Low-Resolution Image Enhancement Assessment

This study aims to address the problem with unrecognisable subject of low-quality images taken from standard resolution web cameras. These images may contain pixelated details, too much noise, and imbalance brightness and contrast. The authors used three algorithms such as Fuzzy Filter Based on Fuzzy Logic for noise reduction, Image Illumination based on Tone Mapping for uneven illumination and Super Resolution Algorithm to reconstruct the facial features of the low-resolution images. After undergoing experiment, results showed that the most acceptable filtering technique among three algorithms is Filtering Fuzzy Filter Based on Fuzzy Logic, Image Illumination Correction based on Tone Mapping for image illumination and with .60-.15-.15 Face Hallucination Super Resolution Parameter significantly improved the quality of face images taken from a low-resolution web camera. Also, results showed that high-resolution versions of low-resolution inputs significantly helped the reconstruction of facial features of low-resolution inputs. 86.67% improvement was recorded from the test images after the processing of images. Thus, the authors concluded that using the combination significantly improved the unprocessed images

Fruit Recognition Using Surface and Geometric Information

One of the interesting topics in image processing and computer vision is Fruit Recognition. The computer vision strategies used to recognise fruits rely on four basic features which are colour, texture, size and shape. In fruit recognition, unrecognised fruit images are caused by different factors. These factors are different illuminations, specular reflections, and different poses of each fruit, variability on the number of elements, and cropping or occlusions. This paper proposes and aims an efficient and effective way to recognise fruits regardless of the said factors by combining the four basic features of the fruit. Fruit recognition involves different processes which are pre-processing, feature extraction, recognition and testing. The recognition is done using the K-Nearest Neighbor based on statistical values of the colour moments, Gray Level Cooccurrence Matrix (GLCM) features, area by pixels for the size and shape roundness. The fruit images comprised of 2633 fruit images from 15 different kinds of fruits. The authors tested different classifiers which are KNN, Naïve Bayes, Decision Tree, and bagging to know what best fits for the images. After testing the classifiers based on the 2633 images, results showed that KNN outperformed the other classifiers. The result showed that combining all the features namely colour, texture, size and shape, the overall recognition rate for all classifiers has increased and it has shown the best output