485,950 research outputs found
Machine Vision - Applications and Systems
none3http://www.intechopen.com/books/machine-vision-applications-and-systemsF. Solari; M. Chessa; S.P. SabatiniSolari, Fabio; Chessa, Manuela; Sabatini, SILVIO PAOL
Advantages of 3D time-of-flight range imaging cameras in machine vision applications
Machine vision using image processing of traditional intensity images is in wide spread use. In many situations environmental conditions or object colours or shades cannot be controlled, leading to difficulties in correctly processing the images and requiring complicated processing algorithms. Many of these complications can be avoided by using range image data, instead of intensity data. This is because range image data represents the physical properties of object location and shape, practically independently of object colour or shading. The advantages of range image processing are presented, along with three example applications that show how robust machine vision results can be obtained with relatively simple range image processing in real-time applications
Understanding How Image Quality Affects Deep Neural Networks
Image quality is an important practical challenge that is often overlooked in
the design of machine vision systems. Commonly, machine vision systems are
trained and tested on high quality image datasets, yet in practical
applications the input images can not be assumed to be of high quality.
Recently, deep neural networks have obtained state-of-the-art performance on
many machine vision tasks. In this paper we provide an evaluation of 4
state-of-the-art deep neural network models for image classification under
quality distortions. We consider five types of quality distortions: blur,
noise, contrast, JPEG, and JPEG2000 compression. We show that the existing
networks are susceptible to these quality distortions, particularly to blur and
noise. These results enable future work in developing deep neural networks that
are more invariant to quality distortions.Comment: Final version will appear in IEEE Xplore in the Proceedings of the
Conference on the Quality of Multimedia Experience (QoMEX), June 6-8, 201
Identifying Implementation Bugs in Machine Learning based Image Classifiers using Metamorphic Testing
We have recently witnessed tremendous success of Machine Learning (ML) in
practical applications. Computer vision, speech recognition and language
translation have all seen a near human level performance. We expect, in the
near future, most business applications will have some form of ML. However,
testing such applications is extremely challenging and would be very expensive
if we follow today's methodologies. In this work, we present an articulation of
the challenges in testing ML based applications. We then present our solution
approach, based on the concept of Metamorphic Testing, which aims to identify
implementation bugs in ML based image classifiers. We have developed
metamorphic relations for an application based on Support Vector Machine and a
Deep Learning based application. Empirical validation showed that our approach
was able to catch 71% of the implementation bugs in the ML applications.Comment: Published at 27th ACM SIGSOFT International Symposium on Software
Testing and Analysis (ISSTA 2018
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