Fractal image compression and recurrent iterated function systems

1996 ◽  
Vol 16 (4) ◽  
pp. 25-33 ◽  
Author(s):  
J.C. Hart
Keyword(s):  
Image Compression ◽  
Iterated Function Systems ◽  
Fractal Image Compression ◽  
Fractal Image ◽  
Iterated Function

Face Detection Based on the Theory of Fractal Compression

2014 ◽  
Vol 945-949 ◽  
pp. 1825-1829
Author(s):  
Qing Sen An ◽  
Yue Bin Chen ◽  
Jing Fan ◽  
Jin Long Wang
Keyword(s):  
Image Compression ◽  
Face Detection ◽  
Iterated Function Systems ◽  
Fractal Image Compression ◽  
Fractal Compression ◽  
Fractal Image ◽  
Iterated Function ◽  
The Face ◽  
Face Similarity

The face detection has been a very important issue, the use of local and global face similarity between faces can be detected. In this paper, based on fractal image compression theory, we construct a local iterated function systems as a description of the face to detect the face.

scholarly journals Partitioned iterated function systems by regression models for head pose estimation

2021 ◽  
Vol 32 (5) ◽  
Author(s):  
Andrea F. Abate ◽  
Paola Barra ◽  
Chiara Pero ◽  
Maurizio Tucci
Keyword(s):  
Pose Estimation ◽  
Regression Models ◽  
Head Rotation ◽  
Iterated Function Systems ◽  
Head Pose Estimation ◽  
Head Pose ◽  
Fractal Image Compression ◽  
Fractal Image ◽  
Iterated Function ◽  
Benchmark Datasets

AbstractHead pose estimation represents an important computer vision technique in different contexts where image acquisition cannot be controlled by an operator, making face recognition of unknown subjects more accurate and efficient. In this work, starting from partitioned iterated function systems to identify the pose, different regression models are adopted to predict the angular value errors (yaw, pitch and roll axes, respectively). This method combines the fractal image compression characteristics, such as self-similar structures in order to identify similar head rotation, with regression analysis prediction. The experimental evaluation is performed on widely used benchmark datasets, i.e., Biwi and AFLW2000, and the results are compared with many existing state-of-the-art methods, demonstrating the robustness of the proposed fusion approach and excellent performance.

Efficient Domain Search for Fractal Image Compression Using Feature Extraction Technique

2012 ◽  
Vol 488-489 ◽  
pp. 1587-1591
Author(s):  
Amol G. Baviskar ◽  
S. S. Pawale
Keyword(s):  
Feature Extraction ◽  
Image Compression ◽  
Low Complexity ◽  
Contractive Mapping ◽  
Fractal Image Compression ◽  
Compression Technique ◽  
Image Encoding ◽  
Fractal Image ◽  
Coding Method ◽  
The Given

Fractal image compression is a lossy compression technique developed in the early 1990s. It makes use of the local self-similarity property existing in an image and finds a contractive mapping affine transformation (fractal transform) T, such that the fixed point of T is close to the given image in a suitable metric. It has generated much interest due to its promise of high compression ratios with good decompression quality. Image encoding based on fractal block-coding method relies on assumption that image redundancy can be efficiently exploited through block-self transformability. It has shown promise in producing high fidelity, resolution independent images. The low complexity of decoding process also suggested use in real time applications. The high encoding time, in combination with patents on technology have unfortunately discouraged results. In this paper, we have proposed efficient domain search technique using feature extraction for the encoding of fractal image which reduces encoding-decoding time and proposed technique improves quality of compressed image.

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