Dongping Zhu

A prototype vision system for analyzing CT imagery of hardwood logs

To fully optimize the value of material produced from a hardwood log requires information about type and location of internal defects in the log. This paper describes a prototype vision system that automatically locates and identifies certain classes of defects in hardwood logs. This system uses computer tomograph (CT) imagery. The system uses a number of processing steps. A set of basic features are defined to capture basic 3-D characteristics of wood defects. For 3-D object (defect) recognition, a set of hypothesis tests are employed that use this set of features. To further help cope with the above mentioned variability, the Dempster-Shafer theory of evidential reasoning is used to classify defect objects. Results of preliminary experiments employing two different types of hardwood logs are given.

CT image sequence processing for wood defect recognition

Explores a nondestructive testing application of X-ray computed tomography (CT) in the forest products industry. This application involves a computer vision system that uses CT to locate and identify internal defects in hardwood logs. The knowledge of log defects is critical in deciding whether to veneer or to saw up a log, and how to position a log so that the boards sawn from it will have as much clear face as possible. To apply CT to these problems requires efficient and robust computer vision methods. This paper addresses the issue of efficient image filtering for suppressing unwanted detail in CT log images. In particular, it describes an image filtering method based on a spatial adaptive least squares filter. Simple in structure and efficient in computation, this filter is not based on assumptions about a signal model, but rather on a fixed filtering structure. In conjunction with image segmentation and region growing procedures, the new filter is used in the machine vision system to produce well defined regions that represent areas of potential wood defects.<<ETX>>

CT image sequence analysis for object recognition-a rule based 3-D computer vision system

The authors present a rule-based, three-dimensional (3-D) vision system for locating and identifying wood defects using topological, geometric and statistical attributes. A number of different features can be derived from the 3-D input scenes. These features and evidence functions are used to compute confidence values for object membership in different defect classes. The use of different knowledge sources in a set of independent and concise rules is illustrated.<<ETX>>

NONDESTRUCTIVE EVALUATION OF HARDWOOD LOGS USING AUTOMATED INTERPRETATION OF CT IMAGES

Knowledge of internal defects within hardwood logs can be useful even prior to a log’s entry into the sawmill. It is in the log yard where the first important decisions are made about processing. First, based upon perceived quality, logs may be sorted as veneer logs or as high-quality sawlogs and sold to domestic veneer mills or for export. Second, roundwood may be bucked into smaller logs to isolate defect areas and to obtain sawlogs with longer sections of clear wood. And third, logs containing metal objects can be identified, thereby preventing headrig saw damage and costly mill down-time.

Stochastic field-based object recognition in computer vision

This study explores the application of a stochastic texture modeling method toward a machine vision system for log inspection in the forest products industry. This machine vision system uses computerized tomography (CT) imaging to locate and identify internal defects in hardwood logs. To apply CT to these industrial vision problems requires efficient and robust image analysis methods. The paper addresses one aspect of the problem of creating such a computer vision system, i.e., the issue of statistical image texture modeling for wood defect recognition using a stochastic field-based approach. In particular, it describes a parametric model-based method for studying the spatial stochastic processes -- wood grain textures, with each grain texture being modeled by a parametric random field model. A robust algorithm for parameter estimation is applied to obtain model parameters for individual defects occurring inside a log. By making use of the estimated model features, a simple minimum distance classifier is constructed to classify an unknown defect into one of the prototypical defects. Experimental results of the proposed method with CT images from red oaks are given to show the efficacy of the proposed approach.

CT image processing for hardwood log inspection

This study explores the application of digital image processing techniques to a machine vision system for log inspection in the forest products industry. This machine vision system uses the computerized tomography (CT) imaging to locate and identify internal defects in hardwood logs. To apply CT to these industrial vision problems requires efficient and robust image processing methods. Several image processing techniques are addressed in this paper: adaptive image smoothing, multi-threshold-based segmentation, morphological filtering, and 3-D connectiveness labeling. Experimental results of these image processing techniques with CT images from two different wood species demonstrate the efficacy of the inspection system.

Three-dimensional CT image segmentation by volume growing

The research reported in this paper is aimed at locating, identifying, and quantifying internal (anatomical or physiological) structures, by 3-D image segmentation. Computerized tomography (CT) images of an object are first processed on a slice-by-slice basis, generating a stack of image slices that have been smoothed and pre-segmented. The image smoothing operation is executed by a spatially adaptive filter, and the 2-D pre-segmentation is achieved by a thresholding process whereby each individual pixel in the input image space is consistently assigned a label, according to its CT number, i.e., the gray-level value. Given a sequence of pre-segmented images as 3-D input scene (a stack of image slices), the spatial connectivity that exists among neighboring image pixels is utilized in a volume growing process which generates a number of well-defined volumetric regions or image solides, each representing an individual anatomical or physiological structure in the input scene. The 3-D segmentation is implemented using a volume growing process so that the aspect of pixel spatial connectivity is incorporated into the image segmentation procedure. To initialize the volume growing process for each volumetric region in the input 3-D scene, a seed location for a region is defined and loaded into a queue data structure called seed queue. The volume growing process consists of a set of procedures that perform different operations on the volumetric data of a CT image sequence. Examples of experiment of the described system with CT image data of several hardwood logs are given to demonstrate usefulness and flexibility of this approach. This allows solutions to industrial web inspection, as well as to several problems in medical image analysis where low-level image segmentation plays an important role toward successful image interpretation tasks.

Three-dimensional image segmentation and recognition in an intelligent vision system

Research is underway to apply computerized tomography (CT) imaging to hardwood log inspection in the forest products industry. For this purpose, an intelligent vision system is being created that is aimed at locating, identifying, and quantifying the internal defects inside logs by analyzing their CT image data. This inspection system is designed to be wood species independent. It is composed of three components: a CT scanner-based data acquisition system; a low-level module for image segmentation; and a high-level module for defect recognition. Defect quantification is attained by computing the volume and orientation of each defect. This paper discusses the problems of segmenting CT image sequence and 3-D object detection by a rule-based expert system approach. Experimental results with real-world images of different hardwood log species are provided to show the usefulness, efficacy, and robustness of the proposed inspection system. This allows solutions to hardwood log inspection, as well as to problems in other nondestructive testing applications where image analysis plays an important role.