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Machine Vision

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Journal Scope
Inventi Rapid/Impact: Machine Vision is the peer reviewed journal of Engineering & Technology. The journal contains the research and review paper related to key technologies in the area manufacturing and quality control. It primarily focuses implementation and use of vision algorithm in practical application is provided and engineering aspects of techniques.



3D MACHINE VISION AND ADDITIVE MANUFACTURING: CONCURRENT PRODUCT AND PROCESS DEVELOPMENT
Ismet P Ilyas

The manufacturing environment rapidly changes in turbulence fashion. Digital manufacturing (DM) plays a significant role and one of the key strategies in setting up vision and strategic planning toward the knowledge based manufacturing. An approach of combining 3D machine vision (3D-MV) and an Additive Manufacturing (AM) may finally be finding its niche in manufacturing. This paper briefly overviews the integration of the 3D machine vision and AM in concurrent product and process development, the challenges and opportunities, the implementation of the 3D-MV and AM at POLMAN Bandung in accelerating product design and process development, and discusses a direct deployment of this approach on a real case from our industrial partners that have placed this as one of the very important and strategic approach in research as well as product/prototype development. The strategic aspects and needs of this combination approach in research, design and development are main concerns of the presentation....
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ACTIVE OBJECT RECOGNITION WITH A SPACE-VARIANT RETINA
Christopher Kanan

When independent component analysis (ICA) is applied to color natural images, the representation it learns has spatiochromatic properties similar to the responses of neurons in primary visual cortex. Existing models of ICA have only been applied to pixel patches. This does not take into account the space-variant nature of human vision. To address this, we use the space-variant logpolar transformation to acquire samples fromcolor natural images, and then we apply ICA to the acquired samples.We analyze the spatiochromatic properties of the learned ICA filters. Qualitatively, the model matches the receptive field properties of neurons in primary visual cortex, including exhibiting the same opponent-color structure and a higher density of receptive fields in the foveal region compared to the periphery.We also adopt the “self-taught learning” paradigm from machine learning to assess the model’s efficacy at active object and face classification, and the model is competitive with the best approaches in computer vision...
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RESECTION-INTERSECTION BUNDLE ADJUSTMENT REVISITED
Ruan Lakemond, Clinton Fookes, Sridha Sridharan

Bundle adjustment is one of the essential components of the computer vision toolbox. This paper revisits the resection-intersection approach, which has previously been shown to have inferior convergence properties. Modifications are proposed that greatly improve the performance of this method, resulting in a fast and accurate approach. Firstly, a linear triangulation step is added to the intersection stage, yielding higher accuracy and improved convergence rate. Secondly, the effect of parameter updates is tracked in order to reduce wasteful computation; only variables coupled to significantly changing variables are updated. This leads to significant improvements in computation time, at the cost of a small, controllable increase in error. Loop closures are handled effectively without the need for additional networkmodelling.The proposed approach is shown experimentally to yield comparable accuracy to a full sparse bundle adjustment (20% error increase) while computation time scales much better with the number of variables. Experiments on a progressive reconstruction system show the proposed method to be more efficient by a factor of 65 to 177, and 4.5 times more accurate (increasing over time) than a localised sparse bundle adjustment approach....
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E- ISSN: 2277-6249
P- ISSN: Awaited


Inventi Impact
Machine Vision



Frequency: Quarterly
E- ISSN: 2277-6249
P- ISSN: Awaited


Abstracted/ Indexed in: Ulrich’s International Periodical Directory & Google Scholar, SCIRUS, getCITED