Content-based commodity retrieval (CCR) faces two major challenges: (1) commodities in real-world scenarios are often captured randomly by users, resulting in significant variations in image backgrounds, poses, shooting angles, and brightness; and (2) many commodities in the CCR dataset have similar appearances but belong to different brands or distinct products within the same brand. We introduce a CCR neural network called CCR-Net, which incorporates both length loss and salient loss. These two losses can operate independently or collaboratively to enhance retrieval quality. CCR-Net offers several advantages, including the ability to (1) minimize data variations in real-world captured images; and (2) differentiate between images containing highly similar but fundamentally distinct commodities, resulting in improved commodity retrieval capabilities. Comprehensive experiments demonstrate that our CCR-Net achieves state-of-the-art performance on the CUB200-2011, Perfect500k, and Stanford Online Products datasets for commodity retrieval tasks.
Research into red, green, blue plus depth salient object detection (SOD) has identified the challenging problem of how to exploit raw depth features and fuse cross-modal (CM) information. To solve this problem, we propose an interactive nonlocal joint learning (INL-JL) network for quality RGB-D SOD. INL-JL benefits from three key components. First, we carry out joint learning to extract common features from RGB and depth images. Second, we adopt simple yet effective CM fusion blocks in lower levels while leveraging the proposed INL blocks in higher levels, aiming to purify the depth features and to make CM fusion more efficient. Third, we utilize a dense multiscale transfer strategy to infer saliency maps. INL-JL advances the state-of-the-art methods on five public datasets, demonstrating its power to promote the quality of RGB-D SOD.
Using deep learning to automatically and quickly extract faults from seismic images is of practical significance. An improved U-Net algorithm is proposed by reducing convolutional layers, designing skip connections, enforcing deep supervision, and improving the loss function and learning rate to build a new model. In the operation, the feature map parameters in the network are further revised, the number of training iterations is increased, a callback function is added, and the parameter adjustment training consumes less time and space and has higher accuracy. Experiments on real public datasets show that the improved network can limit the time required to extract a 128 × 128 × 128 three-dimensional image within 200 ms, which not only requires less time and computing power than existing methods but also has an extraction accuracy as high as 97.6%.
Understanding vasculatures is important for endovascular intervention simulation (EIS). A recent trend attempts to represent vasculatures with three-dimensional (3D) surface meshes by multiview image and graphics-based techniques used in optical and laser scanners. Conversion from image volume data to 3D surface meshes, however, suffers from staircases and noise. Most existing methods in geometric processing often consider mesh smoothing in their independent settings. However, this approach either is ineffective in removing the artifacts or introduces additional types of artifacts (e.g., volume shrinkage and shape distortion). We propose an effective yet vascular-oriented mesh optimization framework. We first employ a weight-adaptive vertex resampling to eliminate staircases; then, we subdivide vascular meshes to denser ones; and finally, we exploit the constrained biquadratic Bezier surface fitting to produce optimized meshes. In EIS, clinicians are interested in the centerline of surface meshes as well as surface meshes. However, centerline extraction from vascular meshes is still challenging. We adopt an existing centerline extraction approach for vascular meshes based on the observation that human blood vessels are generally composed of piecewise cylindrical shapes. Tests on vascular data illustrate that our optimization approach achieves higher quality results regarding surface smoothness compared to previous approaches. In addition, our extraction approach effectively obtains complete centerlines from real data.
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