On the Detection of Motion and the Computation of Optical Flow
IEEE Transactions on Pattern Analysis and Machine Intelligence
International Journal of Computer Vision
Performance of optical flow techniques
International Journal of Computer Vision
Digital image processing
Data clustering using a model granular magnet
Neural Computation
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Digital Image Processing
IEEE Transactions on Pattern Analysis and Machine Intelligence
Markov Random Field Models for Unsupervised Segmentation of Textured Color Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Segmentation and Tracking Using Color Mixture Models
ACCV '98 Proceedings of the Third Asian Conference on Computer Vision-Volume I - Volume I
Smoothness in Layers: Motion segmentation using nonparametric mixture estimation.
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Image Segmentation Using Local Variation
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Face and Facial Feature Extraction from Color Image
FG '96 Proceedings of the 2nd International Conference on Automatic Face and Gesture Recognition (FG '96)
FG '96 Proceedings of the 2nd International Conference on Automatic Face and Gesture Recognition (FG '96)
Multiple motion estimation and segmentation in transparency
ICASSP '00 Proceedings of the Acoustics, Speech, and Signal Processing, 2000. on IEEE International Conference - Volume 04
Temporal segmentation based on video coding
WiCOM'09 Proceedings of the 5th International Conference on Wireless communications, networking and mobile computing
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In this paper we present an algorithm which forms the preprocessing stage of a system for automatically classifying Amazon forest monkeys captured on video in their natural habitat. The work is motivated by a desire to automatically monitor animal populations in natural forest environments. The method applies a graph-theoretical clustering approach to spatial and motion fields to automatically segment monkeys moving in the foreground from trees and other vegetation in the background. The algorithm is described as follows: First a d'Alembertian of a spatial-temporal Gaussian filter is convolved with a sequence of image frames to obtain an image of temporal zero crossings. Subsequently, the magnitude of the visual motion vector in the image plane is estimated at each pixel of the image of temporal zero crossings and spatial-motion-based graph-theoretical clustering is applied to the resulting velocity image. The clustered pixels are then backprojected into the original color image for each subsequent frame to obtain a segmented image sequence. By applying a threshold to the velocity image, motion due to background vegetation and camera movement can be rejected, while segments extracted from animals are retained. This is extremely important for our application as the recognizer relies on color features that are extracted from the monkeys' fur. Experimental results are presented which show that the approach can successfully extract patches of monkey skin from video shot with a simple hand held camera.