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This paper presents a novel approach for both fast and accurately retrieving similar shapes. A hierarchical string cuts (HSC) method is proposed to partition a shape into multiple level curve segments of different lengths from a point moving around the contour to describe the shape gradually and completely from the global information to the finest details. At each hierarchical level, the curve segments are cut by strings to extract features that characterize the geometric and distribution properties in that particular level of details. The translation, rotation, scale and mirror invariant HSC descriptor enables a fast metric based matching to achieve the desired high accuracy. Encouraging experimental results on four databases demonstrated that the proposed method can consistently achieve higher (or similar) retrieval accuracies than the state-of-the-art benchmarks with a more than 120 times faster speed. This may suggest a new way of developing shape retrieval techniques in which a high accuracy can be achieved by a fast metric matching algorithm without using the time-consuming correspondence optimisation strategy.

In the field of connectomics, neuroscientists seek to identify cortical connectivity comprehensively. Neuronal boundary detection from the Electron Microscopy (EM) images is often done to assist the automatic reconstruction of neuronal circuit. But the segmentation of EM images is a challenging problem, as it requires the detector to be able to detect both filament-like thin and blob-like thick membrane, while suppressing the ambiguous intracellular structure. In this paper, we propose multi-stage multi-recursiveinput fully convolutional networks to address this problem. The multiple recursive inputs for one stage, i.e., the multiple side outputs with different receptive field sizes learned from the lower stage, provide multi-scale contextual boundary information for the consecutive learning. This design is biologically-plausible, as it likes a human visual system to compare different possible segmentation solutions to address the ambiguous boundary issue. Our multi-stage networks are trained end-to-end. It achieves promising results on two public available EM segmentation datasets, the mouse piriform cortex dataset and the ISBI 2012 EM dataset.

In this paper, a novel hyperspectral anomaly detector based on low-rank representation (LRR) and learned dictionary (LD) has been proposed. This method assumes that a two-dimensional matrix transformed from a three-dimensional hyperspectral imagery can be decomposed into two parts: a low rank matrix representing the background and a sparse matrix standing for the anomalies. The direct application of LRR model is sensitive to a tradeoff parameter that balances the two parts. To mitigate this problem, a learned dictionary is introduced into the decomposition process. The dictionary is learned from the whole image with a random selection process and therefore can be viewed as the spectra of the background only. It also requires a less computational cost with the learned dictionary. The statistic characteristic of the sparse matrix allows the application of basic anomaly detection method to obtain detection results. Experimental results demonstrate that, compared to other anomaly detection methods, the proposed method based on LRR and LD shows its robustness and has a satisfactory anomaly detection result. are significantly different from their neighbors. These two main features are widely utilized for AD. The Reed-Xiaoli (RX) algorithm [8], as the benchmark AD method, assumes that the background follows a multivariate normal distribution. Based on this assumption, the Mahalanobis distance between the spectrum of the pixel under test (PUT) and its background samples is used to retrieve the detection result. Two versions named global RX (GRX) and local RX (LRX), which estimate the global and local background statistics (i.e., mean and covariance matrix), respectively, have been studied. However, the performance of RX is highly related to the accuracy of the estimated covariance matrix of background. Derived from the RX algorithm, many other modified methods have been proposed [9,10]. To list, kernel strategy was introduced into the RX method to tackle non-linear AD problem [11,12]; weight RX and a random-selection-based anomaly detector were developed to reduce target contamination problem [13,14]; the effect of windows was also discussed [15,16]; and sub-pixel anomaly detection problem was targeted [17,18]. Generally speaking, two major problems exist in the RX and its modified algorithms: (1) in most cases, the normal distribution does not hold in real hyperspectral data; and (2) backgrounds are sometimes contaminated with the signal of anomalies.To avoid obtaining accurate covariance matrix of background, cluster based detector [19], support vector description detector (SVDD) [20,21], graph pixel selection based detector [22], two-dimensional crossing-based anomaly detector (2DCAD) [23], and subspaces based detector [24] were proposed. Meanwhile, sparse representation (SR), first proposed in the field of classification [25,26], was introduced to tackle supervised target detection [27]. In the theory of SR, spectrum of PUT can be sparsely represented by an over-complete dictionary consisting of background spectra. L...