Jump point detection using empirical mode decomposition.

Real estate is an important form of investment in Hong Kong. Recent researches on the analysis of real estate market have revealed that jump points in the housing price time series play an essential role in the Hong Kong economy. Detecting such jump points thus becomes important as they represent vital findings that enable policy-makers and investors to look forward.

Multi scale and slice-based approach for automatic spine detection.

Dendritic spines play an essential role in the central nervous system. Recent experiments have revealed that neuron functional properties are highly correlated with the statistical and morphological changes of the dendritic spines. In this paper, we propose a new multi scale approach for detecting dendritic spines in a 2D Maximum Intensity Projection (MIP) image of the 3D neuron data stacks collected from a 2-photon laser scanning confocal microscope.

A novel and effective multistage classification system for microscopic starch grain images.

This article presents a novel and effective multistage system for classifying Chinese Materia Medica microscopic starch grain images. The proposed classification system is constructed based on the Gaussian mixture model-based clustering, the feature assignment algorithm, and the similarity measurement. Several features for each starch grain image are extracted and every class of drug is represented by a set of characteristic features.

Identification of starch grains in microscopic images based on granulometric operations.

This study demonstrates the feasibility of feature extraction and similarity measurement for the identification of starch grains, which are often found in microscopic images of Chinese Materia Medica (CMM) and as such is an important feature for use in the authentication of CMM with abundance of starch grains and otherwise without other special microscopic characteristics. Our proposed image signature, namely, the size distribution, indicates that it is effective to characterize starch grains in microscopic images of those CMM. Experimental results in a small scale study show that the recognition rate for the proposed signature using different measures of discrepancy is around 90%, which is considered very good while the benchmark wavelet-based energy signature has only about 60% recognition.

A fast and effective model for wavelet subband histograms and its application in texture image retrieval.

This paper presents a novel, effective, and efficient characterization of wavelet subbands by bit-plane extractions. Each bit plane is associated with a probability that represents the frequency of 1-bit occurrence, and the concatenation of all the bit-plane probabilities forms our new image signature. Such a signature can be extracted directly from the code-block code-stream, rather than from the de-quantized wavelet coefficients, making our method particularly adaptable for image retrieval in the compression domain such as JPEG2000 format images.