In this paper, we explore transformed spaces, represented by image illuminant maps, to propose a methodology for selecting complementary forms of characterizing visual properties for an effective and ...automated detection of image forgeries. We combine statistical telltales provided by different image descriptors that explore color, shape, and texture features. We focus on detecting image forgeries containing people and present a method for locating the forgery, specifically, the face of a person in an image. Experiments performed on three different open-access data sets show the potential of the proposed method for pinpointing image forgeries containing people. In the two first data sets (DSO-1 and DSI-1), the proposed method achieved a classification accuracy of 94% and 84%, respectively, a remarkable improvement when compared with the state-of-the-art methods. Finally, when evaluating the third data set comprising questioned images downloaded from the Internet, we also present a detailed analysis of target images.
Plant stomata are essential structures (pores) that control the exchange of gases between plant leaves and the atmosphere, and also they influence plant adaptation to climate through photosynthesis ...and transpiration stream. Many works in literature aim for a better understanding of these structures and their role in the evolution process and the behavior of plants. Although stomata studies in dicots species have advanced considerably in the past years, even there is not much knowledge about the stomata of cereal grasses. Due to the high morphological variation of stomata traits intra- and inter-species, detecting and classifying stomata automatically becomes challenging. For this reason, in this work, we propose a new system for automatic stomata classification and detection in microscope images for maize cultivars based on transfer learning strategy of different deep convolution neural netwoks (DCNN). Our performed experiments show that our system achieves an approximated accuracy of 97.1% in identifying stomata regions using classifiers based on deep learning features, which figures out as a nearly perfect classification system. As the stomata are responsible for several plant functionalities, this work represents an important advance for maize research, providing an accurate system in replacing the current manual task of categorizing these pores on microscope images. Furthermore, this system can also be a reference for studies using images from different cereal grasses.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Optical image sensors are the most common remote sensing data acquisition devices present in Unmanned Aerial Systems (UAS). In this context, assigning a location in a geographic frame of reference to ...the acquired image is a necessary task in the majority of the applications. This process is denominated direct georeferencing when ground control points are not used. Despite it applies simple mathematical fundamentals, the complete direct georeferencing process involves much information, such as camera sensor characteristics, mounting measurements, attitude and position of the UAS, among others. In addition, there are many rotations and translations between the different reference frames, among many other details, which makes the whole process a considerable complex operation. Another problem is that manufacturers and software tools may use different reference frames posing additional difficulty when implementing the direct georeferencing. As this information is spread among many sources, researchers may face difficulties on having a complete vision of the method. In fact, there is absolutely no paper in the literature that explain this process in a comprehensive way. In order to supply this implicit demand, this paper presents a comprehensive method for direct georeferencing of aerial images acquired by cameras mounted on UAS, where all required information, mathematical operations and implementation steps are explained in detail. Finally, in order to show the practical use of the method and to prove its accuracy, both simulated and real flights were performed, where objects of the acquired images were georeferenced.
Brain image registration fuses and aligns sets of structural or functional images within individual and population studies. The similarity metric is an image registration component used for detecting ...the same target region in different images. Multi-modal image registration constitutes one of the greatest challenges in medical imaging as it adds even more variability to the tissue and organ appearance, shape, and positioning. This paper contains two contributions to solve this complex problem: (1) we propose a solution to compute the similarity metric based on a deep ensemble method. It combines multiple traditional and deep similarity metrics into a single improved similarity map; (2) we propose novel evaluation metrics to validate the results. Experiment results in the context of T1- and T2-weighted MR images of the human brain show a major improvement to the state-of-the-art, especially in reducing the false-positive region occurrences.
Connective-tissue growth factor (CTGF/CCN2) is a matricellular-secreted protein involved in complex processes such as wound healing, angiogenesis, fibrosis and metastasis, in the regulation of cell ...proliferation, migration and extracellular matrix remodeling. Glioblastoma (GBM) is the major malignant primary brain tumor and its adaptation to the central nervous system microenvironment requires the production and remodeling of the extracellular matrix. Previously, we published an in vitro approach to test if neurons can influence the expression of the GBM extracellular matrix. We demonstrated that neurons remodeled glioma cell laminin. The present study shows that neurons are also able to modulate CTGF expression in GBM. CTGF immnoreactivity and mRNA levels in GBM cells are dramatically decreased when these cells are co-cultured with neonatal neurons. As proof of particular neuron effects, neonatal neurons co-cultured onto GBM cells also inhibit the reporter luciferase activity under control of the CTGF promoter, suggesting inhibition at the transcription level. This inhibition seems to be contact-mediated, since conditioned media from embryonic or neonatal neurons do not affect CTGF expression in GBM cells. Furthermore, the inhibition of CTGF expression in GBM/neuronal co-cultures seems to affect the two main signaling pathways related to CTGF. We observed inhibition of TGFβ luciferase reporter assay; however phopho-SMAD2 levels did not change in these co-cultures. In addition levels of phospho-p44/42 MAPK were decreased in co-cultured GBM cells. Finally, in transwell migration assay, CTGF siRNA transfected GBM cells or GBM cells co-cultured with neurons showed a decrease in the migration rate compared to controls. Previous data regarding laminin and these results demonstrating that CTGF is down-regulated in GBM cells co-cultured with neonatal neurons points out an interesting view in the understanding of the tumor and cerebral microenvironment interactions and could open up new strategies as well as suggest a new target in GBM control.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Plant stomata are essential structures (pores) that control the exchange of gases between plant leaves and the atmosphere, and also they influence plant adaptation to climate through photosynthesis ...and transpiration stream. Many works in literature aim for a better understanding of these structures and their role in the evolution process and the behavior of plants. Although stomata studies in dicots species have advanced considerably in the past years, even there is not much knowledge about the stomata of cereal grasses. Due to the high morphological variation of stomata traits intra- and inter-species, detecting and classifying stomata automatically becomes challenging. For this reason, in this work, we propose a new system for automatic stomata classification and detection in microscope images for maize cultivars based on transfer learning strategy of different deep convolution neural netwoks (DCNN). Our performed experiments show that our system achieves an approximated accuracy of 97.1% in identifying stomata regions using classifiers based on deep learning features, which figures out as a nearly perfect classification system. As the stomata are responsible for several plant functionalities, this work represents an important advance for maize research, providing an accurate system in replacing the current manual task of categorizing these pores on microscope images. Furthermore, this system can also be a reference for studies using images from different cereal grasses.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
The large variety of medical image modalities (e.g. Computed Tomography, Magnetic Resonance Imaging, and Positron Emission Tomography) acquired from the same body region of a patient together with ...recent advances in computer architectures with faster and larger CPUs and GPUs allows a new, exciting, and unexplored world for image registration area. A precise and accurate registration of images makes possible understanding the etiology of diseases, improving surgery planning and execution, detecting otherwise unnoticed health problem signals, and mapping functionalities of the brain. The goal of this paper is to present a review of the state-of-the-art in medical image registration starting from the preprocessing steps, covering the most popular methodologies of the literature and finish with the more recent advances and perspectives from the application of Deep Learning architectures.
•Proposal of a new representation of time series based on recurrence plots technique for Plant Recognition.•Effectiveness analysis of the recurrence plots approach in different hours of ...day.•Comparative study between recurrence plots and visual rhythm approaches.•Correlation analysis between recurrence plots and visual rhythm approaches.•Use of a successful classifier fusion framework to combine the most suitable classifiers using both approaches.
Nowadays, global warming and its resulting environmental changes is a hot topic in different biology research area. Phenology is one effective way of tracking such environmental changes through the study of plant’s periodic events and their relationship to climate. One promising research direction in this area relies on the use of vegetation images to track phenology changes over time. In this scenario, the creation of effective image-based plant identification systems is of paramount importance. In this paper, we propose the use of a new representation of time series to improve plants recognition rates. This representation, called recurrence plot (RP), is a technique for nonlinear data analysis, which represents repeated events on time series into a two-dimensional representation (an image). Therefore, image descriptors can be used to characterize visual properties from this RP images so that these features can be used as input of a classifier. To the best of our knowledge, this is the first work that uses recurrence plot for plant recognition task. Performed experiments show that RP can be a good solution to describe time series. In addition, in a comparison with visual rhythms (VR), another technique used for time series representation, RP shows a better performance to describe texture properties than VR. On the other hand, a correlation analysis and the adoption of a well successful classifier fusion framework show that both representations provide complementary information that is useful for improving classification accuracies.
Certain aspects of gait may depend on cognition, and the effects of dual-tasking (DT) on gait asymmetry (GA) during obstacle circumvention in people with Parkinson's disease (pwPD) may be walking ...phase-dependent. While the approaching phase requires allocating significant cognitive resources to perform the task, outweighing the available resources may increase GA. During the avoiding phase, the obstacle could be used as an external cue, enhancing the attention to the obstacle and, consequently, improving gait control and regulating GA.
The study investigated the effects of obstacle circumvention during gait with DT on GA in pwPD, considering the circumvention phase.
Thirty participants, 15 pwPD and 15 neurologically healthy individuals, circumvented an obstacle 20 times (in total) according to side (right and left side) and DT (presence and absence). Spatial-temporal stride parameters, medial-lateral and horizontal trajectory deviations from the original path, strategy to obstacle circumvention (lead limb away from or close to the obstacle during the crossing step), and gaze parameters were calculated during the approaching and avoiding phases. The parameters were grouped considering the side that the obstacle was circumvented during each task, and the symmetry index was calculated.
The results showed greater asymmetry of time of fixations, width, duration, and velocity during the approaching phase (p < 0.02), whereas the avoiding phase decreased asymmetry of medial-lateral and horizontal trajectory deviations parameters (p < 0.001) during obstacle circumvention with DT, in both pwPD and control group.
The findings offer a basis for considering that goal-directed movement control may regulate GA in pwPD.
Display omitted
•Asymmetry on gait with dual tasking in Parkinson's disease is walking phase-dependent.•Asymmetry of gait parameters increases during the approaching phase with dual tasking.•Asymmetry of body clearance reduces during obstacle circumvention with dual tasking.•Goal-directed control of movement may regulate gait asymmetry in Parkinson's disease.
PDF
