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This paper presents a novel feature based multi-resolution framework for hand detection. In the algorithm, Histogram of Oriented Gradient (HOG) is used for basic feature representation. To avoid the time consuming down-sampling procedure, features of increasing resolutions are directly extracted from the proposed Gradient-Orientation Image (GOI) under decreasing scales. For efficient detection, cascade is trained by letting the high stage use more discriminative high resolution features. The earlier stages can reject a large quantity of negatives by just using computational cheap low resolution features, and the later stages will carefully diagnose a small number of remaining candidate regions using powerful and expensive high resolution features. Extensive experiments are implemented to demonstrate its improvement and efficiency under complicate scenarios.

This paper represents a hand posture recognition method that combines both skin and shape cues. In the algorithm, implicit skin image is firstly computed to suppress the background disturbances as well as to enhance the hand region; and vanishing component analysis (VCA) algorithm is applied to each posture category to learn a group of Approximate Vanishing Ideal Generators (AVIGs) which are used for the follow-up feature extraction. Each generator is essentially an effective characterization for geometrical structure of corresponding hand posture. In recognition phase, features acquired from implicit skin image and AVIGs are inputted to a soft-max model for classification. A dataset comprising 5 hand postures are constructed for its evaluation. The proposed algorithm is demonstrated to be robust to complex environment and challenging illuminations.Index Terms-hand posture recognition, approximate vanishing ideal generators, softmax classification.

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Sliding-window based multiclass hand posture detections are often performed by detecting postures of each predefined category using an independent detector, which makes it lack efficiency and results in high postures confusion rates in real-time applications. To tackle such problems, in this work, an efficient cascade detector that integrates multiple softmax-based binary (SftB) models and a softmax-based multiclass (SftM) model is investigated to perform multiclass posture detection in parallel. The SftB models are used to distinguish the predefined postures from the background regions, and the SftM model is applied to discriminate among all the predefined hand posture categories. Another usage of the cascade structure is that it could effectively decompose the complexity of background pattern space and therefore improve the detection accuracy. In addition, to balance the detection accuracy and efficiency, the HOG features of increasing resolutions will be adopted by classifiers of increasing stage-levels in the cascade structure. The experiments are implemented under various scenarios with complicated background and challenging lightings. Results show the superiority of the proposed SftB classifiers over the traditional binary classifiers such as logistic regression, as well as the accuracy and efficiency improvements brought by the softmax-based cascade architecture compared with the noncascade multiclass softmax detectors.

Background Kabuki syndrome (KS) is a rare congenital condition with cardinal manifestations of typical facial features, developmental delays, skeletal anomalies, abnormal dermatoglyphic presentations, and mild to moderate intellectual disability. Pathogenic variants in two epigenetic modifier genes, KMT2D and KDM6A, are responsible for KS1 and KS2, respectively. Case presentation A Chinese girl had persistent neonatal hypoglycemia and Dandy-Walker variant. Whole-exome sequencing identified a novel single nucleotide deletion in KMT2D (NM_003482.3 c.12165del p.(Glu4056Serfs*10)) that caused frameshift and premature termination. The mutation was de novo. According to the American College of Medical Genetics and Genomics (ACMG) guidelines, this variant is considered pathogenic. The patient was diagnosed with KS by molecular testing. Conclusion A single novel mutation in KMT2D was identified in a KS patients with hypoglycemia and Dandy-Walker variant in the neonatal stage. A molecular test was conducted to diagnose KS at an early stage.

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In this paper, a novel method of conformal parameterization for triangular meshes is presented. Firstly, based on geodesic on a mesh, an algorithm constructing local barycentric coordinates is proposed. Then, these local coordinates are merged via a linear system to form a global conformal parameterization of the mesh. The conformal mesh parameterization method here can be viewed as a development of the shape-preserving method proposed by M. S. Floater. It avoids error of locally approximating the so-called geodesic polar mapping and hence giving better results. Experimental results are given to illustrate the effectiveness of proposed methods.