An automated algorithm for the quantification of hCG level in novel fabric-based home pregnancy test kits

We report a new image processing algorithm that extracts quantitative information about the concentration of human chorionic gonadotropin (hCG), an important early pregnancy marker, from commercially available qualitative home pregnancy kits. The algorithm could potentially be ported onto a simple camera based cell phone making it a low-cost, portable point-of-care device as opposed to costly and time consuming clinical labs for accurate quantitative determination of hCG. The algorithm takes the image of the test result as input, classifies and determines the hCG concentration based on the RGB intensities of the test line. The efficacy of the algorithm is demonstrated using control samples on commercially available strips as well as novel fabric based strips designed for this application

直養と春海 : 縣門江戸派の系譜

Packet loss artifacts are perhaps the most commonly occurring distortion type in video streaming applications. We present a perceptually motivated no-reference video quality assessment (NR-VQA) algorithm for assessing the quality of videos subject to IP and wireless distortions. The proposed algorithm is composed of a spatial quality assessment stage, a temporal quality assessment stage, followed by a spatio-temporal pooling stage. Each of these stages are perceptually motivated - the spatial stage is inspired by the sparse representation of natural scenes in the human visual system, the temporal stage is motivated by optical flow statistics, and the pooling stage by the sensitivity of the human visual system of spatio-temporal stimulus. We show that the spatio-temporal pooling results in significantly higher performance relative to the stand-alone performance of the spatial and temporal assessment stages. The performance of the algorithm is shown to be promising on a subset of the LIVE dataset

A Statistical Evaluation of Sparsity-based Distance Measure (SDM) as an Image Quality Assessment Algorithm

Sparsity-based Distance Measure (SDM), a sparse reconstruction-based image similarity measure was recently proposed and shown to have promising applications in image classification, clustering and retrieval. In this paper, we present a statistical evaluation of SDM’s performance as an image qual- ity assessment (IQA) algorithm. This evaluation is carried out on the LIVE image database. We show that the SDM performs fairly in comparison with the state-of-the-art while possessing several attractive properties. Specifically, we demonstrate its robustness to rotation ( 90 o , 180 o ), scaling, and combinations of distortions – properties that are highly desirable of any IQA algorith

The Kuhnel Triangulation of the complex projective plane from the view point of complex crystallography Part II

Automated Face Quality Assessment (FQA) plays a key role in improving face recognition accuracy and increasing computational efficiency. In the context of video, it is very common to acquire multiple face images of a single person. If one were to use all the acquired face images for the recognition task, the computational load for Face Recognition (FR) increases while recognition accuracy decreases due to outliers. This impediment necessitates a strategy to optimally choose the good quality face images from the pool of images in order to improve the performance of the FR algorithm. Toward this end, we propose a FQA algorithm that is based on mimicking the recognition capability of a given FR algorithm using a Convolutional Neural Network (CNN). In this way, we select those face images that are of high quality with respect to the FR algorithm. The proposed algorithm is simple and can be used in conjunction with any FR algorithm. Preliminary results demonstrate that the proposed method is on par with the state-of-the-art FQA methods in improving the performance of FR algorithms in a surveillance scenario

No-reference image quality assessment using statistics of sparse representations

We present a no-reference (NR) image quality assessment (IQA) algorithm that is inspired by the representation of visual scenes in the primary visual cortex of the human visual system. Specifically, we use the sparse coding model of the area V1 to construct an overcomplete dictionary for sparsely representing pristine (undistorted) natural images. First, we empirically demonstrate that the distribution of the sparse representation coefficients of natural images have sharp peaks and heavy tails, and can therefore be modeled using a Univariate Generalized Gaussian Distribution (UGGD). We then show that the UGGD model parameters form good features for distortion estimation and formulate our no-reference IQA algorithm based on this observation. Subsequently, we find UGGD model parameters that are representative of the class of pristine natural images. This is achieved using a training set of undistorted natural images. The perceptual quality of a test image is then defined to be the likelihood of its sparse coefficients being generated from the pristine UGGD model. We show that the proposed algorithm correlates well with subjective evaluation over several standard image databases. Further, the proposed method allows us to construct a distortion map that has several useful applications like distortion localization, adaptive rate allocation etc. Finally and importantly, the proposed NR-IQA algorithm does not make use of any distortion information or subjective scores during the training process

3rd National Conference on Image Processing, Computing, Communication, Networking and Data Analytics

This volume contains contributed articles presented in the conference NCICCNDA 2018, organized by the Department of Computer Science and Engineering, GSSS Institute of Engineering and Technology for Women, Mysore, Karnataka (India) on 28th April 2018