Showing papers by "Takeo Kanade published in 2018"

Synthesizing a Scene-Specific Pedestrian Detector and Pose Estimator for Static Video Surveillance: Can We Learn Pedestrian Detectors and Pose Estimators Without Real Data?

Abstract: We consider scenarios where we have zero instances of real pedestrian data (e.g., a newly installed surveillance system in a novel location in which no labeled real data or unsupervised real data exists yet) and a pedestrian detector must be developed prior to any observations of pedestrians. Given a single image and auxiliary scene information in the form of camera parameters and geometric layout of the scene, our approach infers and generates a large variety of geometrically and photometrically accurate potential images of synthetic pedestrians along with purely accurate ground-truth labels through the use of computer graphics rendering engine. We first present an efficient discriminative learning method that takes these synthetic renders and generates a unique spatially-varying and geometry-preserving pedestrian appearance classifier customized for every possible location in the scene. In order to extend our approach to multi-task learning for further analysis (i.e., estimating pose and segmentation of pedestrians besides detection), we build a more generalized model employing a fully convolutional neural network architecture for multi-task learning leveraging the “free" ground-truth annotations that can be obtained from our pedestrian synthesizer. We demonstrate that when real human annotated data is scarce or non-existent, our data generation strategy can provide an excellent solution for an array of tasks for human activity analysis including detection, pose estimation and segmentation. Experimental results show that our approach (1) outperforms classical models and hybrid synthetic-real models, (2) outperforms various combinations of off-the-shelf state-of-the-art pedestrian detectors and pose estimators that are trained on real data, and (3) surprisingly, our method using purely synthetic data is able to outperform models trained on real scene-specific data when data is limited.

Phase contrast time-lapse microscopy datasets with automated and manual cell tracking annotations.

Abstract: Phase contrast time-lapse microscopy is a non-destructive technique that generates large volumes of image-based information to quantify the behaviour of individual cells or cell populations. To guide the development of algorithms for computer-aided cell tracking and analysis, 48 time-lapse image sequences, each spanning approximately 3.5 days, were generated with accompanying ground truths for C2C12 myoblast cells cultured under 4 different media conditions, including with fibroblast growth factor 2 (FGF2), bone morphogenetic protein 2 (BMP2), FGF2 + BMP2, and control (no growth factor). The ground truths generated contain information for tracking at least 3 parent cells and their descendants within these datasets and were validated using a two-tier system of manual curation. This comprehensive, validated dataset will be useful in advancing the development of computer-aided cell tracking algorithms and function as a benchmark, providing an invaluable opportunity to deepen our understanding of individual and population-based cell dynamics for biomedical research. Machine-accessible metadata file describing the reported data (ISA-Tab format)

3D level set method for blastomere segmentation of preimplantation embryos in fluorescence microscopy images

Abstract: Detailed and accurate characteristics of preimplantation embryos are fundamental for a deep understanding of their development. Recent studies indicate that various geometric features of cells, such as size, shape, volume, and position play a significant role in embryo growth. However, a quantitative assessment of these characteristics first needs a segmentation of the individual cells. The manual separation and labeling of cells is extremely inefficient, and an automated approach is highly desirable. This paper presents an automatic method for early stage embryo segmentation into its constituent cells and membranes using three-dimensional (3D) data. The input data consist of two Z-stacks of fluorescence microscope images containing nuclei and membranes. The method uses a 3D level set segmentation algorithm. Its evaluation is based on a dataset composed of 20 mouse embryos, each with 4---32 blastomeres. Segmentation accuracy was evaluated by calculating F-scores with ground truth obtained by manually labeling desired regions. We also compared output of our method with the one acquired with a watershed algorithm. The proposed approach was able to achieve more than $$90\%$$90% accuracy for embryos with 4 and 8 cells, while for embryos with higher number of cells it was lower, reaching $$75\%$$75% for 32-cell embryo.

Guest Editorial: The Computational Face

Abstract: The papers in this special section examine the concept of automated face analysis (AFA). AFA has received special attention from the computer vision and pattern recognition communities. Research progress often gives the impression that problems such as face recognition and face detection are solved, at least for some scenarios. Several aspects of face analysis remain open problems, including the implementation of large scale face recognition/detection methods for in the wild images, emotion recognition, micro-expression analysis, and others. The community keeps making rapid progress on these topics, with continual improvement of current methods and creation of new ones that push the state-of-the-art. Applications are countless, including security and video surveillance, human computer/robot interaction, communication, entertainment, and commerce, while having an important social impact in assistive technologies for education and health. The importance of face analysis, together with the vast amount of work on the subject and the latest developments in the field, motivated us to organize a special section on this theme. The scope of the compilation comprises all aspects of face analysis from a computer vision perspective. Including, but not limited to: recognition, detection, alignment, reconstruction of faces, pose estimation of faces, gaze analysis, age, emotion, gender, and facial attributes estimation, and applications among others.