Use of digital image analysis for the flower color evaluation in ornamental sunflower

Sunflower (Helianthus annuus L.) is broadly used as an ornamental plant in landscape gardening, but also as a potted plant and a cut flower. Since aesthetic traits, including color, are most important for newly developed ornamental plants, sunflower petal (ray floret) color has a high value for the development of new genotypes and its position on the horticulture market. The most common methodology for the evaluation of sunflower petals is based on UPOV guidelines for sunflower. By the guidelines, the color of sunflower ray florets can be described as yellowish white, light yellow, medium yellow, orange yellow, orange, purple, reddish brown and multicolored. Although there are photographs to define these color categories, the provided material does not give clear information on the color definition, especially the multicolored category. The main obstacle of this methodology is its high subjectivity and necessity of high expertise for evaluators. In order to make the process of evaluation of sunflower petal color more objective, we propose a new methodology that combines image segmentation (pixel-based classification), and UPOV sunflower guidelines for the definition of color groups (classes). Images of six sunflower genotypes (Ring of Fire, CMS1-30, Heliopa, Dwarf, Neoplanta and Pacino Gold) were used in the software analysis. Visual results of this process of image segmentation presented different colors for the examined varieties. This visual presentation serves as a guideline for an evaluator to determine whether there is more than one dominant color in the examined genotypes

Explainable cardiac pathology classification on cine MRI with motion characterization by semi-supervised learning of apparent flow

We propose a method to classify cardiac pathology based on a novel approach to extract image derived features to characterize the shape and motion of the heart. An original semi-supervised learning procedure, which makes efficient use of a large amount of non-segmented images and a small amount of images segmented manually by experts, is developed to generate pixel-wise apparent flow between two time points of a 2D+t cine MRI image sequence. Combining the apparent flow maps and cardiac segmentation masks, we obtain a local apparent flow corresponding to the 2D motion of myocardium and ventricular cavities. This leads to the generation of time series of the radius and thickness of myocardial segments to represent cardiac motion. These time series of motion features are reliable and explainable characteristics of pathological cardiac motion. Furthermore, they are combined with shape-related features to classify cardiac pathologies. Using only nine feature values as input, we propose an explainable, simple and flexible model for pathology classification. On ACDC training set and testing set, the model achieves 95% and 94% respectively as classification accuracy. Its performance is hence comparable to that of the state-of-the-art. Comparison with various other models is performed to outline some advantages of our model

Physical Representation-based Predicate Optimization for a Visual Analytics Database

Querying the content of images, video, and other non-textual data sources requires expensive content extraction methods. Modern extraction techniques are based on deep convolutional neural networks (CNNs) and can classify objects within images with astounding accuracy. Unfortunately, these methods are slow: processing a single image can take about 10 milliseconds on modern GPU-based hardware. As massive video libraries become ubiquitous, running a content-based query over millions of video frames is prohibitive. One promising approach to reduce the runtime cost of queries of visual content is to use a hierarchical model, such as a cascade, where simple cases are handled by an inexpensive classifier. Prior work has sought to design cascades that optimize the computational cost of inference by, for example, using smaller CNNs. However, we observe that there are critical factors besides the inference time that dramatically impact the overall query time. Notably, by treating the physical representation of the input image as part of our query optimization---that is, by including image transforms, such as resolution scaling or color-depth reduction, within the cascade---we can optimize data handling costs and enable drastically more efficient classifier cascades. In this paper, we propose Tahoma, which generates and evaluates many potential classifier cascades that jointly optimize the CNN architecture and input data representation. Our experiments on a subset of ImageNet show that Tahoma's input transformations speed up cascades by up to 35 times. We also find up to a 98x speedup over the ResNet50 classifier with no loss in accuracy, and a 280x speedup if some accuracy is sacrificed.Comment: Camera-ready version of the paper submitted to ICDE 2019, In Proceedings of the 35th IEEE International Conference on Data Engineering (ICDE 2019