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The ripeness of bananas is the most significant factor affecting nutrient composition and demand. Conventionally, cutting and ripeness analysis requires expert knowledge and substantial human intervention, and different studies have been conducted to automate and substantially reduce human effort. Using the Preferred Reporting Items for the Systematic Reviews approach, 1548 studies were extracted from journals and conferences, using different research databases, and 35 were included in the final review for key parameters. These studies suggest the dominance of banana fingers as input data, a sensor camera as the preferred capturing device, and appropriate features, such as color, that can provide better detection. Among six stages of ripeness, the studies employing the four mentioned stages performed better in terms of accuracy and coefficient of determination value. Among all the works for detecting ripeness stages prediction, convolutional neural networks were found to perform sufficiently well with large datasets, whereas conventional artificial neural networks and support vector machines attained better performance for sensor-related data. However, insufficient information on the dataset and capturing device, limited data availability, and exploitation of data augmentation techniques are limitations in existing studies. Thus, effectively addressing these shortcomings and close collaboration with experts to predict the ripeness stages should be pursued. [ABSTRACT FROM AUTHOR]

SUMMARY: The main objective in rearing animals is to maximize the efficiencies during production while maintaining their welfare. There is also now significant interest from consumers to understand the conditions under which animals are grown. Assessing welfare is a challenge due to the extensive number of factors that affect welfare; and there is currently no agreement on quantitative measures to describe suitable performance in animal management that are not based on growth performance. Most farm managers use visual observations of behavior as one of the significant features for assessing the state of the animals. Advances in imaging technology allow for easier and more accurate ways to compute and evaluate different indicators by using visual cues.This work looks at the potential to use region feature comparisons by using features derived from video data to aid in conducting these assessments in the environment. This approach supports a more holistic approach to management that incorporates the traditional measures to optimize the production system.Manual analysis of videos can be labor intensive and automated techniques could improve efficiencies and provide significant benefits. In this work, correlations between features generated from the video analysis and physical observations were determined to be significant. The use of this technology aids in the measurement of parameters that could be instrumental in defining behavior and could provide quantitative descriptors of management outcomes. However, more research needs to be conducted associating the outputs of new image processing algorithms to metabolic and physiological changes in the birds. Quantifiable measures such as these could them be used to more consistently describe conditions under which birds are being raised allowing researchers and producers to make informed decisions in designing integrated systems for production management.

With no consensus document or guideline to help us compute the psychological make-up of rhinoplasty candidates and in the light of new perspectives of some key opinion leaders in the field of patient selection, the goals of this article are to offer a glimpse of the current literature together with the knowledge gaps, introduce some new tools for the preoperative consultation, help us identify who among our patients is at greatest risk for a poor outcome, and explain how childhood trauma can be linked to body shame and postoperative dissatisfaction. [ABSTRACT FROM AUTHOR]

The ripeness of bananas is the most significant factor affecting nutrient composition and demand. Conventionally, cutting and ripeness analysis requires expert knowledge and substantial human intervention, and different studies have been conducted to automate and substantially reduce human effort. Using the Preferred Reporting Items for the Systematic Reviews approach, 1548 studies were extracted from journals and conferences, using different research databases, and 35 were included in the final review for key parameters. These studies suggest the dominance of banana fingers as input data, a sensor camera as the preferred capturing device, and appropriate features, such as color, that can provide better detection. Among six stages of ripeness, the studies employing the four mentioned stages performed better in terms of accuracy and coefficient of determination value. Among all the works for detecting ripeness stages prediction, convolutional neural networks were found to perform sufficiently well with large datasets, whereas conventional artificial neural networks and support vector machines attained better performance for sensor-related data. However, insufficient information on the dataset and capturing device, limited data availability, and exploitation of data augmentation techniques are limitations in existing studies. Thus, effectively addressing these shortcomings and close collaboration with experts to predict the ripeness stages should be pursued.

Despite the recognized value of morphing in the literature, this preoperative tool has never been studied in the context of selection process in rhinoplasty. The main purpose of this article is to identify the use of morphing as a filter for unsuitable patients, the attrition rate from the initial consultation to surgery, and whether patients' appreciation on morphing influence their decision-making process. Three-hundred thirty-four consecutive patients, seeking rhinoplasty, underwent two-dimensional computer imaging and completed a 14-question survey about their opinion on morphing. Based on the presence or absence of patient/physician consensus on the expected outcomes during simulation, patients were divided into accepted or rejected candidates for surgery. Accepted candidates were scheduled for rhinoplasty and subdivided into those who underwent surgery, those who postponed their surgery (static), and those who cancelled their procedure. Their responses to the survey were compared between different patients' categories. Forty-four patients (13.2%) were rejected for rhinoplasty since consensus was not achieved during morphing. From 290 accepted patients, 178 underwent their operation (53.3%), 74 patients (22.1%) postponed their rhinoplasty, and 38 (11.4%) cancelled their surgery. Fifty-seven percent of rejected patients and 42% of the static group were not satisfied with the proposed results of morphing, in contrast with 16% of the operated group. Sixty-four percent of rejected patients, and 47% of the static group were not reassured after morphing, compared with 26% of the operated group. Presence or absence of consensus during morphing can guide the surgeon regarding a given patients' suitability for surgery. Patient satisfaction and reassurance with the morphed images can be a good predictor of patients who will proceed to surgery, calling attention to the value of morphing as a selection tool for surgeons and patients alike. [ABSTRACT FROM AUTHOR]

Brain decoding—the process of inferring a person’s momentary cognitive state from their brain activity—has enormous potential in the field of human-computer interaction. In this study we propose a zero-shot EEG-to-image brain decoding approach which makes use of state-of-the-art EEG preprocessing and feature selection methods, and which maps EEG activity to biologically inspired computer vision and linguistic models. We apply this approach to solve the problem of identifying viewed images from recorded brain activity in a reliable and scalable way. We demonstrate competitive decoding accuracies across two EEG datasets, using a zero-shot learning framework more applicable to real-world image retrieval than traditional classification techniques. [ABSTRACT FROM AUTHOR]

Methods for measuring the properties of individual cells within their native 3D environment will enable a deeper understanding of embryonic development, tissue regeneration, and tumorigenesis. However, current methods for segmenting nuclei in 3D tissues are not designed for situations in which nuclei are densely packed, nonspherical, or heterogeneous in shape, size, or texture, all of which are true of many embryonic and adult tissue types as well as in many cases for cells differentiating in culture. Here, we overcome this bottleneck by devising a novel method based on labelling the nuclear envelope (NE) and automatically distinguishing individual nuclei using a tree-structured ridge-tracing method followed by shape ranking according to a trained classifier. The method is fast and makes it possible to process images that are larger than the computer’s memory. We consistently obtain accurate segmentation rates of >90%, even for challenging images such as mid-gestation embryos or 3D cultures. We provide a 3D editor and inspector for the manual curation of the segmentation results as well as a program to assess the accuracy of the segmentation. We have also generated a live reporter of the NE that can be used to track live cells in 3 dimensions over time. We use this to monitor the history of cell interactions and occurrences of neighbour exchange within cultures of pluripotent cells during differentiation. We provide these tools in an open-access user-friendly format. [ABSTRACT FROM AUTHOR]

The classification of high-resolution satellite images is an open research problem for computer vision research community. In last few decades, the Bag of Visual Word (BoVW) model has been used for the classification of satellite images. In BoVW model, an orderless histogram of visual words without any spatial information is used as image signature. The performance of BoVW model suffers due to this orderless nature and addition of spatial clues are reported beneficial for scene and geographical classification of images. Most of the image representations that can compute image spatial information as are not invariant to rotations. A rotation invariant image representation is considered as one of the main requirement for satellite image classification. This paper presents a novel approach that computes the spatial clues for the histograms of BoVW model that is robust to the image rotations. The spatial clues are calculated by computing the histograms of orthogonal vectors. This is achieved by calculating the magnitude of orthogonal vectors between Pairs of Identical Visual Words (PIVW) relative to the geometric center of an image. The comparative analysis is performed with recently proposed research to obtain the best spatial feature representation for the satellite imagery. We evaluated the proposed research for image classification using three standard image benchmarks of remote sensing. The results and comparisons conducted to evaluate this research show that the proposed approach performs better in terms of classification accuracy for a variety of datasets based on satellite images. [ABSTRACT FROM AUTHOR]

Thus far, anatomical studies have reported data on the cervical pedicle, with the focus remaining on the pedicle itself. It was necessary to obtain more comprehensive data about the relationships between the lateral mass, pedicle, and transverse foramen for cervical pedicle screwing (CPS) and paravertebral foramen screwing (PVFS), a new technique. The purpose of this study was to describe the relationships between the lateral mass, pedicle, and transverse foramen. This study analyzed computed tomography images from 77 patients (42 female, 35 male; mean age: 63.95 years). The anatomical pedicle transverse angle (PTA) and linear parameters of the lateral mass were measured, and the relationship between the calculated angles and the anatomical PTA was investigated. θp was defined as the convergence angle from the posterolateral edge of the lateral mass to the pedicle, and θc was defined as the convergence angle from the posterolateral edge of the lateral mass to the anterolateral corner of the vertebral foramen. The thickness of the cortical bone of the medial wall of the lateral mass (cT) and the medial (mT) and lateral (lT) walls of the pedicle at C3–7 were also measured. The PTA was similar to θp and θc at C3–6, but different at C7. In all cases, the transverse foramen was located more anterior to the posterior wall of the cervical body at C3–6, but not at C7. mT and cT were significantly thicker than lT at all levels. Lateral fluoroscopic images show that when the probe is inserted along θc, it meets the counter corner of the lateral mass at C3–6 without invasion of the transverse foramen if it does not cross the posterior wall of the vertebral body. This can be significant when performing CPS and PVFS. [ABSTRACT FROM AUTHOR]

Computer assisted image acquisition techniques, including confocal microscopy, require efficient tools for an automatic sorting of vast amount of generated image data. The complexity of the classification process, absence of adequate tools, and insufficient amount of reference data has made the automated processing of images challenging. Mastering of this issue would allow implementation of statistical analysis in research areas such as in research on formation of t-tubules in cardiac myocytes. We developed a system aimed at automatic assessment of cardiomyocyte development stages (SAACS). The system classifies confocal images of cardiomyocytes with fluorescent dye stained sarcolemma. We based SAACS on a densely connected convolutional network (DenseNet) topology. We created a set of labelled source images, proposed an appropriate data augmentation technique and designed a class probability graph. We showed that the DenseNet topology, in combination with the augmentation technique is suitable for the given task, and that high-resolution images are instrumental for image categorization. SAACS, in combination with the automatic high-throughput confocal imaging, will allow application of statistical analysis in the research of the tubular system development or remodelling and loss. [ABSTRACT FROM AUTHOR]

Introduction: A Cloud-ORiented Engine for advanced MRI simulations (coreMRI) is presented in this study. The aim was to develop the first advanced MR simulation platform delivered as a web service through an on-demand, scalable cloud-based and GPU-based infrastructure. We hypothesized that such an online MR simulation platform could be utilized as a virtual MRI scanner but also as a cloud-based, high-performance engine for advanced MR simulations in simulation-based quantitative MR (qMR) methods. Methods and results: The simulation framework of coreMRI was based on the solution of the Bloch equations and utilized a ground-up-approach design based on the principles already published in the literature. The development of a front-end environment allowed the connection of the end-users to the GPU-equipped instances on the cloud. The coreMRI simulation platform was based on a modular design where individual modules (such as the Gadgetron reconstruction framework and a newly developed Pulse Sequence Designer) could be inserted in the main simulation framework. Different types and sources of pulse sequences and anatomical models were utilized in this study revealing the flexibility that the coreMRI simulation platform offers to the users. The performance and scalability of coreMRI were also examined on multi-GPU configurations on the cloud, showing that a multi-GPU computer on the cloud equipped with a newer generation of GPU cards could significantly mitigate the prolonged execution times that accompany more realistic MRI and qMR simulations. Conclusions: coreMRI is available to the entire MR community, whereas its high performance and scalability allow its users to configure advanced MRI experiments without the constraints imposed by experimentation in a true MRI scanner (such as time constraint and limited availability of MR scanners), without upfront investment for purchasing advanced computer systems and without any user expertise on computer programming or MR physics. coreMRI is available to the users through the webpage . [ABSTRACT FROM AUTHOR]

Free amino acids are an important indicator of the freshness of yellow tea. This study investigated a novel procedure for predicting the free amino acid (FAA) concentration of yellow tea. It was developed based on the combined spectral and textural features from hyperspectral images. For the purposes of exploration and comparison, hyperspectral images of yellow tea (150 samples) were captured and analyzed. The raw spectra were preprocessed with Savitzky-Golay (SG) smoothing. To reduce the dimension of spectral data, five feature wavelengths were extracted using the successive projections algorithm (SPA). Five textural features (angular second moment, entropy, contrast, correlation, and homogeneity) were extracted as textural variables from the characteristic grayscale images of the five characteristic wavelengths using the gray-level co-occurrence matrix (GLCM). The FAA content prediction model with different variables was established by a genetic algorithm-support vector regression (GA-SVR) algorithm. The results showed that better prediction results were obtained by combining the feature wavelengths and textural variables. Compared with other data, this prediction result was still very satisfactory in the GA-SVR model, indicating that data fusion was an effective way to enhance hyperspectral imaging ability for the determination of free amino acid values in yellow tea. [ABSTRACT FROM AUTHOR]

The traditional image mosaic result based on SIFT feature points extraction, to some extent, has distortion errors: the larger the input image set, the greater the spliced panoramic distortion. To achieve the goal of creating a high-quality panorama, a new and improved algorithm based on the A-KAZE feature is proposed in this paper. This includes changing the way reference image are selected and putting forward a method for selecting a reference image based on the binary tree model, which takes the input image set as the leaf node set of a binary tree and uses the bottom-up approach to construct a complete binary tree. The root node image of the binary tree is the ultimate panorama obtained by stitching. Compared with the traditional way, the novel method improves the accuracy of feature points detection and enhances the stitching quality of the panorama. Additionally, the improved method proposes an automatic image straightening model to rectify the panorama, which further improves the panoramic distortion. The experimental results show that the proposed method cannot only enhance the efficiency of image stitching processing, but also reduce the panoramic distortion errors and obtain a better quality panoramic result. [ABSTRACT FROM AUTHOR]

Digital imaging has become one of the most important techniques in environmental monitoring and exploration. In the case of the marine environment, mobile platforms such as autonomous underwater vehicles (AUVs) are now equipped with high-resolution cameras to capture huge collections of images from the seabed. However, the timely evaluation of all these images presents a bottleneck problem as tens of thousands or more images can be collected during a single dive. This makes computational support for marine image analysis essential. Computer-aided analysis of environmental images (and marine images in particular) with machine learning algorithms is promising, but challenging and different to other imaging domains because training data and class labels cannot be collected as efficiently and comprehensively as in other areas. In this paper, we present Machine learning Assisted Image Annotation (MAIA), a new image annotation method for environmental monitoring and exploration that overcomes the obstacle of missing training data. The method uses a combination of autoencoder networks and Mask Region-based Convolutional Neural Network (Mask R-CNN), which allows human observers to annotate large image collections much faster than before. We evaluated the method with three marine image datasets featuring different types of background, imaging equipment and object classes. Using MAIA, we were able to annotate objects of interest with an average recall of 84.1% more than twice as fast as compared to “traditional” annotation methods, which are purely based on software-supported direct visual inspection and manual annotation. The speed gain increases proportionally with the size of a dataset. The MAIA approach represents a substantial improvement on the path to greater efficiency in the annotation of large benthic image collections. [ABSTRACT FROM AUTHOR]

The article presents the first Russian and international experience in the application of ultrasound contrast to evaluation the effectiveness of superselective embolization of arteries prostate benign hyperplasia her. Thoroughly describes the methodology of the arteries embolization prostate hyperplasia her. Given a clear algorithm and the necessary tools in this type of operations. Presented rationale for the use of microspheres Embozene as the most innovative embolization material. These microspheres exhibit shape memory, which allows them to shrink to 35 % and immediately recover its original volume after the release of the microcatheter. Studies have shown that the microspheres Embozene retain its original volume is not fragmented, which is a very important fact for the target artery embolization and radical prostate. Attentive readership presents the rationale and mechanical properties of ultrasound contrast, which is only selectively within the lumen of blood vessels and eliminates early relapse of prostate adenoma after embolization her arteries in its application. This area will allow the use of ultrasound contrast followed by researchers to determine the nature of the blood supply to the prostate and its changes after embolization of the arteries of the prostate. The article presents the clinical and uroflowmetry results of patients who underwent embolization. Denotes the vector of this technology in the elimination of the continued growth of the local and local recurrence of prostate cancer.

In recent years, finding the cause of pathogenesis is expected by observing the cell images. In this paper, we propose a cell particle detection method in cell images. However, there are mainly two kinds of problems in particle detection in cell image. The first is the different properties between cell images and standard images used in computer vision researches. Edges of cell particles are ambiguous, and overlaps between cell particles are often occurred in dense regions. It is difficult to detect cell particles by simple detection method using a binary classifier. The second is the ground truth made by cell biologists. The number of training samples for training a classifier is limited, and incorrect samples are included by the subjectivity of observers. From the background, we propose a cell particle detection method to address those problems. In our proposed method, we predict the center of a cell particle from the peripheral regions by convolutional neural network, and the prediction results are voted. By using the obvious peripheral edges, we can robustly detect overlapped cell particles because all edges of overlapping cell particles are not ambiguous. In addition, voting from peripheral views enables reliable detection. Moreover, our method is useful in practical applications because we can prepare many training samples from a cell particle. In experiments, we evaluate our detection methods on two kinds of cell detection datasets. One is challenging dataset for synthetic cells, and our method achieved the state-of-the-art performance. The other is real dataset of lipid droplets, and our method outperformed the conventional detector using CNN with binary outputs for particles and non-particles classification. [ABSTRACT FROM AUTHOR]

Most of real-world image distortions are multiply distortion rather than single distortion. To address this issue, in this paper we propose a quaternion wavelet transform (QWT) based full reference image quality assessment (FR IQA) metric for multiply distorted images, which jointly considers the local similarity of phase and magnitude of each subband via QWT. Firstly, the reference images and distorted images are decomposed by QWT, and then the similarity of amplitude and phase are calculated on each subband, thirdly the IQA metric is constructed by the weighting method considering human visual system (HVS) characteristics, and lastly the scores of each subband are averaged to get the quality score of test image. Experimental results show that the proposed method outperforms the state of art in multiply distorted IQA. [ABSTRACT FROM AUTHOR]

Recently, sparse representation, which relies on the underlying assumption that samples can be sparsely represented by their labeled neighbors, has been applied with great success to image classification problems. Through sparse representation-based classification (SRC), the label can be assigned with minimum residual between the sample and its synthetic version with class-specific coding, which means that the coding scheme is the most significant factor for classification accuracy. However, conventional SRC-based coding schemes ignore dependency among the samples, which leads to an undesired result that similar samples may be coded into different categories due to quantization sensitivity. To address this problem, in this paper, a novel approach based on self-supervised sparse representation is proposed for image classification. In the proposed approach, the manifold structure of samples is firstly exploited with low rank representation. Next, the low-rank representation matrix is used to characterize the similarity of samples in order to establish a self-supervised sparse coding model, which aims to preserve the local structure of codings for similar samples. Finally, a numerical algorithm utilizing the alternating direction method of multipliers (ADMM) is developed to obtain the approximate solution. Experiments on several publicly available datasets validate the effectiveness and efficiency of our proposed approach compared with existing state-of-the-art methods. [ABSTRACT FROM AUTHOR]

The Bag-of-Visual-Words (BoVW) model is widely used for image classification, object recognition and image retrieval problems. In BoVW model, the local features are quantized and 2-D image space is represented in the form of order-less histogram of visual words. The image classification performance suffers due to the order-less representation of image. This paper presents a novel image representation that incorporates the spatial information to the inverted index of BoVW model. The spatial information is added by calculating the global relative spatial orientation of visual words in a rotation invariant manner. For this, we computed the geometric relationship between triplets of identical visual words by calculating an orthogonal vector relative to each point in the triplets of identical visual words. The histogram of visual words is calculated on the basis of the magnitude of these orthogonal vectors. This calculation provides the unique information regarding the relative position of visual words when they are collinear. The proposed image representation is evaluated by using four standard image benchmarks. The experimental results and quantitative comparisons demonstrate that the proposed image representation outperforms the existing state-of-the-art in terms of classification accuracy. [ABSTRACT FROM AUTHOR]

Miniaturized imaging devices have pushed the boundaries of point-of-care imaging, but existing mobile-phone-based imaging systems do not exploit the full potential of smart phones. This work demonstrates the use of simple imaging configurations to deliver superior image quality and the ability to handle a wide range of biological samples. Results presented in this work are from analysis of fluorescent beads under fluorescence imaging, as well as helminth eggs and freshwater mussel larvae under white light imaging. To demonstrate versatility of the systems, real time analysis and post-processing results of the sample count and sample size are presented in both still images and videos of flowing samples. [ABSTRACT FROM AUTHOR]

Osteoarthritis (OA) is a considerable health problem worldwide, and the prevalence of OA varies in different regions. In this study, the prevalence of OA in Kashin-Beck disease (KBD) and non-KBD endemic areas was examined, respectively. According to monitoring data, 4 types of regions (including none, mild, moderate and high KBD endemic areas) in Heilongjiang and Jilin provinces were selected. All local residents were eligible for inclusion criteria have undergone X-ray images of hands and anteroposterior image of knees. A total of 1673 cases were collected, 1446 cases were analyzed after removing the KBD patients (227). The overall hand OA and knee OA detection rates were 33.3% (481/1446) and 56.6% (818/1446), respectively. After being standardized by age, the detection rate of hand OA in the KBD endemic areas was significantly higher than that in the non-endemic endemic areas. Differently, there was no significant difference in the detection rates of knee OA between the KBD endemic areas and the non-endemic area. The correlation coefficient between the severity of OA and the severity of knee OA was 0.358 and 0.197 in the KBD and non-KBD endemic areas, respectively. Where the KBD historical prevalence level was higher, the severity of the residents’ hand OA was more serious. The detection rates of hand OA and knee OA increased with age. The detection rate of knee OA increased with the increase in body mass index. The prevalence of hand OA was closely related to the pathogenic factors of Kashin-Beck disease, and the prevalence of knee OA had no significant correlation with KBD pathogenic factors. [ABSTRACT FROM AUTHOR]

Image registration is widely used in many fields, but the adaptability of the existing methods is limited. This work proposes a novel image registration method with high precision for various complex applications. In this framework, the registration problem is divided into two stages. First, we detect and describe scale-invariant feature points using modified computer vision-oriented fast and rotated brief (ORB) algorithm, and a simple method to increase the performance of feature points matching is proposed. Second, we develop a new local constraint of rough selection according to the feature distances. Evidence shows that the existing matching techniques based on image features are insufficient for the images with sparse image details. Then, we propose a novel matching algorithm via geometric constraints, and establish local feature descriptions based on geometric invariances for the selected feature points. Subsequently, a new price function is constructed to evaluate the similarities between points and obtain exact matching pairs. Finally, we employ the progressive sample consensus method to remove wrong matches and calculate the space transform parameters. Experimental results on various complex image datasets verify that the proposed method is more robust and significantly reduces the rate of false matches while retaining more high-quality feature points. [ABSTRACT FROM AUTHOR]

The application of electroencephalogram (EEG) generated by human viewing images is a new thrust in image retrieval technology. A P300 component in the EEG is induced when the subjects see their point of interest in a target image under the rapid serial visual presentation (RSVP) experimental paradigm. We detected the single-trial P300 component to determine whether a subject was interested in an image. In practice, the latency and amplitude of the P300 component may vary in relation to different experimental parameters, such as target probability and stimulus semantics. Thus, we proposed a novel method, Target Recognition using Image Complexity Priori (TRICP) algorithm, in which the image information is introduced in the calculation of the interest score in the RSVP paradigm. The method combines information from the image and EEG to enhance the accuracy of single-trial P300 detection on the basis of traditional single-trial P300 detection algorithm. We defined an image complexity parameter based on the features of the different layers of a convolution neural network (CNN). We used the TRICP algorithm to compute for the complexity of an image to quantify the effect of different complexity images on the P300 components and training specialty classifier according to the image complexity. We compared TRICP with the HDCA algorithm. Results show that TRICP is significantly higher than the HDCA algorithm (Wilcoxon Sign Rank Test, p<0.05). Thus, the proposed method can be used in other and visual task-related single-trial event-related potential detection. [ABSTRACT FROM AUTHOR]

Melanoma is the deadliest form of skin cancer, and early detection is crucial for patient survival. Computer systems can assist in melanoma detection, but are not widespread in clinical practice. In 2016, an open challenge in classification of dermoscopic images of skin lesions was announced. A training set of 900 images with corresponding class labels and semi-automatic/manual segmentation masks was released for the challenge. An independent test set of 379 images, of which 75 were of melanomas, was used to rank the participants. This article demonstrates the impact of ranking criteria, segmentation method and classifier, and highlights the clinical perspective. We compare five different measures for diagnostic accuracy by analysing the resulting ranking of the computer systems in the challenge. Choice of performance measure had great impact on the ranking. Systems that were ranked among the top three for one measure, dropped to the bottom half when changing performance measure. Nevus Doctor, a computer system previously developed by the authors, was used to participate in the challenge, and investigate the impact of segmentation and classifier. The diagnostic accuracy when using an automatic versus the semi-automatic/manual segmentation is investigated. The unexpected small impact of segmentation method suggests that improvements of the automatic segmentation method w.r.t. resemblance to semi-automatic/manual segmentation will not improve diagnostic accuracy substantially. A small set of similar classification algorithms are used to investigate the impact of classifier on the diagnostic accuracy. The variability in diagnostic accuracy for different classifier algorithms was larger than the variability for segmentation methods, and suggests a focus for future investigations. From a clinical perspective, the misclassification of a melanoma as benign has far greater cost than the misclassification of a benign lesion. For computer systems to have clinical impact, their performance should be ranked by a high-sensitivity measure. [ABSTRACT FROM AUTHOR]

Spectral analysis techniques are valuable tools at the disposal of paleoclimate scientists in their research of cyclical phenomena potentially responsible for past climatic fluctuations. Advances in computing and an increased availability of climate time series have helped to consolidate this approach. Yet the visual representation of spectral analysis results has not improved at the same pace. Time-frequency analysis aims to identify periodic signals that vary over time using 2-D color graphs, depicting spectral bands theoretically discernible from the image background. The paleoclimate literature is full of examples such as the continuous wavelet analysis, the evolutionary fast Fourier transform (FFT) spectrogram, or even the more recent synchrosqueezing transform. Our approach is based on the stack of non-evolutive (assuming stationary behaviour) spectral analysis results from thousands of fixed interval time series, previously split from a longer and unevenly spaced (irregular sampling) paleoclimate time series. As illustrative examples, the targeted time series are derived from the LR04 Global Pliocene-Pleistocene Benthic δ18O stack and from the 65 °N summer insolation for the last 5.3 Myr, by means of the Lomb-Scargle periodogram technique. Enhanced and clearer visualization is achieved through the novel incorporation of the terrain analysis techniques: slope, hillshading and color mapping, and posterior blending of the individual images, using Python code. The result consists of a high resolution graphical output, allowing for better qualitative and quantitative interpretations of the obtained cyclicity, as the code incorporates the import of the achieved confidence levels from the spectral technique and the option to obscure the pixels under a certain threshold value. The application of terrain analysis techniques on visualization of spectral analysis results has the purpose of improving previous graphical representations of paleoclimate time-series, especially those time-frequency aspects of the involved phenomena. New developments of our approach may be applied to time-frequency analysis directly, supporting present and future paleoclimate studies. • New Python code for display of 2-D spectral analysis time series into evolutive form. • Novel approach by adding terrain analysis techniques into the visualization pipeline. • Method is validated with Pliocene-Pleistocene δ18O time series from benthic records. [ABSTRACT FROM AUTHOR]

• The new singulating and rotating mechanism for in-field grading and sorting was evaluated using apples painted with different paint colors. • The singulating and rotating mechanism provided continuous rotation of the apples while conveying and enabled consistent exposure of the apple surface to the imaging system. • The imaging system was able to image the entire surface of each apple for more than 4 times at the conveying speed of 1 apple/s and for at least 1.4 times at the speed of 3 apples/s. • The singulating and rotating mechanism is suitable for in-field grading of apples at an overall throughput of 9 or more fruit/s. Automated, computer-imaging based in-field grading and sorting of apples at the time of harvest has the potential to help growers achieve significant cost savings in postharvest storage and packing. Singulation and rotation of fruit are essential steps for a computer imaging-based grading/sorting system. We have recently developed a novel compact, low-cost in-field apple sorting system, which consists of pairs of multi-stage helical conveyance drives to singulate, rotate and advance apples so that they can be inspected by a computer vision unit for quality grading. This study was aimed at evaluating the singulating and rotating performance of the sorting system for automatic inspection and grading of apples, in terms of conveying speed, fruit size, and the percentage of apple exposure area for imaging. Two apple varieties (i.e., 'Golden Delicious' and 'Delicious') were used for experimental evaluations. To determine the rotational behavior of apples, each quadrant of the apple surface was painted with different colors. Images were acquired for the apples at 15 frames per second and then automatically segmented for the estimation of exposed areas in terms of pixels. It was found that the number of images acquired for each fruit varied from 24 to 9 images when the speed of the sorting system was changed from 1 to 3 apples per second per lane. The singulating and rotating mechanism (SRM) provided continuous and relatively even rotation of the apples while conveying, which was confirmed by cumulative percentages of the four colored areas of 'Golden Delicious' and 'Delicious' apples at the three sorting speeds. At the sorting speed of 1 apple/s, the entire surface of each fruit was imaged for more than 4 times, while at 3 apples/s, the surface of the apples was imaged for at least 1.4 times. Hence, the sorting system with 3 sorting lanes is able to perform in-field quality grading of apples at a throughput of 9 or more apples/s. [ABSTRACT FROM AUTHOR]

This work presents a comparison of three autoradiography techniques for imaging biological samples contaminated with actinides: emulsion-based, plastic-based autoradiography and a quantitative digital technique, the iQID camera, based on the numerical analysis of light from a scintillator screen. In radiation toxicology it has been important to develop means of imaging actinide distribution in tissues as these radionuclides may be heterogeneously distributed within and between tissues after internal contamination. Actinide distribution determines which cells are exposed to alpha radiation and is thus potentially critical for assessing absorbed dose. The comparison was carried out by generating autoradiographs of the same biological samples contaminated with actinides with the three autoradiography techniques. These samples were cell preparations or tissue sections collected from animals contaminated with different physico-chemical forms of actinides. The autoradiograph characteristics and the performances of the techniques were evaluated and discussed mainly in terms of acquisition process, activity distribution patterns, spatial resolution and feasibility of activity quantification. The obtained autoradiographs presented similar actinide distribution at low magnification. Out of the three techniques, emulsion autoradiography is the only one to provide a highly-resolved image of the actinide distribution inherently superimposed on the biological sample. Emulsion autoradiography is hence best interpreted at higher magnifications. However, this technique is destructive for the biological sample. Both emulsion- and plastic-based autoradiography record alpha tracks and thus enabled the differentiation between ionized forms of actinides and oxide particles. This feature can help in the evaluation of decorporation therapy efficacy. The most recent technique, the iQID camera, presents several additional features: real-time imaging, separate imaging of alpha particles and gamma rays, and alpha activity quantification. The comparison of these three autoradiography techniques showed that they are complementary and the choice of the technique depends on the purpose of the imaging experiment. [ABSTRACT FROM AUTHOR]

As a structure-based image compression technology, fractal image compression (FIC) has been applied not only in image coding but also in many important image processing algorithms. However, two main bottlenecks restrained the develop and application of FIC for a long time. First, the encoding phase of FIC is time-consuming. Second, the quality of the reconstructed images for some images which have low structure-similarity is usually unacceptable. Based on the absolute value of Pearson’s correlation coefficient (APCC), we had proposed an accelerating method to significantly speed up the encoding of FIC. In this paper, we make use of the sparse searching strategy to greatly improve the quality of the reconstructed images in FIC. We call it the sparse fractal image compression (SFIC). Furthermore, we combine both the APCC-based accelerating method and the sparse searching strategy to propose the fast sparse fractal image compression (FSFIC), which can effectively improve the two main bottlenecks of FIC. The experimental results show that the proposed algorithm greatly improves both the efficiency and effectiveness of FIC. [ABSTRACT FROM AUTHOR]

In this paper, we present a robust text detection approach in natural images which is based on region proposal mechanism. A powerful low-level detector named saliency enhanced-MSER extended from the widely-used MSER is proposed by incorporating saliency detection methods, which ensures a high recall rate. Given a natural image, character candidates are extracted from three channels in a perception-based illumination invariant color space by saliency-enhanced MSER algorithm. A discriminative convolutional neural network (CNN) is jointly trained with multi-level information including pixel-level and character-level information as character candidate classifier. Each image patch is classified as strong text, weak text and non-text by double threshold filtering instead of conventional one-step classification, leveraging confident scores obtained via CNN. To further prune non-text regions, we develop a recursive neighborhood search algorithm to track credible texts from weak text set. Finally, characters are grouped into text lines using heuristic features such as spatial location, size, color, and stroke width. We compare our approach with several state-of-the-art methods, and experiments show that our method achieves competitive performance on public datasets ICDAR 2011 and ICDAR 2013. [ABSTRACT FROM AUTHOR]

Objective: The objective of this study was to quantify the use of computer imaging by facial plastic surgeons. Methods: AAFPRS Facial plastic surgeons were surveyed about their use of computer imaging during rhinoplasty consultations. The survey collected information about surgeon demographics, practice settings, practice patterns, and rates of computer imaging (CI) for primary and revision rhinoplasty. For those surgeons who used CI, additional information was also collected, which included who performed the imaging and whether the patient was given the morphed images after the consultation. Results: A total of 238 out of 1200 (19.8%) facial plastic surgeons responded to the survey. Out of those who responded, 195 surgeons (83%) were board certified by the American Board of Facial Plastic and Reconstructive Surgeons (ABFPRS). The majority of respondents (150 surgeons, 63%) used CI during rhinoplasty consultation. Of the surgeons who use CI, 92% performed the image morphing themselves. Approximately two-thirds of surgeons who use CI gave their patient a printout of the morphed images after the consultation. Conclusions: Computer imaging (CI) is a frequently utilized tool for facial plastic surgeons during cosmetic consultations with patients. Based on these results of this study, it can be suggested that the majority of facial plastic surgeons who use CI do so for both primary and revision rhinoplasty. As more sophisticated systems become available, it is possible that utilization of CI modalities will increase. This provides the surgeon with further tools to use at his or her disposal during discussion of aesthetic surgery. Level of Evidence V: This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors . [ABSTRACT FROM AUTHOR]

Near-infrared (874–1734 nm) hyperspectral imaging (NIR-HSI) technique combined with chemometric methods was used to trace origins of 1200 Chinese wolfberry samples, which from Ningxia, Inner Mongolia, Sinkiang and Qinghai in China. Two approaches, named pixel-wise and object-wise, were investigated to discriminative the origin of these Chinese wolfberries. The pixel-wise classification assigned a class to each pixel from individual Chinese wolfberries, and with this approach, the differences in the Chinese wolfberries from four origins were reflected intuitively. Object-wise classification was performed using mean spectra. The average spectral information of all pixels of each sample in the hyperspectral image was extracted as the representative spectrum of a sample, and then discriminant analysis models of the origins of Chinese wolfberries were established based on these average spectra. Specifically, the spectral curves of all samples were collected, and after removal of obvious noise, the spectra of 972–1609 nm were viewed as the spectra of wolfberry. Then, the spectral curves were pretreated with moving average smoothing (MA), and discriminant analysis models including support vector machine (SVM), neural network with radial basis function (NN-RBF) and extreme learning machine (ELM) were established based on the full-band spectra, the extracted characteristic wavelengths from loadings of principal component analysis (PCA) and 2nd derivative spectra, respectively. Among these models, the recognition accuracies of the calibration set and prediction set of the ELM model based on extracted characteristic wavelengths from loadings of PCA were higher than 90%. The model not only ensured a high recognition rate but also simplified the model and was conducive to future rapid on-line testing. The results revealed that NIR-HSI combined with PCA loadings-ELM could rapidly trace the origins of Chinese wolfberries. [ABSTRACT FROM AUTHOR]

In this paper we consider the problem of restoration of a image contaminated by a mixture of Gaussian and impulse noises. We propose a new statistic called ROADGI which improves the well-known Rank-Ordered Absolute Differences (ROAD) statistic for detecting points contaminated with the impulse noise in this context. Combining ROADGI statistic with the method of weights optimization we obtain a new algorithm called Optimal Weights Mixed Filter (OWMF) to deal with the mixed noise. Our simulation results show that the proposed filter is effective for mixed noises, as well as for single impulse noise and for single Gaussian noise. [ABSTRACT FROM AUTHOR]

Multispectral palmprint recognition has shown broad prospects for personal identification due to its high accuracy and great stability. In this paper, we develop a novel illumination-invariant multispectral palmprint recognition method. To combine the information from multiple spectral bands, an image-level fusion framework is completed based on a fast and adaptive bidimensional empirical mode decomposition (FABEMD) and a weighted Fisher criterion. The FABEMD technique decomposes the multispectral images into their bidimensional intrinsic mode functions (BIMFs), on which an illumination compensation operation is performed. The weighted Fisher criterion is to construct the fusion coefficients at the decomposition level, making the images be separated correctly in the fusion space. The image fusion framework has shown strong robustness against illumination variation. In addition, a tensor-based extreme learning machine (TELM) mechanism is presented for feature extraction and classification of two-dimensional (2D) images. In general, this method has fast learning speed and satisfying recognition accuracy. Comprehensive experiments conducted on the PolyU multispectral palmprint database illustrate that the proposed method can achieve favorable results. For the testing under ideal illumination, the recognition accuracy is as high as 99.93%, and the result is 99.50% when the lighting condition is unsatisfied. [ABSTRACT FROM AUTHOR]

Given their powerful feature representation for recognition, deep convolutional neural networks (DCNNs) have been driving rapid advances in high-level computer vision tasks. However, their performance in semantic image segmentation is still not satisfactory. Based on the analysis of visual mechanism, we conclude that DCNNs in a bottom-up manner are not enough, because semantic image segmentation task requires not only recognition but also visual attention capability. In the study, superpixels containing visual attention information are introduced in a top-down manner, and an extensible architecture is proposed to improve the segmentation results of current DCNN-based methods. We employ the current state-of-the-art fully convolutional network (FCN) and FCN with conditional random field (DeepLab-CRF) as baselines to validate our architecture. Experimental results of the PASCAL VOC segmentation task qualitatively show that coarse edges and error segmentation results are well improved. We also quantitatively obtain about 2%-3% intersection over union (IOU) accuracy improvement on the PASCAL VOC 2011 and 2012 test sets. [ABSTRACT FROM AUTHOR]

In this paper, we present a novel image mosaicking method that is based on Speeded-Up Robust Features (SURF) of line segments, aiming to achieve robustness to incident scaling, rotation, change in illumination, and significant affine distortion between images in a panoramic series. Our method involves 1) using a SURF detection operator to locate feature points; 2) rough matching using SURF features of directed line segments constructed via the feature points; and 3) eliminating incorrectly matched pairs using RANSAC (RANdom SAmple Consensus). Experimental results confirm that our method results in high-quality panoramic mosaics that are superior to state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Rorschach inkblots have had a striking impact on the worlds of art and science because of the remarkable variety of associations with recognizable and namable objects they induce. Originally adopted as a projective psychological tool to probe mental health, psychologists and artists have more recently interpreted the variety of induced images simply as a signature of the observers’ creativity. Here we analyze the relationship between the spatial scaling parameters of the inkblot patterns and the number of induced associations, and suggest that the perceived images are induced by the fractal characteristics of the blot edges. We discuss how this relationship explains the frequent observation of images in natural scenery. [ABSTRACT FROM AUTHOR]

Decoupling shading and reflectance from complex scene-images is a long-standing problem in computer vision. We introduce a framework for decomposing an image into the product of an illumination component and a reflectance component. Due to the ill-posed nature of the problem, prior information on shading and reflectance is mandatory. The proposed method adopts the premise that pixels in a region with similar chromaticity values should have the same reflectance. This assumption was used to minimize the l2 norm of the local per-pixel reflectance gradients to extract the shading and reflectance components. To obtain smooth chromatic regions, texture was treated in a new style. Texture was removed in the first step of the algorithm and the smooth image was processed for intrinsic decomposition. In the final step, texture details were added to the intrinsic components based on the material of each pixel. In addition, user-assistance was used to further refine the results. The qualitative and quantitative evaluation on the MIT intrinsic dataset indicated that the quality of intrinsic image decomposition was improved in comparison with previous methods. [ABSTRACT FROM AUTHOR]

This paper describes the development of a prototype of a low-cost digital fluorescent microscope built from commercial off-the-shelf (COTS) components. The prototype was tested to detect malignant tumor cells taken from a living organism in a preclinical setting. This experiment was accomplished by using Alexa Fluor 488 conjugate dye attached to the cancer cells. Our prototype utilizes a torch along with an excitation filter as a light source for fluorophore excitation, a dichroic mirror to reflect the excitation and pass the emitted green light from the sample under test and a barrier filter to permit only appropriate wavelength. The system is designed out of a microscope using its optical zooming property and an assembly of exciter filter, dichroic mirror and transmitter filter. The microscope is connected to a computer or laptop through universal serial bus (USB) that allows real-time transmission of captured florescence images; this also offers real-time control of the microscope. The designed system has comparable features of high-end commercial fluorescent microscopes while reducing cost, power, weight and size. [ABSTRACT FROM AUTHOR]

The main objective in rearing animals is to maximize the efficiencies during production while maintaining their welfare. There is also now significant interest from consumers to understand the conditions under which animals are grown. Assessing welfare is a challenge due to the extensive number of factors that affect welfare; and there is currently no agreement on quantitative measures to describe suitable performance in animal management that are not based on growth performance. Most farm managers use visual observations of behavior as one of the significant features for assessing the state of the animals. Advances in imaging technology allow for easier and more accurate ways to compute and evaluate different indicators by using visual cues. This work looks at the potential to use region feature comparisons by using features derived from video data to aid in conducting these assessments in the environment. This approach supports a more holistic approach to management that incorporates the traditional measures to optimize the production system. Manual analysis of videos can be labor intensive and automated techniques could improve efficiencies and provide significant benefits. In this work, correlations between features generated from the video analysis and physical observations were determined to be significant. The use of this technology aids in the measurement of parameters that could be instrumental in defining behavior and could provide quantitative descriptors of management outcomes. However, more research needs to be conducted associating the outputs of new image processing algorithms to metabolic and physiological changes in the birds. Quantifiable measures such as these could them be used to more consistently describe conditions under which birds are being raised allowing researchers and producers to make informed decisions in designing integrated systems for production management. [ABSTRACT FROM AUTHOR]

Stereo matching is essential and fundamental in computer vision tasks. In this paper, a novel stereo matching algorithm based on disparity propagation using edge-aware filtering is proposed. By extracting disparity subsets for reliable points and customizing the cost volume, the initial disparity map is refined through filtering-based disparity propagation. Then, an edge-aware filter with low computational complexity is adopted to formulate the cost column, which makes the proposed method independent on the local window size. Experimental results demonstrate the effectiveness of the proposed scheme. Bad pixels in our output disparity map are considerably decreased. The proposed method greatly outperforms the adaptive support-weight approach and other conditional window-based local stereo matching algorithms. [ABSTRACT FROM AUTHOR]

The classification of pollen species and types is an important task in many areas like forensic palynology, archaeological palynology and melissopalynology. This paper presents the first annotated image dataset for the Brazilian Savannah pollen types that can be used to train and test computer vision based automatic pollen classifiers. A first baseline human and computer performance for this dataset has been established using 805 pollen images of 23 pollen types. In order to access the computer performance, a combination of three feature extractors and four machine learning techniques has been implemented, fine tuned and tested. The results of these tests are also presented in this paper. [ABSTRACT FROM AUTHOR]