The recognition of fine-grained objects is crucial for future remote sensing applications, but this task faced with the few-shot problem due to limited labeled data. In addition, existing learning methods do not consider unique characteristics objects, i.e., complex backgrounds and difficulty extracting features, leading suboptimal performance. study, we developed an improved sampling strategy ...

Most animals use multiple sensory modalities to obtain information about objects in their environment. There is a clear adaptive advantage to being able to recognize objects cross-modally and spontaneously (without prior training with the sense being tested) as this increases the flexibility of a multisensory system, allowing an animal to perceive its world more accurately and react to environm...

Stroke is a major cause of disability and health care expenditure around the world. Existing stroke rehabilitation methods can be effective but are costly and need to be improved. Even modest improvements in the effectiveness of rehabilitation techniques could produce large benefits in terms of quality of life. The work reported here is part of an ongoing effort to integrate virtual reality and...

Image segmentation is to partition an image into meaningful regions with respect to a particular application. Object recognition is the task of finding a given object in an image or video sequence. This paper discusses the interaction between image segmentation and object recognition in the framework of the Expectation-Maximization (EM) algorithm. Threshold is image processing technique for con...

There is good evidence that the medial prefrontal cortex (mPFC) is involved in different aspects of recognition memory. However, the mPFC is a heterogeneous structure, and the contribution of the prelimbic (PL) and infralimbic (IL) cortices to recognition memory has not been investigated. Similarly, the role of different neuromodulators within the mPFC in these processes is poorly understood. T...

3D object recognition under partial object viewing is a difficult pattern recognition task. In this paper, we introduce a neural-network solution that is robust to partial viewing of objects and noise corruption. This method directly utilizes the acquired 3D data and requires no feature extraction. The object is first parametrically represented by a continuous distance transform neural network ...