Concealed Object Perception and Recognition Using a Photometric Stereo Strategy

Abstract- Deep learning is an emerging research area in machine learning and pattern recognition field which has been presented with the goal of drawing Machine Learning nearer to one of its unique objectives, Artificial Intelligence. It tries to mimic the human brain, which is capable of processing and learning from the complex input data and solving different kinds of complicated tasks well. Deep learning (DL) basically based on a set of supervised and unsupervised algorithms that attempt to model higher level abstractions in data and make it self-learning for hierarchical representation for classification. In the recent years, it has attracted much attention due to its state-of-the-art performance in diverse areas like object perception, speech recognition, computer vision, collaborative filtering and natural language processing. This paper will present a survey on different deep learning techniques for remote sensing image classification.

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Faculty Opinions recommendation of Physical experience leads to enhanced object perception in parietal cortex: insights from knot tying.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.717965450.793466458 ◽

2012 ◽

Author(s):

Patrick Haggard

Keyword(s):

Parietal Cortex ◽

Object Perception ◽

Knot Tying

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A fast LED calibration method under near field lighting based on photometric stereo

Optics and Lasers in Engineering ◽

10.1016/j.optlaseng.2021.106749 ◽

2021 ◽

Vol 147 ◽

pp. 106749

Author(s):

Long Ma ◽

Yuzhe Liu ◽

Jirui Liu ◽

Shengwei Guo ◽

Xin Pei ◽

...

Keyword(s):

Near Field ◽

Calibration Method ◽

Photometric Stereo

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Photometric stereo-based high-speed inline battery electrode inspection

tm - Technisches Messen ◽

10.1515/teme-2021-0024 ◽

2021 ◽

Vol 0 (0) ◽

Author(s):

Christian Kapeller ◽

Ernst Bodenstorfer

Keyword(s):

Quality Control ◽

System Architecture ◽

High Speed ◽

Shape From Shading ◽

Production Quality ◽

Photometric Stereo ◽

Inspection System ◽

Coating Materials ◽

Production Quality Control ◽

Reconstruction Performance

Abstract Battery technology is a key component in current electric vehicle applications and an important building block for upcoming smart grid technologies. The performance of batteries depends largely on quality control during their production process. Defects introduced in the production of electrodes can lead to degraded performance and, more importantly, to short circuits in final cells, which is highly safety-critical. In this paper, we propose an inspection system architecture that can detect defects, such as missing coating, agglomerates, and pinholes on coated electrodes. Our system is able to acquire valuable production quality control metrics, like surface roughness. By employing photometric stereo techniques, a shape from shading algorithm, our system surmounts difficulties that arise while optically inspecting the black to dark gray battery coating materials. We present in detail the acquisition concept of the proposed system architecture, and analyze its acquisition-, as well as, its surface reconstruction performance in experiments. We carry these out utilizing two different implementations that can operate at a production speed of up to 2000 mm/s at a resolution of 50 µm per pixel. In this work we aim to provide a system architecture that can provide a reliable contribution to ensuring optimal performance of produced battery cells.

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