Deep Learning for Assistive Computer Vision

Leo, Marco; Furnari, Antonino; Medioni, Gérard; Trivedi, Mohan M.; Farinella, Giovanni Maria

doi:10.1007/978-3-030-11024-6_1

Cited by 42 publications

(24 citation statements)

References 47 publications

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“…Anomalous change detection for the implementation of intelligent surveillance systems is one of the most challenging and long-standing tasks in computer vision [10,11]. Various simple methods for the implementation of intelligent surveillance systems have been proposed to detect changes in an image.…”

Section: Introductionmentioning

confidence: 99%

An online graph-based anomalous change detection strategy for unsupervised video surveillance

Kim

Cho

2019

J Image Video Proc.

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Due to various accidents and crime threats to an unspecified number of people, many surveillance technologies have been studied as an interest in individual security continues to increase throughout society. In particular, intelligent video surveillance technology is one of the most active research areas in the field of surveillance; this popularity has been spurred by recent advances in computer vision/image processing and machine learning. The main goal is to automatically detect, recognize, and analyze objects of interest from collected sensor information and then efficiently extract/utilize this useful information, such as by detecting abnormal events or intruders and recognizing objects. Anomalous event detection is a key component of security, and many existing anomaly detection algorithms rely on a foreground subtraction process to detect changes in the foreground scene. By comparing input image frames with a reference image, changed areas of the image can be efficiently detected. However, this technique can be insensitive to static changes and has difficulties in noisy environments since it depends on a reference image. We propose a new strategy for improved dynamic/static change detection that complements the weak points of existing detection methods, which have low robustness in noisy environments. To achieve this goal, we employed a self-organizing map (SOM) for data clustering and regarded the cluster distribution of neurons, represented by the weight of the optimized SOM, as a directed graph problem. We then applied the shortest path algorithm to recognize anomalous events. The real-time monitoring capability of the proposed change detection system was verified by applying it to self-produced test data and the CDnet-2014 dataset. This system showed robustness against noise that was superior to other surveillance systems in various environments.

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Section: Introductionmentioning

confidence: 99%

An online graph-based anomalous change detection strategy for unsupervised video surveillance

Kim

Cho

2019

J Image Video Proc.

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“…It is these findings that allow us to explore further. As Marco Leo et al proposed, deep learning can also be used to implement scenario understanding so that we can fully consider the context of electronic components on the PCB [28]. We will perform the detection of the resistor and capacitor according to the silkscreen printing on the PCB.…”

Section: Discussionmentioning

confidence: 99%

Application Research of Improved YOLO V3 Algorithm in PCB Electronic Component Detection

Huang

et al. 2019

Applied Sciences

117

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Target detection of electronic components on PCB (Printed circuit board) based on vision is the core technology for 3C (Computer, Communication and Consumer Electronics) manufacturing companies to achieve quality control and intelligent assembly of robots. However, the number of electronic components on PCB is large, and the shape is different. At present, the accuracy of the algorithm for detecting all electronic components is not high. This paper proposes an improved algorithm based on YOLO (you only look once) V3 (Version 3), which uses a real PCB picture and a virtual PCB picture with synthesized data as a joint training dataset, which greatly increases the recognizability of training electronic components and provides the greatest possibility for data enhancement. After analyzing the feature distribution of the five dimensionality-reduced output layers of Darknet-53 and the size distribution of the detection target, it is proposed to adjust the original three YOLO output layers to four YOLO output layers and generate 12 anchor boxes for electronic component detection. The experimental results show that the mean average precision (mAP) of the improved YOLO V3 algorithm can achieve 93.07%.

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“…In this paper we focus on semantic segmentation. Image segmentation has applications in health care for detecting diseases or cancer cells [1][2][3][4], in agriculture for weed and crop detection or detecting plant diseases [5][6][7][8][9], in autonomous driving for detecting traffic signals, cars, pedestrians [10][11][12], and in other numerous fields of artificial intelligence (AI) [13]. It also poses a main obstacle in the further advancements of computer vision, that we need to overcome.…”

Section: Introductionmentioning

confidence: 99%

SEEK: A Framework of Superpixel Learning with CNN Features for Unsupervised Segmentation

et al. 2020

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Supervised semantic segmentation algorithms have been a hot area of exploration recently, but now the attention is being drawn towards completely unsupervised semantic segmentation. In an unsupervised framework, neither the targets nor the ground truth labels are provided to the network. That being said, the network is unaware about any class instance or object present in the given data sample. So, we propose a convolutional neural network (CNN) based architecture for unsupervised segmentation. We used the squeeze and excitation network, due to its peculiar ability to capture the features’ interdependencies, which increases the network’s sensitivity to more salient features. We iteratively enable our CNN architecture to learn the target generated by a graph-based segmentation method, while simultaneously preventing our network from falling into the pit of over-segmentation. Along with this CNN architecture, image enhancement and refinement techniques are exploited to improve the segmentation results. Our proposed algorithm produces improved segmented regions that meet the human level segmentation results. In addition, we evaluate our approach using different metrics to show the quantitative outperformance.

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Deep Learning for Assistive Computer Vision

Cited by 42 publications

References 47 publications

An online graph-based anomalous change detection strategy for unsupervised video surveillance

An online graph-based anomalous change detection strategy for unsupervised video surveillance

Application Research of Improved YOLO V3 Algorithm in PCB Electronic Component Detection

SEEK: A Framework of Superpixel Learning with CNN Features for Unsupervised Segmentation

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