Videoplethysmography is currently recognized as a promising noninvasive heart rate measurement method advantageous for ubiquitous monitoring of humans in natural living conditions. Although the method is considered for application in several areas including telemedicine, sports and assisted living, its dependence on lighting conditions and camera performance is still not investigated enough. In this paper we report on research of various image acquisition aspects including the lighting spectrum, frame rate and compression. In the experimental part, we recorded five video sequences in various lighting conditions (fluorescent artificial light, dim daylight, infrared light, incandescent light bulb) using a programmable frame rate camera and a pulse oximeter as the reference. For a video sequence-based heart rate measurement we implemented a pulse detection algorithm based on the power spectral density, estimated using Welch’s technique. The results showed that lighting conditions and selected video camera settings including compression and the sampling frequency influence the heart rate detection accuracy. The average heart rate error also varies from 0.35 beats per minute (bpm) for fluorescent light to 6.6 bpm for dim daylight.

Efforts to predict the germination ability of acorns using their shape, length, diameter and density are reported in the literature. These methods, however, are not efficient enough. As such, a visual assessment of the viability of seeds based on the appearance of cross-sections of seeds following their scarification is used. This procedure is more robust but demands significant effort from experienced employees over a short period of time. In this article an automated method of acorn scarification and assessment has been announced. This type of automation requires the specific setup of a machine vision system and application of image processing algorithms for evaluation of sections of seeds in order to predict their viability. In the stage of the analysis of pathological changes, it is important to point out image features that enable efficient classification of seeds in respect of viability. The article shows the results of the binary separation of seeds into two fractions (healthy or spoiled) using average components of regular red-green-blue and perception-based hue-saturation-value colour space. Analysis of accuracy of discrimination was performed on sections of 400 scarified acorns acquired using two various setups: machine vision camera under uncontrolled varying illumination and commodity high-resolution camera under controlled illumination. The accuracy of automatic classification has been compared with predictions completed by experienced professionals. It has been shown that both automatic and manual methods reach an accuracy level of 84%, assuming that the images of the sections are properly normalised. The achieved recognition ratio was higher when referenced to predictions provided by professionals. Results of discrimination by means of Bayes classifier have been also presented as a reference.

Due to technological progress in forestry, seedlings with covered root systems—especially those grown in container nurseries—have become increasingly important in forest nursery production. One the trees that is most commonly grown this way is the common oak (Quercus robur L.). For an acorn to be sown in a container, it is necessary to remove its upper part during mechanical scarification, and evaluate its sowing suitability. At present, this is mainly done manually and by visual assessment. The low effectiveness of this method of acorn preparation has encouraged a search for unconventional solutions. One of them is the use of an automated device that consists of a computer vision-based module. For economic reasons related to the cost of growing seedlings in container nurseries, it is beneficial to minimize the contribution of unhealthy seeds. The maximum accuracy, which is understood as the number of correct seed diagnoses relative to the total number of seeds being assessed, was adopted as a criterion for choosing a separation threshold. According to the method proposed, the intensity and red components of the images of scarified acorns facilitated the best results in terms of the materials examined during the experiment. On average, a 10% inaccuracy of separation was observed. A secondary outcome of the presented research is an evaluation of the ergonomic parameters of the user interface that is attached to the unit controlling the device when it is running in its autonomous operation mode.

The basic principle of silviculture is the rational use of natural regeneration. The acceleration and equalisation of seed germination and an increase of the field seed germination ability are affected by seed scarification, which results in the destruction or weakening of the seed cover. Acorn scarification is performed manually, in the standing position, most often in adapted work stations, whose geometry is adjusted by the staff to their own anthropometric dimensions. An added value of acorn scarification consists in the ability to visually assess the health status of the cotyledons visible on the cross-section, making it possible to infer the potential use of a seed for sowing. However, due to the scope and duration of the activities involved, manual scarification is a process that is monotonous and physically as well as psychologically tiring for its performer. Automating of this process allows for effective replacement of human labour. The results obtained from the use of the vision system designed to determine the length and orientation of acorns may be considered satisfactory. The implementation of the seed orientation detection algorithm using the Harris detector was 90% accurate. Studies and analyses have shown that the process of acorn scarification has a positive effect on the later improvement of uniformity and acceleration of seedling emergence. In the case of seeds subjected to scarification, 83% of the acorns germinated within 4 to 6 weeks after sowing.

In the paper, the method of poseaware silhouette processing is presented. Morphological closing is proposed to enhance segmented silhouette object. The contribution of the work is adaptation of structuring element used for mathematical morphology erosions and dilations. It is proposed to use camera parameters, 3D model of the scene, model of the silhouette and its position to compute structuring element adequate to the individual projected to the camera image. Structuring element computation and basic morphology operators were implemented in OpenCL environment and tested on parallel GPU platform. Comparison with utility software packages is provided and results are briefly discussed.