10,286 research outputs found
Convolutional Nets and Watershed Cuts for Real-Time Semantic Labeling of RGBD Videos
International audienceThis work addresses multi-class segmentation of indoor scenes with RGB-D inputs. While this area of research has gained much attention recently, most works still rely on handcrafted features. In contrast, we apply a multiscale convolutional network to learn features directly from the images and the depth information. Using a frame by frame labeling, we obtain nearly state-of-the-art performance on the NYU-v2 depth dataset with an accuracy of 64.5%. We then show that the labeling can be further improved by exploiting the temporal consistency in the video sequence of the scene. To that goal, we present a method producing temporally consistent superpixels from a streaming video. Among the di erent methods producing superpixel segmentations of an image, the graph-based approach of Felzenszwalb and Huttenlocher is broadly employed. One of its interesting properties is that the regions are computed in a greedy manner in quasi-linear time by using a minimum spanning tree. In a framework exploiting minimum spanning trees all along, we propose an efficient video segmentation approach that computes temporally consistent pixels in a causal manner, filling the need for causal and real-time applications. We illustrate the labeling of indoor scenes in video sequences that could be processed in real-time using appropriate hardware such as an FPGA
Learning the dynamics and time-recursive boundary detection of deformable objects
We propose a principled framework for recursively segmenting deformable objects across a sequence
of frames. We demonstrate the usefulness of this method on left ventricular segmentation across a cardiac
cycle. The approach involves a technique for learning the system dynamics together with methods of
particle-based smoothing as well as non-parametric belief propagation on a loopy graphical model capturing
the temporal periodicity of the heart. The dynamic system state is a low-dimensional representation
of the boundary, and the boundary estimation involves incorporating curve evolution into recursive state
estimation. By formulating the problem as one of state estimation, the segmentation at each particular
time is based not only on the data observed at that instant, but also on predictions based on past and future
boundary estimates. Although the paper focuses on left ventricle segmentation, the method generalizes
to temporally segmenting any deformable object
Massively Parallel Video Networks
We introduce a class of causal video understanding models that aims to
improve efficiency of video processing by maximising throughput, minimising
latency, and reducing the number of clock cycles. Leveraging operation
pipelining and multi-rate clocks, these models perform a minimal amount of
computation (e.g. as few as four convolutional layers) for each frame per
timestep to produce an output. The models are still very deep, with dozens of
such operations being performed but in a pipelined fashion that enables
depth-parallel computation. We illustrate the proposed principles by applying
them to existing image architectures and analyse their behaviour on two video
tasks: action recognition and human keypoint localisation. The results show
that a significant degree of parallelism, and implicitly speedup, can be
achieved with little loss in performance.Comment: Fixed typos in densenet model definition in appendi
Region based analysis of video sequences with a general merging algorithm
Connected operators [4] and Region Growing [2] algorithms have been created in different context and applications. However, they all are based on the same fundamental merging process. This paper discusses the basic issues of the merging algorithm and presents different applications ranging from simple frame segmentation to video sequence analysis.Peer ReviewedPostprint (published version
Video Propagation Networks
We propose a technique that propagates information forward through video
data. The method is conceptually simple and can be applied to tasks that
require the propagation of structured information, such as semantic labels,
based on video content. We propose a 'Video Propagation Network' that processes
video frames in an adaptive manner. The model is applied online: it propagates
information forward without the need to access future frames. In particular we
combine two components, a temporal bilateral network for dense and video
adaptive filtering, followed by a spatial network to refine features and
increased flexibility. We present experiments on video object segmentation and
semantic video segmentation and show increased performance comparing to the
best previous task-specific methods, while having favorable runtime.
Additionally we demonstrate our approach on an example regression task of color
propagation in a grayscale video.Comment: Appearing in Computer Vision and Pattern Recognition, 2017 (CVPR'17
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