We propose a quantitative analysis method using cell segmentation to evaluate the coating quality of anodic oxide. In order to identify each cell in a titania surface, a boundary region is adaptively detected according to the regional intensity distribution, and a boundary distance map is projected onto the scanning electron microscope (SEM) image of the titania surface. Each cell in the projected image is divided via clustering centered on the local maximum point. The uniformities of the cell distribution, size, and thickness are measured using the angle difference, size difference, and brightness difference between adjacent cells to comprehensively evaluate the quantitative alignment of the titania surface. The proposed method demonstrates the best performance in both cell detection and cell segmentation for SEM images of the titania surface compared with the state-of-the-art methods. The quantitative analysis of the anodic oxide film alignment was verified using color coding, histogram, and quantification of uniformity.

We identify cells in microscopy images with stained nuclei, using the following process: Candidate seeds for nuclei are identified as extrema in a Laplacian-of-Gaussian space, and weak candidates are eliminated from clusters obtained by ellipse fitting; a region of interest for each nucleus is then defined by combining local and global thresholding; and these regions are repeatedly merged and split by modeling the shape of a nucleus and measuring the roughness of the shared boundaries connected nuclei. This method showed superior abilities to detect the nucleus regions and to split the boundaries of connected nuclei. Our experiments show higher scores in comparison with five other techniques in terms of eight evaluation metrics.

Accurate cell segmentation is one of the critical, yet challenging problems in microscopy images due to ambiguous boundaries as well as a wide variation of shapes and sizes of cells. Even though a number of existing methods have achieved decent results for cell segmentation, boundary vagueness between adjoining cells tended to cause generation of perceptually inaccurate segmentation of stained nuclei. We propose a segmentation method of cells based on structural dissimilarity between embodied and imaged cells. From assumption that the shape of the region of adjoining cells follows a 2D Gaussian mixture model, the cell region is divided by an expectation-maximization method. The lowest structural dissimilarity using embodied cells decides on the number of components of the 2D Gaussian mixture model. The region of interest is extracted by implementation of both global and local thresholdings, which performs binarization of the local image with a seed at the center, where the seed is obtained by the maximally stable extremal regions. Our approach presented considerably higher evaluation scores compared with other five existing methods in terms of both accuracies of region of interest (ROI) detection and boundary discrimination.

Existing maximum-margin support vector machines (SVMs) generate a hyperplane which produces the clearest separation between positive and negative feature vectors. These SVMs are effective when datasets are large. However, when few training samples are available, the hyperplane is easily influenced by outliers that are geometrically located in the opposite class. We propose a modified SVM which weights feature vectors to reflect the local density of support vectors and quantifies classification uncertainty in terms of the local classification capability of each training sample. We derive a primal formulation of an SVM that incorporates those modifications, and implement an RC-margin SVM of the simplest form. We evaluated our model on the recognition of handwritten numerals, and obtained higher recognition rate than a standard maximum-margin SVM, a weighted SVM, or an SVM which accounts for classification uncertainty.

Blocking artifacts, commonly introduced during video encoding, are one of the major causes of reduced perceptual video quality. The trade-off between these artifacts and bitrate can be improved by adaptively selecting frames from a set of video copies encoded at different bitrates, prior to actual video encoding. We propose a new direction of constructing mixed bitrate video based on content-based image analysis on each video frame, which was posed as a problem of pre-analysis for the final video encoding step. The proposed method consists of the following steps: First, we define a simple and fast impact metric in order to identify the blocking artifacts in each frame of multiple videos, encoded at different bitrates. Based on the impact metric, we generate a blocking artifact density functions for the available bitrates, on the whole video. Finally, we define and optimize our objective function from the blocking artifact density functions in order to select a bitrate with minimum perceptual blockiness and file size for each frame. We validated our method throughout multiple types of videos, showing improved visual quality for the same file size based on commonly used quality assessment measures, such as MSU blocking, MSU blurring, SSIM, 3SSIM, and stSSIM. The reduction rates of average file size and average blocking artifact were about 4.9% and 8.3% over maximum bitrate encoding, respectively.

Quantitative analysis of nanostructures from scanning electron microscope (SEM) images requires a clear segmentation of grains and their boundaries. This is not provided by active contour models, which also require user guidance. Our automatic technique creates a rough representation of grain boundaries by adaptive thresholding. It then performs raycasting from a rectangular grid of seed points to ensure that the grain shapes are convex, and selects the best result for each grain. The whole process can be repeated several times to improve the segmentation. We present results for images of titanium foil, which show that our approach compares favorably in terms of speed and segmentation quality with four competing techniques.