Many contour-based image corner detectors are based on the curvature scale-space (CSS). We identify the weaknesses of the CSS-based detectors. First, the ldquocurvaturerdquo itself by its ...ldquodefinitionrdquo is very much sensitive to the local variation and noise on the curve, unless an appropriate smoothing is carried out beforehand. In addition, the calculation of curvature involves derivatives of up to second order, which may cause instability and errors in the result. Second, the Gaussian smoothing causes changes to the curve and it is difficult to select an appropriate smoothing-scale, resulting in poor performance of the CSS corner detection technique. We propose a complete corner detection technique based on the chord-to-point distance accumulation (CPDA) for the discrete curvature estimation. The CPDA discrete curvature estimation technique is less sensitive to the local variation and noise on the curve. Moreover, it does not have the undesirable effect of the Gaussian smoothing. We provide a comprehensive performance study. Our experiments showed that the proposed technique performs better than the existing CSS-based and other related methods in terms of both average repeatability and localization error.
There are many applications, such as image copyright protection, where transformed images of a given test image need to be identified. The solution to this identification problem consists of two main ...stages. In stage one, certain representative features, such as corners, are detected in all images. In stage two, the representative features of the test image and the stored images are compared to identify the transformed images for the test image. Curvature scale-space (CSS) corner detectors look for curvature maxima or inflection points on planar curves. However, the arc-length used to parameterize the planar curves by the existing CSS detectors is not invariant to geometric transformations such as scaling. As a solution to stage one, this paper presents an improved CSS corner detector using the affine-length parameterization which is relatively invariant to affine transformations. We then present an improved corner matching technique as a solution to the stage two. Finally, we apply the proposed corner detection and matching techniques to identify the transformed images for a given image and report the promising results.
Nowadays, more and more images are available. However, to find a required image for an ordinary user is a challenging task. Large amount of researches on image retrieval have been carried out in the ...past two decades. Traditionally, research in this area focuses on content based image retrieval. However, recent research shows that there is a semantic gap between content based image retrieval and image semantics understandable by humans. As a result, research in this area has shifted to bridge the semantic gap between low level image features and high level semantics. The typical method of bridging the semantic gap is through the automatic image annotation (AIA) which extracts semantic features using machine learning techniques. In this paper, we focus on this latest development in image retrieval and provide a comprehensive survey on automatic image annotation. We analyse key aspects of the various AIA methods, including both feature extraction and semantic learning methods. Major methods are discussed and illustrated in details. We report our findings and provide future research directions in the AIA area in the conclusions
► Comprehensive covering of feature representations in AIA research. ► Comprehensive review of classification and annotation methods in AIA. ► Graphic illustration and interpretation of variety of annotation methods in AIA. ► Findings and future direction of research in AIA.
In order to improve the retrieval accuracy of content-based image retrieval systems, research focus has been shifted from designing sophisticated low-level feature extraction algorithms to reducing ...the ‘semantic gap’ between the visual features and the richness of human semantics. This paper attempts to provide a comprehensive survey of the recent technical achievements in high-level semantic-based image retrieval. Major recent publications are included in this survey covering different aspects of the research in this area, including low-level image feature extraction, similarity measurement, and deriving high-level semantic features. We identify five major categories of the state-of-the-art techniques in narrowing down the ‘semantic gap’: (1) using object ontology to define high-level concepts; (2) using machine learning methods to associate low-level features with query concepts; (3) using relevance feedback to learn users’ intention; (4) generating semantic template to support high-level image retrieval; (5) fusing the evidences from HTML text and the visual content of images for WWW image retrieval. In addition, some other related issues such as image test bed and retrieval performance evaluation are also discussed. Finally, based on existing technology and the demand from real-world applications, a few promising future research directions are suggested.
Music information retrieval (MIR) is an emerging research area that receives growing attention from both the research community and music industry. It addresses the problem of querying and retrieving ...certain types of music from large music data set. Classification is a fundamental problem in MIR. Many tasks in MIR can be naturally cast in a classification setting, such as genre classification, mood classification, artist recognition, instrument recognition, etc. Music annotation, a new research area in MIR that has attracted much attention in recent years, is also a classification problem in the general sense. Due to the importance of music classification in MIR research, rapid development of new methods, and lack of review papers on recent progress of the field, we provide a comprehensive review on audio-based classification in this paper and systematically summarize the state-of-the-art techniques for music classification. Specifically, we have stressed the difference in the features and the types of classifiers used for different classification tasks. This survey emphasizes on recent development of the techniques and discusses several open issues for future research.
Background:
Although previous studies reported that 26S proteasome non-ATPase regulatory subunit 2 (
PSMD2
) is involved in many human cancers. However, its clinical significance and function in lung ...adenocarcinoma remain unclear. Here, we examined the prognostic and immunological role of
PSMD2
in lung adenocarcinoma.
Methods:
The Cancer Genome Atlas (TCGA) was conducted to analyze
PSMD2
expression and verified using UALCAN. PrognoScan and Kaplan-Meier curves were utilized to assess the effect of
PSMD2
on survival. cBioPortal database was conducted to identify the mutation characteristics of
PSMD2
. Functional enrichment was performed to determine
PSMD2
-related function. Cancer Single-cell State Atlas (CancerSEA) was used to explore the cancer functional status of
PSMD2
at single-cell resolution.
PSMD2-
related immune infiltration analysis was conducted. Tumor-Immune system interaction database (TISIDB) was performed to verify the correlation between
PSMD2
expression and tumor-infiltrating lymphocytes (TILs).
Results:
Both mRNA and protein expression of
PSMD2
were significantly elevated in lung adenocarcinoma. High expression of
PSMD2
was significantly correlated with high T stage (
p
= 0.014), lymph node metastases (
p
< 0.001), and TNM stage
p
= 0.005). Kaplan-Meier curves indicated that high expression of
PSMD2
was correlated with poor overall survival (38.2 vs. 59.7 months,
p
< 0.001) and disease-specific survival (59.9 months vs. not available,
p
= 0.004). Multivariate analysis suggested that
PSMD2
was an independent biomarker for poor overall survival (HR 1.471, 95%CI, 1.024–2.114,
p
= 0.037).
PSMD2
had a high mutation frequency of 14% in lung adenocarcinoma. The genetic mutation of
PSMD2
was also correlated with poor overall survival, disease-specific survival, and progression-free survival in lung adenocarcinoma. Functional enrichment suggested
PSMD2
expression was involved in the cell cycle, RNA transport, and cellular senescence. CancerSEA analysis indicated
PSMD2
expression was positively correlated with cell cycle, DNA damage, and DNA repair. Immune infiltration analysis suggested that
PSMD2
expression was correlated with immune cell infiltration levels and abundance of TILs.
Conclusion:
The upregulation of
PSMD2
is significantly correlated with poor prognosis and immune infiltration levels in lung adenocarcinoma. Our findings suggest that
PSMD2
is a potential biomarker for poor prognosis and immune therapeutic target in lung adenocarcinoma.
Abscisic Acid (ABA) is an important phytohormone involved in abiotic stress resistance in plants. A group of bZIP transcription factors play important roles in the ABA signaling pathway in ...Arabidopsis. However, little is known about the function of their orthologs in rice, where they may hold a great potential for developing drought resistant food crops. In this study, our phylogenetic analysis showed that this group of bZIPs was evolutionarily conserved between Arabidopsis and rice, which implies that they may share similar functions. We demonstrated with quantitative RT-PCR that the expressions of most of these OsbZIPs were significantly induced by ABA, ACC, and abiotic stresses. OsbZIP72, a member of this group, was proved to be an ABRE binding factor in rice using the yeast hybrid systems. We showed that it could bind to ABRE and transactivate the downstream reporter genes in yeast, and the transactivity was depending on its N-terminal region. Transgenic rice overexpressing OsbZIP72 showed a hypersensitivity to ABA, elevated levels of expression of ABA response gene such as LEAs, and an enhanced ability of drought tolerance. These results suggest that OsbZIP72 plays a positive role in drought resistance through ABA signaling, and is potential useful for engineering drought tolerant rice.
The plant-specific NAC (NAM, ATAF1/2, CUC2) transcription factors play diverse roles in plant development and stress responses. In this study, a rice
NAC gene,
ONAC045, was functionally ...characterized, especially with regard to its role in abiotic stress resistance. Expression analysis revealed that
ONAC045 was induced by drought, high salt, and low temperature stresses, and abscisic acid (ABA) treatment in leaves and roots. Transcriptional activation assay in yeast indicated that ONAC045 functioned as a transcriptional activator. Transient expression of
GFP-ONAC045 in onion epidermal cells revealed that ONAC045 protein was localized in the nucleus. Transgenic rice plants overexpressing
ONAC045 showed enhanced tolerance to drought and salt treatments. Two stress-responsive genes were upregulated in transgenic rice. Together, these results suggest that
ONAC045 encodes a novel stress-responsive NAC transcription factor and is potential useful for engineering drought and salt tolerant rice.
Corner detectors have many applications in computer vision and image identification and retrieval. Contour-based corner detectors directly or indirectly estimate a significance measure (e.g., ...curvature) on the points of a planar curve, and select the curvature extrema points as corners. While an extensive number of contour-based corner detectors have been proposed over the last four decades, there is no comparative study of recently proposed detectors. This paper is an attempt to fill this gap. The general framework of contour-based corner detection is presented, and two major issues-curve smoothing and curvature estimation, which have major impacts on the corner detection performance, are discussed. A number of promising detectors are compared using both automatic and manual evaluation systems on two large datasets. It is observed that while the detectors using indirect curvature estimation techniques are more robust, the detectors using direct curvature estimation techniques are faster.
PDF
