Cumulative Object Categorization in Clutter
نویسندگان
چکیده
In this paper we present an approach based on sceneor part-graphs for geometrically categorizing touching and occluded objects. We use additive RGBD feature descriptors and hashing of graph configuration parameters for describing the spatial arrangement of constituent parts. The presented experiments quantify that this method outperforms our earlier part-voting and sliding window classification. We evaluated our approach on cluttered scenes, and by using a 3D dataset containing over 15000 Kinect scans of over 100 objects which were grouped into general geometric categories. Additionally, color, geometric, and combined features were compared for categorization tasks.
منابع مشابه
Context based object categorization: A critical survey
Please cite this article in press as: C. Galleguillos doi:10.1016/j.cviu.2010.02.004 The goal of object categorization is to locate and identify instances of an object category within an image. Recognizing an object in an image is difficult when images include occlusion, poor quality, noise or background clutter, and this task becomes even more challenging when many objects are present in the s...
متن کاملVisual Categorization with Bags of Keypoints
We present a novel method for generic visual categorization: the problem of identifying the object content of natural images while generalizing across variations inherent to the object class. This bag of keypoints method is based on vector quantization of affine invariant descriptors of image patches. We propose and compare two alternative implementations using different classifiers: Naïve Baye...
متن کاملFinding Optimal Combination of Kernels using Genetic Programming
In Computer Vision, problem of identifying or classifying the objects present in an image is called Object Categorization. It is a challenging problem, especially when the images have clutter background, occlusions or different lighting conditions. Many vision features have been proposed which aid object categorization even in such adverse conditions. Past research has shown that, employing mul...
متن کاملRobust Object Categorization and Segmentation Motivated by Visual Contexts in the Human Visual System
Categorizing visual elements is fundamentally important for autonomous mobile robots to get intelligence such as novel object learning and topological place recognition. The main difficulties of visual categorization are two folds: large internal and external variations caused by surface markings and background clutters, respectively. In this paper, we present a new object categorization method...
متن کاملCategorization via Agglomerative Correspondence Clustering
This paper presents computationally efficient object detection, matching and categorization via Agglomerative Correspondence Clustering (ACC). We implement ACC for feature correspondence and object-based image matching exploiting both photometric similarity and geometric consistency from local invariant features. Objectbased image matching is formulated here as an unsupervised multi-class clust...
متن کامل