Directional difference chain codes with quasi-lossless compression and run-length encoding

Liu, Yongkui; alik, Borut; Wang, Pengjie; Podgorelec, David

doi:10.1016/j.image.2012.07.008

Cited by 15 publications

(11 citation statements)

References 21 publications

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“…Through comparing the performance of descriptors computed for local interest regions, Mikolajczyk also proposed an extension of the SIFT descriptor that outperforms the original method [12]. Liu presented a contour-based representation of binary objects in raster images [13]. For existing studies using chain codes to represent the image signal, the chain code might be too small to reflect micro-features that can involve considerable data [14].…”

Section: Related Workmentioning

confidence: 99%

A Signal-Processing Neural Model Based on Biological Retina

Wei

Wang

Wang³

et al. 2019

Electronics

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Image signal processing has considerable value in artificial intelligence. However, due to the diverse disturbance (e.g., color, noise), the image signal processing, especially the representation of the signal, remains a big challenge. In the human visual system, it has been justified that simple cells in the primary visual cortex are obviously sensitive to vision signals with partial orientation features. In other words, the image signals are extracted and described along the pathway of visual processing. Inspired by this neural mechanism of the primary visual cortex, it is possible to build an image signal-processing model as the neural architecture. In this paper, we presented a method to process the image signal involving a multitude of disturbance. For image signals, we first extracted 4 rivalry pathways via the projection of color. Secondly, we designed an algorithm in which the computing process of the stimulus with partial orientation features can be altered into a process of analytical geometry, resulting in that the signals with orientation features can be extracted and characterized. Finally, through the integration of characterizations from the 4 different rivalry pathways, the image signals can be effectively interpreted and reconstructed. Instead of data-driven methods, the presented approach requires no prior training. With the use of geometric inferences, the method tends to be interpreted and applied in the signal processor. The extraction and integration of rivalry pathways of different colors allow the method to be effective and robust to the signals with the image noise and disturbance of colors. Experimental results showed that the approach can extract and describing the image signal with diverse disturbance. Based on the characterization of the image signal, it is possible to reconstruct signal features which can effectively represent the important information from the original image signal.

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Section: Related Workmentioning

confidence: 99%

A Signal-Processing Neural Model Based on Biological Retina

Wei

Wang

Wang³

et al. 2019

Electronics

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“…To unify the compressions of the chain codes, a Universal Chain Code Compression (UCCC) algorithm has recently been demonstrated [23]. Nunes et al [4] and Liu et al [24] proposed near-lossless approaches, where both used Huffman coding.…”

Section: Memory Requirementsmentioning

confidence: 99%

Unsigned Manhattan chain code

Žalik

Mongus

Liu

et al. 2016

Journal of Visual Communication and Image Representation

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“…Sánchez-Cruz [19] also proposed a newer compression method for F8 by finding repetitive substrings within the chain code. Liu et al [20] have recently proposed another quasi-lossless chain code compression method, which replaces less frequent angular differences (i.e. 135 and 180 ) in DDCC with pairs of more frequent ones.…”

Section: Related Work On Chain Code Compressionmentioning

confidence: 99%