SURABHI: Self-Training Using Rectified Annotations-Based Hard Instances for Eidetic Cattle Recognition.

We propose a self-training scheme, SURABHI, that trains deep-learning keypoint detection models on machine-annotated instances, together with the methodology to generate those instances. SURABHI aims to improve the keypoint detection accuracy not by altering the structure of a deep-learning-based keypoint detector model but by generating highly effective training instances. The machine-annotated instances used in SURABHI are hard instances-instances that require a rectifier to correct the keypoints misplaced by the keypoint detection model.

Designing a Computer-Vision Application: A Case Study for Hand-Hygiene Assessment in an Open-Room Environment.

Hand-hygiene is a critical component for safe food handling. In this paper, we apply an iterative engineering process to design a hand-hygiene action detection system to improve food-handling safety. We demonstrate the feasibility of a baseline RGB-only convolutional neural network (CNN) in the restricted case of a single scenario; however, since this baseline system performs poorly across scenarios, we also demonstrate the application of two methods to explore potential reasons for its poor performance.

Multi-View Hand-Hygiene Recognition for Food Safety.

A majority of foodborne illnesses result from inappropriate food handling practices. One proven practice to reduce pathogens is to perform effective hand-hygiene before all stages of food handling. In this paper, we design a multi-camera system that uses video analytics to recognize hand-hygiene actions, with the goal of improving hand-hygiene effectiveness.

ISOBlue HD: An Open-Source Platform for Collecting Context-Rich Agricultural Machinery Datasets.

This paper introduces an open-source platform called for acquisition of context-rich data from agricultural machinery. We call these datasets context-rich, because they enable the identification of machine status and farming logistics by properly labeling, fusing, and processing the data. The system includes a single board computer, a cellular modem, local storage, and power-over-ethernet switch to sensors.

Full-reference quality estimation for images with different spatial resolutions.

Multimedia communication is becoming pervasive because of the progress in wireless communications and multimedia coding. Estimating the quality of the visual content accurately is crucial in providing satisfactory service. State of the art visual quality assessment approaches are effective when the input image and reference image have the same resolution.

A versatile model for packet loss visibility and its application to packet prioritization.

In this paper, we propose a generalized linear model for video packet loss visibility that is applicable to different group-of-picture structures. We develop the model using three subjective experiment data sets that span various encoding standards (H.264 and MPEG-2), group-of-picture structures, and decoder error concealment choices.

Quality evaluation of motion-compensated edge artifacts in compressed video.

Little attention has been paid to an impairment common in motion-compensated video compression: the addition of high-frequency (HF) energy as motion compensation displaces blocking artifacts off block boundaries. In this paper, we employ an energy-based approach to measure this motion-compensated edge artifact, using both compressed bitstream information and decoded pixels. We evaluate the performance of our proposed metric, along with several blocking and blurring metrics, on compressed video in two ways.