BONSAI: 3D object recognition using constrained search

نویسندگان

  • Patrick J. Flynn
  • Anil K. Jain
چکیده

Computer vision systems that identify and localize instances of predefined 3-D models in images offer many potential benefits to industrial and other environments. In many of these areas, solid models of the parts to be recognized already exist, and redesign of the part geometry for vision tasks should be avoided. This paper describes BONSAI, which is a model-based 3-D object recognition system, which identifies and localizes 3-D objects in range images of one or more parts that have been designed on a computer-aided design (CAD) system. Recognition is performed via constrained search of the interpretation tree, using unary and binary constraints (derived automatically from the CAD models) to prune the search space. We focus our attention on the recognition procedure, but we also outline the model-building, image acquisition, and segmentation procedures. Experiments with over 200 images demonstrate that the constrained search approach to 3-D object recognition has comparable accuracy to other existing systems. 1929): a potted plant (as a tree) dwarfed by special methods of culture; also: the art of growing such a plant.-from Webster 's Ninth New Collegiate Dictionary

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

3D Models Recognition in Fourier Domain Using Compression of the Spherical Mesh up to the Models Surface

Representing 3D models in diverse fields have automatically paved the way of storing, indexing, classifying, and retrieving 3D objects. Classification and retrieval of 3D models demand that the 3D models represent in a way to capture the local and global shape specifications of the object. This requires establishing a 3D descriptor or signature that summarizes the pivotal shape properties of th...

متن کامل

A Bayesian Approach to 3D Object Recognition Using Linear Combination of 2D Views

In this work, we introduce a Bayesian approach for pose-invariant recognition of the images of 3d objects modelled by a small number of stored 2d intensity images taken from nearby but otherwise arbitrary viewpoints. A linear combination of views approach is used to combine images from two viewpoints of a 3d object and synthesise novel views of that object. Recognition is performed by matching ...

متن کامل

Constrained Multi-Objective Optimization Problems in Mechanical Engineering Design Using Bees Algorithm

Many real-world search and optimization problems involve inequality and/or equality constraints and are thus posed as constrained optimization problems. In trying to solve constrained optimization problems using classical optimization methods, this paper presents a Multi-Objective Bees Algorithm (MOBA) for solving the multi-objective optimal of mechanical engineering problems design. In the pre...

متن کامل

Resource-efficient Machine Learning in 2 KB RAM for the Internet of Things

This paper develops a novel tree-based algorithm, called Bonsai, for efficient prediction on IoT devices – such as those based on the Arduino Uno board having an 8 bit ATmega328P microcontroller operating at 16 MHz with no native floating point support, 2 KB RAM and 32 KB read-only flash. Bonsai maintains prediction accuracy while minimizing model size and prediction costs by: (a) developing a ...

متن کامل

A Framework for 3D Object Recognition Using the Kernel Constrained Mutual Subspace Method

This paper introduces the kernel constrained mutual subspace method (KCMSM) and provides a new framework for 3D object recognition by applying it to multiple view images. KCMSM is a kernel method for classifying a set of patterns. An input pattern x is mapped into the high-dimensional feature space F via a nonlinear function φ, and the mapped pattern φ(x) is projected onto the kernel generalize...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

عنوان ژورنال:

دوره   شماره 

صفحات  -

تاریخ انتشار 1990