Close The Journal of Pattern Recognition Research (JPRR) provides an international forum for the electronic publication of high-quality research and industrial experience articles in all areas of pattern recognition, machine learning, and artificial intelligence. JPRR is committed to rigorous yet rapid reviewing. Final versions are published electronically (ISSN 1558-884X) immediately upon acceptance. Submit Manuscript Pattern Similarity Score Based on One Dimensional Time Series Analysis Paawan Sharma, Mukul Kumar Gupta, Amit Kumar Mondal, Vivek Kaundal JPRR Vol 12, No 1 (2017); doi:10.13176/11.744

The Journal of Pattern Recognition Research (JPRR) provides an open access forum for the publication of research articles in areas of pattern recognition, machine learning, artificial intelligence, computational algorithms, and fuzzy learning

Close The Journal of Pattern Recognition Research (JPRR) provides an international forum for the electronic publication of high-quality research and industrial experience articles in all areas of pattern recognition, machine learning, and artificial intelligence. JPRR is committed to rigorous yet rapid reviewing. Final versions are published electronically (ISSN 1558-884X) immediately upon acceptance. Submit Manuscript Application of Approximate Equality for Reduction of Feature Vector Dimension Kumar Sankar Ray, Srikanta Kolay JPRR Vol 11, No 1 (2016); doi:10.13176/11.639

Fuzzy C-Means With Local Membership Based Weighted Pixel Distance and KL Divergence for Image Segmentation

Ecient representation of the background texture in video image frames, motivates compression strategies based on good perceptual reconstruction quality, instead of just bit-accurate reconstruction. This is especially true for video image frames in applications such as videos with structural patterns, and Bi-Directional Reectance Distribution Function (BRDF) image frames of an object, where different images of an object in a single pose are taken in dierent illumination conditions. This paper investigates a new approach for an ecient representation of a class of images from textured videos and dierent

The Journal of Pattern Recognition Research (JPRR) provides an open access forum for the publication of research articles in areas of pattern recognition, machine learning, artificial intelligence, computational algorithms, and fuzzy learning

Close The Journal of Pattern Recognition Research (JPRR) provides an international forum for the electronic publication of high-quality research and industrial experience articles in all areas of pattern recognition, machine learning, and artificial intelligence. JPRR is committed to rigorous yet rapid reviewing. Final versions are published electronically (ISSN 1558-884X) immediately upon acceptance. Submit Manuscript An Image Visual Quality Assessment Method Based on SIFT Features Guangyi Chen, Stephane Coulombe JPRR Vol 8, No 1 (2013); doi:10.13176/11.511

The Journal of Pattern Recognition Research (JPRR) provides an open access forum for the publication of research articles in areas of pattern recognition, machine learning, artificial intelligence, computational algorithms, and fuzzy learning

As many other inverse problems, single-frame image super-resolution is an ill-posed problem. The problem has been approached in the context of machine learning. However, the proposed method in this paper is different from other learning based methods regarding how the input/output are formulated as well as how the learning is done. The assumption behind example based methods is the local similarity across seemingly different images. The assumption is illustrated by examples of image coding. Because of the differences in formulating the input/output and the implementation of Support Vector Regression (SVR), it is shown that the proposed approach outperforms the competing SVR method and the kernel regression method in terms of Peak Signal-to-Noise Ratio (PSNR), objective measurements of image quality. Since example based approaches are based on training, in which we know exactly what the output shall be. Therefore, it is proper to objectively measure the performance since the trained model is expected to “correctly” restore the image rather than to enhance the image, e.g. sharpening.