Editorial: Current Trends in Image Processing and Pattern Recognition

KC Santosh

Abstract: With this theme, we opened a call for papers on Current Trends in Image Processing & Pattern Recognition that exactly followed 3 rd International Conference on Recent Trends in Image Processing & Pattern Recognition (RTIp2R), 2020 (URL: http://rtip2r-conference.org). Our call was not limited to rtp2r 2020, it was open to all. Altogether, 12 papers were submitted and seven of them were accepted for publication:1) CNNAI: A Convolution Neural Network-Based Latent Fingerprint Matching using Combination of Nearest Neighbor Arrangement Indexing 2) Automatic Detection and Classification of Knee Osteoarthritis using Hu's Invariant In "CNNAI: A Convolution Neural Network-Based Latent Fingerprint Matching using Combination of Nearest Neighbor Arrangement Indexing," authors addressed the use of global fingerprint information like ridge flow, ridge frequency and delta or core points for fingerprint alignment. With a local minutia-based Convolution Neural Network (CNN) matching model named "Combination of Nearest-Neighbor Arrangement Indexing (CNNAI)," on datasets: FVC2004 and NIST SD27 latent fingerprint databases, their highest rank-I identification rate of 84.5% was achieved. Authors claimed that their results can be compared with the state-of-the-art algorithms and their system was robust to rotation and scale.In "Automatic Detection and Classification of Knee Osteoarthritis using Hu's Invariant Moments," authors extracted distinguishing features that were geometrically distorted or transformed by taking Hu's Invariant Moments into account. With this, authors focused on early detection and gradation of Knee Osteoarthritis, and they claimed that their results were validated by ortho surgeons and rheumatologists.In "End-to-end automated latent fingerprint identification with improved DCNN-FFT enhancement," authors ensured the robustness of fingerprint matching against low quality latent fingerprint images, where the importance of minutiae extraction and matching was addressed. Their minutiae extraction results showed significant improvement in precision, recall and F1 scores. We obtained the highest Rank-1 identification rate of 100% for FVC2002/2004 and 84.5% for NIST SD27 fingerprint databases. As claimed by the authors, their results were comparable to state-of-the-art systems.In "Deep Learned Quantization-based Codec for 3D Airborne LiDAR Point Cloud Images," authors introduced a novel deep learned quantization-based coding for 3D Airborne LiDAR (Light detection and ranging) point cloud (PCD) image (DLQCPCD). In their experimental results, authors showed that the proposed model compressed every PCD image into constant 16-bits of data and decompressed the image with approximately 160 dB of PSNR value, 174.46 secs execution time with 0.6 secs execution speed per instruction and proved that it outperforms the other existing algorithms regarding space and time.In "Symptom-Based Identification of G-4 Chili Leaf Diseases Based on Rotation Invariant," authors carefully inspected possible signs of plant leaf diseases. They employed the concept of feature learning and observed the correlation and/or similarity between symptoms that are related to diseases, so their disease identification is possible.In "A Benchmark Environment for Neuromorphic Stereo Vision," authors proposed a benchmark environment to compare multiple algorithms for depth reconstruction from twoevent based sensors. In their evaluation, a stereo matching algorithm was implemented as a testing candidate and multiple experiments with different settings and camera parameters have been carried out. Authors claimed that this work was a foundation for a robust and flexible evaluation of the multitude of new techniques for event-based stereo vision, allowing a meaningful comparison.In "Behavioral Biometric Data Analysis for Gender Classification using Feature Fusion and Machine Learning," authors employed handwritten signature to better understand the behavioral biometric trait for authenticating the documents like letters, contracts, wills, and so on. They used handcrafter features such as LBP and HOG to extract features from 4790 signatures so shallow learning can efficiently be applied. For classification, k-NN, decision tree and Support Vector Machine classifiers were used. They reported promising performance.