Real-Time Display Recognition System for Visually Impaired

Rasines, Irati; Iriondo, Pedro; Díez, Ibai

doi:10.1007/978-3-642-31534-3_91

Cited by 5 publications

(3 citation statements)

References 4 publications

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“…Rasines et al [13] developed a display detection algorithm for displays with a back light. The algorithm detects regions based on thresholds of the channels in the YUV colour space (the Y component determines the brightness of the image, whereas the U and V components determine the colour).…”

Section: Previous Workmentioning

confidence: 99%

“…Two methods for blob extraction were combined: connected components of a binarised image and Maximally Stable Extremal Regions (MSER). The first method was adapted from a process used in the vast majority of literature in this field ( [10][11][12][13][14]). This process used the property that the contrast between the intensities of the background of the display and a seven-segment digit is large.…”

Section: Blob Extractionmentioning

confidence: 99%

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Automated method for detecting and reading seven-segment digits from images of blood glucose metres and blood pressure monitors

Finnegan

Villarroel

Velardo

et al. 2019

Journal of Medical Engineering & Technology

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There is an increasing need for fast and accurate transfer of readings from blood glucose metres and blood pressure monitors to a smartphone mHealth application, without a dependency on Bluetooth technology. Most of the medical devices recommended for home monitoring use a seven-segment display to show the recorded measurement to the patient. We aimed to achieve accurate detection and reading of the seven-segment digits displayed on these medical devices using an image taken in a realistic scenario by a smartphone camera. A synthetic dataset of seven-segment digits was developed in order to train and test a digit classifier. A dataset containing realistic images of blood glucose metres and blood pressure monitors using a variety of smartphone cameras was also created. The digit classifier was evaluated on a dataset of sevensegment digits manually extracted from the medical device images. These datasets along with the code for its development have been made public. The developed algorithm first preprocessed the input image using retinex with two bilateral filters and adaptive histogram equalisation. Subsequently, the digit segments were automatically located within the image by two techniques operating in parallel: Maximally Stable Extremal Regions (MSER) and connected components of a binarised image. A filtering and clustering algorithm was then designed to combine digit segments to form seven-segment digits. The resulting digits were classified using a Histogram of Orientated Gradients (HOG) feature set and a neural network trained on the synthetic digits. The model achieved 93% accuracy on digits found on the medical devices. The digit location algorithm achieved a F1 score of 0.87 and 0.80 on images of blood glucose metres and blood pressure monitors respectively. Very few assumptions were made of the locations of the digits on the devices so that the proposed algorithm can be easily implemented on new devices.

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Section: Previous Workmentioning

confidence: 99%

Section: Blob Extractionmentioning

confidence: 99%

Automated method for detecting and reading seven-segment digits from images of blood glucose metres and blood pressure monitors

Finnegan

Villarroel

Velardo

et al. 2019

Journal of Medical Engineering & Technology

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“…The digits in timer display are recognized by developing a real time display detector and digital character recognition application using techniques based on the connected components approach [11]. A computer vision system has been designed to automatically read the displays of digital instrumentation [12].…”

Section: Related Workmentioning

confidence: 99%

Real time recognition of traffic light and their signal count-down timings

Sathiya

Balasubramanian

Priya

2014

International Conference on Information Communication and Embedded Systems (ICICES2014)

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This paper describes an efficient method to detect traffic lights and recognize their corresponding signal count-down timings. Driver Assistance System (DAS) are applications installed in vehicles that provide guidance for safe driving. Traffic Light detection is also a part of the DAS. The work is split into two parts of which, the first part deals with the detection of traffic lights. The changing traffic lights are detected by extracting the candidate region of traffic light and timer display by color segmentation using thresholding. The primary traffic light is detected by comparing the count of red pixels with the count of green pixels that is obtained as a result of thresholding. The second part recognizes the LED digits in the timer display by color segmentation, which is segmented and extracted by horizontal and vertical scanning. The extracted image is then resized to a standard size which is then converted into its binary form and bounding boxes of static width is placed over each segment of the extracted LED. The on/off status of each LED segment is obtained for all the seven segments and based on the different combination of each LED segment status, the digits can be recognized. DAS provides better realization of comfort, fuel economy and safety. The proposed method is able to detect the predominant traffic light and signal timings in a reasonable time. Experimental results show that an accuracy of 96% and 88% has been achieved for detection of traffic lights and recognition of LED digits respectively.

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