Strong edge features for image coding

A two-component model is proposed for perceptual image coding. For the first component of the model, the watershed operator is used to detect strong edge features. Then, an efficient morphological interpolation algorithm reconstructs the smooth areas of the image from the extracted edge information, also known as sketch data. The residual component, containing fine textures, is separately coded by a subband coding scheme. The morphological operators involved in the coding of the primary component perform very efficiently compared to conventional techniques like the LGO operator, used for the edge extraction, or the diffusion filters, iteratively applied for the interpolation of smooth areas in previously reported sketch-based coding schemes.Peer ReviewedPostprint (published version

New approaches to texture coding in segmentation and feature-based image coding schemes

Peer ReviewedPostprint (published version

Coding of details in very low bit-rate video systems

In this paper, the importance of including small image features at the initial levels of a progressive second generation video coding scheme is presented. It is shown that a number of meaningful small features called details should be coded, even at very low data bit-rates, in order to match their perceptual significance to the human visual system. We propose a method for extracting, perceptually selecting and coding of visual details in a video sequence using morphological techniques. Its application in the framework of a multiresolution segmentation-based coding algorithm yields better results than pure segmentation techniques at higher compression ratios, if the selection step fits some main subjective requirements. Details are extracted and coded separately from the region structure and included in the reconstructed images in a later stage. The bet of considering the local background of a given detail for its perceptual selection breaks the concept ofPeer ReviewedPostprint (published version

Prediction error image coding using a modified stochastic vector quantization scheme

The objective of this paper is to provide an efficient and yet simple method to encode the prediction error image of video sequences, based on a stochastic vector quantization (SVQ) approach that has been modified to cope with the intrinsic decorrelated nature of the prediction error image of video signals. In the SVQ scheme, the codewords are generated by stochastic techniques instead of being generated by a training set representative of the expected input image as is normal use in VQ. The performance of the scheme is shown for the particular case of segmentation-based video coding although the technique can be also applied to motion-compensated hybrid coding schemes.Peer ReviewedPostprint (published version

Morphological filter for lossless image subsampling

We present a morphological filter for lossless image subsampling for a given downsampling-upsampling strategy. This filter is applied in a multiresolution decomposition and results in a more efficient scheme for image coding purposes than other lossy sampling schemes. Its main advantage is a greatly reduced computational load compared to multiresolution schemes performed with linear filters.Peer ReviewedPostprint (published version

A new approach to texture coding using stochastic vector quantization

A new method for texture coding which combines 2-D linear prediction and stochastic vector quantization is presented in this paper. To encode a texture, a linear predictor is computed first. Next, a codebook following the prediction error model is generated and the prediction error is encoded with VQ, using an algorithm which takes into account the pixels surrounding the block being encoded. In the decoder, the error image is decoded first and then filtered as a whole, using the prediction filter. Hence, correlation between pixels is not lost from one block to another and a good reproduction quality can be achieved.Peer ReviewedPostprint (published version

An efficient technique of texture representation in segmentation-based image coding schemes

In segmentation-based image coding techniques the image to be compressed is first segmented. Then, the information is coded describing the shape and the interior of the regions. A new method to encode the texture obtained in segmentation-based coding schemes is presented. The approach combines 2-D linear prediction and stochastic vector quantization. To encode a texture, a linear predictor is computed first. Next, a codebook following the prediction error model is generated and the prediction error is encoded with VQ. In the decoder, the error image is decoded first and then filtered as a whole, using the prediction filter. Hence, correlation between pixels is not lost from one block to another and a good reproduction quality can be achieved.Peer ReviewedPostprint (published version

3D point cloud video segmentation oriented to the analysis of interactions

Given the widespread availability of point cloud data from consumer depth sensors, 3D point cloud segmentation becomes a promising building block for high level applications such as scene understanding and interaction analysis. It benefits from the richer information contained in real world 3D data compared to 2D images. This also implies that the classical color segmentation challenges have shifted to RGBD data, and new challenges have also emerged as the depth information is usually noisy, sparse and unorganized. Meanwhile, the lack of 3D point cloud ground truth labeling also limits the development and comparison among methods in 3D point cloud segmentation. In this paper, we present two contributions: a novel graph based point cloud segmentation method for RGBD stream data with interacting objects and a new ground truth labeling for a previously published data set. This data set focuses on interaction (merge and split between ’object’ point clouds), which differentiates itself from the few existing labeled RGBD data sets which are more oriented to Simultaneous Localization And Mapping (SLAM) tasks. The proposed point cloud segmentation method is evaluated with the 3D point cloud ground truth labeling. Experiments show the promising result of our approach.Postprint (published version

Feature-based annealing particle filter for robust body pose estimation

This paper presents a new annealing method for particle filtering in the context of body pose estimation. The feature-based annealing is inferred from the weighting functions obtained with common image features used for the likelihood approximation. We introduce a complementary weighting function based on the foreground extraction and we balance the different measures through the annealing layers in order to improve the posterior estimate. This technique is applied to estimate the upper body pose of a subject in a realistic multi-view environment. Comparative results between the proposed method and the common annealing strategy are presented to assess the robustness of the algorithm.Postprint (published version

Representing and retrieving regions using binary partition trees

This paper discusses the interest of Binary Partition Trees for image and region representation in the context of indexing and similarity based retrieval. Binary Partition Trees concentrate in a compact and structured way the set of regions that compose an image. Since the tree is able to represent images in a multiresolution way, only simple descriptors need to be attached to the nodes. Moreover, this representation is used for similarity based region retrieval.Peer ReviewedPostprint (published version