The NWRD Dataset: An Open-Source Annotated Segmentation Dataset of Diseased Wheat Crop.

Wheat stripe rust disease (WRD) is extremely detrimental to wheat crop health, and it severely affects the crop yield, increasing the risk of food insecurity. Manual inspection by trained personnel is carried out to inspect the disease spread and extent of damage to wheat fields. However, this is quite inefficient, time-consuming, and laborious, owing to the large area of wheat plantations.

Corrigendum: The NMT Scalp EEG Dataset: An Open-Source Annotated Dataset of Healthy and Pathological EEG Recordings for Predictive Modeling.

[This corrects the article DOI: 10.3389/fnins.2021.

Bacterial prediction using internet of things (IoT) and machine learning.

Water is a basic and primary resource which is required for sustenance of life on the Earth. The importance of water quality is increasing with the ascending water pollution owing to industrialization and depletion of fresh water sources. The countries having low control on reducing water pollution are likely to retain poor public health.

The NMT Scalp EEG Dataset: An Open-Source Annotated Dataset of Healthy and Pathological EEG Recordings for Predictive Modeling.

Electroencephalogram (EEG) is widely used for the diagnosis of neurological conditions like epilepsy, neurodegenerative illnesses and sleep related disorders. Proper interpretation of EEG recordings requires the expertise of trained neurologists, a resource which is scarce in the developing world. Neurologists spend a significant portion of their time sifting through EEG recordings looking for abnormalities.

Correction to: Two-stage framework for optic disc localization and glaucoma classification in retinal fundus images using deep learning.

Following publication of the original article.

Converting a Common Low-Cost Document Scanner into a Multispectral Scanner.

Forged documents and counterfeit currency can be better detected with multispectral imaging in multiple color channels instead of the usual red, green and blue. However, multispectral cameras/scanners are expensive. We propose the construction of a low cost scanner designed to capture multispectral images of documents.

Two-stage framework for optic disc localization and glaucoma classification in retinal fundus images using deep learning.

Background: With the advancement of powerful image processing and machine learning techniques, Computer Aided Diagnosis has become ever more prevalent in all fields of medicine including ophthalmology. These methods continue to provide reliable and standardized large scale screening of various image modalities to assist clinicians in identifying diseases. Since optic disc is the most important part of retinal fundus image for glaucoma detection, this paper proposes a two-stage framework that first detects and localizes optic disc and then classifies it into healthy or glaucomatous.

3DAirSig: A Framework for Enabling In-Air Signatures Using a Multi-Modal Depth Sensor.

In-air signature is a new modality which is essential for user authentication and access control in noncontact mode and has been actively studied in recent years. However, it has been treated as a conventional online signature, which is essentially a 2D spatial representation. Notably, this modality bears a lot more potential due to an important hidden depth feature.

Dense 3D Face Correspondence.

We present an algorithm that automatically establishes dense correspondences between a large number of 3D faces. Starting from automatically detected sparse correspondences on the outer boundary of 3D faces, the algorithm triangulates existing correspondences and expands them iteratively by matching points of distinctive surface curvature along the triangle edges. After exhausting keypoint matches, further correspondences are established by generating evenly distributed points within triangles by evolving level set geodesic curves from the centroids of large triangles.

Urdu Nasta'liq text recognition using implicit segmentation based on multi-dimensional long short term memory neural networks.

The recognition of Arabic script and its derivatives such as Urdu, Persian, Pashto etc. is a difficult task due to complexity of this script. Particularly, Urdu text recognition is more difficult due to its Nasta'liq writing style.

Discriminative Bayesian Dictionary Learning for Classification.

We propose a Bayesian approach to learn discriminative dictionaries for sparse representation of data. The proposed approach infers probability distributions over the atoms of a discriminative dictionary using a finite approximation of Beta Process. It also computes sets of Bernoulli distributions that associate class labels to the learned dictionary atoms.

Prenatal testosterone exposure is related to sexually dimorphic facial morphology in adulthood.

Prenatal testosterone may have a powerful masculinizing effect on postnatal physical characteristics. However, no study has directly tested this hypothesis. Here, we report a 20-year follow-up study that measured testosterone concentrations from the umbilical cord blood of 97 male and 86 female newborns, and procured three-dimensional facial images on these participants in adulthood (range: 21-24 years).

Joint Group Sparse PCA for Compressed Hyperspectral Imaging.

A sparse principal component analysis (PCA) seeks a sparse linear combination of input features (variables), so that the derived features still explain most of the variations in the data. A group sparse PCA introduces structural constraints on the features in seeking such a linear combination. Collectively, the derived principal components may still require measuring all the input features.

Is spectral reflectance of the face a reliable biometric?

Over a decade ago, Pan et al. [IEEE TPAMI 25, 1552 (2003)] performed face recognition using only the spectral reflectance of the face at six points and reported around 95% recognition rate. Since their database is private, no one has been able to replicate these results.

Sexually dimorphic facial features vary according to level of autistic-like traits in the general population.

Background: In a recent study, Bejerot et al. observed that several physical features (including faces) of individuals with an autism spectrum disorder (ASD) were more androgynous than those of their typically developed counterparts, suggesting that ASD may be understood as a 'gender defiant' disorder. These findings are difficult to reconcile with the hypermasculinisation account, which proposes that ASD may be an exaggerated form of cognitive and biological masculinity.

Visual Saliency Models for Text Detection in Real World.

This paper evaluates the degree of saliency of texts in natural scenes using visual saliency models. A large scale scene image database with pixel level ground truth is created for this purpose. Using this scene image database and five state-of-the-art models, visual saliency maps that represent the degree of saliency of the objects are calculated.

Geometric facial gender scoring: objectivity of perception.

Gender score is the cognitive judgement of the degree of masculinity or femininity of a face which is considered to be a continuum. Gender scores have long been used in psychological studies to understand the complex psychosocial relationships between people. Perceptual scores for gender and attractiveness have been employed for quality assessment and planning of cosmetic facial surgery.

The effect of border noise on the performance of projection-based page segmentation methods.

Projection methods have been used in the analysis of bitonal document images for different tasks such as page segmentation and skew correction for more than two decades. However, these algorithms are sensitive to the presence of border noise in document images. Border noise can appear along the page border due to scanning or photocopying.

Performance evaluation and benchmarking of six-page segmentation algorithms.

Informative benchmarks are crucial for optimizing the page segmentation step of an OCR system, frequently the performance limiting step for overall OCR system performance. We show that current evaluation scores are insufficient for diagnosing specific errors in page segmentation and fail to identify some classes of serious segmentation errors altogether. This paper introduces a vectorial score that is sensitive to, and identifies, the most important classes of segmentation errors (over-, under-, and mis-segmentation) and what page components (lines, blocks, etc.