Continual Learning Strategy in One-Stage Object Detection Framework Based on Experience Replay for Autonomous Driving Vehicle.

Object detection is an important aspect for autonomous driving vehicles (ADV), which may comprise of a machine learning model that detects a range of classes. As the deployment of ADV widens globally, the variety of objects to be detected may increase beyond the designated range of classes. Continual learning for object detection essentially ensure a robust adaptation of a model to detect additional classes on the fly. This study proposes a novel continual learning method for object detection that learns new object class(es) along with cumulative memory of classes from prior learning rounds to avoid any catastrophic forgetting. The results of PASCAL VOC 2007 have suggested that the proposed ER method obtains 4.3% of mAP drop compared against the all-classes learning, which is the lowest amongst other prior arts.

Depth-based dynamic lightness adjustment power-saving algorithm for AMOLED in head-mounted display.

Most of the head-mounted displays take the active-matrix organic light emitting diode (AMOLED) as the primary display panel because of its displaying superiorities. Yet, the AMOLED displays are still regarded as power-hungry components; in order to reduce the power consumption of AMOLED displays, the input image would be suppressed based on the proposed dynamic lightness adjustment algorithm that incorporates the depth information from the stereoscopic images which indicates the saliency, and the lightness of image pixel-wisely. The experiments reveal that the proposed method could achieve the approximately high power-saving rate with lower computational overheads compared to the existing methods.

An Effective Occipitomental View Enhancement Based on Adaptive Morphological Texture Analysis.

This paper aims to present an algorithm that specifically enhances maxillary sinuses using a novel contrast enhancement technique based on the adaptive morphological texture analysis for occipitomental view radiographs. First, the skull X-ray (SXR) is decomposed into rotational blocks (RBs). Second, each RB is rotated into various directions and processed using morphological kernels to obtain the dark and bright features. Third, a gradient-based block segmentation decomposes the interpolated feature maps into feature blocks (FBs). Finally, the histograms of FBs are equalized and overlaid locally to the input SXR. The performance of the proposed method was evaluated on an independent dataset, which comprises of 145 occipitomental view-based human SXR images. According to the experimental results, the proposed method is able to increase the diagnosis accuracy by 83.45% compared with the computed tomography modality as the gold standard.