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1) The document discusses using high-performance distributed computing to solve large-scale multimedia problems like analyzing vast amounts of video footage. 2) It describes Parallel-Horus, a tool that makes parallel and distributed computing transparent to users through sequential programming patterns and lazy parallelization. 3) Experiments show Parallel-Horus can achieve real-time performance for tasks like color-based object recognition by distributing the work across multiple computer clusters.
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cloud. Acting on the feature detector influences the final result: PPT can work both
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Slides by Miriam Bellver from the Computer Vision Reading Group at the Universitat Politecnica de Catalunya about the paper:
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Abstract:
State-of-the-art object detection systems rely on an accurate set of region proposals. Several recent methods use a neural network architecture to hypothesize promising object locations. While these approaches are computationally efficient, they rely on fixed image regions as anchors for predictions. In this paper we propose to use a search strategy that adaptively directs computational resources to sub-regions likely to contain objects. Compared to methods based on fixed anchor locations, our approach naturally adapts to cases where object instances are sparse and small. Our approach is comparable in terms of accuracy to the state-of-the-art Faster R-CNN approach while using two orders of magnitude fewer anchors on average. Code is publicly available.
Robust techniques for background subtraction in urban
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深度學習在AOI的應用
This document discusses using fully convolutional neural networks for defect inspection. It begins with an agenda that outlines image segmentation using FCNs and defect inspection. It then provides details on data preparation including labeling guidelines, data augmentation, and model setup using techniques like deconvolution layers and the U-Net architecture. Metrics for evaluating the model like Dice score and IoU are also covered. The document concludes with best practices for successful deep learning projects focusing on aspects like having a large reusable dataset, feasibility of the problem, potential payoff, and fault tolerance.
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みなさんこんにちはこれ何文字まで入るの？40文字以下不可とか本当に意味わからないけどこれ限界文字数書いてないからマジでやばい文字数いけるんじゃないの？えこ...
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みなさんこんにちはこれ何文字まで入るの？40文字以下不可とか本当に意味わからないけどこれ限界文字数書いてないからマジでやばい文字数いけるんじゃないの？えこ...
みなさんこんにちはこれ何文字まで入るの？40文字以下不可とか本当に意味わからないけどこれ限界文字数書いてないからマジでやばい文字数いけるんじゃないの？えこ...
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Das3 Fjseins
- 1. High-Performance Distributed Multimedia Computing Frank Seinstra, Jan-Mark Geusebroek Intelligent Systems Lab Amsterdam Informatics Institute University of Amsterdam MultimediaN (BSIK Project)
- 10. A Real Problem, part 2… + LambdaRAM ?? may be time-critical…!
- 14. See also: http://www.science.uva.nl/~fjseins/aibo.html Example: Object Recognition
- 15. Example: Object Recognition Demonstrated live (a.o.) at ECCV 2006, June 8-11, 2006, Graz, Austria (see video2.wmv)
- 20. The End (see video3.avi)