Shape classification and matching is an important branch of computer vision. It is widely used in image retrieval and target tracking. Shape context method, curvature scale space (CSS) operator and its improvement have been the main algorithms of shape matching and classification. The shape classification network (SCN) algorithm is proposed inspired by LeNet5 basic network structure. Then, the network structure of SCN is introduced and analyzed in detail, and the specific parameters of the network structure are explained. In the experimental part, SCN is used to perform classification tasks on three shape datasets, and the advantages and limitations of our algorithm are analyzed in detail according to the experimental results. SCN performs better than many traditional shape classification algorithms. Accordingly, a practical example is given to show that SCN can save computing resources.

Superpixels group perceptually similar pixels into homogeneous sub-regions that act as meaningful features for advanced tasks. However, there is still a contradiction between color homogeneity and shape regularity in existing algorithms, which hinders their performance in further processing. In this work, a novel Contour Optimized Non-Iterative Clustering (CONIC) method is presented. It incorporates contour prior into the non-iterative clustering framework, aiming to provide a balanced trade-off between segmentation accuracy and visual uniformity. After the conventional grid sampling initialization, a regional inter-seed correlation is first established by the joint color-spatial-contour distance. It then guides a global redistribution of all seeds to modify the number and positions iteratively. This is done to avoid clustering falling into the local optimum and achieve the exact number of user-expectation. During the clustering process, an improved feature distance is elaborated to measure the color similarity that considers contour constraint and prevents the boundary pixels from being wrongly assigned. Consequently, superpixels acquire better visual quality and their boundaries are more consistent with the object contours. Experimental results show that CONIC performs as well as or even better than the state-of-the-art superpixel segmentation algorithms, in terms of both efficiency and segmentation effects.

Background Psychological conditions have been found to be associated with an increased risk of incident benign paroxysmal positional vertigo (BPPV). However, much less is known on whether and how psychological conditions such as anxiety, insomnia and obsessive–compulsive disorder (OCD) affect the recurrence of BPPV. Methods A retrospective cohort study of 2,612 outpatients and inpatients diagnosed with BPPV between September 2012 and August 2020. BPPV recurrence was followed up until February 2021. The Cox proportional hazard regression was used to analyze the association between psychological conditions and the risk of the first recurrence. Poisson regression was applied to analyze the association between psychological conditions and the number of recurrences in patients with at least one relapse. Results During the follow-up, 391 patients had at least one BPPV recurrence. Female BPPV patients were more likely than male patients to experience relapses than male patients, but the characteristics of BPPV recurrence (number of recurrences and duration between recurrences) did not differ between men and women. After adjustment for sex, age and comorbidities, a heightened risk of first BPPV recurrence was found to be associated with anxiety (hazard ratio [HR]: 1.30, 95% confidence interval [CI]: 1.01, 1.68) and OCD (HR: 2.15, 95% CI: 1.31, 3.52). An increased risk of first BPPV recurrence associated with insomnia was only observed in male patients (HR: 2.22, 95% CI: 1.24, 3.98) but not in female patients (HR: 0.91, 95% CI: 0.63, 1.31). None of these psychological conditions were associated with the number of recurrences in patients who experienced recurrence. Conclusions The presence of anxiety and OCD increased the risk of first BPPV recurrence, as well as insomnia for male patients. These psychological conditions were not associated with the number of BPPV recurrences. Diagnosis and treatment of these psychological conditions could be a useful strategy to prevent the recurrence of BPPV.

Superpixels could aggregate pixels with similar properties, thus reducing the number of image primitives for subsequent advanced computer vision tasks. Nevertheless, existing algorithms are not effective enough to tackle computing redundancy and inaccurate segmentation. To this end, an optimized superpixel generation framework termed Boundary Awareness and Content Adaptation (BACA) is presented. Firstly, an adaptive seed sampling method based on content complexity is proposed in the initialization stage. Different from the conventional uniform mesh initialization, it takes content differentiation into consideration to incipiently eliminate the redundancy of seed distribution. In addition to the efficient initialization strategy, this work also leverages contour prior information to strengthen the boundary adherence from whole to part. During the similarity calculation of inspecting the unlabeled pixels in the non-iterative clustering framework, a multi-feature associated measurement is put forward to ameliorate the misclassification of boundary pixels. Experimental results indicate that the two optimizations could generate a synergistic effect. The integrated BACA achieves an outstanding under-segmentation error (3.34%) on the BSD dataset over the state-of-the-art performances with a minimum number of superpixels (345). Furthermore, it is not limited to image segmentation and can be facilitated by remote sensing imaging analysis.

Extraction of the magnetic anomaly signal is one of the difficulties in the magnetic anomaly detection as the weak features extracted are easily disturbed by strong background noise. To address this problem, a sparse feature extraction method based on the tunable Q-factor wavelet transform and overlapping group shrinkage is developed in this paper. Compared with the traditional wavelet transform, the proposed method has excellent characteristics, which can flexibly tune the Q-factor according to the oscillation characteristics of the useful features. In this way, the sparsity of extracted features can be induced more effectively. In addition, the non-convex overlapping group shrinkage can effectively extract weak features from signals with group property, enhancing the extraction accuracy of features. The practical experiment verifies the effectiveness of the proposed method in the magnetic anomaly detection.