O.Robert Mitchell

Hemispheric asymmetries during six- to eight- year-olds performance of piagetian conservation and reading tasks☆

Abstract To investigate childrens hemispheric processing, their EEGs were recorded and alpha asymmetries were computed for each task or subtask. Piagetian conservation and reading tasks shifted from greater right-hemispheric processing during encoding of information to greater left-hemispheric processing during retrieval and verbal/logical expression of the information, which suggests interhemispheric processing within these tasks. Conserving responses and high reading scores were related to greater interhemispheric or bilateral processing during information retrieval. It is suggested that Piagetian Conservation tasks are behavioral measurements of interhemispheric integration and that progression through Piagetian stages may parallel cortical myelin development.

Global shape recognition of 3-D objects using a differential library storage

Abstract Shape recognition of three-dimensional rigid objects when viewed by a two-dimensional imaging system is discussed. Fourier descriptor features are used that are invariant to scale, translation, and rotation about the viewing axis. A differential library storage method is developed which allows compact storage of the many shapes generated by viewing an object from different aspect angles. Experimental results are presented using aircraft and normalized Fourier descriptors which show the effectiveness of the differential library storage. An automatic shape learning system is also demonstrated.

Segmentation And Classification Of Targets In Flir Imagery

An approach is described for detecting and classifying tactical targets in FLIR imagery. The basic assumption used for segmenting objects from their background is that the objects to be detected differ from the background in grey level, edge, properties, or texture. Potential targets are selected from a large frame, by locating combinations of grey level, edge, value, and texture that occur infrequently over the entire frame. Once potential objects are obtained, they are segmented from their backgrounds using the identical process as above, except applied on a local level. The segmented objects are classified into three, types of vehicles or into false, alarms. The classification procedure uses features measured on projections made through the segmented objects. Results are shown for 32 test images.

Recent Results in Precision Measurements of Edges, Angles, Areas and Perimeters

This paper presents recent results in precision measurements using computer vision. An edge operator based on two-dimensional spatial moments is used. This operator can locate correctly modeled edges to hundredths of a pixel. This accuracy is unaffected by additive or multiplicative changes to the data values. The precision is achieved by correcting for many of the deterministic errors caused by non-ideal edge profiles using a look-up table to correct the original estimates of edge orientation and location. This table is generated using a synthesized edge which is located at various subpixel locations and various orientations. The edge locator is then used to estimate area and perimeter of imaged objects. Area and perimeter are also measured using averages of estimates from binary images generated by thresholding at many gray values. This method of threshold decomposition is compared to the edge detection methods. The application of these techniques to measurement of imaged machined metal parts is also presented.

Skeletonization And Distance Transformation By Greyscale Morphology

Mathematical morphology applied to image processing which deals directly with shape is a more direct and faster approach to feature measurements than traditional techniques. It has grown to include many applications and architectures in image analysis. Binary morphology has been successfully extended to greyscale morphology which allows a new set of applications. In this paper, the distance transformation, skeletonization, and reconstruction algorithms using the greyscale morphology approach are described and proven to be remarkably simple. The distance transformation of an object is the minimum distance from inner points to the background of an object. The algorithm is a recursive greyscale erosion of the image with a small size structuring element. The distance can be Euclidean, chessboard, or city-block distance which depends on the selection of its structuring element. The skeleton extracted is the Medial Axis Transformation (MAT) which is produced from the result of the distance transformation. The values of the distance transform along the skeleton are maintained to represent distance to the closest boundary. We can easily reconstruct the distance transform from the skeleton by iterative greyscale dilations with the same struc-turing element. In order for this method to be useful for grey level images, a simple adaptive threshold algorithm using greyscale ero-sion with a non-linear structuring element has been developed.21 A decomposition technique which reduces the large size non-linear structuring element into a recursive operation with a small window allows real-time implementation.

Shape Representation And Recognition With Depth Information

Many global shape recognition techniques, such as moments and Fourier Descriptors, are used almost exclusively with two-dimensional images. It would be desirable to extend these global shape recognition concepts to three dimensional images. Specifically, the concepts associated with Fourier Descriptors will be extended to both three dimensional object representation and recognition and the representation and recognition of objects which are described by depth data. With Fourier Descriptors, two dimensional shape boundaries are described in terms of a set of complex sinusoidal basis functions. Extending this concept to three dimensions, the surface of a shape will be described in terms of a set of three .dimensional basis functions. The basis functions which will be used are known as spherical harmonics. Spherical harmonics can be used to describe a function on the surface of the unit sphere. In this application, the function on the unit sphere will describe the shape to be represented. The representation presented here is restricted to the class of objects for which each ray from the origin intersects the surface of the object only once. Basic definitions and properties of spherical harmonics will be discussed. A distance measure for shape discrimination will be derived as a function of the spherical harmonic coefficients for two shapes. The question of representation of objects described by depth data will then be addressed. A functional description for the objects will be introduced, along with methods of normalizing the spherical harmonic coefficients for scale, translation, and orientation so that meaningful library comparisons might be possible. Classification results obtained with a set of simple objects will be discussed.

Global And Partial Shape Discrimination For Computer Vision

The use of Fourier descriptors for rapid recognition of complete shapes is discussed. Also a Fourier-Mellin correlation procedure for recognition of partial shapes is presented.

Use of Moment Preserving Quantizers In Differential Pulse Code Modulation (DPCM) Image Coding

In this paper we present results of a study using a moment preserving (MP) quantizer in predictive (DPCM) coding of images. The MP quantizer is designed such that the quantizer preserves statistical moments of the input and output of the quantizer. This quantizer has previously been shown by the authors to work quite well in a PCM image compression scheme. In this paper we extend the use of MP quantizers to predictive coding. We show that the moment preserving quantizer works quite well at data rates of 1.18 bits/pixel. The quantizer scheme is block adaptive, i.e. the picture to be coded is divided into non-overlapping blocks and from block to block the quantizer parameters are changed. A theoretical analysis is presented whereby necessary and sufficient conditions are derived relating the moment preserving properties of the quantizer to the moment preserving properties of the original image data. The performance of the moment preserving quantizer in DPCM is compared to classical minimum mean square error quantizers. These methods are also compared in the presence of channel errors.