Automatic Image Annotation for Description of Urban and Outdoor Scenes

Cruz-Perez, Claudia; Starostenko, Oleg; Alarcón-Aquino, Vicente; Rodrı́guez-Asomoza, J.

doi:10.1007/978-3-319-06773-5_20

Cited by 5 publications

(3 citation statements)

References 15 publications

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“…The use of formalism for knowledge representation and reasoning for solution of complex problems improves accuracy, recall, and increments speed of these methods. Under controlled conditions, they provide good results, but often they are sensitive to rotation and scaling as well as requiring a structured environment [10,19,20].…”

Section: Modern Techniques For Qr Code Detection and Recognitionmentioning

confidence: 99%

Binary Large Object-Based Approach for QR Code Detection in Uncontrolled Environments

López-Rincón

Starostenko

Alarcón-Aquino

et al. 2017

Journal of Electrical and Computer Engineering

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Quick Response QR barcode detection in nonarbitrary environment is still a challenging task despite many existing applications for finding 2D symbols. The main disadvantage of recent applications for QR code detection is a low performance for rotated and distorted single or multiple symbols in images with variable illumination and presence of noise. In this paper, a particular solution for QR code detection in uncontrolled environments is presented. The proposal consists in recognizing geometrical features of QR code using a binary large object-(BLOB-) based algorithm with subsequent iterative filtering QR symbol position detection patterns that do not require complex processing and training of classifiers frequently used for these purposes. The high precision and speed are achieved by adaptive threshold binarization of integral images. In contrast to well-known scanners, which fail to detect QR code with medium to strong blurring, significant nonuniform illumination, considerable symbol deformations, and noising, the proposed technique provides high recognition rate of 80%-100% with a speed compatible to real-time applications. In particular, speed varies from 200 ms to 800 ms per single or multiple QR code detected simultaneously in images with resolution from 640 × 480 to 4080 × 2720, respectively.

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Section: Modern Techniques For Qr Code Detection and Recognitionmentioning

confidence: 99%

Binary Large Object-Based Approach for QR Code Detection in Uncontrolled Environments

López-Rincón

Starostenko

Alarcón-Aquino

et al. 2017

Journal of Electrical and Computer Engineering

Self Cite

View full text Add to dashboard Cite

show abstract

“…This work contrasts to previous approaches in several aspects: first, sentences are generated from scratch, instead of retrieving (11,27) or summarising existing text fragments associated with an image (3,7). Second, textual descriptions are generated for specific and real contents of videos, whereas related (but subtly different) work in automatic caption generation created news text (12) or encyclopedic text (2) that was contextually relevant but not closely pertinent to the specific content of images.…”

Section: Related Workmentioning

confidence: 99%

“…Human emotion can be estimated using eyes, lips and their measures (gradient, distance of eyelids or lips). The same set of facial features and measures can be used to identify a human gender 7 .…”

Section: High Level Features Extraction From Videomentioning

confidence: 99%

Generating natural language tags for video information management

Khan

Gotoh

2017

Machine Vision and Applications

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This exploratory work is concerned with generation of natural language descriptions that can be used for video retrieval applications. It is a step ahead of keyword based tagging as it captures relations between keywords associated with videos. Firstly we prepare hand annotations consisting of descriptions for video segments crafted from a TREC Video dataset. Analysis of this data presents insights into human's interests on video contents. Secondly we develop a framework for creating smooth and coherent description of video streams. It builds on conventional image processing techniques that extract high level features from individual video frames. Natural language description is then produced based on high level features. Although feature extraction processes are erroneous at various levels, we explore approaches to putting them together to produce a coherent, smooth and well phrased description by incorporating spatial and temporal information. Evaluation is made by calculating ROUGE scores between human annotated and machine generated descriptions. Further we introduce a task based evaluation by human subjects which provides qualitative evaluation of generated descriptions.

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