A computer vision system for oocyte counting using images captured by smartphone

Best practices and current implementation of emerging smartphone-based (bio)sensors – Part 1: Data handling and ethics

“…An RGB image is composed of millions of pixels; each pixel quantifies the red, green, and blue light sampled at the corresponding location (together covering the visible spectrum from approximately 400 nme700 nm) [37]. When many smartphone images are collected (e.g., one per second) and analysed together, these can be used for the real-time monitoring of dynamic processes [67]. Comparatively, an RGB video contains even more data than an image, i.e., up to 60 frames per second (FPS) collected by the smartphone camera [68].…”

“…As highlighted above, low-cost and customizable attachments can convert conventional smartphones into biosensing devices replicating the functions of expensive and inaccessible laboratory equipment. Furthermore, conditions can be semi-controlled by imposing requirements or restrictions on data acquisition, such as requiring a certain distance or viewing angle between the test/ sample and the SbS [33,40,49,67,77]. In contrast, uncontrolled conditions have no specific restrictions for image capture but require additional data processing for normalization and noise removal before result interpretation [31,73].…”

“…Segmentation groups homogeneous regions and keeps any two inhomogeneous adjacent regions as ROIs [106]. In one approach, SbSacquired images were clustered into groups of similar pixels, called superpixels [67]. After clustering, the superpixels were segmented into classes, called light background, dark background, dirt, or oocyte, making it possible to differentiate the target (oocyte) from the other parts of the image.…”

“…Measuring and correcting non-linear sensor response to a linear input to output mapping, e.g., camera response calibration using standard calibration references [94]. Spectral and radiometric calibration of smartphone cameras [43,95] Segmentation Superpixel Grouping of similar pixels to form larger homogeneous regions, known as superpixels [67]; Pixel values in a superpixel are homogenous while it is not in adjacent superpixels [96].…”

Identifying plant species in kettle holes using UAV images and deep learning techniques

“…The attribute extraction can be understood as mathematical operations performed in the abstract binary data of the digital image to group regions with similarities. The chosen attributes for extraction were based on previous approaches (Costa et al, 2019) and improved with the implementation of the K-curvature extraction algorithm (Abu Bakar et al, 2015). The algorithm uses K-means to cluster similar pixels and thereby separate the image into small pieces, that is, superpixels.…”