A rational cubic spline with tension

A rational cubic spline curve is described which has tension control parameters for manipulating the shape of the curve. The spline is presented in both interpolatory and rational B-spline forms, and the behaviour of the resulting representations is analysed with respect to variation of the control parameters

Deep Perceptual Mapping for Thermal to Visible Face Recognition

Cross modal face matching between the thermal and visible spectrum is a much de- sired capability for night-time surveillance and security applications. Due to a very large modality gap, thermal-to-visible face recognition is one of the most challenging face matching problem. In this paper, we present an approach to bridge this modality gap by a significant margin. Our approach captures the highly non-linear relationship be- tween the two modalities by using a deep neural network. Our model attempts to learn a non-linear mapping from visible to thermal spectrum while preserving the identity in- formation. We show substantive performance improvement on a difficult thermal-visible face dataset. The presented approach improves the state-of-the-art by more than 10% in terms of Rank-1 identification and bridge the drop in performance due to the modality gap by more than 40%.Comment: BMVC 2015 (oral

Acute-on-chronic Liver Failure: MELD Score 30-day Mortality Predictability and Etiology in a Pakistani Population

Background: Cirrhosis is a pathological condition that ultimately leads to liver failure. Acute on chronic liver failure (ACLF) has a high short term mortality rate. Viral hepatitis is the most common cause of liver failure in our local population. We carried out this study to identity the 30-day mortality and etiology of patients presenting with ACLF using Model for End-Stage Liver Disease (MELD) score predictability. Methodology: This was a descriptive case series, conducted at Sheikh Zayed Hospital, Lahore, Pakistan from January 31, 2018 to July 30, 2018. One hundred and eighty five patients who met the inclusion criteria were enrolled using 95% confidence level and 4% margin of error. Data was entered and analyzed with SPSS version 23.0. Numerical variables including age was presented by Mean ± S.D. Categorical variables i.e. gender, etiology of acute-on-chronic liver failure and 30-day mortality were presented by frequency and percentage. Data was stratified for age, gender, duration of chronic liver disease and MELD grade to address the effect modifiers. Post-stratification chi-square test was calculated using 95% significance (p≤0.05). Results: Majority of the enrolled patients were male (74.6%) while only 25.4% of the patients were female. One hundred and thirty patients (70.3%) had underlying viral hepatitis while twelve patients (6.5%) and forty three patients (23.2%) presented with alcoholic liver disease and drug-induced ACLF, respectively. Eighty patients (43.2%) died within 30 days of admission.The 30-day mortality with respect to MELD grade was statistically significant (p<0.001) with the highest mortality noted in grade-IV and thirty five patients (43.8%) dying within 30 days of admission (p<0.001). Grade-II and III MELD scores also contributed to the 30-day mortality with twenty three patients (28.8%) and nineteen patients (23.8%) dying within 30 days of admission (p<0.001). Conclusion: MELD scores are able to accurately predict the short-term mortality in patients with ACLF and viral hepatitis was the most common etiology in our population. Early detection and use of appropriate prognostic models may alleviate mortality and morbidity in paitents with ACLF

A Pose-Sensitive Embedding for Person Re-Identification with Expanded Cross Neighborhood Re-Ranking

Person re identification is a challenging retrieval task that requires matching a person's acquired image across non overlapping camera views. In this paper we propose an effective approach that incorporates both the fine and coarse pose information of the person to learn a discriminative embedding. In contrast to the recent direction of explicitly modeling body parts or correcting for misalignment based on these, we show that a rather straightforward inclusion of acquired camera view and/or the detected joint locations into a convolutional neural network helps to learn a very effective representation. To increase retrieval performance, re-ranking techniques based on computed distances have recently gained much attention. We propose a new unsupervised and automatic re-ranking framework that achieves state-of-the-art re-ranking performance. We show that in contrast to the current state-of-the-art re-ranking methods our approach does not require to compute new rank lists for each image pair (e.g., based on reciprocal neighbors) and performs well by using simple direct rank list based comparison or even by just using the already computed euclidean distances between the images. We show that both our learned representation and our re-ranking method achieve state-of-the-art performance on a number of challenging surveillance image and video datasets. The code is available online at: https://github.com/pse-ecn/pose-sensitive-embeddingComment: CVPR 2018: v2 (fixes, added new results on PRW dataset

Deep View-Sensitive Pedestrian Attribute Inference in an end-to-end Model

Pedestrian attribute inference is a demanding problem in visual surveillance that can facilitate person retrieval, search and indexing. To exploit semantic relations between attributes, recent research treats it as a multi-label image classification task. The visual cues hinting at attributes can be strongly localized and inference of person attributes such as hair, backpack, shorts, etc., are highly dependent on the acquired view of the pedestrian. In this paper we assert this dependence in an end-to-end learning framework and show that a view-sensitive attribute inference is able to learn better attribute predictions. Our proposed model jointly predicts the coarse pose (view) of the pedestrian and learns specialized view-specific multi-label attribute predictions. We show in an extensive evaluation on three challenging datasets (PETA, RAP and WIDER) that our proposed end-to-end view-aware attribute prediction model provides competitive performance and improves on the published state-of-the-art on these datasets.Comment: accepted BMVC 201