Abstract Semi-automated segmentation algorithms hold promise for improving extraction and identification of objects in images such as tumors in medical images of human tissue, counting plants or flowers for crop yield prediction or other tasks where object numbers and appearance vary from image to image. By blending markup from human annotators to algorithmic classifiers, the accuracy and reproducability of image segmentation can be raised to very high levels. At least, that is the promise of this approach, but the reality is less than clear. In this paper, we review the state-of-the-art in semi-automated image segmentation performance assessment and demonstrate it to be lacking the level of experimental rigour needed to ensure that claims about algorithm accuracy and reproducability can be considered valid. We follow this review with two experiments that vary the type of markup that annotators make on images, either points or strokes, in tightly controlled experimental conditions in order to investigate the effect that this one particular source of variation has on the accuracy of these types of systems. In both experiments, we found that accuracy substantially increases when participants use a stroke-based interaction. In light of these results, the validity of claims about algorithm performance are brought into sharp focus, and we reflect on the need for a far more control on variables for benchmarking the impact of annotators and their context on these types of systems.

Unmanned Aerial Vehicles (UAVs) paired with image detection and segmentation techniques can be used to extract plant phenotype information of individual breeding or research plots. Each plot contains plants of a single genetic line. Breeders are interested in selecting lines with preferred phenotypes (physical traits) that increase crop yield or resilience. Automated detection and segmentation of plots would enable automatic monitoring and quantification of plot phenotypes, allowing a faster selection process that requires much fewer person-hours compared with manual assessment. A detection algorithm based on Laplacian of Gaussian (LoG) blob detection and a segmentation algorithm based on a combination of unsupervised clustering and random walker image segmentation are proposed to detect and segment lentil plots from multi-spectral aerial images. Our algorithm detects and segments lentil plots from normalized difference vegetative index (NDVI) images. The detection algorithm exhibited an average precision and recall of 96.3% and 97.2% respectively. The average Dice similarity coefficient between a detected segmented plot and its ground truth was 0.906.

Lodging in agricultural crops is the permanent displacement of a plant from its upright position [2]. It may be caused by several weather and environmental conditions. Harvesting severely lodged crops may take twice as much time and results in reduced yield. Plant breeders seek to identify and select for lodging-resistant varieties. Currently lodging in plant breeding trials is assessed by manual observation. This can be both time consuming and subjective. The objective of this work is to automate detection of lodging in breeding plots using drone imagery. Texture features were extracted from drone images of wheat and canola breeding plots using Gray Level Co-occurrence Matrix (GLCM), local binary patterns (LBP) and Gabor filters. For each type of feature, a Support Vector Machine (SVM) was trained and used to predict if plots were lodged. The GLCM features exhibited the best performance, achieving 96.0% prediction accuracy for canola and 92.6% accuracy for wheat. Qualitative visualizations were constructed to show spatial distribution across and within plots. This system will be of assistance to breeders for selecting lodgingresistant varieties.

Three-dimensional (3D) reconstruction of neutron tomographic projection images is an important tool for research on animal and plant tissues. Neutron scattering images contain impulsive noise caused by background cosmic gamma radiation that can significantly affect the reconstruction quality. Common de-noising methods are computationally efficient, but may also blur edges in the signal reducing the quality of reconstruction and may require careful parameter selection. Moreover, prior to reconstruction we must correct for rotation axis misalignment during data acquisition and suppress statistical noise due to variations in the neutron source. Currently many of these steps require manual intervention and parameter selection to maximize reconstruction quality. We have developed a more automatic algorithm which performs comparably to the semi-automatic state-of-the-art process.

Archaeologists are currently producing huge numbers of digitized photographs to record and preserve artefact finds. These images are used to identify and categorize artefacts and reason about connections between artefacts and perform outreach to the public. However, finding specific types of images within collections remains a major challenge. Often, the metadata associated with images is sparse or is inconsistent. This makes keyword-based exploratory search difficult, leaving researchers to rely on serendipity and slowing down the research process. We present an image-based retrieval system that addresses this problem for biface artefacts. In order to identify artefact characteristics that need to be captured by image features, we conducted a contextual inquiry study with experts in bifaces. We then devised several descriptors for matching images of bifaces with similar artefacts. We evaluated the performance of these descriptors using measures that specifically look at the differences between the sets of images returned by the search system using different descriptors. Through this nuanced approach, we have provided a comprehensive analysis of the strengths and weaknesses of the different descriptors and identified implications for design in the search systems for archaeology.

This paper proposes a novel colour image enhancement method which uses wavelet-based texture characteristic morphological component analysis (WT-TC-MCA) to enhance the textural differences in the luminance channel of the colour image. The image enhancement method is intended to be the preprocessing method prior to the use of the colour image segmentation. The input colour image is firstly transformed to CIELab colour space to separate the luminance channel from the chromatic channels. Then only the luminance channel is enhanced by the WT-TC-MCA method to enhance the textural differences between different textures. Therefore, the colour image is enhanced with more differentiate textures while preserving the chromatic information. The experimental results show that the proposed method can enhance different colour image segmentation algorithms more than the state-of-the-art colour image enhancement method.

Defocus blur is extremely common in images captured using optical imaging systems. It may be undesirable, but may also be an intentional artistic effect, thus it can either enhance or inhibit our visual perception of the image scene. For tasks, such as image restoration and object recognition, one might want to segment a partially blurred image into blurred and non-blurred regions. In this paper, we propose a sharpness metric based on local binary patterns and a robust segmentation algorithm to separate in- and out-of-focus image regions. The proposed sharpness metric exploits the observation that most local image patches in blurry regions have significantly fewer of certain local binary patterns compared with those in sharp regions. Using this metric together with image matting and multi-scale inference, we obtained high-quality sharpness maps. Tests on hundreds of partially blurred images were used to evaluate our blur segmentation algorithm and six comparator methods. The results show that our algorithm achieves comparative segmentation results with the state of the art and have big speed advantage over the others.

This paper proposes a new texture enhancement method which uses an image decomposition that allows different visual characteristics of textures to be represented by separate components in contrast with previous methods which either enhance texture indirectly or represent all texture information using a single image component. Our method is intended to be used as a preprocessing step prior to the use of texture-based image segmentation algorithms. Our method uses a modification of morphological component analysis (MCA) which allows texture to be separated into multiple morphological components each representing a different visual characteristic of texture. We select four such texture characteristics and propose new dictionaries to extract these components using MCA. We then propose procedures for modifying each texture component and recombining them to produce a texture-enhanced image. We applied our method as a preprocessing step prior to a number of texture-based segmentation methods and compared the accuracy of the results, finding that our method produced results superior to comparator methods for all segmentation algorithms tested. We also demonstrate by example the main mechanism by which our method produces superior results, namely that it causes the clusters of local texture features of each distinct image texture to mutually diverge within the multidimensional feature space to a vastly superior degree versus the comparator enhancement methods.