With the rapid growth of industries like e-commerce, food delivery, and ride-hailing, research on the Vehicle Routing Problem (VRP) is becoming increasingly relevant. However, most of the research in the field of VRP is based on static delivery tasks. In such tasks, the information about customer and order requests is provided before the delivery vehicle departs from the depot, and it remains constant during delivery. However, in the real world, the delivery tasks are often dynamic, where only some orders are known before the vehicle departs from the depot, and the rest of the orders are disclosed over time [1].

Detecting cells in blood smear images is of great significance for automatic diagnosis of blood diseases. However, this task is rather challenging, mainly because there are dense cells that are often overlapping, making some of the occluded boundary parts invisible. In this paper, we propose a generic and effective detection framework that exploits non-overlapping regions (NOR) for providing discriminative and confident information to compensate the intensity deficiency. In particular, we propose a feature masking (FM) to exploit the NOR mask generated from the original annotation information, which can guide the network to extract NOR features as supplementary information. Furthermore, we exploit NOR features to directly predict the NOR bounding boxes (NOR BBoxes). NOR BBoxes are combined with the original BBoxes for generating one-to-one corresponding BBox-pairs that are used for further improving the detection performance. Different from the non-maximum suppression (NMS), our proposed non-overlapping regions NMS (NOR-NMS) uses the NOR BBoxes in the BBox-pairs to calculate intersection over union (IoU) for suppressing redundant BBoxes, and consequently retains the corresponding original BBoxes, circumventing the dilemma of NMS. We conducted extensive experiments on two publicly available datasets, with positive results demonstrating the effectiveness of the proposed method against existing methods.

We present a novel deep learning model equipped with a new region-aware global context modeling technique for automatic nerve segmentation from ultrasound images, which is a challenging task due to (1) the large variation and blurred boundaries of targets, (2) the large amount of speckle noise in ultrasound images, and (3) the inherent real-time requirement of this task. It is essential to efficiently capture long-range dependencies by global context modeling for a segmentation network to overcome these challenges. Traditional global context modeling techniques usually explore pixel-aware correlations to establish long-range dependencies, which are usually computation-intensive and greatly degrade time performance. In addition, in this application, pixel-aware modeling may inevitably introduce much speckle noise in the computation and potentially degrade segmentation performance. In this paper, we propose a novel region-aware modeling technique to establish long-range dependencies based on different regions to improve segmentation accuracy while maintaining real-time performance; we call it region-aware pyramid aggregation (RPA) module. In order to adaptively divide the feature maps into a set of semantic-independent regions, we develop an attention mechanism and integrate it into the spatial pyramid network to evaluate the semantic similarity of different regions. We further develop an adaptive pyramid fusion (APF) module to dynamically fuse the multi-level features generated from the decoder to refining the segmentation results. We conducted extensive experiments on a famous public ultrasound nerve image segmentation dataset. Experimental results demonstrate that our method consistently outperforms our rivals in terms of segmentation accuracy. The code is available at https://github.com/jsonliu-szu/RAGCM.

Fluorescence in situ hybridization (FISH) is a well-recognized technique for evaluating the human epidermal growth factor receptor 2 (HER2) gene status in breast cancer, where HER2 genes are seen as the red stained spots, and the chromosome centromere (CEN17) on which HER2 is located are visible as the green stained spots under a fluorescent microscope. The traditional manual analysis of FISH images is low efficient and has great subjective differences. Many researches focus on automatically calculating the average ratio of red/green spots in a cell and have achieved good performance. However, there are still a need to improve the segmentation accuracy of cells and HER2/CEN17 signals in FISH images with poor quality. In this paper, we propose a robust approach to locate HER2 and CEN17 signals based on the image standardization of varied FISH images. First we propose to use offset operation to reduce the fluorescence scattering, and to use exposure ratio map estimation and brightness transformation to enhance the weak fluorescence. Then we detect red/green spots by sequentially using top-hat filtering, thresholding and post-processing operations. The experimental results have shown that our proposed method can work well for the clinical FISH images with different quality.

In multi-touch interactive systems, it is of great significance to distinguish which hand of the user is touching the surface in real time. Left-right hand distinction is essential for recognizing the multi-finger gestures and further fully exploring the potential of bimanual interaction. However, left-right hand distinction is beyond the capability of most existing multi-touch systems. In this paper, we present a new method for left and right hand distinction based on the human anatomy, work area, finger orientation and finger position. Considering the ergonomics principles of gesture designing, the body-forearm triangle model was proposed. Furthermore, a heuristic algorithm was introduced to group multi-touch contact points and then made left-right hand distinction. A dataset of 2880 images has been set up to evaluate the proposed left-right hand distinction method. The experimental results demonstrate that our method can guarantee the high recognition accuracy and real time performance in freely bimanual multi-touch interactions.