- Image/Video Storage, Retrieval
- Image/Video Processing
- Image/Video Coding
- Image Scanning, Display, and Printing
- Image Formation
- Read more about Prostate detection and segmentation based on convolutional neural network and topological derivative
- Log in to post comments
The topological derivative (TD) for shape analysis has been employed
in image segmentation, and machine learning schemes based on
convolutional neural network (CNN) provide the high performance in
the image processing. The supervised and unsupervised approaches
have different roles and advantages according to their concepts. To
maximize the benefits of two approaches, we propose CNN-TD based
segmentation approach. A CNN-based segmentation scheme is employed
to faithfully consider the characteristics of an object to be
- Categories:
- Read more about Dual-Fisheye Lens Stiching for 360-Degree Imaging
- Log in to post comments
- Categories:
- Read more about STOCHASTIC TRUNCATED WIRTINGER FLOW ALGORITHM FOR PHASE RETRIEVAL USING BOOLEAN CODED APERTURES
- Log in to post comments
- Categories:
- Read more about STOCHASTIC TRUNCATED WIRTINGER FLOW ALGORITHM FOR PHASE RETRIEVAL USING BOOLEAN CODED APERTURES
- Log in to post comments
- Categories:
- Read more about Pre-processing And Classification Of Hyperspectral Imagery Via Selective Inpainting Presentation
- Log in to post comments
We propose a semi-supervised algorithm for processing and classification of hyperspectral imagery. For initialization, we keep 20% of the data intact, and use Principal Component Analysis to discard voxels from noisier bands and pixels. Then, we use either an Accelerated Proximal Gradient algorithm (APGL), or a modified APGL algorithm with a penalty term for distance between inpainted pixels and endmembers (APGL Hyp), on the initialized datacube to inpaint the missing data. APGL and APGL Hyp are distinguished by performance on datasets with full pixels removed or extreme noise.
- Categories:
- Read more about FAST HUMAN SEGMENTATION USING COLOR AND DEPTH
- Log in to post comments
Accurate segmentation of humans from live videos is an important problem to be solved in developing immersive video experience. We propose to extract the human segmentation information from color and depth cues in a video using multiple modeling techniques. The prior information from human skeleton data is also fused along with the depth and color models to obtain the final segmentation inside a graph-cut framework. The proposed method runs real time on live videos using single CPU and is shown to be quantitatively outperforming the methods that directly fuse color and depth data.
- Categories:
- Read more about RECONSTRUCTION OF 3D SURFACE FROM 2D HOLOGRAPHIC SIGNAL BASED ON KALMAN FILTER
- Log in to post comments
- Categories:
Group sparsity or nonlocal image representation has shown great potential in image denoising. However, most existing methods only consider the nonlocal self-similarity (NSS) prior of noisy input image, that is, the similar patches collected only from degraded input, which makes the quality of image denoising largely depend on the input itself. In this paper we propose a new prior model for image denoising, called group sparsity residual constraint (GSRC).
ICASSP--2017.pdf

- Categories: