Computer-Vision-Oriented Adaptive Sampling in Compressive Sensing.

Compressive sensing (CS) is recognized for its adeptness at compressing signals, making it a pivotal technology in the context of sensor data acquisition. With the proliferation of image data in Internet of Things (IoT) systems, CS is expected to reduce the transmission cost of signals captured by various sensor devices. However, the quality of CS-reconstructed signals inevitably degrades as the sampling rate decreases, which poses a challenge in terms of the inference accuracy in downstream computer vision (CV) tasks.

Multi-scale Xception based depthwise separable convolution for single image super-resolution.

The main target of Single image super-resolution is to recover high-quality or high-resolution image from degraded version of low-quality or low-resolution image. Recently, deep learning-based approaches have achieved significant performance in image super-resolution tasks. However, existing approaches related with image super-resolution fail to use the features information of low-resolution images as well as do not recover the hierarchical features for the final reconstruction purpose.

Non-Contact Respiration Monitoring and Body Movements Detection for Sleep Using Thermal Imaging.

Monitoring of respiration and body movements during sleep is a part of screening sleep disorders related to health status. Nowadays, thermal-based methods are presented to monitor the sleeping person without any sensors attached to the body to protect privacy. A non-contact respiration monitoring based on thermal videos requires visible facial landmarks like nostril and mouth.

Application of the real-time Retinex image enhancement for endoscopic images.

This paper presents a real-time image enhancement technique for gastric endoscopy, which is based on the variational approach of the Retinex theory. In order to efficiently reduce the computational cost required for image enhancement, processing layers and repeat counts of iterations are determined in accordance with software evaluation result, and as for processing architecture, the pipelining architecture can handle high resolution pictures in real-time. To show its potential, performance comparison between with and without the proposed image enhancement technique is shown using several video images obtained by endoscopy for different parts of digestive organ.

A speed-up scheme based on multiple-instance pruning for pedestrian detection using a support vector machine.

In pedestrian detection, as sophisticated feature descriptors are used for improving detection accuracy, its processing speed becomes a critical issue. In this paper, we propose a novel speed-up scheme based on multiple-instance pruning (MIP), one of the soft cascade methods, to enhance the processing speed of support vector machine (SVM) classifiers. Our scheme mainly consists of three steps.