of research behind current efforts and with the availability of powerful, inexpensive computers, there is a common belief that computer vision is poised to deliver reliable solutions. The area of empirical evaluation of computer vision algorithms is developing the methods and tools for measuring the ability of algorithms to meet requirements to be fielded, for determining the state-of-the-art, and for pointing out future research directions. The goal of this special theme section of IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI) is to highlight progress in empirical evaluation and identify it as a maturing area of computer vision. Out of 18 submissions, three were accepted for this special section. In addition, one submission was accepted to appear in a regular issue, and two others are being revised for consideration as regular papers. “Filtering for Supervised Texture Segmentation: A Comparative Study” by T. Randen and J.H. Husoy presents a comparative study of methods for texture classification. The emphasis of the study is filtering methods from signal processing. Most major filtering approaches are evaluated. For reference, a statistical algorithm and a model-based algorithm are also evaluated. The paper presents performance results on a number of mosaic texture images. In a first for PAMI, the raw image files for these images are being made available as part of the electronic version of the paper. (The electronic version of the paper is part of the Computer Society’s digital library, accessible online at www.computer.org.) It is hoped that future papers on texture segmentation will take advantage of this in order to present directly comparable experimental results. “Performance Evaluation and Analysis of Monocular Building Extraction From Aerial Imagery” by J.A. Shufelt evaluates end-to-end performance of four systems on their ability to extract buildings from 83 aerial images of 18 sites. The methodology allows for an examination of traditional assumptions made in designing algorithms that extract buildings from monocular imagery. “Evaluation of Methods for Ridge and Valley Detection” by A.M. Lopez, F. Lumbreras, and J. Serrat evaluates ridge and valley detectors. The authors discuss what are desirable properties of ridge and valley detectors and the methods for measuring desirable properties. Then they present an evaluation using these methods. We hope the papers in this special section are interesting and present challenges for future researchers.