Graph Clustering, Variational Image Segmentation Methods and Hough Transform Scale Detection for Object Measurement in Images

© 2016, Springer Science+Business Media New York. We consider the problem of scale detection in images where a region of interest is present together with a measurement tool (e.g. a ruler). For the segmentation part, we focus on the graph-based method presented in Bertozzi and Flenner (Multiscale Model Simul 10(3):1090–1118, 2012) which reinterprets classical continuous Ginzburg–Landau minimisation models in a totally discrete framework. To overcome the numerical difficulties due to the large size of the images considered, we use matrix completion and splitting techniques. The scale on the measurement tool is detected via a Hough transform-based algorithm. The method is then applied to some measurement tasks arising in real-world applications such as zoology, medicine and archaeology

Action recognition of insects using spectral clustering

We propose a technique to recognize actions of grasshoppers based on spectral clustering. We track the object in 3D and construct features using 3D object movement in segments of video which discriminate between different classes of actions. We sample from these feature vectors and compute the eigenvalues and eigenvectors of affinity or similarity matrix. Then, we perform K-means algorithm to cluster component from each of dominant eigenvectors of the affinity matrix. These dominant eigenvectors are embedding coordinate of video segments in our embedding space. We experimented with our method on a noisy track of one insect to validate our approach.