scholarly journals Chimpanzee face recognition from videos in the wild using deep learning

2019 ◽  
Vol 5 (9) ◽  
pp. eaaw0736 ◽  
Author(s):  
Daniel Schofield ◽  
Arsha Nagrani ◽  
Andrew Zisserman ◽  
Misato Hayashi ◽  
Tetsuro Matsuzawa ◽  
...  
Keyword(s):  
Large Scale ◽  
Video Recording ◽  
Automated Analysis ◽  
Sex Recognition ◽  
Identity Recognition ◽  
Face Images ◽  
Social Network Structure ◽  
In The Wild ◽  
Future Potential ◽  
Video Archives

Video recording is now ubiquitous in the study of animal behavior, but its analysis on a large scale is prohibited by the time and resources needed to manually process large volumes of data. We present a deep convolutional neural network (CNN) approach that provides a fully automated pipeline for face detection, tracking, and recognition of wild chimpanzees from long-term video records. In a 14-year dataset yielding 10 million face images from 23 individuals over 50 hours of footage, we obtained an overall accuracy of 92.5% for identity recognition and 96.2% for sex recognition. Using the identified faces, we generated co-occurrence matrices to trace changes in the social network structure of an aging population. The tools we developed enable easy processing and annotation of video datasets, including those from other species. Such automated analysis unveils the future potential of large-scale longitudinal video archives to address fundamental questions in behavior and conservation.

Development and study of a method for cell separation during white blood cell segmentation on images of bone marrow preparations in information and measurement systems for diagnostics of acute leukemia

2020 ◽  
pp. 68-72
Author(s):  
V.G. Nikitaev ◽  
A.N. Pronichev ◽  
V.V. Dmitrieva ◽  
E.V. Polyakov ◽  
A.D. Samsonova ◽  
...  
Keyword(s):  
Bone Marrow ◽  
Blood Cell ◽  
Acute Leukemia ◽  
White Blood Cell ◽  
Cell Separation ◽  
Large Scale ◽  
Bone Marrow Cells ◽  
Automated Analysis ◽  
Cell Segmentation ◽  
Measurement Systems

The issues of using of information and measurement systems based on processing of digital images of microscopic preparations for solving large-scale tasks of automating the diagnosis of acute leukemia are considered. The high density of leukocyte cells in the preparation (hypercellularity) is a feature of microscopic images of bone marrow preparations. It causes the proximity of cells to eachother and their contact with the formation of conglomerates. Measuring of the characteristics of bone marrow cells in such conditions leads to unacceptable errors (more than 50%). The work is devoted to segmentation of contiguous cells in images of bone marrow preparations. A method of cell separation during white blood cell segmentation on images of bone marrow preparations under conditions of hypercellularity of the preparation has been developed. The peculiarity of the proposed method is the use of an approach to segmentation of cell images based on the watershed method with markers. Key stages of the method: the formation of initial markers and builds the lines of watershed, a threshold binarization, shading inside the outline. The parameters of the separation of contiguous cells are determined. The experiment confirmed the effectiveness of the proposed method. The relative segmentation error was 5 %. The use of the proposed method in information and measurement systems of computer microscopy for automated analysis of bone marrow preparations will help to improve the accuracy of diagnosis of acute leukemia.

scholarly journals VIPPrint: Validating Synthetic Image Detection and Source Linking Methods on a Large Scale Dataset of Printed Documents

2021 ◽  
Vol 7 (3) ◽  
pp. 50
Author(s):  
Anselmo Ferreira ◽  
Ehsan Nowroozi ◽  
Mauro Barni
Keyword(s):  
Large Scale ◽  
State Of The Art ◽  
Child Pornography ◽  
Forensic Analysis ◽  
Synthetic Image ◽  
Image Detection ◽  
Face Images ◽  
Large Scale Dataset ◽  
Scanned Images ◽  
Analysis Of The Images

The possibility of carrying out a meaningful forensic analysis on printed and scanned images plays a major role in many applications. First of all, printed documents are often associated with criminal activities, such as terrorist plans, child pornography, and even fake packages. Additionally, printing and scanning can be used to hide the traces of image manipulation or the synthetic nature of images, since the artifacts commonly found in manipulated and synthetic images are gone after the images are printed and scanned. A problem hindering research in this area is the lack of large scale reference datasets to be used for algorithm development and benchmarking. Motivated by this issue, we present a new dataset composed of a large number of synthetic and natural printed face images. To highlight the difficulties associated with the analysis of the images of the dataset, we carried out an extensive set of experiments comparing several printer attribution methods. We also verified that state-of-the-art methods to distinguish natural and synthetic face images fail when applied to print and scanned images. We envision that the availability of the new dataset and the preliminary experiments we carried out will motivate and facilitate further research in this area.

scholarly journals Shape My Face: Registering 3D Face Scans by Surface-to-Surface Translation

2021 ◽  
Author(s):  
Mehdi Bahri ◽  
Eimear O’ Sullivan ◽  
Shunwang Gong ◽  
Feng Liu ◽  
Xiaoming Liu ◽  
...  
Keyword(s):  
Large Scale ◽  
Training Time ◽  
3D Face ◽  
In The Wild ◽  
Previous State ◽  
Surface Translation ◽  
Visual Attention Mechanism ◽  
Diverse Data ◽  
Human Faces ◽  
Robust To Noise

AbstractStandard registration algorithms need to be independently applied to each surface to register, following careful pre-processing and hand-tuning. Recently, learning-based approaches have emerged that reduce the registration of new scans to running inference with a previously-trained model. The potential benefits are multifold: inference is typically orders of magnitude faster than solving a new instance of a difficult optimization problem, deep learning models can be made robust to noise and corruption, and the trained model may be re-used for other tasks, e.g. through transfer learning. In this paper, we cast the registration task as a surface-to-surface translation problem, and design a model to reliably capture the latent geometric information directly from raw 3D face scans. We introduce Shape-My-Face (SMF), a powerful encoder-decoder architecture based on an improved point cloud encoder, a novel visual attention mechanism, graph convolutional decoders with skip connections, and a specialized mouth model that we smoothly integrate with the mesh convolutions. Compared to the previous state-of-the-art learning algorithms for non-rigid registration of face scans, SMF only requires the raw data to be rigidly aligned (with scaling) with a pre-defined face template. Additionally, our model provides topologically-sound meshes with minimal supervision, offers faster training time, has orders of magnitude fewer trainable parameters, is more robust to noise, and can generalize to previously unseen datasets. We extensively evaluate the quality of our registrations on diverse data. We demonstrate the robustness and generalizability of our model with in-the-wild face scans across different modalities, sensor types, and resolutions. Finally, we show that, by learning to register scans, SMF produces a hybrid linear and non-linear morphable model. Manipulation of the latent space of SMF allows for shape generation, and morphing applications such as expression transfer in-the-wild. We train SMF on a dataset of human faces comprising 9 large-scale databases on commodity hardware.

Wireless parathyroid detection device using autofluorescence and smart glasses: A preliminary study

2021 ◽  
pp. 1-9
Author(s):  
Chang Hwan Ryu ◽  
Jungirl Seok ◽  
Yu Lim Choi ◽  
Seok-ki Kim ◽  
Yuh-Seog Jung ◽  
...  
Keyword(s):  
Prospective Study ◽  
Large Scale ◽  
Video Recording ◽  
Controlled Study ◽  
Autofluorescence Imaging ◽  
Agreement Rate ◽  
Smart Glasses ◽  
Practical Effect ◽  
Preliminary Study ◽  
Positive Agreement

BACKGROUND: Autofluorescence imaging technology has been utilized for preserving or identifying parathyroid glands (PTGs) during thyroid surgery. We developed a wireless PTGs detection device linked with smart glasses that allows for real-time video recording and screen switching according to the light source. OBJECTIVE: This study aimed to confirm the feasibility of the device and whether it would help preserve the PTG during the surgery. METHODS: This prospective study was conducted in 30 patients with 66 PTGs. The device’s agreement with the physician’s judgment was evaluated, and we determined how many PTGs were preserved from thyroidectomy. RESULTS: The positive agreement rate for PTGs detection between the surgeon and device was 70.9%. Inadvertent parathyroidectomy was identified in surgical specimens of 6 patients (20%). No PTG was removed when it was confirmed by the device (0/39). Of the 27 glands not detected by the device, there was inadvertent removal of 6 PTGs. CONCLUSIONS: PTGs can be preserved successfully when the detection of them by the device is consistent with the surgeon’s discretion. A large-scale controlled study is necessary to demonstrate the practical effect of this device on hypoparathyroidism after thyroidectomy.

scholarly journals Hybrid Attention Cascade Network for Facial Expression Recognition

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 2003 ◽  
Author(s):  
Xiaoliang Zhu ◽  
Shihao Ye ◽  
Liang Zhao ◽  
Zhicheng Dai
Keyword(s):  
Facial Expression ◽  
Facial Expressions ◽  
Facial Expression Recognition ◽  
Expression Recognition ◽  
Spatial Features ◽  
Face Images ◽  
Temporal Features ◽  
The Face ◽  
In The Wild ◽  
Fusion Features

As a sub-challenge of EmotiW (the Emotion Recognition in the Wild challenge), how to improve performance on the AFEW (Acted Facial Expressions in the wild) dataset is a popular benchmark for emotion recognition tasks with various constraints, including uneven illumination, head deflection, and facial posture. In this paper, we propose a convenient facial expression recognition cascade network comprising spatial feature extraction, hybrid attention, and temporal feature extraction. First, in a video sequence, faces in each frame are detected, and the corresponding face ROI (range of interest) is extracted to obtain the face images. Then, the face images in each frame are aligned based on the position information of the facial feature points in the images. Second, the aligned face images are input to the residual neural network to extract the spatial features of facial expressions corresponding to the face images. The spatial features are input to the hybrid attention module to obtain the fusion features of facial expressions. Finally, the fusion features are input in the gate control loop unit to extract the temporal features of facial expressions. The temporal features are input to the fully connected layer to classify and recognize facial expressions. Experiments using the CK+ (the extended Cohn Kanade), Oulu-CASIA (Institute of Automation, Chinese Academy of Sciences) and AFEW datasets obtained recognition accuracy rates of 98.46%, 87.31%, and 53.44%, respectively. This demonstrated that the proposed method achieves not only competitive performance comparable to state-of-the-art methods but also greater than 2% performance improvement on the AFEW dataset, proving the significant outperformance of facial expression recognition in the natural environment.

scholarly journals TrackingNet: A Large-Scale Dataset and Benchmark for Object Tracking in the Wild

2018 ◽  
pp. 310-327 ◽  
Author(s):  
Matthias Müller ◽  
Adel Bibi ◽  
Silvio Giancola ◽  
Salman Alsubaihi ◽  
Bernard Ghanem
Keyword(s):  
Object Tracking ◽  
Large Scale ◽  
Large Scale Dataset ◽  
In The Wild

scholarly journals Automated analysis of liver fat, muscle and adipose tissue distribution from CT suitable for large-scale studies

2017 ◽  
Vol 7 (1) ◽  
Author(s):  
Joel Kullberg ◽  
Anders Hedström ◽  
John Brandberg ◽  
Robin Strand ◽  
Lars Johansson ◽  
...  
Keyword(s):  
Adipose Tissue ◽  
Tissue Distribution ◽  
Large Scale ◽  
Automated Analysis ◽  
Liver Fat ◽  
Adipose Tissue Distribution

scholarly journals High-throughput super-resolution analysis of influenza virus pleomorphism reveals insights into viral spatial organization

2021 ◽  
Author(s):  
Andrew McMahon ◽  
Rebecca Andrews ◽  
Sohail V Ghani ◽  
Thorben Cordes ◽  
Achillefs N Kapanidis ◽  
...  
Keyword(s):  
Large Scale ◽  
Spatial Organization ◽  
Structural Information ◽  
Virus Assembly ◽  
Super Resolution ◽  
Automated Analysis ◽  
Size Analysis ◽  
Analysis Pipeline ◽  
Single Experiment ◽  
Viral Immunology

Many viruses form highly pleomorphic particles; in influenza, these particles range from spheres of ~ 100 nm in diameter to filaments of several microns in length. Virion structure is of interest, not only in the context of virus assembly, but also because pleomorphic variations may correlate with infectivity and pathogenicity. Detailed images of virus morphology often rely on electron microscopy, which is generally low throughput and limited in molecular identification. We have used fluorescence super-resolution microscopy combined with a rapid automated analysis pipeline to image many thousands of individual influenza virions, gaining information on their size, morphology and the distribution of membrane-embedded and internal proteins. This large-scale analysis revealed that influenza particles can be reliably characterised by length, that no spatial frequency patterning of the surface glycoproteins occurs, and that RNPs are preferentially located towards filament ends within Archetti bodies. Our analysis pipeline is versatile and can be adapted for use on multiple other pathogens, as demonstrated by its application for the size analysis of SARS-CoV-2. The ability to gain nanoscale structural information from many thousands of viruses in just a single experiment is valuable for the study of virus assembly mechanisms, host cell interactions and viral immunology, and should be able to contribute to the development of viral vaccines, anti-viral strategies and diagnostics.

scholarly journals Memory and Perception-based Facial Image Reconstruction

2017 ◽  
Author(s):  
Chi-Hsun Chang ◽  
Dan Nemrodov ◽  
Andy C. H. Lee ◽  
Adrian Nestor
Keyword(s):  
Image Reconstruction ◽  
Visual Memory ◽  
Long Term Memory ◽  
Facial Features ◽  
Identity Recognition ◽  
Facial Identity ◽  
Face Images ◽  
Computer Based ◽  
Novel Applications

AbstractVisual memory for faces has been extensively researched, especially regarding the main factors that influence face memorability. However, what we remember exactly about a face, namely, the pictorial content of visual memory, remains largely unclear. The current work aims to elucidate this issue by reconstructing face images from both perceptual and memory-based behavioural data. Specifically, our work builds upon and further validates the hypothesis that visual memory and perception share a common representational basis underlying facial identity recognition. To this end, we derived facial features directly from perceptual data and then used such features for image reconstruction separately from perception and memory data. Successful levels of reconstruction were achieved in both cases for newly-learned faces as well as for familiar faces retrieved from long-term memory. Theoretically, this work provides insights into the content of memory-based representations while, practically, it opens the path to novel applications, such as computer-based ‘sketch artists’.

scholarly journals Assessing species coverage and assembly quality of rapidly accumulating sequenced genomes

2021 ◽  
Author(s):  
Romain Feron ◽  
Robert Michael Waterhouse
Keyword(s):  
Large Scale ◽  
Automated Analysis ◽  
Single Copy ◽  
The United States ◽  
Community Resource ◽  
Assembly Quality ◽  
Sequencing Technologies ◽  
Analysis Workflow ◽  
Genome Assemblies

Ambitious initiatives to coordinate genome sequencing of Earth's biodiversity mean that the accumulation of genomic data is growing rapidly. In addition to cataloguing biodiversity, these data provide the basis for understanding biological function and evolution. Accurate and complete genome assemblies offer a comprehensive and reliable foundation upon which to advance our understanding of organismal biology at genetic, species, and ecosystem levels. However, ever-changing sequencing technologies and analysis methods mean that available data are often heterogeneous in quality. In order to guide forthcoming genome generation efforts and promote efficient prioritisation of resources, it is thus essential to define and monitor taxonomic coverage and quality of the data. Here we present an automated analysis workflow that surveys genome assemblies from the United States National Center for Biotechnology Information (NCBI), assesses their completeness using the relevant Benchmarking Universal Single-Copy Orthologue (BUSCO) datasets, and collates the results into an interactively browsable resource. We apply our workflow to produce a community resource of available assemblies from the phylum Arthropoda, the Arthropoda Assembly Assessment Catalogue. Using this resource, we survey current taxonomic coverage and assembly quality at the NCBI, we examine how key assembly metrics relate to gene content completeness, and we compare results from using different BUSCO lineage datasets. These results demonstrate how the workflow can be used to build a community resource that enables large-scale assessments to survey species coverage and data quality of available genome assemblies, and to guide prioritisations for ongoing and future sampling, sequencing, and genome generation initiatives.

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