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Digital Imaging Processing, Sensing, and Object Recognition
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Digital Imaging Processing, Sensing, and Object Recognition

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensing and Imaging".

Deadline for manuscript submissions: 25 February 2025 | Viewed by 1884

Special Issue Editors

Dr. Marco Buzzelli

E-Mail Website
Guest Editor
Department of Informatics, Systems and Communication, University of Milan-Bicocca, 20126 Milano, Italy
Interests: signal/image/video processing and understanding; color imaging; machine learning
Special Issues, Collections and Topics in MDPI journals
Dr. Flavio Piccoli

E-Mail Website
Guest Editor
Department of Informatics, Systems and Communication, University of Milano-Bicocca, 20126 Milan, Italy
Interests: image enhancement; speaker recognition; earth observation; remote sensing; computer vision; deep learning
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Alain Tremeau

E-Mail Website
Guest Editor
Laboratoire Hubert Curien UMR 5516, Université Jean Monnet, 42023 Saint-Étienne, France
Interests: human body pose estimation; human body tracking and trajectories estimation; environmental remote sensing; computer vision; color imaging
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Digital image processing, a critical component of modern sensor systems, involves the manipulation and interpretation of images by computers. It plays a pivotal role in various fields, from environmental monitoring to medical diagnostics, offering enhanced capabilities when analyzing and understanding visual data. Object recognition, on the other hand, involves identifying and classifying objects within images, and is a key function in automated systems and AI-driven applications.

This Special Issue aims to present research that bridges the gap between advanced image processing techniques and the practical challenges of object recognition, highlighting innovative approaches and applications in sensor technology. We encourage submissions that not only present novel research but also demonstrate the practical implications and potential sensor applications of digital image processing and object recognition.

We invite contributions that explore novel methodologies, algorithms, and applications in areas including, but not limited to, the following:

  • Advanced algorithms for image segmentation, enhancement, and reconstruction.
  • Deep learning and machine learning approaches for object detection and classification.
  • The integration of image processing in sensor networks for real-time analysis.
  • The application of image processing in autonomous systems, such as drones and robots.
  • Techniques for 3D object recognition and modeling.
  • Image processing in challenging environments (such as underwater, aerial, and low-light environments).
  • Multi-spectral and hyper-spectral imaging for object identification.
  • Real-world applications in security, surveillance, healthcare, environmental monitoring, and more.
  • Ethical considerations and privacy issues in object recognition systems.

Dr. Marco Buzzelli
Dr. Flavio Piccoli
Prof. Dr. Alain Tremeau
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • digital imaging processing
  • object recognition
  • sensors
  • object classification
  • object identification

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (2 papers)

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13 pages, 2185 KiB  
Article
Diagnosis of Pancreatic Ductal Adenocarcinoma Using Deep Learning
by Fulya Kavak, Sebnem Bora, Aylin Kantarci, Aybars Uğur, Sumru Cagaptay, Deniz Gokcay, Anıl Aysal, Burcin Pehlivanoglu and Ozgul Sagol
Sensors 2024, 24(21), 7005; https://doi.org/10.3390/s24217005 - 31 Oct 2024
Viewed by 587
Abstract
Recent advances in artificial intelligence (AI) research, particularly in image processing technologies, have shown promising applications across various domains, including health care. There is a significant effort to use AI for the early diagnosis and detection of diseases, offering cost-effective and timely solutions [...] Read more.
Recent advances in artificial intelligence (AI) research, particularly in image processing technologies, have shown promising applications across various domains, including health care. There is a significant effort to use AI for the early diagnosis and detection of diseases, offering cost-effective and timely solutions to enhance patient outcomes. This study introduces a deep learning network aimed at analyzing pathology images for the accurate diagnosis of pancreatic cancer, specifically pancreatic ductal adenocarcinoma (PDAC). Utilizing a novel dataset comprised of cases diagnosed with PDAC and/or chronic pancreatitis, this study applies deep learning algorithms to assess the effectiveness and reliability of the diagnostic process. The dataset was enhanced through image duplication and the creation of a second dataset with varied dimensions, facilitating the training of advanced transfer learning models including InceptionV3, DenseNet, ResNet, VGG, EfficientNet, and a specially designed deep neural network. The study presents a convolutional neural network model, optimized for the rapid and accurate detection of pancreatic cancer, and conducts a comparative analysis with other models to select the most accurate algorithm for a decision support system. The results from Dataset 1 show that EfficientNetB0 achieved a high success rate of 92%. In Dataset 2, VGG16 was found to have high performance, with a success rate of 92%. On the other hand, ResNet50 achieved a remarkable success rate of 96% despite a moderate training time and showed high precision, recall, F1 score, and accuracy. These results provide valuable data to demonstrate and share the relevance of different deep learning models in pancreatic cancer diagnosis. Full article
(This article belongs to the Special Issue Digital Imaging Processing, Sensing, and Object Recognition)
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23 pages, 15945 KiB  
Article
ISLS: An Illumination-Aware Sauce-Packet Leakage Segmentation Method
by Shuai You, Shijun Lin, Yujian Feng, Jianhua Fan, Zhenzheng Yan, Shangdong Liu and Yimu Ji
Sensors 2024, 24(10), 3216; https://doi.org/10.3390/s24103216 - 18 May 2024
Viewed by 836
Abstract
The segmentation of abnormal regions is vital in smart manufacturing. The blurring sauce-packet leakage segmentation task (BSLST) is designed to distinguish the sauce packet and the leakage’s foreground and background at the pixel level. However, the existing segmentation system for detecting sauce-packet leakage [...] Read more.
The segmentation of abnormal regions is vital in smart manufacturing. The blurring sauce-packet leakage segmentation task (BSLST) is designed to distinguish the sauce packet and the leakage’s foreground and background at the pixel level. However, the existing segmentation system for detecting sauce-packet leakage on intelligent sensors encounters an issue of imaging blurring caused by uneven illumination. This issue adversely affects segmentation performance, thereby hindering the measurements of leakage area and impeding the automated sauce-packet production. To alleviate this issue, we propose the two-stage illumination-aware sauce-packet leakage segmentation (ISLS) method for intelligent sensors. The ISLS comprises two main stages: illumination-aware region enhancement and leakage region segmentation. In the first stage, YOLO-Fastestv2 is employed to capture the Region of Interest (ROI), which reduces redundancy computations. Additionally, we propose image enhancement to relieve the impact of uneven illumination, enhancing the texture details of the ROI. In the second stage, we propose a novel feature extraction network. Specifically, we propose the multi-scale feature fusion module (MFFM) and the Sequential Self-Attention Mechanism (SSAM) to capture discriminative representations of leakage. The multi-level features are fused by the MFFM with a small number of parameters, which capture leakage semantics at different scales. The SSAM realizes the enhancement of valid features and the suppression of invalid features by the adaptive weighting of spatial and channel dimensions. Furthermore, we generate a self-built dataset of sauce packets, including 606 images with various leakage areas. Comprehensive experiments demonstrate that our ISLS method shows better results than several state-of-the-art methods, with additional performance analyses deployed on intelligent sensors to affirm the effectiveness of our proposed method. Full article
(This article belongs to the Special Issue Digital Imaging Processing, Sensing, and Object Recognition)
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