A contour-based approach to binary shape coding using a multiple grid chain code

This paper presents a contour-based approach to efficiently code binary shape information in the context of object-based video coding. This approach meets some of the most important requirements identified for the MPEG-4 standard, notably efficient coding and low delay. The proposed methods support both object-based lossless and quasi-lossless coding modes. For the cases where low delay is a primary requirement, a macroblock-based coding mode is proposed which can take advantage of inter-frame coding to improve the coding efficiency. The approach presented here relies on a grid different from that used for the pixels to represent the shape – the hexagonal grid – which simplifies the task of contour coding. Using this grid, an appraoch based on a differential chain code (DCC) is proposed for the lossless mode while, for the quasi-lossless case, an approach based on the multiple grid chain code (MGCC) principle is proposed. The MGCC combines both contour simplification and contour prediction to reduce the number of bits needed to code the shapes. Results for alpha plane coding of MPEG-4 video test sequences are presented in order to illustrate the performance of the several modes of operation, and a comparison is made with the shape-coding tool chosen by MPEG-4.Peer ReviewedPostprint (published version

Fourier Transform to Detect Pine Seedlings in a Digital Image

Each year, u.s. forest nurseries produce approximately 200 million pine seedlings. Forest companies depend on an adequate number of seedlings in order to replant timber land. To monitor the progress of seedlings, nurseries periodically conduct an inventory. The procedure is performed manually and is based on a statistical estimate. The process is slow, tedious, and imprecise. Automating the inventory procedure is subject of this dissertation. A digital image processing technique to visually count pine seedlings is investigated. The technique is based on a proposed imaging system which resides on a platform behind a tractor. As the system passes over the seedling bed, image sensors capture an overhead view of individual seedlings. A computer analyzes the sensor values in order to detect and count individual seedlings. This dissertation is concerned with developing a computer algorithm. Several test images were obtained. Pertinent seedling features in the images are gray level contrast, lines formed by the needles, and circular distribution of the needles. Four different techniques were investigated in an attempt to use these features to detect pine seedlings. These techniques are gray level peaks geometric intersection of needle lines, gray level contour encoding 1 and a technique based on the Fourier transform.Agricultural Engineerin