1. An algorithm for voxelised solids representation using chain codesBlaž Repnik, Libor Váša, Borut Žalik, 2025, izvirni znanstveni članek Opis: The paper introduces a new method to describe the surfaces of voxelised solids. It operates in three stages: a hierarchical linked list of chain code sequences is created first; the linked lists are pruned; and, finally, the content of the data structure is stored. The method uses chain codes from either a three- or nine-symbols alphabet. In the first case, two chain code symbols are needed to access the next face, while, in the second case, this is done by one symbol. The pair of chain codes from the three-symbols alphabet, or the individual symbol from the nine-symbols alphabet are considered as tokens. The sets of tokens are, in both cases, extended by two tokens, indicating the beginning and ending of the list. The method processes solids of any shape, including those containing holes, cavities, or multiple components existing in the same voxel space. Edge-connectivity is permitted. The method was compared against the method proposed by Lemus et al., which is designed for solids without holes. Although supporting a broader set of voxelised solids, the proposed method generates sequences of tokens that are, on average, up to 10% shorter. Since the information entropy of the sequences of tokens produced by the proposed method is also smaller, the obtained sequences are more compressible, as confirmed by applying gzip and bzip2 data compressors.
Ključne besede: voxels, chain code, data structures, pruning, information entropy, data compression
Objavljeno v DKUM: 19.05.2025; Ogledov: 0; Prenosov: 1
 Celotno besedilo (11,28 MB) |
2. Efficient compressed storage and fast reconstruction of large binary images using chain codesDamjan Strnad, Danijel Žlaus, Andrej Nerat, Borut Žalik, 2024, izvirni znanstveni članek Opis: Large binary images are used in many modern applications of image processing. For instance, they serve as inputs or target masks for training machine learning (ML) models in computer vision and image segmentation. Storing large binary images in limited memory and loading them repeatedly on demand, which is common in ML, calls for efficient image encoding and decoding mechanisms. In the paper, we propose an encoding scheme for efficient compressed storage of large binary images based on chain codes, and introduce a new single-pass algorithm for fast parallel reconstruction of raster images from the encoded representation. We use three large real-life binary masks to test the efficiency of the proposed method, which were derived from vector layers of single-class objects – a building cadaster, a woody vegetation landscape feature map, and a road network map. We show that the masks encoded by the proposed method require significantly less storage space than standard lossless compression formats. We further compared the proposed method for mask reconstruction from chain codes with a recent state-of-the-art algorithm, and achieved between and faster reconstruction on test data
Ključne besede: binary mask, machine learning, chain code, binary encoding, bitmap reconstruction
Objavljeno v DKUM: 29.01.2025; Ogledov: 0; Prenosov: 150
Celotno besedilo (1,45 MB) |
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4. An efficient chain code with Huffman codingYong Kui Liu, Borut Žalik, 2005, izvirni znanstveni članek Opis: This paper presents a new chain code based on the eight-direction Freeman code. Each element in the chain is coded as a relative angle difference between it and the previous element. Statistical analysis showed that the probabilities of the Freeman codes differ importantly. Therefore, the Huffman coding was applied. The proposed chain code requires 1.97 bits/code, its chainlength is small, it allows representation of non-closed patterns and is rotationally independent.
Ključne besede: computer science, pattern recognition, chain code, Huffman code, object representations, chain code compression
Objavljeno v DKUM: 01.06.2012; Ogledov: 2051; Prenosov: 111
 Povezava na celotno besedilo |
