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Advances in Image Processing, Analysis and Recognition Technology 2021

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A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (22 December 2021) | Viewed by 70506
Related Special Issue: Advances in Image Processing, Analysis and Recognition Technology

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Special Issue Editor

Dr. Dariusz Frejlichowski

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Faculty of Computer Science and Information Technology, West Pomeranian University of Technology, Szczecin Zolnierska 52, 71-210 Szczecin, Poland
Interests: machine vision; computer vision; image processing; image recognition; biometrics; medical images analysis; shape description; binary images representation; fusion of various features representing an object of interest; content-based image retrieval; practical applications of image processing; analysis and recognition algorithms
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Special Issue Information

Dear Colleagues,

For many decades, researchers have been trying to make computer analysis of images as effective as the human vision system is. For this purpose, many algorithms and systems have been created so far. The whole process covers various stages, including image processing, representation and recognition. The results of this work find many applications in computer-assisted areas of everyday life. They improve particular activities, provide useful tools, sometimes only for entertainment, but quite often significantly increasing our safety. In fact, the practical implementation of image processing algorithms is particularly wide. Moreover, the rapid growth of computational complexity and computer efficiency is allowed for the development of more sophisticated and effective algorithms and tools. Although significant progress has been made so far, many issues still remain open, resulting in the need for the development of novel approaches.

The aim of this Special Issue on “Advances in Image Processing, Analysis and Recognition Technology” is to give researchers the opportunity to provide new trends, latest achievements and research directions, as well as to present their current work on the important problem of image processing, analysis and recognition.

Potential topics of interest for this Special Issue include (but are not limited to) the following areas:

Image acquisition
Image quality analysis
Image filtering, restoration and enhancement
Image segmentation
Biomedical image processing
Color image processing
Multispectral image processing
Hardware and architectures for image processing
Image databases
Image retrieval and indexing
Image compression
Low-level and high-level image description
Mathematical methods in image processing, analysis and representation
Artificial intelligence tools in image analysis
Pattern recognition algorithms applied for images
Digital watermarking
Digital photography
Practical applications of image processing, analysis and recognition algorithms in medicine, surveillance, biometrics, document analysis, multimedia, intelligent transportation systems, stereo vision, remote sensing, computer vision, robotics, etc.

Dr. Dariusz Frejlichowski
Guest Editor

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Keywords

image processing
image analysis
image recognition
computer vision

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Published Papers (10 papers)

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Research

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19 pages, 1723 KiB

Open AccessFeature PaperArticle

Combined No-Reference Image Quality Metrics for Visual Quality Assessment Optimized for Remote Sensing Images

by Andrii Rubel, Oleg Ieremeiev, Vladimir Lukin, Jarosław Fastowicz and Krzysztof Okarma

Appl. Sci. 2022, 12(4), 1986; https://doi.org/10.3390/app12041986 - 14 Feb 2022

Cited by 9 | Viewed by 2626

Abstract

No-reference image quality assessment is one of the most demanding areas of image analysis for many applications where the results of the analysis should be strongly correlated with the quality of an input image and the corresponding reference image is unavailable. One of the examples might be remote sensing since the transmission of such obtained images often requires the use of lossy compression and they are often distorted, e.g., by the presence of noise and blur. Since the practical usefulness of acquired and/or preprocessed images is directly related to their quality, there is a need for the development of reliable and adequate no-reference metrics that do not need any reference images. As the performance and universality of many existing metrics are quite limited, one of the possible solutions is the design and application of combined metrics. Several possible approaches to their composition have been previously proposed and successfully used for full-reference metrics. In the paper, three possible approaches to the development and optimization of no-reference combined metrics are investigated and verified for the dataset of images containing distortions typical for remote sensing. The proposed approach leads to good results, significantly improving the correlation of the obtained results with subjective quality scores. Full article

(This article belongs to the Special Issue Advances in Image Processing, Analysis and Recognition Technology 2021)

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19 pages, 1421 KiB

Open AccessArticle

Do Background Colors Have an Impact on Preferences and Catch the Attention of Users?

by Anna Lewandowska and Agnieszka Olejnik-Krugly

Appl. Sci. 2022, 12(1), 225; https://doi.org/10.3390/app12010225 - 27 Dec 2021

Cited by 5 | Viewed by 6974

Abstract

In recent years, our environment has become more invasive and stimulating than ever. People must choose carefully what to look for in their over-stimulated daily lives. One way to attract visual attention, which may even interrupt the cognitive task being performed, is color. However, a question arises: Does each color attract the attention of users in a similar way? In this paper, we attempt to answer this question. Our goal is to investigate whether there are colors that have a greater visual power than other colors and, thus, can capture the attention of users more strongly, independent of the background (e.g., color or image). We also discuss which mode of visual attention (divided or sustained) is particularly susceptible to such visual messages. For this purpose, a perceptual experiment was developed, in which user preferences concerning user-friendly and readable color compositions were acquired. At the same time, we measured the unconscious reactions of users related to their first impression, thus indicating the color composition which first (from a displayed pair of images) draws the attention of users. Reactions were measured using an eye tracker. As a result of this research, we found that the background color, in the case of some colors, does not have a significant impact on the perception of the visual message, even if it is intended to attract and maintain the attention of the user. Full article

(This article belongs to the Special Issue Advances in Image Processing, Analysis and Recognition Technology 2021)

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24 pages, 5034 KiB

Open AccessFeature PaperArticle

Action Classification for Partially Occluded Silhouettes by Means of Shape and Action Descriptors

by Katarzyna Gościewska and Dariusz Frejlichowski

Appl. Sci. 2021, 11(18), 8633; https://doi.org/10.3390/app11188633 - 16 Sep 2021

Cited by 1 | Viewed by 1875

Abstract

This paper presents an action recognition approach based on shape and action descriptors that is aimed at the classification of physical exercises under partial occlusion. Regular physical activity in adults can be seen as a form of non-communicable diseases prevention, and may be aided by digital solutions that encourages individuals to increase their activity level. The application scenario includes workouts in front of the camera, where either the lower or upper part of the camera’s field of view is occluded. The proposed approach uses various features extracted from sequences of binary silhouettes, namely centroid trajectory, shape descriptors based on the Minimum Bounding Rectangle, action representation based on the Fourier transform and leave-one-out cross-validation for classification. Several experiments combining various parameters and shape features are performed. Despite the presence of occlusion, it was possible to obtain about 90% accuracy for several action classes, with the use of elongation values observed over time and centroid trajectory. Full article

(This article belongs to the Special Issue Advances in Image Processing, Analysis and Recognition Technology 2021)

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18 pages, 5478 KiB

Open AccessArticle

Activity Recognition with Combination of Deeply Learned Visual Attention and Pose Estimation

by Jisu Kim and Deokwoo Lee

Appl. Sci. 2021, 11(9), 4153; https://doi.org/10.3390/app11094153 - 1 May 2021

Cited by 11 | Viewed by 2818

Abstract

While human activity recognition and pose estimation are closely related, these two issues are usually treated as separate tasks. In this thesis, two-dimension and three-dimension pose estimation is obtained for human activity recognition in a video sequence, and final activity is determined by combining it with an activity algorithm with visual attention. Two problems can be solved efficiently using a single architecture. It is also shown that end-to-end optimization leads to much higher accuracy than separated learning. The proposed architecture can be trained seamlessly with different categories of data. For visual attention, soft visual attention is used, and a multilayer recurrent neural network using long short term memory that can be used both temporally and spatially is used. The image, pose estimated skeleton, and RGB-based activity recognition data are all synthesized to determine the final activity to increase reliability. Visual attention evaluates the model in UCF-11 (Youtube Action), HMDB-51 and Hollywood2 data sets, and analyzes how to focus according to the scene and task the model is performing. Pose estimation and activity recognition are tested and analyzed on MPII, Human3.6M, Penn Action and NTU data sets. Test results are Penn Action 98.9%, NTU 87.9%, and NW-UCLA 88.6%. Full article

(This article belongs to the Special Issue Advances in Image Processing, Analysis and Recognition Technology 2021)

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25 pages, 8687 KiB

Open AccessArticle

Adaptive Multi-View Image Mosaic Method for Conveyor Belt Surface Fault Online Detection

by Rui Gao, Changyun Miao and Xianguo Li

Appl. Sci. 2021, 11(6), 2564; https://doi.org/10.3390/app11062564 - 12 Mar 2021

Cited by 7 | Viewed by 2788

Abstract

In order to improve the accuracy and real-time of image mosaic, realize the multi-view conveyor belt surface fault online detection, and solve the problem of longitudinal tear of conveyor belt, we in this paper propose an adaptive multi-view image mosaic (AMIM) method based on the combination of grayscale and feature. Firstly, the overlapping region of two adjacent images is preliminarily estimated by establishing the overlapping region estimation model, and then the grayscale-based method is used to register the overlapping region. Secondly, the image of interest (IOI) detection algorithm is used to divide the IOI and the non-IOI. Thirdly, only for the IOI, the feature-based partition and block registration method is used to register the images more accurately, the overlapping region is adaptively segmented, the speeded up robust features (SURF) algorithm is used to extract the feature points, and the random sample consensus (RANSAC) algorithm is used to achieve accurate registration. Finally, the improved weighted smoothing algorithm is used to fuse the two adjacent images. The experimental results showed that the registration rate reached 97.67%, and the average time of stitching was less than 500 ms. This method is accurate and fast, and is suitable for conveyor belt surface fault online detection. Full article

(This article belongs to the Special Issue Advances in Image Processing, Analysis and Recognition Technology 2021)

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20 pages, 3349 KiB

Open AccessArticle

Spectra-Based Selective Searching for Hyperspectral Anomaly Detection

by Chensong Yin, Chengshan Han, Xucheng Xue and Liang Huang

Appl. Sci. 2021, 11(1), 175; https://doi.org/10.3390/app11010175 - 27 Dec 2020

Cited by 4 | Viewed by 2281

Abstract

The research on hyperspectral anomaly detection algorithms has become a hotspot, driven by a lot of practical applications, such as mineral exploration, environmental monitoring and the national defense force. However, most existing hyperspectral anomaly detectors are designed with a single pixel as unit, which may not make full use of the spatial and spectral information in the hyperspectral image to detect anomalies. In this paper, to fully combine and utilize the spatial and spectral information of hyperspectral images, we propose a novel spectral-based selective searching method for hyperspectral anomaly detection, which firstly combines adjacent pixels with the same spectral characteristics into regions with adaptive shape and size and then treats those regions as one processing unit. Then, by fusing adjacent regions with similar spectral characteristics, the anomaly can be successfully distinguished from background. Two standard hyperspectral datasets are introduced to verify the feasibility and effectiveness of the proposed method. The detection performance is depicted by intuitive detection images, receiver operating characteristic curves and area under curve values. Comparing the results of the proposed method with five popular and state-of-the-art methods proves that the spectral-based selective searching method is an accurate and effective method to detect anomalies. Full article

(This article belongs to the Special Issue Advances in Image Processing, Analysis and Recognition Technology 2021)

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19 pages, 1151 KiB

Open AccessArticle

Zero-Shot Recognition Enhancement by Distance-Weighted Contextual Inference

by Doo Soo Chang, Gun Hee Cho and Yong Suk Choi

Appl. Sci. 2020, 10(20), 7234; https://doi.org/10.3390/app10207234 - 16 Oct 2020

Cited by 3 | Viewed by 2154

Abstract

Zero-shot recognition (ZSR) aims to perform visual classification by category in the absence of training samples. The focus in most traditional ZSR models is using semantic knowledge about familiar categories to represent unfamiliar categories with only the visual appearance of an unseen object. In this research, we consider not only visual information but context to enhance the classifier’s cognitive ability in a multi-object scene. We propose a novel method, contextual inference, that uses external resources such as knowledge graphs and semantic embedding spaces to obtain similarity measures between an unseen object and its surrounding objects. Using the intuition that close contexts involve more related associations than distant ones, distance weighting is applied to each piece of surrounding information with a newly defined distance calculation formula. We integrated contextual inference into traditional ZSR models to calibrate their visual predictions, and performed extensive experiments on two different datasets for comparative evaluations. The experimental results demonstrate the effectiveness of our method through significant enhancements in performance. Full article

(This article belongs to the Special Issue Advances in Image Processing, Analysis and Recognition Technology 2021)

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16 pages, 1916 KiB

Open AccessArticle

Gesture-Based User Interface for Vehicle On-Board System: A Questionnaire and Research Approach

by Krzysztof Małecki, Adam Nowosielski and Mateusz Kowalicki

Appl. Sci. 2020, 10(18), 6620; https://doi.org/10.3390/app10186620 - 22 Sep 2020

Cited by 8 | Viewed by 5516

Abstract

Touchless interaction with electronic devices using gestures is gaining popularity and along with speech-based communication offers their users natural and intuitive control methods. Now, these interaction modes go beyond the entertainment industry and are successfully applied in real-life scenarios such as a car interior. In the paper, we analyse the potential of hand gesture interaction in the vehicle environment by physically challenged drivers. A survey conducted with potential users shows that the knowledge of gesture-based interaction and its practical use by people with disabilities is low. Based on these results we proposed a gesture-based system for vehicle on-board system. It has been developed on the available state-of-the-art solutions and investigated in terms of usability on a group of people with different physical limitations who drive a car on daily basis mostly using steering aid tools. The obtained results are compared with the performance of users without any disabilities. Full article

(This article belongs to the Special Issue Advances in Image Processing, Analysis and Recognition Technology 2021)

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18 pages, 8930 KiB

Open AccessArticle

Efficient Video Frame Interpolation Using Generative Adversarial Networks

by Quang Nhat Tran and Shih-Hsuan Yang

Appl. Sci. 2020, 10(18), 6245; https://doi.org/10.3390/app10186245 - 8 Sep 2020

Cited by 10 | Viewed by 7310

Abstract

Frame interpolation, which generates an intermediate frame given adjacent ones, finds various applications such as frame rate up-conversion, video compression, and video streaming. Instead of using complex network models and additional data involved in the state-of-the-art frame interpolation methods, this paper proposes an approach based on an end-to-end generative adversarial network. A combined loss function is employed, which jointly considers the adversarial loss (difference between data models), reconstruction loss, and motion blur degradation. The objective image quality metric values reach a PSNR of 29.22 dB and SSIM of 0.835 on the UCF101 dataset, similar to those of the state-of-the-art approach. The good visual quality is notably achieved by approximately one-fifth computational time, which entails possible real-time frame rate up-conversion. The interpolated output can be further improved by a GAN based refinement network that better maintains motion and color by image-to-image translation. Full article

(This article belongs to the Special Issue Advances in Image Processing, Analysis and Recognition Technology 2021)

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Review

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25 pages, 3595 KiB

Open AccessReview

Surface Defect Detection Methods for Industrial Products: A Review

by Yajun Chen, Yuanyuan Ding, Fan Zhao, Erhu Zhang, Zhangnan Wu and Linhao Shao

Appl. Sci. 2021, 11(16), 7657; https://doi.org/10.3390/app11167657 - 20 Aug 2021

Cited by 161 | Viewed by 33579

Abstract

The comprehensive intelligent development of the manufacturing industry puts forward new requirements for the quality inspection of industrial products. This paper summarizes the current research status of machine learning methods in surface defect detection, a key part in the quality inspection of industrial products. First, according to the use of surface features, the application of traditional machine vision surface defect detection methods in industrial product surface defect detection is summarized from three aspects: texture features, color features, and shape features. Secondly, the research status of industrial product surface defect detection based on deep learning technology in recent years is discussed from three aspects: supervised method, unsupervised method, and weak supervised method. Then, the common key problems and their solutions in industrial surface defect detection are systematically summarized; the key problems include real-time problem, small sample problem, small target problem, unbalanced sample problem. Lastly, the commonly used datasets of industrial surface defects in recent years are more comprehensively summarized, and the latest research methods on the MVTec AD dataset are compared, so as to provide some reference for the further research and development of industrial surface defect detection technology. Full article

(This article belongs to the Special Issue Advances in Image Processing, Analysis and Recognition Technology 2021)

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