Resource Allocation in Computer Vision

We broadly examine resource allocation in several computer vision problems. We consider human resource or computational resource constraints. Human resources, such as human operators monitoring a camera network, provide reliable information, but are typically limited by the huge amount of data to be processed. Computational resources refer to the resources used by machines, such as running time, to execute the programs. It is important to develop algorithms to make effective use of these resources in computer vision applications. We approach human resource constraints with a frame retrieval problem in a camera network. This work addresses the problem of using active inference to direct human attention in searching a camera network for people that match a query image. We find that by representing the camera network using a graphical model, we can more accurately determine which video frames match the query, and improve our ability to direct human attention. We experiment with different methods to determine from which frames to sample expert information from humans, and discover that a method that learns to predict which frame is misclassified gives the best performance. We approach the problem of allocating computational resource in a video processing task. We consider a video processing application in which we combine the outputs from two algorithms so that the budget-limited computationally more expensive algorithm is run in the most useful video frames to maximize processing performance. We model the video frames as a chain graphical model and extend a dynamic programming algorithm to determine on which frames to run the more expensive algorithm. We perform experiments on moving object detection and face detection to demonstrate the effectiveness of our approaches. Finally, we consider an idea for saving computational resources and maintaining program performance. We work on a problem of learning model complexity in latent variable models. Specifically, we learn the latent variable state space complexity in latent support vector machines using group norm regularization. We apply our method to handwritten digit recognition and object detection with deformable part models. Our approach reduces latent variable state size and performs faster inference with similar or better performance

Group Norm for Learning Structured SVMs with Unstructured Latent Variables

Latent variables models have been applied to a number of computer vision problems. However, the complexity of the latent space is typically left as a free design choice. A larger latent space results in a more expressive model, but such models are prone to over fitting and are slower to perform inference with. The goal of this paper is to regularize the complexity of the latent space and learn which hidden states are really relevant for prediction. Specifically, we propose using group-sparsity-inducing regularizers such as ℓ[subscript 1]-ℓ[subscript 2] to estimate the parameters of Structured SVMs with unstructured latent variables. Our experiments on digit recognition and object detection show that our approach is indeed able to control the complexity of latent space without any significant loss in accuracy of the learnt model.Quanta Computer (Firm)Google (Firm

Coupling the effects of extreme temperature and air pollution on non-accidental mortality in Rencheng, China

BackgroundExtreme temperatures and air pollution have raised widespread concerns about their impact on population health.AimTo explore the quantitative exposure risks of high/low temperatures and types of air pollutants on the health of various populations in urban areas in China, this study assessed the effects of temperature and air pollutants on daily non-accidental deaths in Rencheng District, Jining City, China from 2019 to 2021.MethodsA combination of Poisson regression models and distributed lag non-linear models was used to examine the relationships between temperature, air pollutants, and daily non-accidental deaths. We found that temperature and air pollutants had a significant non-linear effect on non-accidental mortality. Both high and low temperatures had a noticeable impact on non-accidental deaths, with heat effects occurring immediately and lasting 2–3 days, while cold effects lasted for 6–12 days. The relative risks of non-accidental deaths from PM2.5, NO2, and SO2 were highest in winter and lowest in autumn. The relative risk of non-accidental deaths from O3 was highest in spring, with no significant variations in other seasons. Older adults (≥75) and outdoor workers were at the greatest risk from temperature and air pollutant exposure.Conclusions/interpretationExposure to extreme temperatures and air pollutants in the Rencheng District was associated with an increased mortality rate. Under the influence of climate change, it is necessary for policymakers to take measures to reduce the risk of non-accidental deaths among residents

Speeding up Queries in a Leaf Image Database

We have an Electronic Field Guide which contains an image database with thousands of leaf images. We have a system which can take a photo of a leaf and use this image to query the database and bring out a set of leaf images which have the most similar shape as the query leaves from different species. The existing query algorithm is based on a brute force approach, and it does not have a very attractive query processing time. After we have tried some approaches to speed up the system, we discovered that two methods using a distance matrix show some usable improvement

ProFold: Protein Fold Classification with Additional Structural Features and a Novel Ensemble Classifier

Protein fold classification plays an important role in both protein functional analysis and drug design. The number of proteins in PDB is very large, but only a very small part is categorized and stored in the SCOPe database. Therefore, it is necessary to develop an efficient method for protein fold classification. In recent years, a variety of classification methods have been used in many protein fold classification studies. In this study, we propose a novel classification method called proFold. We import protein tertiary structure in the period of feature extraction and employ a novel ensemble strategy in the period of classifier training. Compared with existing similar ensemble classifiers using the same widely used dataset (DD-dataset), proFold achieves 76.2% overall accuracy. Another two commonly used datasets, EDD-dataset and TG-dataset, are also tested, of which the accuracies are 93.2% and 94.3%, higher than the existing methods. ProFold is available to the public as a web-server