An Image Visual Quality Assessment Method Based on SIFT Features

Close The Journal of Pattern Recognition Research (JPRR) provides an international forum for the electronic publication of high-quality research and industrial experience articles in all areas of pattern recognition, machine learning, and artificial intelligence. JPRR is committed to rigorous yet rapid reviewing. Final versions are published electronically (ISSN 1558-884X) immediately upon acceptance. Submit Manuscript Application of Approximate Equality for Reduction of Feature Vector Dimension Kumar Sankar Ray, Srikanta Kolay JPRR Vol 11, No 1 (2016); doi:10.13176/11.639

Close The Journal of Pattern Recognition Research (JPRR) provides an international forum for the electronic publication of high-quality research and industrial experience articles in all areas of pattern recognition, machine learning, and artificial intelligence. JPRR is committed to rigorous yet rapid reviewing. Final versions are published electronically (ISSN 1558-884X) immediately upon acceptance. Submit Manuscript Pattern Similarity Score Based on One Dimensional Time Series Analysis Paawan Sharma, Mukul Kumar Gupta, Amit Kumar Mondal, Vivek Kaundal JPRR Vol 12, No 1 (2017); doi:10.13176/11.744

Close The Journal of Pattern Recognition Research (JPRR) provides an international forum for the electronic publication of high-quality research and industrial experience articles in all areas of pattern recognition, machine learning, and artificial intelligence. JPRR is committed to rigorous yet rapid reviewing. Final versions are published electronically (ISSN 1558-884X) immediately upon acceptance. Submit Manuscript GrepVS - a Combined Approach for Graph Matching Diego Reforgiato Recupero JPRR Vol 4, No 1 (2009); doi:10.13176/11.85

Close The Journal of Pattern Recognition Research (JPRR) provides an international forum for the electronic publication of high-quality research and industrial experience articles in all areas of pattern recognition, machine learning, and artificial intelligence. JPRR is committed to rigorous yet rapid reviewing. Final versions are published electronically (ISSN 1558-884X) immediately upon acceptance. Submit Manuscript Bangla Basic Character Recognition Using Digital Curvelet Transform Majumdar Angshul JPRR Vol 2, No 1 (2007); doi:10.13176/11.27

Knowledge and use of visual soil structure assessment methods in Brazil – A survey

Background Early detection of mandibular bone changes is critical for preventing complications during dental surgeries. Radiographic assessment of mandibular trabeculae using visual index assessment and fractal dimension (FD) analysis represents a significant advancement in dental diagnostics. These approaches enhance the identification of individuals at risk of low bone mass and facilitate the monitoring of bone changes, thereby optimizing treatment planning. Objective This study aimed to visually assess changes in the trabecular pattern of the mandible on digital panoramic images and compare these changes with fractal dimension analysis to support optimal treatment planning. Methods This retrospective cross-sectional study was conducted in the Department of Oral Diagnostic Sciences, Aminu Kano Teaching Hospital, Kano. Two regions of interest (ROIs)—between the interdental region of the second premolar and first molar—were analyzed on both sides of the mandible using dedicated software for visual and FD assessment. Data on age, sex, trabecular pattern type, and FD values were recorded. Statistical analyses, including Chi-square, independent t-test, and ANOVA, were performed to assess associations among variables. Results A total of 412 digital panoramic images were analyzed, comprising 219 (53.2%) males and 193 (46.8%) females, with a mean age (SD) of 35.3 (11.9) years. Sparse trabecular patterns were more prevalent among females (56.0% right; 50.3% left), while significantly higher FD values were observed in males (p = 0.02 right; p = 0.006 left). Both visual assessment and FD analysis identified sparse trabecular patterns on the right (51.7%, 51.9%) and left (44.2%, 48.3%) sides. Agreement between methods was moderate on the right and fair on the left side of the mandible (Kappa = 0.47 and 0.36, respectively). Conclusion Both visual index and FD assessment methods effectively identified sparse trabecular patterns, though neither alone is entirely sufficient. A combined approach yields more reliable data for improved treatment planning.

The design of campus landscape is expected to reflect the institution values, provide the character and the spirit of place to campus environment. It is highly influenced by visual experience and impression of campus environment. There are two methods in landscape visual quality assessment: descriptive assessment method and evaluative assessment method. Both assessment methods cannot be done simultaneously, but as a sequence phase. Descriptive assessment method should be done first to obtain a reference for evaluative assessment method. Landscape visual quality evaluative assessment method is used to measure the level of public assessment about visual quality and visual response. Information Processing Theory is used to develop the visual quality evaluative assessment method and to obtain unified integration between descriptive assessment and evaluative assessment. The development of evaluative assessment method includes the process of comparing, averaging, or determine the ranking of each environmental hue or landscape areas in campus, based on the public or college user community preferences.

The 2025 3rd International Conference on Computer, Machine Learning and Artificial Intelligence (CMLAI 2025) had been held in San Francisco, USA during March 29-30, 2025. CMLAI 2025 is an international forum devoted specifically to facilitate synergies in research and development in the areas of computer science, machine learning, neural networks, algorithm design, and artificial intelligence. It provides the communication platform for those who are leading experts and scholars from around the world. The development of artificial intelligence and their corresponding technologies and industries has also been regarded as "national development strategy" for the world's leading countries and gradually developed into a new "trump card" in the game between countries. This conference was attended by over 60 researchers, scholars, academic and industry experts from many countries. During the two-day conference, 4 keynote speeches with topics on cloud computing, machine learning, neural networks, and artificial intelligence were delivered. And 24 oral and invited presentations were presented and discussed in-depth in 4 technical sessions, with interactive communication among the attendees. The conference successfully promoted exchanging new ideas and perspective discourse among the participants in their respective fields. CMLAI Organizing Committees San Francisco, USA

Technical note: An acoustic method for assessing the respiration rate of free-grazing dairy cattle

The Journal of Pattern Recognition Research (JPRR) provides an open access forum for the publication of research articles in areas of pattern recognition, machine learning, artificial intelligence, computational algorithms, and fuzzy learning

Artificial Intelligence in the American Healthcare Industry: Looking Forward to 2030

Gingival phenotype (GP) is determined based on the thickness and width of the gingival tissue. An evaluation of GP is essential for adequate treatment planning and outcome monitoring, including orthodontic treatments in a paediatric population. The present study aimed to compare the reliability of the visual and TRAN methods with that of the ultrasound biometer measurements in the early transitional dentition phase. One hundred ninety three generally healthy, 7-year-old children were examined. An assessment of GP was performed by a paedodontist and a periodontist. The average thickness of the gingiva was 0.76 ± 0.36 mm, which was classified as a thin GP. The agreement between a visual assessment and the biometric ultrasound measurements reached the highest (94%) level when assessing a very thin GP (Spearman's rank correlation coefficient r = 0.37, p < 0.01). Similarly, 99% agreement in the diagnosis of a thin GP was recorded for the TRAN and ultrasound methods (Spearman's rank correlation coefficient r = 0.49, p < 0.001). In total, 86% of cases diagnosed as having a thick GP using the TRAN method turned out to be thin according to the ultrasound measurements. The dentist's specialization and professional experience in the assessment of GP were irrelevant (Spearman's rank correlation coefficient r = 0.49, p < 0.001). All methods tested in the present study were proven to be easy to perform and well accepted by the children. The visual assessment and TRAN methods, despite the fact that they enabled the diagnosis of a thin GP (crucial for treatment planning), cannot be recommended during the teeth replacement period. A misdiagnosis of thick GP may deprive a young at-risk patient of special supervision, which may develop into mucogingival deformities. A biometric ultrasound, although expensive, allows for reliable assessment of the gingiva thickness when needed.

Visual discomfort is one of the most frequent complaints of the viewers when watching stereo images or videos. It has been the subject of considerable research in relation to stereo display system. To predict the effects induced by three-dimensional contents on visual discomfort, a new objective visual comfort assessment (VCA) method of stereo image is proposed based on scene mode classification. Scene modes are defined and real scenes are classified into ten kinds of scene modes according to the properties of two aspects. These aspects include the crossed / uncrossed disparity type of foreground object (FGO) and background region (BGR), and the other is the whether locate on zone of comfortable viewing of FGO and BGR in scene. In the process of mode classification and selection, disparity map is first utilized to segment stereo image into FGOs and BGRs adaptively. Then, some features, including disparity angle of both the FGOs and BGRs, and width angle of FGOs, are utilized to build an objective VCA models in various scene modes. The experimental results show that the proposed method performs higher assessment accuracy than some state-of-the-art methods.

An air curtain is used to ensure an adequate separation between two compartments that is aimed to restrict, a clean one and a contaminated one. The air curtain performance is improved with the use of an air extraction from the “contaminated” compartment. The aerodynamic sealing of an optimized air curtain is assessed regarding particulate matter. The evaluation using visual assessment of the aerodynamic sealing using a cold smoke source is validated against PM 10 detection. It is shown that the air curtain presents a good aerodynamic sealing for the particulate of PM10 and PM2.5 classes and that the visual assessment method can lead to similar results. An equation was obtained with a view to be used to predict the level of aerodynamic sealing of the air curtain when the exhaust rate becomes too low. Another equation was obtained for predicting the average velocity through the door protected by the air curtain required to obtain an aerodynamic sealing, as a function of the jet parameters (nozzle average velocity and thickness). These test results prove that the air curtain, complemented with an air exhaust from the “contaminated compartment”, is suitable to provide an acceptable aerodynamic sealing for the particulate matter. Practical application: The findings of this research are applicable when it is necessary to retain the contamination due to particulate matter using a soft boundary, as air plane jets. This is applicable to medical care centres to reduce the possibility of contamination due to bacteria between different compartments or in pharmaceutical process units to avoid cross contamination among them.

The effective management of municipal road infrastructure requires up-to-date, standardized and reliable condition information to support sustainable maintenance. While visual road-condition assessment methods based on established standards are widely applied to municipal roads, they remain largely manual, time-consuming, costly and subjective. This study presents an end-to-end workflow for the automated visual inspection and condition assessment of municipal road infrastructure using high-resolution, 3D street-level imagery acquired by professional mobile mapping systems. The proposed approach integrates an efficient preprocessing pipeline for precise road-surface extraction with deep learning models trained for the specific task and an advanced postprocessing method for robust results aggregation. For this purpose, a large dataset covering approximately 352 km of municipal roads across eight municipalities was created by combining street-level imagery with expert-annotated road-condition index (RCI) values. Two neural network variants were implemented: a regression model predicting standardized RCI values and a binary classifier distinguishing between roads requiring maintenance and those in good condition. To ensure decision-oriented outputs at the infrastructure-asset level, frame-based predictions are aggregated into homogeneous road segments using outlier detection and change-point analysis along the road axis. The regression model achieved a mean absolute error of 0.48 RCI values at frame level and 0.40 RCI values at road-segment level, outperforming conventional inter-expert variability, while the binary classification model reached an F1-score of 0.85. These findings demonstrate that AI-based visual road-condition assessment using professional mobile mapping data can provide accurate, standardized and scalable condition information for municipal road infrastructure. The proposed workflow supports maintenance prioritization and infrastructure management decisions without requiring explicit detection of individual pavement defects, offering a practical pathway toward automated, cost-effective road-condition monitoring.