Region-based segmentation of images using syntactic visual features

This paper presents a robust and efficient method for segmentation of images into large regions that reflect the real world objects present in the scene. We propose an extension to the well known Recursive Shortest Spanning Tree (RSST) algorithm based on a new color model and so-called syntactic features [1]. We introduce practical solutions, integrated within the RSST framework, to structure analysis based on the shape and spatial configuration of image regions. We demonstrate that syntactic features provide a reliable basis for region merging criteria which prevent formation of regions spanning more than one semantic object, thereby significantly improving the perceptual quality of the output segmentation. Experiments indicate that the proposed features are generic in nature and allow satisfactory segmentation of real world images from various sources without adjustment to algorithm parameters

The aceToolbox: low-level audiovisual feature extraction for retrieval and classification

In this paper we present an overview of a software platform that has been developed within the aceMedia project, termed the aceToolbox, that provides global and local lowlevel feature extraction from audio-visual content. The toolbox is based on the MPEG-7 eXperimental Model (XM), with extensions to provide descriptor extraction from arbitrarily shaped image segments, thereby supporting local descriptors reflecting real image content. We describe the architecture of the toolbox as well as providing an overview of the descriptors supported to date. We also briefly describe the segmentation algorithm provided. We then demonstrate the usefulness of the toolbox in the context of two different content processing scenarios: similarity-based retrieval in large collections and scene-level classification of still images

Graph Spectral Image Processing

Recent advent of graph signal processing (GSP) has spurred intensive studies of signals that live naturally on irregular data kernels described by graphs (e.g., social networks, wireless sensor networks). Though a digital image contains pixels that reside on a regularly sampled 2D grid, if one can design an appropriate underlying graph connecting pixels with weights that reflect the image structure, then one can interpret the image (or image patch) as a signal on a graph, and apply GSP tools for processing and analysis of the signal in graph spectral domain. In this article, we overview recent graph spectral techniques in GSP specifically for image / video processing. The topics covered include image compression, image restoration, image filtering and image segmentation

A Novel Point Cloud Compression Algorithm for Vehicle Recognition Using Boundary Extraction

Recently, research on the hardware system for generating point cloud data through 3D LiDAR scanning has improved, which has important applications in autonomous driving and 3D reconstruction. However, point cloud data may contain defects such as duplicate points, redundant points, and an unordered mass of points, which put higher demands on the performance of hardware systems for processing data. Simplifying and compressing point cloud data can improve recognition speed in subsequent processes. This paper studies a novel algorithm for identifying vehicles in the environment using 3D LiDAR to obtain point cloud data. The point cloud compression method based on the nearest neighbor point and boundary extraction from octree voxels center points is applied to the point cloud data, followed by the vehicle point cloud identification algorithm based on image mapping for vehicle recognition. The proposed algorithm is tested using the KITTI dataset, and the results show improved accuracy compared to other methods