399 research outputs found
LiveCap: Real-time Human Performance Capture from Monocular Video
We present the first real-time human performance capture approach that
reconstructs dense, space-time coherent deforming geometry of entire humans in
general everyday clothing from just a single RGB video. We propose a novel
two-stage analysis-by-synthesis optimization whose formulation and
implementation are designed for high performance. In the first stage, a skinned
template model is jointly fitted to background subtracted input video, 2D and
3D skeleton joint positions found using a deep neural network, and a set of
sparse facial landmark detections. In the second stage, dense non-rigid 3D
deformations of skin and even loose apparel are captured based on a novel
real-time capable algorithm for non-rigid tracking using dense photometric and
silhouette constraints. Our novel energy formulation leverages automatically
identified material regions on the template to model the differing non-rigid
deformation behavior of skin and apparel. The two resulting non-linear
optimization problems per-frame are solved with specially-tailored
data-parallel Gauss-Newton solvers. In order to achieve real-time performance
of over 25Hz, we design a pipelined parallel architecture using the CPU and two
commodity GPUs. Our method is the first real-time monocular approach for
full-body performance capture. Our method yields comparable accuracy with
off-line performance capture techniques, while being orders of magnitude
faster
Analysis of Hand Segmentation in the Wild
A large number of works in egocentric vision have concentrated on action and
object recognition. Detection and segmentation of hands in first-person videos,
however, has less been explored. For many applications in this domain, it is
necessary to accurately segment not only hands of the camera wearer but also
the hands of others with whom he is interacting. Here, we take an in-depth look
at the hand segmentation problem. In the quest for robust hand segmentation
methods, we evaluated the performance of the state of the art semantic
segmentation methods, off the shelf and fine-tuned, on existing datasets. We
fine-tune RefineNet, a leading semantic segmentation method, for hand
segmentation and find that it does much better than the best contenders.
Existing hand segmentation datasets are collected in the laboratory settings.
To overcome this limitation, we contribute by collecting two new datasets: a)
EgoYouTubeHands including egocentric videos containing hands in the wild, and
b) HandOverFace to analyze the performance of our models in presence of similar
appearance occlusions. We further explore whether conditional random fields can
help refine generated hand segmentations. To demonstrate the benefit of
accurate hand maps, we train a CNN for hand-based activity recognition and
achieve higher accuracy when a CNN was trained using hand maps produced by the
fine-tuned RefineNet. Finally, we annotate a subset of the EgoHands dataset for
fine-grained action recognition and show that an accuracy of 58.6% can be
achieved by just looking at a single hand pose which is much better than the
chance level (12.5%).Comment: Accepted at CVPR 201
HW/SW Co-design and Prototyping Approach for Embedded Smart Camera: ADAS Case Study
In 1968, Volkswagen integrated an electronic circuit as a new control fuel injection system, called the “Little Black Box”, it is considered as the first embedded system in the automotive industry. Currently, automobile constructors integrate several embedded systems into any of their new model vehicles. Behind these automobile’s electronics systems, a sophisticated Hardware/Software (HW/SW) architecture, which is based on heterogeneous components, and multiple CPUs is built. At present, they are more oriented toward visionbased systems using tiny embedded smart camera. This visionbased system in real time aspects represents one of the most challenging issues, especially in the domain of automobile’s applications. On the design side, one of the optimal solutions adopted by embedded systems designer for system performance, is to associate CPUs and hardware accelerators in the same design, in order to reduce the computational burden on the CPU and to speed-up the data processing. In this paper, we present a hardware platform-based design approach for fast embedded smart Advanced Driver Assistant System (ADAS) design and prototyping, as an alternative for the pure time-consuming simulation technique. Based on a Multi-CPU/FPGA platform, we introduced a new methodology/flow to design the different HW and SW parts of the ADAS system. Then, we shared our experience in designing and prototyping a HW/SW vision based on smart embedded system as an ADAS that helps to increase the safety of car’s drivers. We presented a real HW/SW prototype of the vision ADAS based on a Zynq FPGA. The system detects the fatigue/drowsiness state of the driver by monitoring the eyes closure and generates a real time alert. A new HW Skin Segmentation step to locate the eyes/face is proposed. Our new approach migrates the skin segmentation step from processing system (SW) to programmable logic (HW) taking the advantage of High-Level Synthesis (HLS) tool flow to accelerate the implementation, and the prototyping of the Vision based ADAS on a hardware platform
Towards Data-Driven Large Scale Scientific Visualization and Exploration
Technological advances have enabled us to acquire extremely large
datasets but it remains a challenge to store, process, and extract
information from them. This dissertation builds upon recent advances
in machine learning, visualization, and user interactions to
facilitate exploration of large-scale scientific datasets. First, we
use data-driven approaches to computationally identify regions of
interest in the datasets. Second, we use visual presentation for
effective user comprehension. Third, we provide interactions for
human users to integrate domain knowledge and semantic information
into this exploration process.
Our research shows how to extract, visualize, and explore informative
regions on very large 2D landscape images, 3D volumetric datasets,
high-dimensional volumetric mouse brain datasets with thousands of
spatially-mapped gene expression profiles, and geospatial trajectories
that evolve over time. The contribution of this dissertation include:
(1) We introduce a sliding-window saliency model that discovers
regions of user interest in very large images; (2) We develop visual
segmentation of intensity-gradient histograms to identify meaningful
components from volumetric datasets; (3) We extract boundary surfaces
from a wealth of volumetric gene expression mouse brain profiles to
personalize the reference brain atlas; (4) We show how to efficiently
cluster geospatial trajectories by mapping each sequence of locations
to a high-dimensional point with the kernel distance framework.
We aim to discover patterns, relationships, and anomalies that would
lead to new scientific, engineering, and medical advances. This work
represents one of the first steps toward better visual understanding
of large-scale scientific data by combining machine learning and human
intelligence
Histopathological image analysis : a review
Over the past decade, dramatic increases in computational power and improvement in image analysis algorithms have allowed the development of powerful computer-assisted analytical approaches to radiological data. With the recent advent of whole slide digital scanners, tissue histopathology slides can now be digitized and stored in digital image form. Consequently, digitized tissue histopathology has now become amenable to the application of computerized image analysis and machine learning techniques. Analogous to the role of computer-assisted diagnosis (CAD) algorithms in medical imaging to complement the opinion of a radiologist, CAD algorithms have begun to be developed for disease detection, diagnosis, and prognosis prediction to complement the opinion of the pathologist. In this paper, we review the recent state of the art CAD technology for digitized histopathology. This paper also briefly describes the development and application of novel image analysis technology for a few specific histopathology related problems being pursued in the United States and Europe
Texture-based Deep Neural Network for Histopathology Cancer Whole Slide Image (WSI) Classification
Automatic histopathological Whole Slide Image (WSI) analysis for cancer classification has been highlighted along with the advancements in microscopic imaging techniques. However, manual examination and diagnosis with WSIs is time-consuming and tiresome. Recently, deep convolutional neural networks have succeeded in histopathological image analysis. In this paper, we propose a novel cancer texture-based deep neural network (CAT-Net) that learns scalable texture features from histopathological WSIs. The innovation of CAT-Net is twofold: (1) capturing invariant spatial patterns by dilated convolutional layers and (2) Reducing model complexity while improving performance. Moreover, CAT-Net can provide discriminative texture patterns formed on cancerous regions of histopathological images compared to normal regions. The proposed method outperformed the current state-of-the-art benchmark methods on accuracy, precision, recall, and F1 score
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